From 35478e0f0d04b00bbaadb62a390a5282af6f0d90 Mon Sep 17 00:00:00 2001
From: Maziyar Panahi <maziyar.panahi@iscpif.fr>
Date: Wed, 2 Aug 2023 18:10:43 +0200
Subject: [PATCH] Models hub (#13913)

---------

Co-authored-by: ahmedlone127 <ahmedlone127@gmail.com>

* 2023-06-27-roberta_embeddings_robertinh_gl (#13868)

* Add model 2023-06-27-roberta_embeddings_robertinh_gl

* Add model 2023-06-27-roberta_embeddings_roberta_base_wechsel_german_de

* Add model 2023-06-27-roberta_embeddings_roberta_base_russian_v0_ru

* Add model 2023-06-27-roberta_embeddings_ruperta_base_finetuned_spa_constitution_en

* Add model 2023-06-27-roberta_embeddings_robasqu_eu

* Add model 2023-06-27-roberta_embeddings_roberta_ko_small_ko

* Add model 2023-06-27-roberta_embeddings_hindi_hi

* Add model 2023-06-27-roberta_embeddings_sundanese_roberta_base_su

* Add model 2023-06-27-roberta_embeddings_roberta_pubmed_en

* Add model 2023-06-27-roberta_embeddings_distilroberta_base_climate_f_en

* Add model 2023-06-27-roberta_embeddings_roberta_urdu_small_ur

* Add model 2023-06-27-roberta_embeddings_BR_BERTo_pt

* Add model 2023-06-27-roberta_embeddings_distilroberta_base_climate_d_s_en

* Add model 2023-06-27-roberta_embeddings_distilroberta_base_climate_d_en

* Add model 2023-06-27-roberta_embeddings_ukr_roberta_base_uk

* Add model 2023-06-27-roberta_embeddings_roberta_base_wechsel_french_fr

* Add model 2023-06-27-roberta_embeddings_Bible_roberta_base_en

* Add model 2023-06-27-roberta_embeddings_bertin_roberta_large_spanish_es

* Add model 2023-06-27-roberta_embeddings_roberta_base_wechsel_chinese_zh

* Add model 2023-06-27-roberta_embeddings_bertin_roberta_base_spanish_es

* Add model 2023-06-27-roberta_embeddings_bertin_base_gaussian_es

* Add model 2023-06-27-roberta_embeddings_bertin_base_random_exp_512seqlen_es

* Add model 2023-06-27-roberta_embeddings_RuPERTa_base_es

* Add model 2023-06-27-roberta_embeddings_roberta_base_bne_es

* Add model 2023-06-27-roberta_embeddings_bertin_base_stepwise_exp_512seqlen_es

* Add model 2023-06-27-roberta_embeddings_MedRoBERTa.nl_nl

* Add model 2023-06-27-roberta_embeddings_bertin_base_random_es

* Add model 2023-06-27-roberta_embeddings_RoBERTalex_es

* Add model 2023-06-27-roberta_embeddings_SecRoBERTa_en

* Add model 2023-06-27-roberta_embeddings_KanBERTo_kn

* Add model 2023-06-27-roberta_embeddings_distilroberta_base_finetuned_jira_qt_issue_title_en

* Add model 2023-06-27-roberta_embeddings_MedRoBERTa.nl_nl

* Add model 2023-06-27-roberta_embeddings_distilroberta_base_finetuned_jira_qt_issue_titles_and_bodies_en

* Add model 2023-06-27-roberta_embeddings_bertin_base_stepwise_es

* Add model 2023-06-27-roberta_embeddings_KanBERTo_kn

* Add model 2023-06-27-roberta_embeddings_bertin_base_gaussian_exp_512seqlen_es

* Add model 2023-06-27-roberta_embeddings_mlm_spanish_roberta_base_es

* Add model 2023-06-27-roberta_embeddings_KNUBert_kn

* Add model 2023-06-27-roberta_embeddings_javanese_roberta_small_jv

* Add model 2023-06-27-roberta_embeddings_indonesian_roberta_base_id

* Add model 2023-06-27-roberta_embeddings_indic_transformers_hi_roberta_hi

* Add model 2023-06-27-roberta_embeddings_indo_roberta_small_id

* Add model 2023-06-27-roberta_embeddings_fairlex_scotus_minilm_en

* Add model 2023-06-27-roberta_embeddings_indic_transformers_te_roberta_te

* Add model 2023-06-27-roberta_embeddings_javanese_roberta_small_imdb_jv

* Add model 2023-06-27-roberta_embeddings_jurisbert_es

* Add model 2023-06-27-roberta_embeddings_roberta_base_indonesian_522M_id

* Add model 2023-06-27-roberta_embeddings_fairlex_ecthr_minilm_en

* Add model 2023-06-27-roberta_embeddings_muppet_roberta_base_en

---------

Co-authored-by: ahmedlone127 <ahmedlone127@gmail.com>

* Add model 2023-06-29-xlmroberta_embeddings_paraphrase_mpnet_base_v2_xx (#13872)

Co-authored-by: Damla-Gurbaz <dml.grbz.01@gmail.com>

* 2023-06-08-instructor_base_en (#13850)

* Add model 2023-06-08-instructor_base_en

* Update 2023-06-08-instructor_base_en.md

* Add model 2023-06-21-e5_base_v2_en

* Add model 2023-06-21-e5_base_en

* Add model 2023-06-21-e5_large_v2_en

* Add model 2023-06-21-e5_large_en

* Add model 2023-06-21-e5_small_v2_en

* Add model 2023-06-21-e5_small_en

* Add model 2023-06-21-instructor_large_en

---------

Co-authored-by: prabod <prabod@rathnayaka.me>
Co-authored-by: Maziyar Panahi <maziyar.panahi@iscpif.fr>

* 2023-06-28-roberta_base_en (#13871)

* Add model 2023-06-28-roberta_base_en

* Add model 2023-06-28-roberta_base_opt_en

* Add model 2023-06-28-roberta_base_quantized_en

* Add model 2023-06-28-small_bert_L2_768_en

* Add model 2023-06-28-small_bert_L2_768_opt_en

* Add model 2023-06-28-small_bert_L2_768_quantized_en

* Add model 2023-06-28-distilbert_base_cased_en

* Add model 2023-06-28-distilbert_base_cased_opt_en

* Add model 2023-06-28-distilbert_base_cased_quantized_en

* Add model 2023-06-28-deberta_v3_base_en

* Add model 2023-06-28-deberta_v3_base_opt_en

* Add model 2023-06-28-deberta_v3_base_quantized_en

* Add model 2023-06-28-distilbert_base_uncased_en

* Add model 2023-06-28-distilbert_base_uncased_opt_en

* Add model 2023-06-28-distilbert_base_uncased_quantized_en

* Add model 2023-06-28-distilbert_base_multilingual_cased_xx

* Add model 2023-06-28-distilbert_base_multilingual_cased_xx

* Add model 2023-06-28-distilbert_base_multilingual_cased_opt_xx

* Add model 2023-06-28-distilbert_base_multilingual_cased_quantized_xx

* Add model 2023-06-28-distilbert_embeddings_distilbert_base_german_cased_de

* Add model 2023-06-28-distilbert_embeddings_distilbert_base_german_cased_opt_de

* Add model 2023-06-28-distilbert_embeddings_distilbert_base_german_cased_quantized_de

* Add model 2023-06-29-bert_base_cased_en

* Add model 2023-06-29-bert_base_cased_opt_en

* Add model 2023-06-29-bert_base_cased_quantized_en

---------

Co-authored-by: ahmedlone127 <ahmedlone127@gmail.com>

* Add model 2023-07-05-image_classifier_convnext_tiny_224_local_en (#13879)

Co-authored-by: gadde5300 <gadde5300@gmail.com>

* Add model 2023-07-06-quora_distilbert_multilingual_en (#13882)

Co-authored-by: purulalwani <purulalwani@gmail.com>

* removed duplicated sections (#13885)

* Add model 2023-07-20-xlm_roberta_large_zero_shot_classifier_xnli_anli_xx (#13900)

Co-authored-by: ahmedlone127 <ahmedlone127@gmail.com>

* Add model 2023-07-28-twitter_xlm_roberta_base_sentiment_en (#13905)

Co-authored-by: veerdhwaj <veerdhwaj@aol.com>

* 2023-07-30-albert_embeddings_ALR_BERT_ro (#13910)

* Add model 2023-07-30-albert_embeddings_ALR_BERT_ro

* Add model 2023-07-30-albert_embeddings_albert_base_japanese_v1_ja

* Add model 2023-07-30-albert_embeddings_albert_large_arabic_ar

* Add model 2023-07-30-albert_embeddings_albert_fa_base_v2_fa

* Add model 2023-07-30-albert_embeddings_albert_german_ner_de

* Add model 2023-07-30-albert_embeddings_albert_fa_zwnj_base_v2_fa

* Add model 2023-07-30-albert_embeddings_marathi_albert_mr

* Add model 2023-07-30-albert_embeddings_albert_tiny_bahasa_cased_ms

* Add model 2023-07-30-albert_embeddings_albert_base_bahasa_cased_ms

* Add model 2023-07-30-albert_embeddings_fralbert_base_fr

* Add model 2023-07-30-albert_embeddings_marathi_albert_v2_mr

* Add model 2023-07-30-albert_embeddings_albert_base_arabic_ar

* Add model 2023-07-30-albert_embeddings_albert_large_bahasa_cased_ms

* Add model 2023-07-30-camembert_embeddings_das22_10_camembert_pretrained_fr

* Add model 2023-07-30-camembert_embeddings_zhenghuabin_generic_model_fr

* Add model 2023-07-30-camembert_embeddings_das22_10_camembert_pretrained_fr

* Add model 2023-07-30-camembert_embeddings_camembert_mlm_fr

* Add model 2023-07-30-camembert_embeddings_edge2992_generic_model_fr

* Add model 2023-07-30-camembert_embeddings_elusive_magnolia_generic_model_fr

* Add model 2023-07-30-camembert_embeddings_zhenghuabin_generic_model_fr

* Add model 2023-07-30-camembert_embeddings_camembert_aux_amandes_mt

* Add model 2023-07-30-camembert_embeddings_elliotsmith_generic_model_fr

* Add model 2023-07-30-camembert_embeddings_dianeshan_generic_model_fr

* fixed wrong version

* Add model 2023-07-31-camembert_embeddings_ankitkupadhyay_generic_model_fr

* Add model 2023-07-31-camembert_embeddings_devtrent_generic_model_fr

* Add model 2023-07-31-camembert_embeddings_eduardopds_generic_model_fr

* Add model 2023-07-31-camembert_embeddings_adeiMousa_generic_model_fr

* Add model 2023-07-31-camembert_embeddings_ericchchiu_generic_model_fr

* Add model 2023-07-31-camembert_embeddings_Sebu_generic_model_fr

* Add model 2023-07-31-camembert_embeddings_Weipeng_generic_model_fr

* Add model 2023-07-31-camembert_embeddings_codingJacob_generic_model_fr

* Add model 2023-07-31-camembert_embeddings_SummFinFR_fr

* Add model 2023-07-31-camembert_embeddings_MYX4567_generic_model_fr

* Add model 2023-07-31-camembert_embeddings_Katster_generic_model_fr

* Add model 2023-07-31-camembert_embeddings_MYX4567_generic_model_fr

* Add model 2023-07-31-camembert_embeddings_JonathanSum_generic_model_fr

* Add model 2023-07-31-camembert_embeddings_Leisa_generic_model_fr

* Add model 2023-07-31-camembert_embeddings_adam1224_generic_model_fr

* Add model 2023-07-31-camembert_embeddings_est_roberta_et

* Add model 2023-07-31-camembert_embeddings_generic2_fr

* Add model 2023-07-31-camembert_embeddings_ysharma_generic_model_2_fr

* Add model 2023-07-31-camembert_embeddings_DoyyingFace_generic_model_fr

* Add model 2023-07-31-camembert_embeddings_Henrywang_generic_model_fr

* Add model 2023-07-31-camembert_embeddings_xkang_generic_model_fr

* Add model 2023-07-31-camembert_embeddings_wangst_generic_model_fr

* Add model 2023-07-31-camembert_embeddings_seyfullah_generic_model_fr

* Add model 2023-07-31-camembert_embeddings_tnagata_generic_model_fr

* Add model 2023-07-31-camembert_embeddings_yancong_generic_model_fr

* Add model 2023-07-31-camembert_embeddings_safik_generic_model_fr

* Add model 2023-07-31-camembert_embeddings_tpanza_generic_model_fr

* Add model 2023-07-31-camembert_embeddings_peterhsu_generic_model_fr

* Add model 2023-07-31-camembert_embeddings_pgperrone_generic_model_fr

* Add model 2023-07-31-camembert_embeddings_osanseviero_generic_model_fr

* Add model 2023-07-31-camembert_embeddings_lijingxin_generic_model_fr

* Add model 2023-08-01-camembert_embeddings_kaushikacharya_generic_model_fr

* Add model 2023-08-01-camembert_embeddings_new_generic_model_fr

* Add model 2023-08-01-camembert_embeddings_mbateman_generic_model_fr

* Add model 2023-08-01-camembert_embeddings_lijingxin_generic_model_2_fr

* Add model 2023-08-01-camembert_embeddings_katrin_kc_generic_model_fr

* Add model 2023-08-01-camembert_embeddings_linyi_generic_model_fr

* Add model 2023-08-01-camembert_embeddings_lewtun_generic_model_fr

* Add model 2023-08-01-camembert_embeddings_joe8zhang_generic_model_fr

* Add model 2023-08-01-camembert_embeddings_sloberta_sl

* Add model 2023-08-01-camembert_embeddings_generic_model_test_fr

* Add model 2023-08-01-camembert_embeddings_jcai1_generic_model_fr

* Add model 2023-08-01-camembert_embeddings_umberto_commoncrawl_cased_v1_it

* Add model 2023-08-01-camembert_embeddings_DataikuNLP_camembert_base_fr

* Add model 2023-08-01-camembert_embeddings_umberto_wikipedia_uncased_v1_it

* Add model 2023-08-01-camembert_base_oscar_4gb_fr

* Add model 2023-08-01-camembert_embeddings_distilcamembert_base_fr

* Add model 2023-08-01-camembert_base_wikipedia_4gb_fr

* Add model 2023-08-01-camembert_base_ccnet_fr

* Add model 2023-08-01-camembert_base_oscar_4gb_fr

* Add model 2023-08-01-camembert_embeddings_hackertec_generic_fr

* Add model 2023-08-01-camembert_base_ccnet_fr

* Add model 2023-08-01-camembert_embeddings_h4d35_generic_model_fr

* Add model 2023-08-01-camembert_embeddings_bertweetfr_base_fr

* Add model 2023-08-01-camembert_base_ccnet_4gb_fr

* Add model 2023-08-01-camembert_base_ccnet_4gb_fr

* Add model 2023-08-01-xlmroberta_embeddings_fairlex_fscs_minilm_xx

* Add model 2023-08-01-xlmroberta_embeddings_fairlex_cail_minilm_zh

* Add model 2023-08-01-camembert_base_fr

* Add model 2023-08-01-camembert_base_opt_fr

* Add model 2023-08-01-camembert_base_quantized_fr

* Add model 2023-08-02-albert_base_uncased_en

* Add model 2023-08-02-albert_base_uncased_opt_en

* Add model 2023-08-02-albert_base_uncased_quantized_en

* Add model 2023-08-02-albert_large_uncased_en

* Add model 2023-08-02-albert_large_uncased_en

* Add model 2023-08-02-albert_large_uncased_opt_en

* Add model 2023-08-02-albert_large_uncased_quantized_en

---------

Co-authored-by: ahmedlone127 <ahmedlone127@gmail.com>

* 2023-07-28-twitter_xlm_roberta_base_sentiment_en (#13906)

* Add model 2023-07-28-twitter_xlm_roberta_base_sentiment_en

* Add model 2023-07-31-twitter_xlm_roberta_base_sentiment_pdc_en

* Add model 2023-07-31-sentiment_twitter_xlm_roBerta_pdc_en

---------

Co-authored-by: veerdhwaj <veerdhwaj@aol.com>

---------

Co-authored-by: jsl-models <74001263+jsl-models@users.noreply.github.com>
Co-authored-by: ahmedlone127 <ahmedlone127@gmail.com>
Co-authored-by: purulalwani <purulalwani@gmail.com>
Co-authored-by: veerdhwaj <veerdhwaj@aol.com>
---
 ...large_zero_shot_classifier_xnli_anli_xx.md | 106 ++++++++++++++++++
 ...023-07-30-albert_embeddings_ALR_BERT_ro.md |  99 ++++++++++++++++
 ...albert_embeddings_albert_base_arabic_ar.md |  99 ++++++++++++++++
 ..._embeddings_albert_base_bahasa_cased_ms.md |  99 ++++++++++++++++
 ...t_embeddings_albert_base_japanese_v1_ja.md |  99 ++++++++++++++++
 ...-albert_embeddings_albert_fa_base_v2_fa.md |  99 ++++++++++++++++
 ...rt_embeddings_albert_fa_zwnj_base_v2_fa.md |  99 ++++++++++++++++
 ...-albert_embeddings_albert_german_ner_de.md |  99 ++++++++++++++++
 ...lbert_embeddings_albert_large_arabic_ar.md |  99 ++++++++++++++++
 ...embeddings_albert_large_bahasa_cased_ms.md |  99 ++++++++++++++++
 ..._embeddings_albert_tiny_bahasa_cased_ms.md |  99 ++++++++++++++++
 ...7-30-albert_embeddings_fralbert_base_fr.md |  99 ++++++++++++++++
 ...-30-albert_embeddings_marathi_albert_mr.md |  99 ++++++++++++++++
 ...-albert_embeddings_marathi_albert_v2_mr.md |  99 ++++++++++++++++
 ...ert_embeddings_camembert_aux_amandes_mt.md |  99 ++++++++++++++++
 ...0-camembert_embeddings_camembert_mlm_fr.md |  99 ++++++++++++++++
 ...ddings_das22_10_camembert_pretrained_fr.md |  99 ++++++++++++++++
 ...t_embeddings_dianeshan_generic_model_fr.md |  99 ++++++++++++++++
 ...rt_embeddings_edge2992_generic_model_fr.md |  99 ++++++++++++++++
 ...embeddings_elliotsmith_generic_model_fr.md |  99 ++++++++++++++++
 ...dings_elusive_magnolia_generic_model_fr.md |  99 ++++++++++++++++
 ...embeddings_zhenghuabin_generic_model_fr.md |  99 ++++++++++++++++
 ...embeddings_DoyyingFace_generic_model_fr.md |  93 +++++++++++++++
 ...t_embeddings_Henrywang_generic_model_fr.md |  93 +++++++++++++++
 ...embeddings_JonathanSum_generic_model_fr.md |  93 +++++++++++++++
 ...ert_embeddings_Katster_generic_model_fr.md |  93 +++++++++++++++
 ...mbert_embeddings_Leisa_generic_model_fr.md |  93 +++++++++++++++
 ...ert_embeddings_MYX4567_generic_model_fr.md |  93 +++++++++++++++
 ...embert_embeddings_Sebu_generic_model_fr.md |  93 +++++++++++++++
 ...07-31-camembert_embeddings_SummFinFR_fr.md |  93 +++++++++++++++
 ...ert_embeddings_Weipeng_generic_model_fr.md |  93 +++++++++++++++
 ...rt_embeddings_adam1224_generic_model_fr.md |  99 ++++++++++++++++
 ...t_embeddings_adeiMousa_generic_model_fr.md |  93 +++++++++++++++
 ...eddings_ankitkupadhyay_generic_model_fr.md |  99 ++++++++++++++++
 ...embeddings_codingJacob_generic_model_fr.md |  93 +++++++++++++++
 ...rt_embeddings_devtrent_generic_model_fr.md |  99 ++++++++++++++++
 ..._embeddings_eduardopds_generic_model_fr.md |  99 ++++++++++++++++
 ..._embeddings_ericchchiu_generic_model_fr.md |  99 ++++++++++++++++
 ...-31-camembert_embeddings_est_roberta_et.md |  99 ++++++++++++++++
 ...-07-31-camembert_embeddings_generic2_fr.md |  99 ++++++++++++++++
 ...t_embeddings_lijingxin_generic_model_fr.md |  99 ++++++++++++++++
 ...embeddings_osanseviero_generic_model_fr.md |  99 ++++++++++++++++
 ...rt_embeddings_peterhsu_generic_model_fr.md |  99 ++++++++++++++++
 ...t_embeddings_pgperrone_generic_model_fr.md |  99 ++++++++++++++++
 ...mbert_embeddings_safik_generic_model_fr.md |  99 ++++++++++++++++
 ...t_embeddings_seyfullah_generic_model_fr.md |  99 ++++++++++++++++
 ...ert_embeddings_tnagata_generic_model_fr.md |  99 ++++++++++++++++
 ...bert_embeddings_tpanza_generic_model_fr.md |  99 ++++++++++++++++
 ...bert_embeddings_wangst_generic_model_fr.md |  99 ++++++++++++++++
 ...mbert_embeddings_xkang_generic_model_fr.md |  99 ++++++++++++++++
 ...ert_embeddings_yancong_generic_model_fr.md |  99 ++++++++++++++++
 ...t_embeddings_ysharma_generic_model_2_fr.md |  99 ++++++++++++++++
 .../2023-08-01-camembert_base_ccnet_4gb_fr.md |  86 ++++++++++++++
 .../2023-08-01-camembert_base_ccnet_fr.md     |  86 ++++++++++++++
 .../2023-08-01-camembert_base_fr.md           |  86 ++++++++++++++
 .../2023-08-01-camembert_base_opt_fr.md       |  86 ++++++++++++++
 .../2023-08-01-camembert_base_oscar_4gb_fr.md |  86 ++++++++++++++
 .../2023-08-01-camembert_base_quantized_fr.md |  86 ++++++++++++++
 ...3-08-01-camembert_base_wikipedia_4gb_fr.md |  86 ++++++++++++++
 ...embeddings_DataikuNLP_camembert_base_fr.md |  93 +++++++++++++++
 ...camembert_embeddings_bertweetfr_base_fr.md |  99 ++++++++++++++++
 ...bert_embeddings_distilcamembert_base_fr.md |  99 ++++++++++++++++
 ...embert_embeddings_generic_model_test_fr.md |  99 ++++++++++++++++
 ...mbert_embeddings_h4d35_generic_model_fr.md |  99 ++++++++++++++++
 ...membert_embeddings_hackertec_generic_fr.md |  99 ++++++++++++++++
 ...mbert_embeddings_jcai1_generic_model_fr.md |  99 ++++++++++++++++
 ...t_embeddings_joe8zhang_generic_model_fr.md |  99 ++++++++++++++++
 ...t_embeddings_katrin_kc_generic_model_fr.md |  99 ++++++++++++++++
 ...eddings_kaushikacharya_generic_model_fr.md |  99 ++++++++++++++++
 ...bert_embeddings_lewtun_generic_model_fr.md |  99 ++++++++++++++++
 ...embeddings_lijingxin_generic_model_2_fr.md |  99 ++++++++++++++++
 ...mbert_embeddings_linyi_generic_model_fr.md |  99 ++++++++++++++++
 ...rt_embeddings_mbateman_generic_model_fr.md |  99 ++++++++++++++++
 ...membert_embeddings_new_generic_model_fr.md |  99 ++++++++++++++++
 ...-08-01-camembert_embeddings_sloberta_sl.md |  99 ++++++++++++++++
 ...eddings_umberto_commoncrawl_cased_v1_it.md |  99 ++++++++++++++++
 ...eddings_umberto_wikipedia_uncased_v1_it.md |  99 ++++++++++++++++
 ...berta_embeddings_fairlex_cail_minilm_zh.md |  99 ++++++++++++++++
 ...berta_embeddings_fairlex_fscs_minilm_xx.md |  99 ++++++++++++++++
 .../2023-08-02-albert_base_uncased_en.md      |  78 +++++++++++++
 .../2023-08-02-albert_base_uncased_opt_en.md  |  78 +++++++++++++
 ...-08-02-albert_base_uncased_quantized_en.md |  78 +++++++++++++
 .../2023-08-02-albert_large_uncased_en.md     |  78 +++++++++++++
 .../2023-08-02-albert_large_uncased_opt_en.md |  78 +++++++++++++
 ...08-02-albert_large_uncased_quantized_en.md |  78 +++++++++++++
 ...8-twitter_xlm_roberta_base_sentiment_en.md |  90 +++++++++++++++
 ...31-sentiment_twitter_xlm_roBerta_pdc_en.md |  94 ++++++++++++++++
 ...itter_xlm_roberta_base_sentiment_pdc_en.md |  86 ++++++++++++++
 88 files changed, 8403 insertions(+)
 create mode 100644 docs/_posts/ahmedlone127/2023-07-20-xlm_roberta_large_zero_shot_classifier_xnli_anli_xx.md
 create mode 100644 docs/_posts/ahmedlone127/2023-07-30-albert_embeddings_ALR_BERT_ro.md
 create mode 100644 docs/_posts/ahmedlone127/2023-07-30-albert_embeddings_albert_base_arabic_ar.md
 create mode 100644 docs/_posts/ahmedlone127/2023-07-30-albert_embeddings_albert_base_bahasa_cased_ms.md
 create mode 100644 docs/_posts/ahmedlone127/2023-07-30-albert_embeddings_albert_base_japanese_v1_ja.md
 create mode 100644 docs/_posts/ahmedlone127/2023-07-30-albert_embeddings_albert_fa_base_v2_fa.md
 create mode 100644 docs/_posts/ahmedlone127/2023-07-30-albert_embeddings_albert_fa_zwnj_base_v2_fa.md
 create mode 100644 docs/_posts/ahmedlone127/2023-07-30-albert_embeddings_albert_german_ner_de.md
 create mode 100644 docs/_posts/ahmedlone127/2023-07-30-albert_embeddings_albert_large_arabic_ar.md
 create mode 100644 docs/_posts/ahmedlone127/2023-07-30-albert_embeddings_albert_large_bahasa_cased_ms.md
 create mode 100644 docs/_posts/ahmedlone127/2023-07-30-albert_embeddings_albert_tiny_bahasa_cased_ms.md
 create mode 100644 docs/_posts/ahmedlone127/2023-07-30-albert_embeddings_fralbert_base_fr.md
 create mode 100644 docs/_posts/ahmedlone127/2023-07-30-albert_embeddings_marathi_albert_mr.md
 create mode 100644 docs/_posts/ahmedlone127/2023-07-30-albert_embeddings_marathi_albert_v2_mr.md
 create mode 100644 docs/_posts/ahmedlone127/2023-07-30-camembert_embeddings_camembert_aux_amandes_mt.md
 create mode 100644 docs/_posts/ahmedlone127/2023-07-30-camembert_embeddings_camembert_mlm_fr.md
 create mode 100644 docs/_posts/ahmedlone127/2023-07-30-camembert_embeddings_das22_10_camembert_pretrained_fr.md
 create mode 100644 docs/_posts/ahmedlone127/2023-07-30-camembert_embeddings_dianeshan_generic_model_fr.md
 create mode 100644 docs/_posts/ahmedlone127/2023-07-30-camembert_embeddings_edge2992_generic_model_fr.md
 create mode 100644 docs/_posts/ahmedlone127/2023-07-30-camembert_embeddings_elliotsmith_generic_model_fr.md
 create mode 100644 docs/_posts/ahmedlone127/2023-07-30-camembert_embeddings_elusive_magnolia_generic_model_fr.md
 create mode 100644 docs/_posts/ahmedlone127/2023-07-30-camembert_embeddings_zhenghuabin_generic_model_fr.md
 create mode 100644 docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_DoyyingFace_generic_model_fr.md
 create mode 100644 docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_Henrywang_generic_model_fr.md
 create mode 100644 docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_JonathanSum_generic_model_fr.md
 create mode 100644 docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_Katster_generic_model_fr.md
 create mode 100644 docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_Leisa_generic_model_fr.md
 create mode 100644 docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_MYX4567_generic_model_fr.md
 create mode 100644 docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_Sebu_generic_model_fr.md
 create mode 100644 docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_SummFinFR_fr.md
 create mode 100644 docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_Weipeng_generic_model_fr.md
 create mode 100644 docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_adam1224_generic_model_fr.md
 create mode 100644 docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_adeiMousa_generic_model_fr.md
 create mode 100644 docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_ankitkupadhyay_generic_model_fr.md
 create mode 100644 docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_codingJacob_generic_model_fr.md
 create mode 100644 docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_devtrent_generic_model_fr.md
 create mode 100644 docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_eduardopds_generic_model_fr.md
 create mode 100644 docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_ericchchiu_generic_model_fr.md
 create mode 100644 docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_est_roberta_et.md
 create mode 100644 docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_generic2_fr.md
 create mode 100644 docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_lijingxin_generic_model_fr.md
 create mode 100644 docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_osanseviero_generic_model_fr.md
 create mode 100644 docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_peterhsu_generic_model_fr.md
 create mode 100644 docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_pgperrone_generic_model_fr.md
 create mode 100644 docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_safik_generic_model_fr.md
 create mode 100644 docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_seyfullah_generic_model_fr.md
 create mode 100644 docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_tnagata_generic_model_fr.md
 create mode 100644 docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_tpanza_generic_model_fr.md
 create mode 100644 docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_wangst_generic_model_fr.md
 create mode 100644 docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_xkang_generic_model_fr.md
 create mode 100644 docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_yancong_generic_model_fr.md
 create mode 100644 docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_ysharma_generic_model_2_fr.md
 create mode 100644 docs/_posts/ahmedlone127/2023-08-01-camembert_base_ccnet_4gb_fr.md
 create mode 100644 docs/_posts/ahmedlone127/2023-08-01-camembert_base_ccnet_fr.md
 create mode 100644 docs/_posts/ahmedlone127/2023-08-01-camembert_base_fr.md
 create mode 100644 docs/_posts/ahmedlone127/2023-08-01-camembert_base_opt_fr.md
 create mode 100644 docs/_posts/ahmedlone127/2023-08-01-camembert_base_oscar_4gb_fr.md
 create mode 100644 docs/_posts/ahmedlone127/2023-08-01-camembert_base_quantized_fr.md
 create mode 100644 docs/_posts/ahmedlone127/2023-08-01-camembert_base_wikipedia_4gb_fr.md
 create mode 100644 docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_DataikuNLP_camembert_base_fr.md
 create mode 100644 docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_bertweetfr_base_fr.md
 create mode 100644 docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_distilcamembert_base_fr.md
 create mode 100644 docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_generic_model_test_fr.md
 create mode 100644 docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_h4d35_generic_model_fr.md
 create mode 100644 docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_hackertec_generic_fr.md
 create mode 100644 docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_jcai1_generic_model_fr.md
 create mode 100644 docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_joe8zhang_generic_model_fr.md
 create mode 100644 docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_katrin_kc_generic_model_fr.md
 create mode 100644 docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_kaushikacharya_generic_model_fr.md
 create mode 100644 docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_lewtun_generic_model_fr.md
 create mode 100644 docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_lijingxin_generic_model_2_fr.md
 create mode 100644 docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_linyi_generic_model_fr.md
 create mode 100644 docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_mbateman_generic_model_fr.md
 create mode 100644 docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_new_generic_model_fr.md
 create mode 100644 docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_sloberta_sl.md
 create mode 100644 docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_umberto_commoncrawl_cased_v1_it.md
 create mode 100644 docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_umberto_wikipedia_uncased_v1_it.md
 create mode 100644 docs/_posts/ahmedlone127/2023-08-01-xlmroberta_embeddings_fairlex_cail_minilm_zh.md
 create mode 100644 docs/_posts/ahmedlone127/2023-08-01-xlmroberta_embeddings_fairlex_fscs_minilm_xx.md
 create mode 100644 docs/_posts/ahmedlone127/2023-08-02-albert_base_uncased_en.md
 create mode 100644 docs/_posts/ahmedlone127/2023-08-02-albert_base_uncased_opt_en.md
 create mode 100644 docs/_posts/ahmedlone127/2023-08-02-albert_base_uncased_quantized_en.md
 create mode 100644 docs/_posts/ahmedlone127/2023-08-02-albert_large_uncased_en.md
 create mode 100644 docs/_posts/ahmedlone127/2023-08-02-albert_large_uncased_opt_en.md
 create mode 100644 docs/_posts/ahmedlone127/2023-08-02-albert_large_uncased_quantized_en.md
 create mode 100644 docs/_posts/veerdhwaj/2023-07-28-twitter_xlm_roberta_base_sentiment_en.md
 create mode 100644 docs/_posts/veerdhwaj/2023-07-31-sentiment_twitter_xlm_roBerta_pdc_en.md
 create mode 100644 docs/_posts/veerdhwaj/2023-07-31-twitter_xlm_roberta_base_sentiment_pdc_en.md

diff --git a/docs/_posts/ahmedlone127/2023-07-20-xlm_roberta_large_zero_shot_classifier_xnli_anli_xx.md b/docs/_posts/ahmedlone127/2023-07-20-xlm_roberta_large_zero_shot_classifier_xnli_anli_xx.md
new file mode 100644
index 00000000000000..467209f7ce1fce
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-07-20-xlm_roberta_large_zero_shot_classifier_xnli_anli_xx.md
@@ -0,0 +1,106 @@
+---
+layout: model
+title: XlmRoBertaZero-Shot Classification Large xlm_roberta_large_zero_shot_classifier_xnli_anli
+author: John Snow Labs
+name: xlm_roberta_large_zero_shot_classifier_xnli_anli
+date: 2023-07-20
+tags: [zero_shot, xx, open_source, tensorflow]
+task: Zero-Shot Classification
+language: xx
+edition: Spark NLP 5.0.2
+spark_version: 3.0
+supported: true
+engine: tensorflow
+annotator: XlmRoBertaForZeroShotClassification
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+This model is intended to be used for zero-shot text classification, especially in English. It is fine-tuned on NLI by using XlmRoberta Large model.
+
+XlmRoBertaForZeroShotClassificationusing a ModelForSequenceClassification trained on NLI (natural language inference) tasks. Equivalent of TFXLMRoBertaForZeroShotClassification models, but these models don’t require a hardcoded number of potential classes, they can be chosen at runtime. It usually means it’s slower but it is much more flexible.
+
+We used TFXLMRobertaForSequenceClassification to train this model and used XlmRoBertaForZeroShotClassification annotator in Spark NLP 🚀 for prediction at scale!
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/xlm_roberta_large_zero_shot_classifier_xnli_anli_xx_5.0.2_3.0_1689886974932.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/xlm_roberta_large_zero_shot_classifier_xnli_anli_xx_5.0.2_3.0_1689886974932.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+document_assembler = DocumentAssembler() \
+.setInputCol('text') \
+.setOutputCol('document')
+
+tokenizer = Tokenizer() \
+.setInputCols(['document']) \
+.setOutputCol('token')
+
+zeroShotClassifier = XlmRobertaForSequenceClassification \
+.pretrained('xlm_roberta_large_zero_shot_classifier_xnli_anli', 'xx') \
+.setInputCols(['token', 'document']) \
+.setOutputCol('class') \
+.setCaseSensitive(True) \
+.setMaxSentenceLength(512) \
+.setCandidateLabels(["urgent", "mobile", "travel", "movie", "music", "sport", "weather", "technology"])
+
+pipeline = Pipeline(stages=[
+document_assembler,
+tokenizer,
+zeroShotClassifier
+])
+
+example = spark.createDataFrame([['I have a problem with my iphone that needs to be resolved asap!!']]).toDF("text")
+result = pipeline.fit(example).transform(example)
+
+```
+```scala
+val document_assembler = DocumentAssembler()
+.setInputCol("text")
+.setOutputCol("document")
+
+val tokenizer = Tokenizer()
+.setInputCols("document")
+.setOutputCol("token")
+
+val zeroShotClassifier = XlmRobertaForSequenceClassification.pretrained("xlm_roberta_large_zero_shot_classifier_xnli_anli", "xx")
+.setInputCols("document", "token")
+.setOutputCol("class")
+.setCaseSensitive(true)
+.setMaxSentenceLength(512)
+.setCandidateLabels(Array("urgent", "mobile", "travel", "movie", "music", "sport", "weather", "technology"))
+
+val pipeline = new Pipeline().setStages(Array(document_assembler, tokenizer, zeroShotClassifier))
+val example = Seq("I have a problem with my iphone that needs to be resolved asap!!").toDS.toDF("text")
+val result = pipeline.fit(example).transform(example)
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|xlm_roberta_large_zero_shot_classifier_xnli_anli|
+|Compatibility:|Spark NLP 5.0.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[token, document]|
+|Output Labels:|[label]|
+|Language:|xx|
+|Size:|2.0 GB|
+|Case sensitive:|true|
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-07-30-albert_embeddings_ALR_BERT_ro.md b/docs/_posts/ahmedlone127/2023-07-30-albert_embeddings_ALR_BERT_ro.md
new file mode 100644
index 00000000000000..86db1efa595bc0
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-07-30-albert_embeddings_ALR_BERT_ro.md
@@ -0,0 +1,99 @@
+---
+layout: model
+title: Romanian ALBERT Embeddings (from dragosnicolae555)
+author: John Snow Labs
+name: albert_embeddings_ALR_BERT
+date: 2023-07-30
+tags: [albert, embeddings, ro, open_source, onnx]
+task: Embeddings
+language: ro
+edition: Spark NLP 5.0.2
+spark_version: 3.0
+supported: true
+engine: onnx
+annotator: AlbertEmbeddings
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained ALBERT Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `ALR_BERT` is a Romanian model orginally trained by `dragosnicolae555`.
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_embeddings_ALR_BERT_ro_5.0.2_3.0_1690752767725.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_embeddings_ALR_BERT_ro_5.0.2_3.0_1690752767725.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+documentAssembler = DocumentAssembler() \
+.setInputCol("text") \
+.setOutputCol("document")
+
+tokenizer = Tokenizer() \
+.setInputCols("document") \
+.setOutputCol("token")
+
+embeddings = AlbertEmbeddings.pretrained("albert_embeddings_ALR_BERT","ro") \
+.setInputCols(["document", "token"]) \
+.setOutputCol("embeddings")
+
+pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings])
+
+data = spark.createDataFrame([["Îmi place Spark NLP"]]).toDF("text")
+
+result = pipeline.fit(data).transform(data)
+```
+```scala
+val documentAssembler = new DocumentAssembler() 
+.setInputCol("text") 
+.setOutputCol("document")
+
+val tokenizer = new Tokenizer() 
+.setInputCols(Array("document"))
+.setOutputCol("token")
+
+val embeddings = AlbertEmbeddings.pretrained("albert_embeddings_ALR_BERT","ro") 
+.setInputCols(Array("document", "token")) 
+.setOutputCol("embeddings")
+
+val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings))
+
+val data = Seq("Îmi place Spark NLP").toDF("text")
+
+val result = pipeline.fit(data).transform(data)
+```
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("ro.embed.ALR_BERT").predict("""Îmi place Spark NLP""")
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|albert_embeddings_ALR_BERT|
+|Compatibility:|Spark NLP 5.0.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[sentence, token]|
+|Output Labels:|[bert]|
+|Language:|ro|
+|Size:|51.7 MB|
+|Case sensitive:|false|
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-07-30-albert_embeddings_albert_base_arabic_ar.md b/docs/_posts/ahmedlone127/2023-07-30-albert_embeddings_albert_base_arabic_ar.md
new file mode 100644
index 00000000000000..22896e9a9cd377
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-07-30-albert_embeddings_albert_base_arabic_ar.md
@@ -0,0 +1,99 @@
+---
+layout: model
+title: Arabic ALBERT Embeddings (Base)
+author: John Snow Labs
+name: albert_embeddings_albert_base_arabic
+date: 2023-07-30
+tags: [albert, embeddings, ar, open_source, onnx]
+task: Embeddings
+language: ar
+edition: Spark NLP 5.0.2
+spark_version: 3.0
+supported: true
+engine: onnx
+annotator: AlbertEmbeddings
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained ALBERT Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `albert-base-arabic` is a Arabic model orginally trained by `asafaya`.
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_embeddings_albert_base_arabic_ar_5.0.2_3.0_1690753212237.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_embeddings_albert_base_arabic_ar_5.0.2_3.0_1690753212237.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+documentAssembler = DocumentAssembler() \
+.setInputCol("text") \
+.setOutputCol("document")
+
+tokenizer = Tokenizer() \
+.setInputCols("document") \
+.setOutputCol("token")
+
+embeddings = AlbertEmbeddings.pretrained("albert_embeddings_albert_base_arabic","ar") \
+.setInputCols(["document", "token"]) \
+.setOutputCol("embeddings")
+
+pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings])
+
+data = spark.createDataFrame([["أنا أحب شرارة NLP"]]).toDF("text")
+
+result = pipeline.fit(data).transform(data)
+```
+```scala
+val documentAssembler = new DocumentAssembler() 
+.setInputCol("text") 
+.setOutputCol("document")
+
+val tokenizer = new Tokenizer() 
+.setInputCols(Array("document"))
+.setOutputCol("token")
+
+val embeddings = AlbertEmbeddings.pretrained("albert_embeddings_albert_base_arabic","ar") 
+.setInputCols(Array("document", "token")) 
+.setOutputCol("embeddings")
+
+val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings))
+
+val data = Seq("أنا أحب شرارة NLP").toDF("text")
+
+val result = pipeline.fit(data).transform(data)
+```
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("ar.embed.albert").predict("""أنا أحب شرارة NLP""")
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|albert_embeddings_albert_base_arabic|
+|Compatibility:|Spark NLP 5.0.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[sentence, token]|
+|Output Labels:|[bert]|
+|Language:|ar|
+|Size:|42.0 MB|
+|Case sensitive:|false|
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-07-30-albert_embeddings_albert_base_bahasa_cased_ms.md b/docs/_posts/ahmedlone127/2023-07-30-albert_embeddings_albert_base_bahasa_cased_ms.md
new file mode 100644
index 00000000000000..f3fca4de758ac0
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-07-30-albert_embeddings_albert_base_bahasa_cased_ms.md
@@ -0,0 +1,99 @@
+---
+layout: model
+title: Malay ALBERT Embeddings (Base)
+author: John Snow Labs
+name: albert_embeddings_albert_base_bahasa_cased
+date: 2023-07-30
+tags: [albert, embeddings, ms, open_source, onnx]
+task: Embeddings
+language: ms
+edition: Spark NLP 5.0.2
+spark_version: 3.0
+supported: true
+engine: onnx
+annotator: AlbertEmbeddings
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained ALBERT Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `albert-base-bahasa-cased` is a Malay model orginally trained by `malay-huggingface`.
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_embeddings_albert_base_bahasa_cased_ms_5.0.2_3.0_1690753174981.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_embeddings_albert_base_bahasa_cased_ms_5.0.2_3.0_1690753174981.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+documentAssembler = DocumentAssembler() \
+.setInputCol("text") \
+.setOutputCol("document")
+
+tokenizer = Tokenizer() \
+.setInputCols("document") \
+.setOutputCol("token")
+
+embeddings = AlbertEmbeddings.pretrained("albert_embeddings_albert_base_bahasa_cased","ms") \
+.setInputCols(["document", "token"]) \
+.setOutputCol("embeddings")
+
+pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings])
+
+data = spark.createDataFrame([["Saya suka Spark NLP"]]).toDF("text")
+
+result = pipeline.fit(data).transform(data)
+```
+```scala
+val documentAssembler = new DocumentAssembler() 
+.setInputCol("text") 
+.setOutputCol("document")
+
+val tokenizer = new Tokenizer() 
+.setInputCols(Array("document"))
+.setOutputCol("token")
+
+val embeddings = AlbertEmbeddings.pretrained("albert_embeddings_albert_base_bahasa_cased","ms") 
+.setInputCols(Array("document", "token")) 
+.setOutputCol("embeddings")
+
+val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings))
+
+val data = Seq("Saya suka Spark NLP").toDF("text")
+
+val result = pipeline.fit(data).transform(data)
+```
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("ms.embed.albert_base_bahasa_cased").predict("""Saya suka Spark NLP""")
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|albert_embeddings_albert_base_bahasa_cased|
+|Compatibility:|Spark NLP 5.0.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[sentence, token]|
+|Output Labels:|[bert]|
+|Language:|ms|
+|Size:|42.9 MB|
+|Case sensitive:|false|
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-07-30-albert_embeddings_albert_base_japanese_v1_ja.md b/docs/_posts/ahmedlone127/2023-07-30-albert_embeddings_albert_base_japanese_v1_ja.md
new file mode 100644
index 00000000000000..ad1185dcccdb33
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-07-30-albert_embeddings_albert_base_japanese_v1_ja.md
@@ -0,0 +1,99 @@
+---
+layout: model
+title: Japanese ALBERT Embeddings (from ken11)
+author: John Snow Labs
+name: albert_embeddings_albert_base_japanese_v1
+date: 2023-07-30
+tags: [albert, embeddings, ja, open_source, onnx]
+task: Embeddings
+language: ja
+edition: Spark NLP 5.0.2
+spark_version: 3.0
+supported: true
+engine: onnx
+annotator: AlbertEmbeddings
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained ALBERT Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `albert-base-japanese-v1` is a Japanese model orginally trained by `ken11`.
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_embeddings_albert_base_japanese_v1_ja_5.0.2_3.0_1690752780150.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_embeddings_albert_base_japanese_v1_ja_5.0.2_3.0_1690752780150.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+documentAssembler = DocumentAssembler() \
+.setInputCol("text") \
+.setOutputCol("document")
+
+tokenizer = Tokenizer() \
+.setInputCols("document") \
+.setOutputCol("token")
+
+embeddings = AlbertEmbeddings.pretrained("albert_embeddings_albert_base_japanese_v1","ja") \
+.setInputCols(["document", "token"]) \
+.setOutputCol("embeddings")
+
+pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings])
+
+data = spark.createDataFrame([["私はSpark NLPを愛しています"]]).toDF("text")
+
+result = pipeline.fit(data).transform(data)
+```
+```scala
+val documentAssembler = new DocumentAssembler() 
+.setInputCol("text") 
+.setOutputCol("document")
+
+val tokenizer = new Tokenizer() 
+.setInputCols(Array("document"))
+.setOutputCol("token")
+
+val embeddings = AlbertEmbeddings.pretrained("albert_embeddings_albert_base_japanese_v1","ja") 
+.setInputCols(Array("document", "token")) 
+.setOutputCol("embeddings")
+
+val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings))
+
+val data = Seq("私はSpark NLPを愛しています").toDF("text")
+
+val result = pipeline.fit(data).transform(data)
+```
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("ja.embed.albert_base_japanese_v1").predict("""私はSpark NLPを愛しています""")
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|albert_embeddings_albert_base_japanese_v1|
+|Compatibility:|Spark NLP 5.0.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[sentence, token]|
+|Output Labels:|[bert]|
+|Language:|ja|
+|Size:|42.8 MB|
+|Case sensitive:|false|
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-07-30-albert_embeddings_albert_fa_base_v2_fa.md b/docs/_posts/ahmedlone127/2023-07-30-albert_embeddings_albert_fa_base_v2_fa.md
new file mode 100644
index 00000000000000..db63deaaa6b3dd
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-07-30-albert_embeddings_albert_fa_base_v2_fa.md
@@ -0,0 +1,99 @@
+---
+layout: model
+title: Persian ALBERT Embeddings (from m3hrdadfi)
+author: John Snow Labs
+name: albert_embeddings_albert_fa_base_v2
+date: 2023-07-30
+tags: [albert, embeddings, fa, open_source, onnx]
+task: Embeddings
+language: fa
+edition: Spark NLP 5.0.2
+spark_version: 3.0
+supported: true
+engine: onnx
+annotator: AlbertEmbeddings
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained ALBERT Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `albert-fa-base-v2` is a Persian model orginally trained by `m3hrdadfi`.
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_embeddings_albert_fa_base_v2_fa_5.0.2_3.0_1690752839758.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_embeddings_albert_fa_base_v2_fa_5.0.2_3.0_1690752839758.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+documentAssembler = DocumentAssembler() \
+.setInputCol("text") \
+.setOutputCol("document")
+
+tokenizer = Tokenizer() \
+.setInputCols("document") \
+.setOutputCol("token")
+
+embeddings = AlbertEmbeddings.pretrained("albert_embeddings_albert_fa_base_v2","fa") \
+.setInputCols(["document", "token"]) \
+.setOutputCol("embeddings")
+
+pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings])
+
+data = spark.createDataFrame([["من عاشق جرقه NLP هستم"]]).toDF("text")
+
+result = pipeline.fit(data).transform(data)
+```
+```scala
+val documentAssembler = new DocumentAssembler() 
+.setInputCol("text") 
+.setOutputCol("document")
+
+val tokenizer = new Tokenizer() 
+.setInputCols(Array("document"))
+.setOutputCol("token")
+
+val embeddings = AlbertEmbeddings.pretrained("albert_embeddings_albert_fa_base_v2","fa") 
+.setInputCols(Array("document", "token")) 
+.setOutputCol("embeddings")
+
+val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings))
+
+val data = Seq("من عاشق جرقه NLP هستم").toDF("text")
+
+val result = pipeline.fit(data).transform(data)
+```
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("fa.embed.albert").predict("""من عاشق جرقه NLP هستم""")
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|albert_embeddings_albert_fa_base_v2|
+|Compatibility:|Spark NLP 5.0.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[sentence, token]|
+|Output Labels:|[bert]|
+|Language:|fa|
+|Size:|66.3 MB|
+|Case sensitive:|false|
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-07-30-albert_embeddings_albert_fa_zwnj_base_v2_fa.md b/docs/_posts/ahmedlone127/2023-07-30-albert_embeddings_albert_fa_zwnj_base_v2_fa.md
new file mode 100644
index 00000000000000..6537edaada379d
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-07-30-albert_embeddings_albert_fa_zwnj_base_v2_fa.md
@@ -0,0 +1,99 @@
+---
+layout: model
+title: Persian (Farsi) ALBERT Embeddings
+author: John Snow Labs
+name: albert_embeddings_albert_fa_zwnj_base_v2
+date: 2023-07-30
+tags: [albert, embeddings, fa, open_source, onnx]
+task: Embeddings
+language: fa
+edition: Spark NLP 5.0.2
+spark_version: 3.0
+supported: true
+engine: onnx
+annotator: AlbertEmbeddings
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained ALBERT Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `albert-fa-zwnj-base-v2` is a Persian model orginally trained by `HooshvareLab`.
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_embeddings_albert_fa_zwnj_base_v2_fa_5.0.2_3.0_1690752897049.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_embeddings_albert_fa_zwnj_base_v2_fa_5.0.2_3.0_1690752897049.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+documentAssembler = DocumentAssembler() \
+.setInputCol("text") \
+.setOutputCol("document")
+
+tokenizer = Tokenizer() \
+.setInputCols("document") \
+.setOutputCol("token")
+
+embeddings = AlbertEmbeddings.pretrained("albert_embeddings_albert_fa_zwnj_base_v2","fa") \
+.setInputCols(["document", "token"]) \
+.setOutputCol("embeddings")
+
+pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings])
+
+data = spark.createDataFrame([["من عاشق جرقه NLP هستم"]]).toDF("text")
+
+result = pipeline.fit(data).transform(data)
+```
+```scala
+val documentAssembler = new DocumentAssembler() 
+.setInputCol("text") 
+.setOutputCol("document")
+
+val tokenizer = new Tokenizer() 
+.setInputCols(Array("document"))
+.setOutputCol("token")
+
+val embeddings = AlbertEmbeddings.pretrained("albert_embeddings_albert_fa_zwnj_base_v2","fa") 
+.setInputCols(Array("document", "token")) 
+.setOutputCol("embeddings")
+
+val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings))
+
+val data = Seq("من عاشق جرقه NLP هستم").toDF("text")
+
+val result = pipeline.fit(data).transform(data)
+```
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("fa.embed.albert_fa_zwnj_base_v2").predict("""من عاشق جرقه NLP هستم""")
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|albert_embeddings_albert_fa_zwnj_base_v2|
+|Compatibility:|Spark NLP 5.0.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[sentence, token]|
+|Output Labels:|[bert]|
+|Language:|fa|
+|Size:|41.9 MB|
+|Case sensitive:|false|
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-07-30-albert_embeddings_albert_german_ner_de.md b/docs/_posts/ahmedlone127/2023-07-30-albert_embeddings_albert_german_ner_de.md
new file mode 100644
index 00000000000000..038a9d98ca32ff
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-07-30-albert_embeddings_albert_german_ner_de.md
@@ -0,0 +1,99 @@
+---
+layout: model
+title: German ALBERT Embeddings (from abhilash1910)
+author: John Snow Labs
+name: albert_embeddings_albert_german_ner
+date: 2023-07-30
+tags: [albert, embeddings, de, open_source, onnx]
+task: Embeddings
+language: de
+edition: Spark NLP 5.0.2
+spark_version: 3.0
+supported: true
+engine: onnx
+annotator: AlbertEmbeddings
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained ALBERT Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `albert-german-ner` is a German model orginally trained by `abhilash1910`.
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_embeddings_albert_german_ner_de_5.0.2_3.0_1690752850054.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_embeddings_albert_german_ner_de_5.0.2_3.0_1690752850054.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+documentAssembler = DocumentAssembler() \
+.setInputCol("text") \
+.setOutputCol("document")
+
+tokenizer = Tokenizer() \
+.setInputCols("document") \
+.setOutputCol("token")
+
+embeddings = AlbertEmbeddings.pretrained("albert_embeddings_albert_german_ner","de") \
+.setInputCols(["document", "token"]) \
+.setOutputCol("embeddings")
+
+pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings])
+
+data = spark.createDataFrame([["Ich liebe Funken NLP"]]).toDF("text")
+
+result = pipeline.fit(data).transform(data)
+```
+```scala
+val documentAssembler = new DocumentAssembler() 
+.setInputCol("text") 
+.setOutputCol("document")
+
+val tokenizer = new Tokenizer() 
+.setInputCols(Array("document"))
+.setOutputCol("token")
+
+val embeddings = AlbertEmbeddings.pretrained("albert_embeddings_albert_german_ner","de") 
+.setInputCols(Array("document", "token")) 
+.setOutputCol("embeddings")
+
+val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings))
+
+val data = Seq("Ich liebe Funken NLP").toDF("text")
+
+val result = pipeline.fit(data).transform(data)
+```
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("de.embed.albert_german_ner").predict("""Ich liebe Funken NLP""")
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|albert_embeddings_albert_german_ner|
+|Compatibility:|Spark NLP 5.0.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[sentence, token]|
+|Output Labels:|[bert]|
+|Language:|de|
+|Size:|42.0 MB|
+|Case sensitive:|false|
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-07-30-albert_embeddings_albert_large_arabic_ar.md b/docs/_posts/ahmedlone127/2023-07-30-albert_embeddings_albert_large_arabic_ar.md
new file mode 100644
index 00000000000000..45627e3aa3b848
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-07-30-albert_embeddings_albert_large_arabic_ar.md
@@ -0,0 +1,99 @@
+---
+layout: model
+title: Arabic ALBERT Embeddings (Large)
+author: John Snow Labs
+name: albert_embeddings_albert_large_arabic
+date: 2023-07-30
+tags: [albert, embeddings, ar, open_source, onnx]
+task: Embeddings
+language: ar
+edition: Spark NLP 5.0.2
+spark_version: 3.0
+supported: true
+engine: onnx
+annotator: AlbertEmbeddings
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained ALBERT Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `albert-large-arabic` is a Arabic model orginally trained by `asafaya`.
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_embeddings_albert_large_arabic_ar_5.0.2_3.0_1690752835564.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_embeddings_albert_large_arabic_ar_5.0.2_3.0_1690752835564.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+documentAssembler = DocumentAssembler() \
+.setInputCol("text") \
+.setOutputCol("document")
+
+tokenizer = Tokenizer() \
+.setInputCols("document") \
+.setOutputCol("token")
+
+embeddings = AlbertEmbeddings.pretrained("albert_embeddings_albert_large_arabic","ar") \
+.setInputCols(["document", "token"]) \
+.setOutputCol("embeddings")
+
+pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings])
+
+data = spark.createDataFrame([["أنا أحب شرارة NLP"]]).toDF("text")
+
+result = pipeline.fit(data).transform(data)
+```
+```scala
+val documentAssembler = new DocumentAssembler() 
+.setInputCol("text") 
+.setOutputCol("document")
+
+val tokenizer = new Tokenizer() 
+.setInputCols(Array("document"))
+.setOutputCol("token")
+
+val embeddings = AlbertEmbeddings.pretrained("albert_embeddings_albert_large_arabic","ar") 
+.setInputCols(Array("document", "token")) 
+.setOutputCol("embeddings")
+
+val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings))
+
+val data = Seq("أنا أحب شرارة NLP").toDF("text")
+
+val result = pipeline.fit(data).transform(data)
+```
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("ar.embed.albert_large_arabic").predict("""أنا أحب شرارة NLP""")
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|albert_embeddings_albert_large_arabic|
+|Compatibility:|Spark NLP 5.0.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[sentence, token]|
+|Output Labels:|[bert]|
+|Language:|ar|
+|Size:|62.8 MB|
+|Case sensitive:|false|
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-07-30-albert_embeddings_albert_large_bahasa_cased_ms.md b/docs/_posts/ahmedlone127/2023-07-30-albert_embeddings_albert_large_bahasa_cased_ms.md
new file mode 100644
index 00000000000000..b1d5a55edb5f34
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-07-30-albert_embeddings_albert_large_bahasa_cased_ms.md
@@ -0,0 +1,99 @@
+---
+layout: model
+title: Malay ALBERT Embeddings (Large)
+author: John Snow Labs
+name: albert_embeddings_albert_large_bahasa_cased
+date: 2023-07-30
+tags: [albert, embeddings, ms, open_source, onnx]
+task: Embeddings
+language: ms
+edition: Spark NLP 5.0.2
+spark_version: 3.0
+supported: true
+engine: onnx
+annotator: AlbertEmbeddings
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained ALBERT Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `albert-large-bahasa-cased` is a Malay model orginally trained by `malay-huggingface`.
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_embeddings_albert_large_bahasa_cased_ms_5.0.2_3.0_1690753388948.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_embeddings_albert_large_bahasa_cased_ms_5.0.2_3.0_1690753388948.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+documentAssembler = DocumentAssembler() \
+.setInputCol("text") \
+.setOutputCol("document")
+
+tokenizer = Tokenizer() \
+.setInputCols("document") \
+.setOutputCol("token")
+
+embeddings = AlbertEmbeddings.pretrained("albert_embeddings_albert_large_bahasa_cased","ms") \
+.setInputCols(["document", "token"]) \
+.setOutputCol("embeddings")
+
+pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings])
+
+data = spark.createDataFrame([["Saya suka Spark NLP"]]).toDF("text")
+
+result = pipeline.fit(data).transform(data)
+```
+```scala
+val documentAssembler = new DocumentAssembler() 
+.setInputCol("text") 
+.setOutputCol("document")
+
+val tokenizer = new Tokenizer() 
+.setInputCols(Array("document"))
+.setOutputCol("token")
+
+val embeddings = AlbertEmbeddings.pretrained("albert_embeddings_albert_large_bahasa_cased","ms") 
+.setInputCols(Array("document", "token")) 
+.setOutputCol("embeddings")
+
+val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings))
+
+val data = Seq("Saya suka Spark NLP").toDF("text")
+
+val result = pipeline.fit(data).transform(data)
+```
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("ms.embed.albert").predict("""Saya suka Spark NLP""")
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|albert_embeddings_albert_large_bahasa_cased|
+|Compatibility:|Spark NLP 5.0.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[sentence, token]|
+|Output Labels:|[bert]|
+|Language:|ms|
+|Size:|63.6 MB|
+|Case sensitive:|false|
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-07-30-albert_embeddings_albert_tiny_bahasa_cased_ms.md b/docs/_posts/ahmedlone127/2023-07-30-albert_embeddings_albert_tiny_bahasa_cased_ms.md
new file mode 100644
index 00000000000000..a50ff855bec557
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-07-30-albert_embeddings_albert_tiny_bahasa_cased_ms.md
@@ -0,0 +1,99 @@
+---
+layout: model
+title: Malay ALBERT Embeddings (Tiny)
+author: John Snow Labs
+name: albert_embeddings_albert_tiny_bahasa_cased
+date: 2023-07-30
+tags: [albert, embeddings, ms, open_source, onnx]
+task: Embeddings
+language: ms
+edition: Spark NLP 5.0.2
+spark_version: 3.0
+supported: true
+engine: onnx
+annotator: AlbertEmbeddings
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained ALBERT Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `albert-tiny-bahasa-cased` is a Malay model orginally trained by `malay-huggingface`.
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_embeddings_albert_tiny_bahasa_cased_ms_5.0.2_3.0_1690752867859.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_embeddings_albert_tiny_bahasa_cased_ms_5.0.2_3.0_1690752867859.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+documentAssembler = DocumentAssembler() \
+.setInputCol("text") \
+.setOutputCol("document")
+
+tokenizer = Tokenizer() \
+.setInputCols("document") \
+.setOutputCol("token")
+
+embeddings = AlbertEmbeddings.pretrained("albert_embeddings_albert_tiny_bahasa_cased","ms") \
+.setInputCols(["document", "token"]) \
+.setOutputCol("embeddings")
+
+pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings])
+
+data = spark.createDataFrame([["Saya suka Spark NLP"]]).toDF("text")
+
+result = pipeline.fit(data).transform(data)
+```
+```scala
+val documentAssembler = new DocumentAssembler() 
+.setInputCol("text") 
+.setOutputCol("document")
+
+val tokenizer = new Tokenizer() 
+.setInputCols(Array("document"))
+.setOutputCol("token")
+
+val embeddings = AlbertEmbeddings.pretrained("albert_embeddings_albert_tiny_bahasa_cased","ms") 
+.setInputCols(Array("document", "token")) 
+.setOutputCol("embeddings")
+
+val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings))
+
+val data = Seq("Saya suka Spark NLP").toDF("text")
+
+val result = pipeline.fit(data).transform(data)
+```
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("ms.embed.albert_tiny_bahasa_cased").predict("""Saya suka Spark NLP""")
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|albert_embeddings_albert_tiny_bahasa_cased|
+|Compatibility:|Spark NLP 5.0.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[sentence, token]|
+|Output Labels:|[bert]|
+|Language:|ms|
+|Size:|21.3 MB|
+|Case sensitive:|false|
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-07-30-albert_embeddings_fralbert_base_fr.md b/docs/_posts/ahmedlone127/2023-07-30-albert_embeddings_fralbert_base_fr.md
new file mode 100644
index 00000000000000..36baf514c0219f
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-07-30-albert_embeddings_fralbert_base_fr.md
@@ -0,0 +1,99 @@
+---
+layout: model
+title: French ALBERT Embeddings (from qwant)
+author: John Snow Labs
+name: albert_embeddings_fralbert_base
+date: 2023-07-30
+tags: [albert, embeddings, fr, open_source, onnx]
+task: Embeddings
+language: fr
+edition: Spark NLP 5.0.2
+spark_version: 3.0
+supported: true
+engine: onnx
+annotator: AlbertEmbeddings
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained ALBERT Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `fralbert-base` is a French model orginally trained by `qwant`.
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_embeddings_fralbert_base_fr_5.0.2_3.0_1690752813444.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_embeddings_fralbert_base_fr_5.0.2_3.0_1690752813444.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+documentAssembler = DocumentAssembler() \
+.setInputCol("text") \
+.setOutputCol("document")
+
+tokenizer = Tokenizer() \
+.setInputCols("document") \
+.setOutputCol("token")
+
+embeddings = AlbertEmbeddings.pretrained("albert_embeddings_fralbert_base","fr") \
+.setInputCols(["document", "token"]) \
+.setOutputCol("embeddings")
+
+pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings])
+
+data = spark.createDataFrame([["J'adore Spark Nlp"]]).toDF("text")
+
+result = pipeline.fit(data).transform(data)
+```
+```scala
+val documentAssembler = new DocumentAssembler() 
+.setInputCol("text") 
+.setOutputCol("document")
+
+val tokenizer = new Tokenizer() 
+.setInputCols(Array("document"))
+.setOutputCol("token")
+
+val embeddings = AlbertEmbeddings.pretrained("albert_embeddings_fralbert_base","fr") 
+.setInputCols(Array("document", "token")) 
+.setOutputCol("embeddings")
+
+val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings))
+
+val data = Seq("J'adore Spark Nlp").toDF("text")
+
+val result = pipeline.fit(data).transform(data)
+```
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("fr.embed.albert").predict("""J'adore Spark Nlp""")
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|albert_embeddings_fralbert_base|
+|Compatibility:|Spark NLP 5.0.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[sentence, token]|
+|Output Labels:|[bert]|
+|Language:|fr|
+|Size:|43.0 MB|
+|Case sensitive:|false|
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-07-30-albert_embeddings_marathi_albert_mr.md b/docs/_posts/ahmedlone127/2023-07-30-albert_embeddings_marathi_albert_mr.md
new file mode 100644
index 00000000000000..f1e0b9468b9294
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-07-30-albert_embeddings_marathi_albert_mr.md
@@ -0,0 +1,99 @@
+---
+layout: model
+title: Marathi ALBERT Embeddings (v1)
+author: John Snow Labs
+name: albert_embeddings_marathi_albert
+date: 2023-07-30
+tags: [albert, embeddings, mr, open_source, onnx]
+task: Embeddings
+language: mr
+edition: Spark NLP 5.0.2
+spark_version: 3.0
+supported: true
+engine: onnx
+annotator: AlbertEmbeddings
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained ALBERT Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `marathi-albert` is a Marathi model orginally trained by `l3cube-pune`.
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_embeddings_marathi_albert_mr_5.0.2_3.0_1690752853424.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_embeddings_marathi_albert_mr_5.0.2_3.0_1690752853424.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+documentAssembler = DocumentAssembler() \
+.setInputCol("text") \
+.setOutputCol("document")
+
+tokenizer = Tokenizer() \
+.setInputCols("document") \
+.setOutputCol("token")
+
+embeddings = AlbertEmbeddings.pretrained("albert_embeddings_marathi_albert","mr") \
+.setInputCols(["document", "token"]) \
+.setOutputCol("embeddings")
+
+pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings])
+
+data = spark.createDataFrame([["मला स्पार्क एनएलपी आवडते"]]).toDF("text")
+
+result = pipeline.fit(data).transform(data)
+```
+```scala
+val documentAssembler = new DocumentAssembler() 
+.setInputCol("text") 
+.setOutputCol("document")
+
+val tokenizer = new Tokenizer() 
+.setInputCols(Array("document"))
+.setOutputCol("token")
+
+val embeddings = AlbertEmbeddings.pretrained("albert_embeddings_marathi_albert","mr") 
+.setInputCols(Array("document", "token")) 
+.setOutputCol("embeddings")
+
+val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings))
+
+val data = Seq("मला स्पार्क एनएलपी आवडते").toDF("text")
+
+val result = pipeline.fit(data).transform(data)
+```
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("mr.embed.albert").predict("""मला स्पार्क एनएलपी आवडते""")
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|albert_embeddings_marathi_albert|
+|Compatibility:|Spark NLP 5.0.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[sentence, token]|
+|Output Labels:|[bert]|
+|Language:|mr|
+|Size:|42.6 MB|
+|Case sensitive:|false|
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-07-30-albert_embeddings_marathi_albert_v2_mr.md b/docs/_posts/ahmedlone127/2023-07-30-albert_embeddings_marathi_albert_v2_mr.md
new file mode 100644
index 00000000000000..401cb9ec3030b0
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-07-30-albert_embeddings_marathi_albert_v2_mr.md
@@ -0,0 +1,99 @@
+---
+layout: model
+title: Marathi ALBERT Embeddings (v2)
+author: John Snow Labs
+name: albert_embeddings_marathi_albert_v2
+date: 2023-07-30
+tags: [albert, embeddings, mr, open_source, onnx]
+task: Embeddings
+language: mr
+edition: Spark NLP 5.0.2
+spark_version: 3.0
+supported: true
+engine: onnx
+annotator: AlbertEmbeddings
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained ALBERT Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `marathi-albert-v2` is a Marathi model orginally trained by `l3cube-pune`.
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_embeddings_marathi_albert_v2_mr_5.0.2_3.0_1690753295251.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_embeddings_marathi_albert_v2_mr_5.0.2_3.0_1690753295251.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+documentAssembler = DocumentAssembler() \
+.setInputCol("text") \
+.setOutputCol("document")
+
+tokenizer = Tokenizer() \
+.setInputCols("document") \
+.setOutputCol("token")
+
+embeddings = AlbertEmbeddings.pretrained("albert_embeddings_marathi_albert_v2","mr") \
+.setInputCols(["document", "token"]) \
+.setOutputCol("embeddings")
+
+pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings])
+
+data = spark.createDataFrame([["मला स्पार्क एनएलपी आवडते"]]).toDF("text")
+
+result = pipeline.fit(data).transform(data)
+```
+```scala
+val documentAssembler = new DocumentAssembler() 
+.setInputCol("text") 
+.setOutputCol("document")
+
+val tokenizer = new Tokenizer() 
+.setInputCols(Array("document"))
+.setOutputCol("token")
+
+val embeddings = AlbertEmbeddings.pretrained("albert_embeddings_marathi_albert_v2","mr") 
+.setInputCols(Array("document", "token")) 
+.setOutputCol("embeddings")
+
+val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings))
+
+val data = Seq("मला स्पार्क एनएलपी आवडते").toDF("text")
+
+val result = pipeline.fit(data).transform(data)
+```
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("mr.embed.albert_v2").predict("""मला स्पार्क एनएलपी आवडते""")
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|albert_embeddings_marathi_albert_v2|
+|Compatibility:|Spark NLP 5.0.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[sentence, token]|
+|Output Labels:|[bert]|
+|Language:|mr|
+|Size:|125.5 MB|
+|Case sensitive:|false|
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-07-30-camembert_embeddings_camembert_aux_amandes_mt.md b/docs/_posts/ahmedlone127/2023-07-30-camembert_embeddings_camembert_aux_amandes_mt.md
new file mode 100644
index 00000000000000..2de5a5480b5f52
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-07-30-camembert_embeddings_camembert_aux_amandes_mt.md
@@ -0,0 +1,99 @@
+---
+layout: model
+title: Maltese CamemBert Embeddings (from fenrhjen)
+author: John Snow Labs
+name: camembert_embeddings_camembert_aux_amandes
+date: 2023-07-30
+tags: [mt, open_source, camembert, embeddings, onnx]
+task: Embeddings
+language: mt
+edition: Spark NLP 5.0.2
+spark_version: 3.0
+supported: true
+engine: onnx
+annotator: CamemBertEmbeddings
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained CamemBert Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `camembert_aux_amandes` is a Maltese model orginally trained by `fenrhjen`.
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_embeddings_camembert_aux_amandes_mt_5.0.2_3.0_1690755523370.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_embeddings_camembert_aux_amandes_mt_5.0.2_3.0_1690755523370.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+documentAssembler = DocumentAssembler() \
+    .setInputCol("text") \
+    .setOutputCol("document")
+
+tokenizer = Tokenizer() \
+    .setInputCols("document") \
+    .setOutputCol("token")
+  
+embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_camembert_aux_amandes","mt") \
+    .setInputCols(["document", "token"]) \
+    .setOutputCol("embeddings")
+    
+pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings])
+
+data = spark.createDataFrame([["I Love Spark NLP"]]).toDF("text")
+
+result = pipeline.fit(data).transform(data)
+```
+```scala
+val documentAssembler = new DocumentAssembler() 
+      .setInputCol("text") 
+      .setOutputCol("document")
+ 
+val tokenizer = new Tokenizer() 
+    .setInputCols(Array("document"))
+    .setOutputCol("token")
+
+val embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_camembert_aux_amandes","mt") 
+    .setInputCols(Array("document", "token")) 
+    .setOutputCol("embeddings")
+
+val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings))
+
+val data = Seq("I Love Spark NLP").toDF("text")
+
+val result = pipeline.fit(data).transform(data)
+```
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("mt.embed.camembert").predict("""I Love Spark NLP""")
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|camembert_embeddings_camembert_aux_amandes|
+|Compatibility:|Spark NLP 5.0.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[sentence, token]|
+|Output Labels:|[embeddings]|
+|Language:|mt|
+|Size:|412.4 MB|
+|Case sensitive:|true|
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-07-30-camembert_embeddings_camembert_mlm_fr.md b/docs/_posts/ahmedlone127/2023-07-30-camembert_embeddings_camembert_mlm_fr.md
new file mode 100644
index 00000000000000..bf7603675b6c3e
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-07-30-camembert_embeddings_camembert_mlm_fr.md
@@ -0,0 +1,99 @@
+---
+layout: model
+title: French CamemBert Embeddings (from Jodsa)
+author: John Snow Labs
+name: camembert_embeddings_camembert_mlm
+date: 2023-07-30
+tags: [fr, open_source, camembert, embeddings, onnx]
+task: Embeddings
+language: fr
+edition: Spark NLP 5.0.2
+spark_version: 3.0
+supported: true
+engine: onnx
+annotator: CamemBertEmbeddings
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained CamemBert Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `camembert_mlm` is a French model orginally trained by `Jodsa`.
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_embeddings_camembert_mlm_fr_5.0.2_3.0_1690755250840.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_embeddings_camembert_mlm_fr_5.0.2_3.0_1690755250840.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+documentAssembler = DocumentAssembler() \
+    .setInputCol("text") \
+    .setOutputCol("document")
+
+tokenizer = Tokenizer() \
+    .setInputCols("document") \
+    .setOutputCol("token")
+  
+embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_camembert_mlm","fr") \
+    .setInputCols(["document", "token"]) \
+    .setOutputCol("embeddings")
+    
+pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings])
+
+data = spark.createDataFrame([["J'adore Spark NLP"]]).toDF("text")
+
+result = pipeline.fit(data).transform(data)
+```
+```scala
+val documentAssembler = new DocumentAssembler() 
+      .setInputCol("text") 
+      .setOutputCol("document")
+ 
+val tokenizer = new Tokenizer() 
+    .setInputCols(Array("document"))
+    .setOutputCol("token")
+
+val embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_camembert_mlm","fr") 
+    .setInputCols(Array("document", "token")) 
+    .setOutputCol("embeddings")
+
+val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings))
+
+val data = Seq("J'adore Spark NLP").toDF("text")
+
+val result = pipeline.fit(data).transform(data)
+```
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("fr.embed.camembert.by_jodsa").predict("""J'adore Spark NLP""")
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|camembert_embeddings_camembert_mlm|
+|Compatibility:|Spark NLP 5.0.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[sentence, token]|
+|Output Labels:|[embeddings]|
+|Language:|fr|
+|Size:|417.9 MB|
+|Case sensitive:|true|
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-07-30-camembert_embeddings_das22_10_camembert_pretrained_fr.md b/docs/_posts/ahmedlone127/2023-07-30-camembert_embeddings_das22_10_camembert_pretrained_fr.md
new file mode 100644
index 00000000000000..88001282ca4e52
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-07-30-camembert_embeddings_das22_10_camembert_pretrained_fr.md
@@ -0,0 +1,99 @@
+---
+layout: model
+title: French CamemBert Embeddings (from HueyNemud)
+author: John Snow Labs
+name: camembert_embeddings_das22_10_camembert_pretrained
+date: 2023-07-30
+tags: [fr, open_source, camembert, embeddings, onnx]
+task: Embeddings
+language: fr
+edition: Spark NLP 5.0.2
+spark_version: 3.0
+supported: true
+engine: onnx
+annotator: CamemBertEmbeddings
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained CamemBert Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `das22-10-camembert_pretrained` is a French model orginally trained by `HueyNemud`.
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_embeddings_das22_10_camembert_pretrained_fr_5.0.2_3.0_1690755233634.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_embeddings_das22_10_camembert_pretrained_fr_5.0.2_3.0_1690755233634.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+documentAssembler = DocumentAssembler() \
+    .setInputCol("text") \
+    .setOutputCol("document")
+
+tokenizer = Tokenizer() \
+    .setInputCols("document") \
+    .setOutputCol("token")
+  
+embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_das22_10_camembert_pretrained","fr") \
+    .setInputCols(["document", "token"]) \
+    .setOutputCol("embeddings")
+    
+pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings])
+
+data = spark.createDataFrame([["J'adore Spark NLP"]]).toDF("text")
+
+result = pipeline.fit(data).transform(data)
+```
+```scala
+val documentAssembler = new DocumentAssembler() 
+      .setInputCol("text") 
+      .setOutputCol("document")
+ 
+val tokenizer = new Tokenizer() 
+    .setInputCols(Array("document"))
+    .setOutputCol("token")
+
+val embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_das22_10_camembert_pretrained","fr") 
+    .setInputCols(Array("document", "token")) 
+    .setOutputCol("embeddings")
+
+val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings))
+
+val data = Seq("J'adore Spark NLP").toDF("text")
+
+val result = pipeline.fit(data).transform(data)
+```
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("fr.embed.camembert.by_hueynemud").predict("""J'adore Spark NLP""")
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|camembert_embeddings_das22_10_camembert_pretrained|
+|Compatibility:|Spark NLP 5.0.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[sentence, token]|
+|Output Labels:|[embeddings]|
+|Language:|fr|
+|Size:|412.8 MB|
+|Case sensitive:|true|
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-07-30-camembert_embeddings_dianeshan_generic_model_fr.md b/docs/_posts/ahmedlone127/2023-07-30-camembert_embeddings_dianeshan_generic_model_fr.md
new file mode 100644
index 00000000000000..2b802a94df3c0a
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-07-30-camembert_embeddings_dianeshan_generic_model_fr.md
@@ -0,0 +1,99 @@
+---
+layout: model
+title: French CamemBert Embeddings (from dianeshan)
+author: John Snow Labs
+name: camembert_embeddings_dianeshan_generic_model
+date: 2023-07-30
+tags: [fr, open_source, camembert, embeddings, onnx]
+task: Embeddings
+language: fr
+edition: Spark NLP 5.0.2
+spark_version: 3.0
+supported: true
+engine: onnx
+annotator: CamemBertEmbeddings
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained CamemBert Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `dummy-model` is a French model orginally trained by `dianeshan`.
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_embeddings_dianeshan_generic_model_fr_5.0.2_3.0_1690755640623.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_embeddings_dianeshan_generic_model_fr_5.0.2_3.0_1690755640623.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+documentAssembler = DocumentAssembler() \
+    .setInputCol("text") \
+    .setOutputCol("document")
+
+tokenizer = Tokenizer() \
+    .setInputCols("document") \
+    .setOutputCol("token")
+  
+embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_dianeshan_generic_model","fr") \
+    .setInputCols(["document", "token"]) \
+    .setOutputCol("embeddings")
+    
+pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings])
+
+data = spark.createDataFrame([["J'adore Spark NLP"]]).toDF("text")
+
+result = pipeline.fit(data).transform(data)
+```
+```scala
+val documentAssembler = new DocumentAssembler() 
+      .setInputCol("text") 
+      .setOutputCol("document")
+ 
+val tokenizer = new Tokenizer() 
+    .setInputCols(Array("document"))
+    .setOutputCol("token")
+
+val embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_dianeshan_generic_model","fr") 
+    .setInputCols(Array("document", "token")) 
+    .setOutputCol("embeddings")
+
+val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings))
+
+val data = Seq("J'adore Spark NLP").toDF("text")
+
+val result = pipeline.fit(data).transform(data)
+```
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("fr.embed.camembert.generic.by_dianeshan").predict("""J'adore Spark NLP""")
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|camembert_embeddings_dianeshan_generic_model|
+|Compatibility:|Spark NLP 5.0.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[sentence, token]|
+|Output Labels:|[embeddings]|
+|Language:|fr|
+|Size:|264.0 MB|
+|Case sensitive:|true|
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-07-30-camembert_embeddings_edge2992_generic_model_fr.md b/docs/_posts/ahmedlone127/2023-07-30-camembert_embeddings_edge2992_generic_model_fr.md
new file mode 100644
index 00000000000000..1696cd080f1959
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-07-30-camembert_embeddings_edge2992_generic_model_fr.md
@@ -0,0 +1,99 @@
+---
+layout: model
+title: French CamemBert Embeddings (from edge2992)
+author: John Snow Labs
+name: camembert_embeddings_edge2992_generic_model
+date: 2023-07-30
+tags: [fr, open_source, camembert, embeddings, onnx]
+task: Embeddings
+language: fr
+edition: Spark NLP 5.0.2
+spark_version: 3.0
+supported: true
+engine: onnx
+annotator: CamemBertEmbeddings
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained CamemBert Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `dummy-model` is a French model orginally trained by `edge2992`.
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_embeddings_edge2992_generic_model_fr_5.0.2_3.0_1690755314295.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_embeddings_edge2992_generic_model_fr_5.0.2_3.0_1690755314295.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+documentAssembler = DocumentAssembler() \
+    .setInputCol("text") \
+    .setOutputCol("document")
+
+tokenizer = Tokenizer() \
+    .setInputCols("document") \
+    .setOutputCol("token")
+  
+embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_edge2992_generic_model","fr") \
+    .setInputCols(["document", "token"]) \
+    .setOutputCol("embeddings")
+    
+pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings])
+
+data = spark.createDataFrame([["J'adore Spark NLP"]]).toDF("text")
+
+result = pipeline.fit(data).transform(data)
+```
+```scala
+val documentAssembler = new DocumentAssembler() 
+      .setInputCol("text") 
+      .setOutputCol("document")
+ 
+val tokenizer = new Tokenizer() 
+    .setInputCols(Array("document"))
+    .setOutputCol("token")
+
+val embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_edge2992_generic_model","fr") 
+    .setInputCols(Array("document", "token")) 
+    .setOutputCol("embeddings")
+
+val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings))
+
+val data = Seq("J'adore Spark NLP").toDF("text")
+
+val result = pipeline.fit(data).transform(data)
+```
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("fr.embed.camembert.generic.by_edge2992").predict("""J'adore Spark NLP""")
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|camembert_embeddings_edge2992_generic_model|
+|Compatibility:|Spark NLP 5.0.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[sentence, token]|
+|Output Labels:|[embeddings]|
+|Language:|fr|
+|Size:|264.0 MB|
+|Case sensitive:|true|
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-07-30-camembert_embeddings_elliotsmith_generic_model_fr.md b/docs/_posts/ahmedlone127/2023-07-30-camembert_embeddings_elliotsmith_generic_model_fr.md
new file mode 100644
index 00000000000000..04b6b4dccd2773
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-07-30-camembert_embeddings_elliotsmith_generic_model_fr.md
@@ -0,0 +1,99 @@
+---
+layout: model
+title: French CamemBert Embeddings (from elliotsmith)
+author: John Snow Labs
+name: camembert_embeddings_elliotsmith_generic_model
+date: 2023-07-30
+tags: [fr, open_source, camembert, embeddings, onnx]
+task: Embeddings
+language: fr
+edition: Spark NLP 5.0.2
+spark_version: 3.0
+supported: true
+engine: onnx
+annotator: CamemBertEmbeddings
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained CamemBert Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `dummy-model` is a French model orginally trained by `elliotsmith`.
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_embeddings_elliotsmith_generic_model_fr_5.0.2_3.0_1690755611886.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_embeddings_elliotsmith_generic_model_fr_5.0.2_3.0_1690755611886.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+documentAssembler = DocumentAssembler() \
+    .setInputCol("text") \
+    .setOutputCol("document")
+
+tokenizer = Tokenizer() \
+    .setInputCols("document") \
+    .setOutputCol("token")
+  
+embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_elliotsmith_generic_model","fr") \
+    .setInputCols(["document", "token"]) \
+    .setOutputCol("embeddings")
+    
+pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings])
+
+data = spark.createDataFrame([["J'adore Spark NLP"]]).toDF("text")
+
+result = pipeline.fit(data).transform(data)
+```
+```scala
+val documentAssembler = new DocumentAssembler() 
+      .setInputCol("text") 
+      .setOutputCol("document")
+ 
+val tokenizer = new Tokenizer() 
+    .setInputCols(Array("document"))
+    .setOutputCol("token")
+
+val embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_elliotsmith_generic_model","fr") 
+    .setInputCols(Array("document", "token")) 
+    .setOutputCol("embeddings")
+
+val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings))
+
+val data = Seq("J'adore Spark NLP").toDF("text")
+
+val result = pipeline.fit(data).transform(data)
+```
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("fr.embed.camembert.generic.by_elliotsmith").predict("""J'adore Spark NLP""")
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|camembert_embeddings_elliotsmith_generic_model|
+|Compatibility:|Spark NLP 5.0.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[sentence, token]|
+|Output Labels:|[embeddings]|
+|Language:|fr|
+|Size:|264.0 MB|
+|Case sensitive:|true|
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-07-30-camembert_embeddings_elusive_magnolia_generic_model_fr.md b/docs/_posts/ahmedlone127/2023-07-30-camembert_embeddings_elusive_magnolia_generic_model_fr.md
new file mode 100644
index 00000000000000..9b0644bf825cea
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-07-30-camembert_embeddings_elusive_magnolia_generic_model_fr.md
@@ -0,0 +1,99 @@
+---
+layout: model
+title: French CamemBert Embeddings (from elusive-magnolia)
+author: John Snow Labs
+name: camembert_embeddings_elusive_magnolia_generic_model
+date: 2023-07-30
+tags: [fr, open_source, camembert, embeddings, onnx]
+task: Embeddings
+language: fr
+edition: Spark NLP 5.0.2
+spark_version: 3.0
+supported: true
+engine: onnx
+annotator: CamemBertEmbeddings
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained CamemBert Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `dummy-model` is a French model orginally trained by `elusive-magnolia`.
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_embeddings_elusive_magnolia_generic_model_fr_5.0.2_3.0_1690755320528.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_embeddings_elusive_magnolia_generic_model_fr_5.0.2_3.0_1690755320528.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+documentAssembler = DocumentAssembler() \
+    .setInputCol("text") \
+    .setOutputCol("document")
+
+tokenizer = Tokenizer() \
+    .setInputCols("document") \
+    .setOutputCol("token")
+  
+embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_elusive_magnolia_generic_model","fr") \
+    .setInputCols(["document", "token"]) \
+    .setOutputCol("embeddings")
+    
+pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings])
+
+data = spark.createDataFrame([["J'adore Spark NLP"]]).toDF("text")
+
+result = pipeline.fit(data).transform(data)
+```
+```scala
+val documentAssembler = new DocumentAssembler() 
+      .setInputCol("text") 
+      .setOutputCol("document")
+ 
+val tokenizer = new Tokenizer() 
+    .setInputCols(Array("document"))
+    .setOutputCol("token")
+
+val embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_elusive_magnolia_generic_model","fr") 
+    .setInputCols(Array("document", "token")) 
+    .setOutputCol("embeddings")
+
+val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings))
+
+val data = Seq("J'adore Spark NLP").toDF("text")
+
+val result = pipeline.fit(data).transform(data)
+```
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("fr.embed.camembert.generic.by_elusive_magnolia").predict("""J'adore Spark NLP""")
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|camembert_embeddings_elusive_magnolia_generic_model|
+|Compatibility:|Spark NLP 5.0.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[sentence, token]|
+|Output Labels:|[embeddings]|
+|Language:|fr|
+|Size:|264.0 MB|
+|Case sensitive:|true|
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-07-30-camembert_embeddings_zhenghuabin_generic_model_fr.md b/docs/_posts/ahmedlone127/2023-07-30-camembert_embeddings_zhenghuabin_generic_model_fr.md
new file mode 100644
index 00000000000000..5bfe25af51d77e
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-07-30-camembert_embeddings_zhenghuabin_generic_model_fr.md
@@ -0,0 +1,99 @@
+---
+layout: model
+title: French CamemBert Embeddings (from zhenghuabin)
+author: John Snow Labs
+name: camembert_embeddings_zhenghuabin_generic_model
+date: 2023-07-30
+tags: [fr, open_source, camembert, embeddings, onnx]
+task: Embeddings
+language: fr
+edition: Spark NLP 5.0.2
+spark_version: 3.0
+supported: true
+engine: onnx
+annotator: CamemBertEmbeddings
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained CamemBert Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `dummy_model` is a French model orginally trained by `zhenghuabin`.
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_embeddings_zhenghuabin_generic_model_fr_5.0.2_3.0_1690755345824.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_embeddings_zhenghuabin_generic_model_fr_5.0.2_3.0_1690755345824.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+documentAssembler = DocumentAssembler() \
+    .setInputCol("text") \
+    .setOutputCol("document")
+
+tokenizer = Tokenizer() \
+    .setInputCols("document") \
+    .setOutputCol("token")
+  
+embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_zhenghuabin_generic_model","fr") \
+    .setInputCols(["document", "token"]) \
+    .setOutputCol("embeddings")
+    
+pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings])
+
+data = spark.createDataFrame([["J'adore Spark NLP"]]).toDF("text")
+
+result = pipeline.fit(data).transform(data)
+```
+```scala
+val documentAssembler = new DocumentAssembler() 
+      .setInputCol("text") 
+      .setOutputCol("document")
+ 
+val tokenizer = new Tokenizer() 
+    .setInputCols(Array("document"))
+    .setOutputCol("token")
+
+val embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_zhenghuabin_generic_model","fr") 
+    .setInputCols(Array("document", "token")) 
+    .setOutputCol("embeddings")
+
+val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings))
+
+val data = Seq("J'adore Spark NLP").toDF("text")
+
+val result = pipeline.fit(data).transform(data)
+```
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("fr.embed.camembert.generic.by_zhenghuabin").predict("""J'adore Spark NLP""")
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|camembert_embeddings_zhenghuabin_generic_model|
+|Compatibility:|Spark NLP 5.0.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[sentence, token]|
+|Output Labels:|[embeddings]|
+|Language:|fr|
+|Size:|264.0 MB|
+|Case sensitive:|true|
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_DoyyingFace_generic_model_fr.md b/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_DoyyingFace_generic_model_fr.md
new file mode 100644
index 00000000000000..eb8cc1f3446f54
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_DoyyingFace_generic_model_fr.md
@@ -0,0 +1,93 @@
+---
+layout: model
+title: French CamemBert Embeddings (from DoyyingFace)
+author: John Snow Labs
+name: camembert_embeddings_DoyyingFace_generic_model
+date: 2023-07-31
+tags: [fr, open_source, camembert, embeddings, onnx]
+task: Embeddings
+language: fr
+edition: Spark NLP 5.0.2
+spark_version: 3.0
+supported: true
+engine: onnx
+annotator: CamemBertEmbeddings
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained CamemBert Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `dummy-model` is a French model orginally trained by `DoyyingFace`.
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_embeddings_DoyyingFace_generic_model_fr_5.0.2_3.0_1690840568040.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_embeddings_DoyyingFace_generic_model_fr_5.0.2_3.0_1690840568040.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+documentAssembler = DocumentAssembler() \
+    .setInputCol("text") \
+    .setOutputCol("document")
+
+tokenizer = Tokenizer() \
+    .setInputCols("document") \
+    .setOutputCol("token")
+  
+embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_DoyyingFace_generic_model","fr") \
+    .setInputCols(["document", "token"]) \
+    .setOutputCol("embeddings")
+    
+pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings])
+
+data = spark.createDataFrame([["J'adore Spark NLP"]]).toDF("text")
+
+result = pipeline.fit(data).transform(data)
+```
+```scala
+val documentAssembler = new DocumentAssembler() 
+      .setInputCol("text") 
+      .setOutputCol("document")
+ 
+val tokenizer = new Tokenizer() 
+    .setInputCols(Array("document"))
+    .setOutputCol("token")
+
+val embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_DoyyingFace_generic_model","fr") 
+    .setInputCols(Array("document", "token")) 
+    .setOutputCol("embeddings")
+
+val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings))
+
+val data = Seq("J'adore Spark NLP").toDF("text")
+
+val result = pipeline.fit(data).transform(data)
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|camembert_embeddings_DoyyingFace_generic_model|
+|Compatibility:|Spark NLP 5.0.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[sentence, token]|
+|Output Labels:|[embeddings]|
+|Language:|fr|
+|Size:|264.0 MB|
+|Case sensitive:|true|
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_Henrywang_generic_model_fr.md b/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_Henrywang_generic_model_fr.md
new file mode 100644
index 00000000000000..850aecb10f995c
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_Henrywang_generic_model_fr.md
@@ -0,0 +1,93 @@
+---
+layout: model
+title: French CamemBert Embeddings (from Henrywang)
+author: John Snow Labs
+name: camembert_embeddings_Henrywang_generic_model
+date: 2023-07-31
+tags: [fr, open_source, camembert, embeddings, onnx]
+task: Embeddings
+language: fr
+edition: Spark NLP 5.0.2
+spark_version: 3.0
+supported: true
+engine: onnx
+annotator: CamemBertEmbeddings
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained CamemBert Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `dummy-model` is a French model orginally trained by `Henrywang`.
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_embeddings_Henrywang_generic_model_fr_5.0.2_3.0_1690840573449.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_embeddings_Henrywang_generic_model_fr_5.0.2_3.0_1690840573449.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+documentAssembler = DocumentAssembler() \
+    .setInputCol("text") \
+    .setOutputCol("document")
+
+tokenizer = Tokenizer() \
+    .setInputCols("document") \
+    .setOutputCol("token")
+  
+embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_Henrywang_generic_model","fr") \
+    .setInputCols(["document", "token"]) \
+    .setOutputCol("embeddings")
+    
+pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings])
+
+data = spark.createDataFrame([["J'adore Spark NLP"]]).toDF("text")
+
+result = pipeline.fit(data).transform(data)
+```
+```scala
+val documentAssembler = new DocumentAssembler() 
+      .setInputCol("text") 
+      .setOutputCol("document")
+ 
+val tokenizer = new Tokenizer() 
+    .setInputCols(Array("document"))
+    .setOutputCol("token")
+
+val embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_Henrywang_generic_model","fr") 
+    .setInputCols(Array("document", "token")) 
+    .setOutputCol("embeddings")
+
+val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings))
+
+val data = Seq("J'adore Spark NLP").toDF("text")
+
+val result = pipeline.fit(data).transform(data)
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|camembert_embeddings_Henrywang_generic_model|
+|Compatibility:|Spark NLP 5.0.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[sentence, token]|
+|Output Labels:|[embeddings]|
+|Language:|fr|
+|Size:|264.0 MB|
+|Case sensitive:|true|
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_JonathanSum_generic_model_fr.md b/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_JonathanSum_generic_model_fr.md
new file mode 100644
index 00000000000000..373bdacf419b42
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_JonathanSum_generic_model_fr.md
@@ -0,0 +1,93 @@
+---
+layout: model
+title: French CamemBert Embeddings (from JonathanSum)
+author: John Snow Labs
+name: camembert_embeddings_JonathanSum_generic_model
+date: 2023-07-31
+tags: [fr, open_source, camembert, embeddings, onnx]
+task: Embeddings
+language: fr
+edition: Spark NLP 5.0.2
+spark_version: 3.0
+supported: true
+engine: onnx
+annotator: CamemBertEmbeddings
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained CamemBert Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `dummy-model` is a French model orginally trained by `JonathanSum`.
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_embeddings_JonathanSum_generic_model_fr_5.0.2_3.0_1690839719364.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_embeddings_JonathanSum_generic_model_fr_5.0.2_3.0_1690839719364.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+documentAssembler = DocumentAssembler() \
+    .setInputCol("text") \
+    .setOutputCol("document")
+
+tokenizer = Tokenizer() \
+    .setInputCols("document") \
+    .setOutputCol("token")
+  
+embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_JonathanSum_generic_model","fr") \
+    .setInputCols(["document", "token"]) \
+    .setOutputCol("embeddings")
+    
+pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings])
+
+data = spark.createDataFrame([["J'adore Spark NLP"]]).toDF("text")
+
+result = pipeline.fit(data).transform(data)
+```
+```scala
+val documentAssembler = new DocumentAssembler() 
+      .setInputCol("text") 
+      .setOutputCol("document")
+ 
+val tokenizer = new Tokenizer() 
+    .setInputCols(Array("document"))
+    .setOutputCol("token")
+
+val embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_JonathanSum_generic_model","fr") 
+    .setInputCols(Array("document", "token")) 
+    .setOutputCol("embeddings")
+
+val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings))
+
+val data = Seq("J'adore Spark NLP").toDF("text")
+
+val result = pipeline.fit(data).transform(data)
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|camembert_embeddings_JonathanSum_generic_model|
+|Compatibility:|Spark NLP 5.0.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[sentence, token]|
+|Output Labels:|[embeddings]|
+|Language:|fr|
+|Size:|264.0 MB|
+|Case sensitive:|true|
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_Katster_generic_model_fr.md b/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_Katster_generic_model_fr.md
new file mode 100644
index 00000000000000..2521d52f8bcab8
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_Katster_generic_model_fr.md
@@ -0,0 +1,93 @@
+---
+layout: model
+title: French CamemBert Embeddings (from Katster)
+author: John Snow Labs
+name: camembert_embeddings_Katster_generic_model
+date: 2023-07-31
+tags: [fr, open_source, camembert, embeddings, onnx]
+task: Embeddings
+language: fr
+edition: Spark NLP 5.0.2
+spark_version: 3.0
+supported: true
+engine: onnx
+annotator: CamemBertEmbeddings
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained CamemBert Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `dummy-model` is a French model orginally trained by `Katster`.
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_embeddings_Katster_generic_model_fr_5.0.2_3.0_1690839314673.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_embeddings_Katster_generic_model_fr_5.0.2_3.0_1690839314673.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+documentAssembler = DocumentAssembler() \
+    .setInputCol("text") \
+    .setOutputCol("document")
+
+tokenizer = Tokenizer() \
+    .setInputCols("document") \
+    .setOutputCol("token")
+  
+embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_Katster_generic_model","fr") \
+    .setInputCols(["document", "token"]) \
+    .setOutputCol("embeddings")
+    
+pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings])
+
+data = spark.createDataFrame([["J'adore Spark NLP"]]).toDF("text")
+
+result = pipeline.fit(data).transform(data)
+```
+```scala
+val documentAssembler = new DocumentAssembler() 
+      .setInputCol("text") 
+      .setOutputCol("document")
+ 
+val tokenizer = new Tokenizer() 
+    .setInputCols(Array("document"))
+    .setOutputCol("token")
+
+val embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_Katster_generic_model","fr") 
+    .setInputCols(Array("document", "token")) 
+    .setOutputCol("embeddings")
+
+val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings))
+
+val data = Seq("J'adore Spark NLP").toDF("text")
+
+val result = pipeline.fit(data).transform(data)
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|camembert_embeddings_Katster_generic_model|
+|Compatibility:|Spark NLP 5.0.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[sentence, token]|
+|Output Labels:|[embeddings]|
+|Language:|fr|
+|Size:|264.0 MB|
+|Case sensitive:|true|
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_Leisa_generic_model_fr.md b/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_Leisa_generic_model_fr.md
new file mode 100644
index 00000000000000..11fe2eba2f4b9e
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_Leisa_generic_model_fr.md
@@ -0,0 +1,93 @@
+---
+layout: model
+title: French CamemBert Embeddings (from Leisa)
+author: John Snow Labs
+name: camembert_embeddings_Leisa_generic_model
+date: 2023-07-31
+tags: [fr, open_source, camembert, embeddings, onnx]
+task: Embeddings
+language: fr
+edition: Spark NLP 5.0.2
+spark_version: 3.0
+supported: true
+engine: onnx
+annotator: CamemBertEmbeddings
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained CamemBert Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `dummy-model` is a French model orginally trained by `Leisa`.
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_embeddings_Leisa_generic_model_fr_5.0.2_3.0_1690839743810.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_embeddings_Leisa_generic_model_fr_5.0.2_3.0_1690839743810.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+documentAssembler = DocumentAssembler() \
+    .setInputCol("text") \
+    .setOutputCol("document")
+
+tokenizer = Tokenizer() \
+    .setInputCols("document") \
+    .setOutputCol("token")
+  
+embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_Leisa_generic_model","fr") \
+    .setInputCols(["document", "token"]) \
+    .setOutputCol("embeddings")
+    
+pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings])
+
+data = spark.createDataFrame([["J'adore Spark NLP"]]).toDF("text")
+
+result = pipeline.fit(data).transform(data)
+```
+```scala
+val documentAssembler = new DocumentAssembler() 
+      .setInputCol("text") 
+      .setOutputCol("document")
+ 
+val tokenizer = new Tokenizer() 
+    .setInputCols(Array("document"))
+    .setOutputCol("token")
+
+val embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_Leisa_generic_model","fr") 
+    .setInputCols(Array("document", "token")) 
+    .setOutputCol("embeddings")
+
+val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings))
+
+val data = Seq("J'adore Spark NLP").toDF("text")
+
+val result = pipeline.fit(data).transform(data)
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|camembert_embeddings_Leisa_generic_model|
+|Compatibility:|Spark NLP 5.0.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[sentence, token]|
+|Output Labels:|[embeddings]|
+|Language:|fr|
+|Size:|264.0 MB|
+|Case sensitive:|true|
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_MYX4567_generic_model_fr.md b/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_MYX4567_generic_model_fr.md
new file mode 100644
index 00000000000000..2fe8dc27a6876f
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_MYX4567_generic_model_fr.md
@@ -0,0 +1,93 @@
+---
+layout: model
+title: French CamemBert Embeddings (from MYX4567)
+author: John Snow Labs
+name: camembert_embeddings_MYX4567_generic_model
+date: 2023-07-31
+tags: [fr, open_source, camembert, embeddings, onnx]
+task: Embeddings
+language: fr
+edition: Spark NLP 5.0.2
+spark_version: 3.0
+supported: true
+engine: onnx
+annotator: CamemBertEmbeddings
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained CamemBert Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `dummy-model` is a French model orginally trained by `MYX4567`.
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_embeddings_MYX4567_generic_model_fr_5.0.2_3.0_1690839423024.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_embeddings_MYX4567_generic_model_fr_5.0.2_3.0_1690839423024.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+documentAssembler = DocumentAssembler() \
+    .setInputCol("text") \
+    .setOutputCol("document")
+
+tokenizer = Tokenizer() \
+    .setInputCols("document") \
+    .setOutputCol("token")
+  
+embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_MYX4567_generic_model","fr") \
+    .setInputCols(["document", "token"]) \
+    .setOutputCol("embeddings")
+    
+pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings])
+
+data = spark.createDataFrame([["J'adore Spark NLP"]]).toDF("text")
+
+result = pipeline.fit(data).transform(data)
+```
+```scala
+val documentAssembler = new DocumentAssembler() 
+      .setInputCol("text") 
+      .setOutputCol("document")
+ 
+val tokenizer = new Tokenizer() 
+    .setInputCols(Array("document"))
+    .setOutputCol("token")
+
+val embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_MYX4567_generic_model","fr") 
+    .setInputCols(Array("document", "token")) 
+    .setOutputCol("embeddings")
+
+val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings))
+
+val data = Seq("J'adore Spark NLP").toDF("text")
+
+val result = pipeline.fit(data).transform(data)
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|camembert_embeddings_MYX4567_generic_model|
+|Compatibility:|Spark NLP 5.0.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[sentence, token]|
+|Output Labels:|[embeddings]|
+|Language:|fr|
+|Size:|264.0 MB|
+|Case sensitive:|true|
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_Sebu_generic_model_fr.md b/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_Sebu_generic_model_fr.md
new file mode 100644
index 00000000000000..916051f233bf31
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_Sebu_generic_model_fr.md
@@ -0,0 +1,93 @@
+---
+layout: model
+title: French CamemBert Embeddings (from Sebu)
+author: John Snow Labs
+name: camembert_embeddings_Sebu_generic_model
+date: 2023-07-31
+tags: [fr, open_source, camembert, embeddings, onnx]
+task: Embeddings
+language: fr
+edition: Spark NLP 5.0.2
+spark_version: 3.0
+supported: true
+engine: onnx
+annotator: CamemBertEmbeddings
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained CamemBert Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `dummy-model` is a French model orginally trained by `Sebu`.
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_embeddings_Sebu_generic_model_fr_5.0.2_3.0_1690838692036.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_embeddings_Sebu_generic_model_fr_5.0.2_3.0_1690838692036.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+documentAssembler = DocumentAssembler() \
+    .setInputCol("text") \
+    .setOutputCol("document")
+
+tokenizer = Tokenizer() \
+    .setInputCols("document") \
+    .setOutputCol("token")
+  
+embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_Sebu_generic_model","fr") \
+    .setInputCols(["document", "token"]) \
+    .setOutputCol("embeddings")
+    
+pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings])
+
+data = spark.createDataFrame([["J'adore Spark NLP"]]).toDF("text")
+
+result = pipeline.fit(data).transform(data)
+```
+```scala
+val documentAssembler = new DocumentAssembler() 
+      .setInputCol("text") 
+      .setOutputCol("document")
+ 
+val tokenizer = new Tokenizer() 
+    .setInputCols(Array("document"))
+    .setOutputCol("token")
+
+val embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_Sebu_generic_model","fr") 
+    .setInputCols(Array("document", "token")) 
+    .setOutputCol("embeddings")
+
+val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings))
+
+val data = Seq("J'adore Spark NLP").toDF("text")
+
+val result = pipeline.fit(data).transform(data)
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|camembert_embeddings_Sebu_generic_model|
+|Compatibility:|Spark NLP 5.0.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[sentence, token]|
+|Output Labels:|[embeddings]|
+|Language:|fr|
+|Size:|264.0 MB|
+|Case sensitive:|true|
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_SummFinFR_fr.md b/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_SummFinFR_fr.md
new file mode 100644
index 00000000000000..24347b800134c1
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_SummFinFR_fr.md
@@ -0,0 +1,93 @@
+---
+layout: model
+title: French CamemBert Embeddings (from Ghani-25)
+author: John Snow Labs
+name: camembert_embeddings_SummFinFR
+date: 2023-07-31
+tags: [fr, open_source, camembert, embeddings, onnx]
+task: Embeddings
+language: fr
+edition: Spark NLP 5.0.2
+spark_version: 3.0
+supported: true
+engine: onnx
+annotator: CamemBertEmbeddings
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained CamemBert Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `SummFinFR` is a French model orginally trained by `Ghani-25`.
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_embeddings_SummFinFR_fr_5.0.2_3.0_1690838983983.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_embeddings_SummFinFR_fr_5.0.2_3.0_1690838983983.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+documentAssembler = DocumentAssembler() \
+    .setInputCol("text") \
+    .setOutputCol("document")
+
+tokenizer = Tokenizer() \
+    .setInputCols("document") \
+    .setOutputCol("token")
+  
+embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_SummFinFR","fr") \
+    .setInputCols(["document", "token"]) \
+    .setOutputCol("embeddings")
+    
+pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings])
+
+data = spark.createDataFrame([["J'adore Spark NLP"]]).toDF("text")
+
+result = pipeline.fit(data).transform(data)
+```
+```scala
+val documentAssembler = new DocumentAssembler() 
+      .setInputCol("text") 
+      .setOutputCol("document")
+ 
+val tokenizer = new Tokenizer() 
+    .setInputCols(Array("document"))
+    .setOutputCol("token")
+
+val embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_SummFinFR","fr") 
+    .setInputCols(Array("document", "token")) 
+    .setOutputCol("embeddings")
+
+val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings))
+
+val data = Seq("J'adore Spark NLP").toDF("text")
+
+val result = pipeline.fit(data).transform(data)
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|camembert_embeddings_SummFinFR|
+|Compatibility:|Spark NLP 5.0.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[sentence, token]|
+|Output Labels:|[embeddings]|
+|Language:|fr|
+|Size:|412.4 MB|
+|Case sensitive:|true|
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_Weipeng_generic_model_fr.md b/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_Weipeng_generic_model_fr.md
new file mode 100644
index 00000000000000..edafd431387b1b
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_Weipeng_generic_model_fr.md
@@ -0,0 +1,93 @@
+---
+layout: model
+title: French CamemBert Embeddings (from Weipeng)
+author: John Snow Labs
+name: camembert_embeddings_Weipeng_generic_model
+date: 2023-07-31
+tags: [fr, open_source, camembert, embeddings, onnx]
+task: Embeddings
+language: fr
+edition: Spark NLP 5.0.2
+spark_version: 3.0
+supported: true
+engine: onnx
+annotator: CamemBertEmbeddings
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained CamemBert Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `dummy-model` is a French model orginally trained by `Weipeng`.
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_embeddings_Weipeng_generic_model_fr_5.0.2_3.0_1690838714201.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_embeddings_Weipeng_generic_model_fr_5.0.2_3.0_1690838714201.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+documentAssembler = DocumentAssembler() \
+    .setInputCol("text") \
+    .setOutputCol("document")
+
+tokenizer = Tokenizer() \
+    .setInputCols("document") \
+    .setOutputCol("token")
+  
+embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_Weipeng_generic_model","fr") \
+    .setInputCols(["document", "token"]) \
+    .setOutputCol("embeddings")
+    
+pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings])
+
+data = spark.createDataFrame([["J'adore Spark NLP"]]).toDF("text")
+
+result = pipeline.fit(data).transform(data)
+```
+```scala
+val documentAssembler = new DocumentAssembler() 
+      .setInputCol("text") 
+      .setOutputCol("document")
+ 
+val tokenizer = new Tokenizer() 
+    .setInputCols(Array("document"))
+    .setOutputCol("token")
+
+val embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_Weipeng_generic_model","fr") 
+    .setInputCols(Array("document", "token")) 
+    .setOutputCol("embeddings")
+
+val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings))
+
+val data = Seq("J'adore Spark NLP").toDF("text")
+
+val result = pipeline.fit(data).transform(data)
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|camembert_embeddings_Weipeng_generic_model|
+|Compatibility:|Spark NLP 5.0.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[sentence, token]|
+|Output Labels:|[embeddings]|
+|Language:|fr|
+|Size:|264.0 MB|
+|Case sensitive:|true|
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_adam1224_generic_model_fr.md b/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_adam1224_generic_model_fr.md
new file mode 100644
index 00000000000000..5fa59759dab81d
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_adam1224_generic_model_fr.md
@@ -0,0 +1,99 @@
+---
+layout: model
+title: French CamemBert Embeddings (from adam1224)
+author: John Snow Labs
+name: camembert_embeddings_adam1224_generic_model
+date: 2023-07-31
+tags: [fr, open_source, camembert, embeddings, onnx]
+task: Embeddings
+language: fr
+edition: Spark NLP 5.0.2
+spark_version: 3.0
+supported: true
+engine: onnx
+annotator: CamemBertEmbeddings
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained CamemBert Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `dummy-model` is a French model orginally trained by `adam1224`.
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_embeddings_adam1224_generic_model_fr_5.0.2_3.0_1690839841421.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_embeddings_adam1224_generic_model_fr_5.0.2_3.0_1690839841421.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+documentAssembler = DocumentAssembler() \
+    .setInputCol("text") \
+    .setOutputCol("document")
+
+tokenizer = Tokenizer() \
+    .setInputCols("document") \
+    .setOutputCol("token")
+  
+embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_adam1224_generic_model","fr") \
+    .setInputCols(["document", "token"]) \
+    .setOutputCol("embeddings")
+    
+pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings])
+
+data = spark.createDataFrame([["J'adore Spark NLP"]]).toDF("text")
+
+result = pipeline.fit(data).transform(data)
+```
+```scala
+val documentAssembler = new DocumentAssembler() 
+      .setInputCol("text") 
+      .setOutputCol("document")
+ 
+val tokenizer = new Tokenizer() 
+    .setInputCols(Array("document"))
+    .setOutputCol("token")
+
+val embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_adam1224_generic_model","fr") 
+    .setInputCols(Array("document", "token")) 
+    .setOutputCol("embeddings")
+
+val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings))
+
+val data = Seq("J'adore Spark NLP").toDF("text")
+
+val result = pipeline.fit(data).transform(data)
+```
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("fr.embed.camembert.generic.by_adam1224").predict("""J'adore Spark NLP""")
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|camembert_embeddings_adam1224_generic_model|
+|Compatibility:|Spark NLP 5.0.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[sentence, token]|
+|Output Labels:|[embeddings]|
+|Language:|fr|
+|Size:|264.0 MB|
+|Case sensitive:|true|
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_adeiMousa_generic_model_fr.md b/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_adeiMousa_generic_model_fr.md
new file mode 100644
index 00000000000000..9d644c783ef626
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_adeiMousa_generic_model_fr.md
@@ -0,0 +1,93 @@
+---
+layout: model
+title: French CamemBert Embeddings (from adeiMousa)
+author: John Snow Labs
+name: camembert_embeddings_adeiMousa_generic_model
+date: 2023-07-31
+tags: [fr, open_source, camembert, embeddings, onnx]
+task: Embeddings
+language: fr
+edition: Spark NLP 5.0.2
+spark_version: 3.0
+supported: true
+engine: onnx
+annotator: CamemBertEmbeddings
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained CamemBert Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `dummy-model` is a French model orginally trained by `adeiMousa`.
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_embeddings_adeiMousa_generic_model_fr_5.0.2_3.0_1690838633068.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_embeddings_adeiMousa_generic_model_fr_5.0.2_3.0_1690838633068.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+documentAssembler = DocumentAssembler() \
+    .setInputCol("text") \
+    .setOutputCol("document")
+
+tokenizer = Tokenizer() \
+    .setInputCols("document") \
+    .setOutputCol("token")
+  
+embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_adeiMousa_generic_model","fr") \
+    .setInputCols(["document", "token"]) \
+    .setOutputCol("embeddings")
+    
+pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings])
+
+data = spark.createDataFrame([["J'adore Spark NLP"]]).toDF("text")
+
+result = pipeline.fit(data).transform(data)
+```
+```scala
+val documentAssembler = new DocumentAssembler() 
+      .setInputCol("text") 
+      .setOutputCol("document")
+ 
+val tokenizer = new Tokenizer() 
+    .setInputCols(Array("document"))
+    .setOutputCol("token")
+
+val embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_adeiMousa_generic_model","fr") 
+    .setInputCols(Array("document", "token")) 
+    .setOutputCol("embeddings")
+
+val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings))
+
+val data = Seq("J'adore Spark NLP").toDF("text")
+
+val result = pipeline.fit(data).transform(data)
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|camembert_embeddings_adeiMousa_generic_model|
+|Compatibility:|Spark NLP 5.0.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[sentence, token]|
+|Output Labels:|[embeddings]|
+|Language:|fr|
+|Size:|264.0 MB|
+|Case sensitive:|true|
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_ankitkupadhyay_generic_model_fr.md b/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_ankitkupadhyay_generic_model_fr.md
new file mode 100644
index 00000000000000..7a4d6aab6d24ab
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_ankitkupadhyay_generic_model_fr.md
@@ -0,0 +1,99 @@
+---
+layout: model
+title: French CamemBert Embeddings (from ankitkupadhyay)
+author: John Snow Labs
+name: camembert_embeddings_ankitkupadhyay_generic_model
+date: 2023-07-31
+tags: [fr, open_source, camembert, embeddings, onnx]
+task: Embeddings
+language: fr
+edition: Spark NLP 5.0.2
+spark_version: 3.0
+supported: true
+engine: onnx
+annotator: CamemBertEmbeddings
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained CamemBert Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `dummy-model` is a French model orginally trained by `ankitkupadhyay`.
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_embeddings_ankitkupadhyay_generic_model_fr_5.0.2_3.0_1690838066435.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_embeddings_ankitkupadhyay_generic_model_fr_5.0.2_3.0_1690838066435.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+documentAssembler = DocumentAssembler() \
+    .setInputCol("text") \
+    .setOutputCol("document")
+
+tokenizer = Tokenizer() \
+    .setInputCols("document") \
+    .setOutputCol("token")
+  
+embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_ankitkupadhyay_generic_model","fr") \
+    .setInputCols(["document", "token"]) \
+    .setOutputCol("embeddings")
+    
+pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings])
+
+data = spark.createDataFrame([["J'adore Spark NLP"]]).toDF("text")
+
+result = pipeline.fit(data).transform(data)
+```
+```scala
+val documentAssembler = new DocumentAssembler() 
+      .setInputCol("text") 
+      .setOutputCol("document")
+ 
+val tokenizer = new Tokenizer() 
+    .setInputCols(Array("document"))
+    .setOutputCol("token")
+
+val embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_ankitkupadhyay_generic_model","fr") 
+    .setInputCols(Array("document", "token")) 
+    .setOutputCol("embeddings")
+
+val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings))
+
+val data = Seq("J'adore Spark NLP").toDF("text")
+
+val result = pipeline.fit(data).transform(data)
+```
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("fr.embed.camembert.generic.by_ankitkupadhyay").predict("""J'adore Spark NLP""")
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|camembert_embeddings_ankitkupadhyay_generic_model|
+|Compatibility:|Spark NLP 5.0.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[sentence, token]|
+|Output Labels:|[embeddings]|
+|Language:|fr|
+|Size:|264.0 MB|
+|Case sensitive:|true|
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_codingJacob_generic_model_fr.md b/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_codingJacob_generic_model_fr.md
new file mode 100644
index 00000000000000..b325e41991bc1c
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_codingJacob_generic_model_fr.md
@@ -0,0 +1,93 @@
+---
+layout: model
+title: French CamemBert Embeddings (from codingJacob)
+author: John Snow Labs
+name: camembert_embeddings_codingJacob_generic_model
+date: 2023-07-31
+tags: [fr, open_source, camembert, embeddings, onnx]
+task: Embeddings
+language: fr
+edition: Spark NLP 5.0.2
+spark_version: 3.0
+supported: true
+engine: onnx
+annotator: CamemBertEmbeddings
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained CamemBert Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `dummy-model` is a French model orginally trained by `codingJacob`.
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_embeddings_codingJacob_generic_model_fr_5.0.2_3.0_1690838696305.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_embeddings_codingJacob_generic_model_fr_5.0.2_3.0_1690838696305.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+documentAssembler = DocumentAssembler() \
+    .setInputCol("text") \
+    .setOutputCol("document")
+
+tokenizer = Tokenizer() \
+    .setInputCols("document") \
+    .setOutputCol("token")
+  
+embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_codingJacob_generic_model","fr") \
+    .setInputCols(["document", "token"]) \
+    .setOutputCol("embeddings")
+    
+pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings])
+
+data = spark.createDataFrame([["J'adore Spark NLP"]]).toDF("text")
+
+result = pipeline.fit(data).transform(data)
+```
+```scala
+val documentAssembler = new DocumentAssembler() 
+      .setInputCol("text") 
+      .setOutputCol("document")
+ 
+val tokenizer = new Tokenizer() 
+    .setInputCols(Array("document"))
+    .setOutputCol("token")
+
+val embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_codingJacob_generic_model","fr") 
+    .setInputCols(Array("document", "token")) 
+    .setOutputCol("embeddings")
+
+val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings))
+
+val data = Seq("J'adore Spark NLP").toDF("text")
+
+val result = pipeline.fit(data).transform(data)
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|camembert_embeddings_codingJacob_generic_model|
+|Compatibility:|Spark NLP 5.0.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[sentence, token]|
+|Output Labels:|[embeddings]|
+|Language:|fr|
+|Size:|264.0 MB|
+|Case sensitive:|true|
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_devtrent_generic_model_fr.md b/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_devtrent_generic_model_fr.md
new file mode 100644
index 00000000000000..9cb2ddef4dc357
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_devtrent_generic_model_fr.md
@@ -0,0 +1,99 @@
+---
+layout: model
+title: French CamemBert Embeddings (from devtrent)
+author: John Snow Labs
+name: camembert_embeddings_devtrent_generic_model
+date: 2023-07-31
+tags: [fr, open_source, camembert, embeddings, onnx]
+task: Embeddings
+language: fr
+edition: Spark NLP 5.0.2
+spark_version: 3.0
+supported: true
+engine: onnx
+annotator: CamemBertEmbeddings
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained CamemBert Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `dummy-model` is a French model orginally trained by `devtrent`.
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_embeddings_devtrent_generic_model_fr_5.0.2_3.0_1690838103819.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_embeddings_devtrent_generic_model_fr_5.0.2_3.0_1690838103819.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+documentAssembler = DocumentAssembler() \
+    .setInputCol("text") \
+    .setOutputCol("document")
+
+tokenizer = Tokenizer() \
+    .setInputCols("document") \
+    .setOutputCol("token")
+  
+embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_devtrent_generic_model","fr") \
+    .setInputCols(["document", "token"]) \
+    .setOutputCol("embeddings")
+    
+pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings])
+
+data = spark.createDataFrame([["J'adore Spark NLP"]]).toDF("text")
+
+result = pipeline.fit(data).transform(data)
+```
+```scala
+val documentAssembler = new DocumentAssembler() 
+      .setInputCol("text") 
+      .setOutputCol("document")
+ 
+val tokenizer = new Tokenizer() 
+    .setInputCols(Array("document"))
+    .setOutputCol("token")
+
+val embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_devtrent_generic_model","fr") 
+    .setInputCols(Array("document", "token")) 
+    .setOutputCol("embeddings")
+
+val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings))
+
+val data = Seq("J'adore Spark NLP").toDF("text")
+
+val result = pipeline.fit(data).transform(data)
+```
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("fr.embed.camembert.generic.by_devtrent").predict("""J'adore Spark NLP""")
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|camembert_embeddings_devtrent_generic_model|
+|Compatibility:|Spark NLP 5.0.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[sentence, token]|
+|Output Labels:|[embeddings]|
+|Language:|fr|
+|Size:|264.0 MB|
+|Case sensitive:|true|
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_eduardopds_generic_model_fr.md b/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_eduardopds_generic_model_fr.md
new file mode 100644
index 00000000000000..b81bdd170972c8
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_eduardopds_generic_model_fr.md
@@ -0,0 +1,99 @@
+---
+layout: model
+title: French CamemBert Embeddings (from eduardopds)
+author: John Snow Labs
+name: camembert_embeddings_eduardopds_generic_model
+date: 2023-07-31
+tags: [fr, open_source, camembert, embeddings, onnx]
+task: Embeddings
+language: fr
+edition: Spark NLP 5.0.2
+spark_version: 3.0
+supported: true
+engine: onnx
+annotator: CamemBertEmbeddings
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained CamemBert Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `dummy-model` is a French model orginally trained by `eduardopds`.
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_embeddings_eduardopds_generic_model_fr_5.0.2_3.0_1690838312633.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_embeddings_eduardopds_generic_model_fr_5.0.2_3.0_1690838312633.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+documentAssembler = DocumentAssembler() \
+    .setInputCol("text") \
+    .setOutputCol("document")
+
+tokenizer = Tokenizer() \
+    .setInputCols("document") \
+    .setOutputCol("token")
+  
+embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_eduardopds_generic_model","fr") \
+    .setInputCols(["document", "token"]) \
+    .setOutputCol("embeddings")
+    
+pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings])
+
+data = spark.createDataFrame([["J'adore Spark NLP"]]).toDF("text")
+
+result = pipeline.fit(data).transform(data)
+```
+```scala
+val documentAssembler = new DocumentAssembler() 
+      .setInputCol("text") 
+      .setOutputCol("document")
+ 
+val tokenizer = new Tokenizer() 
+    .setInputCols(Array("document"))
+    .setOutputCol("token")
+
+val embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_eduardopds_generic_model","fr") 
+    .setInputCols(Array("document", "token")) 
+    .setOutputCol("embeddings")
+
+val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings))
+
+val data = Seq("J'adore Spark NLP").toDF("text")
+
+val result = pipeline.fit(data).transform(data)
+```
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("fr.embed.camembert.generic.by_eduardopds").predict("""J'adore Spark NLP""")
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|camembert_embeddings_eduardopds_generic_model|
+|Compatibility:|Spark NLP 5.0.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[sentence, token]|
+|Output Labels:|[embeddings]|
+|Language:|fr|
+|Size:|264.0 MB|
+|Case sensitive:|true|
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_ericchchiu_generic_model_fr.md b/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_ericchchiu_generic_model_fr.md
new file mode 100644
index 00000000000000..32e911654222d4
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_ericchchiu_generic_model_fr.md
@@ -0,0 +1,99 @@
+---
+layout: model
+title: French CamemBert Embeddings (from ericchchiu)
+author: John Snow Labs
+name: camembert_embeddings_ericchchiu_generic_model
+date: 2023-07-31
+tags: [fr, open_source, camembert, embeddings, onnx]
+task: Embeddings
+language: fr
+edition: Spark NLP 5.0.2
+spark_version: 3.0
+supported: true
+engine: onnx
+annotator: CamemBertEmbeddings
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained CamemBert Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `dummy-model` is a French model orginally trained by `ericchchiu`.
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_embeddings_ericchchiu_generic_model_fr_5.0.2_3.0_1690838666122.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_embeddings_ericchchiu_generic_model_fr_5.0.2_3.0_1690838666122.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+documentAssembler = DocumentAssembler() \
+    .setInputCol("text") \
+    .setOutputCol("document")
+
+tokenizer = Tokenizer() \
+    .setInputCols("document") \
+    .setOutputCol("token")
+  
+embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_ericchchiu_generic_model","fr") \
+    .setInputCols(["document", "token"]) \
+    .setOutputCol("embeddings")
+    
+pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings])
+
+data = spark.createDataFrame([["J'adore Spark NLP"]]).toDF("text")
+
+result = pipeline.fit(data).transform(data)
+```
+```scala
+val documentAssembler = new DocumentAssembler() 
+      .setInputCol("text") 
+      .setOutputCol("document")
+ 
+val tokenizer = new Tokenizer() 
+    .setInputCols(Array("document"))
+    .setOutputCol("token")
+
+val embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_ericchchiu_generic_model","fr") 
+    .setInputCols(Array("document", "token")) 
+    .setOutputCol("embeddings")
+
+val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings))
+
+val data = Seq("J'adore Spark NLP").toDF("text")
+
+val result = pipeline.fit(data).transform(data)
+```
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("fr.embed.camembert.generic.by_ericchchiu").predict("""J'adore Spark NLP""")
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|camembert_embeddings_ericchchiu_generic_model|
+|Compatibility:|Spark NLP 5.0.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[sentence, token]|
+|Output Labels:|[embeddings]|
+|Language:|fr|
+|Size:|264.0 MB|
+|Case sensitive:|true|
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_est_roberta_et.md b/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_est_roberta_et.md
new file mode 100644
index 00000000000000..e6db30b8ea753c
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_est_roberta_et.md
@@ -0,0 +1,99 @@
+---
+layout: model
+title: Estonian CamemBert Embeddings (from EMBEDDIA)
+author: John Snow Labs
+name: camembert_embeddings_est_roberta
+date: 2023-07-31
+tags: [et, open_source, camembert, embeddings, onnx]
+task: Embeddings
+language: et
+edition: Spark NLP 5.0.2
+spark_version: 3.0
+supported: true
+engine: onnx
+annotator: CamemBertEmbeddings
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained CamemBert Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `est-roberta` is a Estonian model orginally trained by `EMBEDDIA`.
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_embeddings_est_roberta_et_5.0.2_3.0_1690839928128.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_embeddings_est_roberta_et_5.0.2_3.0_1690839928128.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+documentAssembler = DocumentAssembler() \
+    .setInputCol("text") \
+    .setOutputCol("document")
+
+tokenizer = Tokenizer() \
+    .setInputCols("document") \
+    .setOutputCol("token")
+
+embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_est_roberta","et") \
+    .setInputCols(["document", "token"]) \
+    .setOutputCol("embeddings")
+
+pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings])
+
+data = spark.createDataFrame([["Ma armastan sädet nlp"]]).toDF("text")
+
+result = pipeline.fit(data).transform(data)
+```
+```scala
+val documentAssembler = new DocumentAssembler()
+      .setInputCol("text")
+      .setOutputCol("document")
+
+val tokenizer = new Tokenizer()
+    .setInputCols(Array("document"))
+    .setOutputCol("token")
+
+val embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_est_roberta","et")
+    .setInputCols(Array("document", "token"))
+    .setOutputCol("embeddings")
+
+val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings))
+
+val data = Seq("Ma armastan sädet nlp").toDF("text")
+
+val result = pipeline.fit(data).transform(data)
+```
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("et.embed.camembert").predict("""Ma armastan sädet nlp""")
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|camembert_embeddings_est_roberta|
+|Compatibility:|Spark NLP 5.0.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[sentence, token]|
+|Output Labels:|[embeddings]|
+|Language:|et|
+|Size:|277.9 MB|
+|Case sensitive:|true|
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_generic2_fr.md b/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_generic2_fr.md
new file mode 100644
index 00000000000000..cadd0e57fa1d59
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_generic2_fr.md
@@ -0,0 +1,99 @@
+---
+layout: model
+title: French CamemBert Embeddings (from hackertec)
+author: John Snow Labs
+name: camembert_embeddings_generic2
+date: 2023-07-31
+tags: [fr, open_source, camembert, embeddings, onnx]
+task: Embeddings
+language: fr
+edition: Spark NLP 5.0.2
+spark_version: 3.0
+supported: true
+engine: onnx
+annotator: CamemBertEmbeddings
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained CamemBert Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `dummy2` is a French model orginally trained by `hackertec`.
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_embeddings_generic2_fr_5.0.2_3.0_1690840126577.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_embeddings_generic2_fr_5.0.2_3.0_1690840126577.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+documentAssembler = DocumentAssembler() \
+    .setInputCol("text") \
+    .setOutputCol("document")
+
+tokenizer = Tokenizer() \
+    .setInputCols("document") \
+    .setOutputCol("token")
+  
+embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_generic2","fr") \
+    .setInputCols(["document", "token"]) \
+    .setOutputCol("embeddings")
+    
+pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings])
+
+data = spark.createDataFrame([["J'adore Spark NLP"]]).toDF("text")
+
+result = pipeline.fit(data).transform(data)
+```
+```scala
+val documentAssembler = new DocumentAssembler() 
+      .setInputCol("text") 
+      .setOutputCol("document")
+ 
+val tokenizer = new Tokenizer() 
+    .setInputCols(Array("document"))
+    .setOutputCol("token")
+
+val embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_generic2","fr") 
+    .setInputCols(Array("document", "token")) 
+    .setOutputCol("embeddings")
+
+val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings))
+
+val data = Seq("J'adore Spark NLP").toDF("text")
+
+val result = pipeline.fit(data).transform(data)
+```
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("fr.embed.camembert.generic.by_hackertec").predict("""J'adore Spark NLP""")
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|camembert_embeddings_generic2|
+|Compatibility:|Spark NLP 5.0.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[sentence, token]|
+|Output Labels:|[embeddings]|
+|Language:|fr|
+|Size:|264.0 MB|
+|Case sensitive:|true|
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_lijingxin_generic_model_fr.md b/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_lijingxin_generic_model_fr.md
new file mode 100644
index 00000000000000..b844d5bc5c7473
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_lijingxin_generic_model_fr.md
@@ -0,0 +1,99 @@
+---
+layout: model
+title: French CamemBert Embeddings (from lijingxin)
+author: John Snow Labs
+name: camembert_embeddings_lijingxin_generic_model
+date: 2023-07-31
+tags: [fr, open_source, camembert, embeddings, onnx]
+task: Embeddings
+language: fr
+edition: Spark NLP 5.0.2
+spark_version: 3.0
+supported: true
+engine: onnx
+annotator: CamemBertEmbeddings
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained CamemBert Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `dummy-model` is a French model orginally trained by `lijingxin`.
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_embeddings_lijingxin_generic_model_fr_5.0.2_3.0_1690842315235.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_embeddings_lijingxin_generic_model_fr_5.0.2_3.0_1690842315235.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+documentAssembler = DocumentAssembler() \
+    .setInputCol("text") \
+    .setOutputCol("document")
+
+tokenizer = Tokenizer() \
+    .setInputCols("document") \
+    .setOutputCol("token")
+  
+embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_lijingxin_generic_model","fr") \
+    .setInputCols(["document", "token"]) \
+    .setOutputCol("embeddings")
+    
+pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings])
+
+data = spark.createDataFrame([["J'adore Spark NLP"]]).toDF("text")
+
+result = pipeline.fit(data).transform(data)
+```
+```scala
+val documentAssembler = new DocumentAssembler() 
+      .setInputCol("text") 
+      .setOutputCol("document")
+ 
+val tokenizer = new Tokenizer() 
+    .setInputCols(Array("document"))
+    .setOutputCol("token")
+
+val embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_lijingxin_generic_model","fr") 
+    .setInputCols(Array("document", "token")) 
+    .setOutputCol("embeddings")
+
+val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings))
+
+val data = Seq("J'adore Spark NLP").toDF("text")
+
+val result = pipeline.fit(data).transform(data)
+```
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("fr.embed.camembert.generic.by_lijingxin").predict("""J'adore Spark NLP""")
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|camembert_embeddings_lijingxin_generic_model|
+|Compatibility:|Spark NLP 5.0.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[sentence, token]|
+|Output Labels:|[embeddings]|
+|Language:|fr|
+|Size:|264.0 MB|
+|Case sensitive:|true|
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_osanseviero_generic_model_fr.md b/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_osanseviero_generic_model_fr.md
new file mode 100644
index 00000000000000..019c9a8252d9b9
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_osanseviero_generic_model_fr.md
@@ -0,0 +1,99 @@
+---
+layout: model
+title: French CamemBert Embeddings (from osanseviero)
+author: John Snow Labs
+name: camembert_embeddings_osanseviero_generic_model
+date: 2023-07-31
+tags: [fr, open_source, camembert, embeddings, onnx]
+task: Embeddings
+language: fr
+edition: Spark NLP 5.0.2
+spark_version: 3.0
+supported: true
+engine: onnx
+annotator: CamemBertEmbeddings
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained CamemBert Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `dummy-model` is a French model orginally trained by `osanseviero`.
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_embeddings_osanseviero_generic_model_fr_5.0.2_3.0_1690842223769.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_embeddings_osanseviero_generic_model_fr_5.0.2_3.0_1690842223769.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+documentAssembler = DocumentAssembler() \
+    .setInputCol("text") \
+    .setOutputCol("document")
+
+tokenizer = Tokenizer() \
+    .setInputCols("document") \
+    .setOutputCol("token")
+  
+embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_osanseviero_generic_model","fr") \
+    .setInputCols(["document", "token"]) \
+    .setOutputCol("embeddings")
+    
+pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings])
+
+data = spark.createDataFrame([["J'adore Spark NLP"]]).toDF("text")
+
+result = pipeline.fit(data).transform(data)
+```
+```scala
+val documentAssembler = new DocumentAssembler() 
+      .setInputCol("text") 
+      .setOutputCol("document")
+ 
+val tokenizer = new Tokenizer() 
+    .setInputCols(Array("document"))
+    .setOutputCol("token")
+
+val embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_osanseviero_generic_model","fr") 
+    .setInputCols(Array("document", "token")) 
+    .setOutputCol("embeddings")
+
+val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings))
+
+val data = Seq("J'adore Spark NLP").toDF("text")
+
+val result = pipeline.fit(data).transform(data)
+```
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("fr.embed.camembert.generic_v2.by_osanseviero").predict("""J'adore Spark NLP""")
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|camembert_embeddings_osanseviero_generic_model|
+|Compatibility:|Spark NLP 5.0.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[sentence, token]|
+|Output Labels:|[embeddings]|
+|Language:|fr|
+|Size:|264.0 MB|
+|Case sensitive:|true|
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_peterhsu_generic_model_fr.md b/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_peterhsu_generic_model_fr.md
new file mode 100644
index 00000000000000..64617bef18c08b
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_peterhsu_generic_model_fr.md
@@ -0,0 +1,99 @@
+---
+layout: model
+title: French CamemBert Embeddings (from peterhsu)
+author: John Snow Labs
+name: camembert_embeddings_peterhsu_generic_model
+date: 2023-07-31
+tags: [fr, open_source, camembert, embeddings, onnx]
+task: Embeddings
+language: fr
+edition: Spark NLP 5.0.2
+spark_version: 3.0
+supported: true
+engine: onnx
+annotator: CamemBertEmbeddings
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained CamemBert Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `dummy-model` is a French model orginally trained by `peterhsu`.
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_embeddings_peterhsu_generic_model_fr_5.0.2_3.0_1690841897272.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_embeddings_peterhsu_generic_model_fr_5.0.2_3.0_1690841897272.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+documentAssembler = DocumentAssembler() \
+    .setInputCol("text") \
+    .setOutputCol("document")
+
+tokenizer = Tokenizer() \
+    .setInputCols("document") \
+    .setOutputCol("token")
+  
+embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_peterhsu_generic_model","fr") \
+    .setInputCols(["document", "token"]) \
+    .setOutputCol("embeddings")
+    
+pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings])
+
+data = spark.createDataFrame([["J'adore Spark NLP"]]).toDF("text")
+
+result = pipeline.fit(data).transform(data)
+```
+```scala
+val documentAssembler = new DocumentAssembler() 
+      .setInputCol("text") 
+      .setOutputCol("document")
+ 
+val tokenizer = new Tokenizer() 
+    .setInputCols(Array("document"))
+    .setOutputCol("token")
+
+val embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_peterhsu_generic_model","fr") 
+    .setInputCols(Array("document", "token")) 
+    .setOutputCol("embeddings")
+
+val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings))
+
+val data = Seq("J'adore Spark NLP").toDF("text")
+
+val result = pipeline.fit(data).transform(data)
+```
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("fr.embed.camembert.generic.by_peterhsu").predict("""J'adore Spark NLP""")
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|camembert_embeddings_peterhsu_generic_model|
+|Compatibility:|Spark NLP 5.0.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[sentence, token]|
+|Output Labels:|[embeddings]|
+|Language:|fr|
+|Size:|264.0 MB|
+|Case sensitive:|true|
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_pgperrone_generic_model_fr.md b/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_pgperrone_generic_model_fr.md
new file mode 100644
index 00000000000000..1ffb45542ea74f
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_pgperrone_generic_model_fr.md
@@ -0,0 +1,99 @@
+---
+layout: model
+title: French CamemBert Embeddings (from pgperrone)
+author: John Snow Labs
+name: camembert_embeddings_pgperrone_generic_model
+date: 2023-07-31
+tags: [fr, open_source, camembert, embeddings, onnx]
+task: Embeddings
+language: fr
+edition: Spark NLP 5.0.2
+spark_version: 3.0
+supported: true
+engine: onnx
+annotator: CamemBertEmbeddings
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained CamemBert Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `dummy-model` is a French model orginally trained by `pgperrone`.
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_embeddings_pgperrone_generic_model_fr_5.0.2_3.0_1690842077639.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_embeddings_pgperrone_generic_model_fr_5.0.2_3.0_1690842077639.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+documentAssembler = DocumentAssembler() \
+    .setInputCol("text") \
+    .setOutputCol("document")
+
+tokenizer = Tokenizer() \
+    .setInputCols("document") \
+    .setOutputCol("token")
+  
+embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_pgperrone_generic_model","fr") \
+    .setInputCols(["document", "token"]) \
+    .setOutputCol("embeddings")
+    
+pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings])
+
+data = spark.createDataFrame([["J'adore Spark NLP"]]).toDF("text")
+
+result = pipeline.fit(data).transform(data)
+```
+```scala
+val documentAssembler = new DocumentAssembler() 
+      .setInputCol("text") 
+      .setOutputCol("document")
+ 
+val tokenizer = new Tokenizer() 
+    .setInputCols(Array("document"))
+    .setOutputCol("token")
+
+val embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_pgperrone_generic_model","fr") 
+    .setInputCols(Array("document", "token")) 
+    .setOutputCol("embeddings")
+
+val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings))
+
+val data = Seq("J'adore Spark NLP").toDF("text")
+
+val result = pipeline.fit(data).transform(data)
+```
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("fr.embed.camembert.generic.by_pgperrone").predict("""J'adore Spark NLP""")
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|camembert_embeddings_pgperrone_generic_model|
+|Compatibility:|Spark NLP 5.0.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[sentence, token]|
+|Output Labels:|[embeddings]|
+|Language:|fr|
+|Size:|264.0 MB|
+|Case sensitive:|true|
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_safik_generic_model_fr.md b/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_safik_generic_model_fr.md
new file mode 100644
index 00000000000000..30ff64246a20f7
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_safik_generic_model_fr.md
@@ -0,0 +1,99 @@
+---
+layout: model
+title: French CamemBert Embeddings (from safik)
+author: John Snow Labs
+name: camembert_embeddings_safik_generic_model
+date: 2023-07-31
+tags: [fr, open_source, camembert, embeddings, onnx]
+task: Embeddings
+language: fr
+edition: Spark NLP 5.0.2
+spark_version: 3.0
+supported: true
+engine: onnx
+annotator: CamemBertEmbeddings
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained CamemBert Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `dummy-model` is a French model orginally trained by `safik`.
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_embeddings_safik_generic_model_fr_5.0.2_3.0_1690841450264.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_embeddings_safik_generic_model_fr_5.0.2_3.0_1690841450264.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+documentAssembler = DocumentAssembler() \
+    .setInputCol("text") \
+    .setOutputCol("document")
+
+tokenizer = Tokenizer() \
+    .setInputCols("document") \
+    .setOutputCol("token")
+  
+embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_safik_generic_model","fr") \
+    .setInputCols(["document", "token"]) \
+    .setOutputCol("embeddings")
+    
+pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings])
+
+data = spark.createDataFrame([["J'adore Spark NLP"]]).toDF("text")
+
+result = pipeline.fit(data).transform(data)
+```
+```scala
+val documentAssembler = new DocumentAssembler() 
+      .setInputCol("text") 
+      .setOutputCol("document")
+ 
+val tokenizer = new Tokenizer() 
+    .setInputCols(Array("document"))
+    .setOutputCol("token")
+
+val embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_safik_generic_model","fr") 
+    .setInputCols(Array("document", "token")) 
+    .setOutputCol("embeddings")
+
+val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings))
+
+val data = Seq("J'adore Spark NLP").toDF("text")
+
+val result = pipeline.fit(data).transform(data)
+```
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("fr.embed.camembert.generic.by_safik").predict("""J'adore Spark NLP""")
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|camembert_embeddings_safik_generic_model|
+|Compatibility:|Spark NLP 5.0.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[sentence, token]|
+|Output Labels:|[embeddings]|
+|Language:|fr|
+|Size:|264.0 MB|
+|Case sensitive:|true|
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_seyfullah_generic_model_fr.md b/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_seyfullah_generic_model_fr.md
new file mode 100644
index 00000000000000..8282ef44896f1f
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_seyfullah_generic_model_fr.md
@@ -0,0 +1,99 @@
+---
+layout: model
+title: French CamemBert Embeddings (from seyfullah)
+author: John Snow Labs
+name: camembert_embeddings_seyfullah_generic_model
+date: 2023-07-31
+tags: [fr, open_source, camembert, embeddings, onnx]
+task: Embeddings
+language: fr
+edition: Spark NLP 5.0.2
+spark_version: 3.0
+supported: true
+engine: onnx
+annotator: CamemBertEmbeddings
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained CamemBert Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `dummy-model` is a French model orginally trained by `seyfullah`.
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_embeddings_seyfullah_generic_model_fr_5.0.2_3.0_1690841115822.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_embeddings_seyfullah_generic_model_fr_5.0.2_3.0_1690841115822.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+documentAssembler = DocumentAssembler() \
+    .setInputCol("text") \
+    .setOutputCol("document")
+
+tokenizer = Tokenizer() \
+    .setInputCols("document") \
+    .setOutputCol("token")
+  
+embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_seyfullah_generic_model","fr") \
+    .setInputCols(["document", "token"]) \
+    .setOutputCol("embeddings")
+    
+pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings])
+
+data = spark.createDataFrame([["J'adore Spark NLP"]]).toDF("text")
+
+result = pipeline.fit(data).transform(data)
+```
+```scala
+val documentAssembler = new DocumentAssembler() 
+      .setInputCol("text") 
+      .setOutputCol("document")
+ 
+val tokenizer = new Tokenizer() 
+    .setInputCols(Array("document"))
+    .setOutputCol("token")
+
+val embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_seyfullah_generic_model","fr") 
+    .setInputCols(Array("document", "token")) 
+    .setOutputCol("embeddings")
+
+val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings))
+
+val data = Seq("J'adore Spark NLP").toDF("text")
+
+val result = pipeline.fit(data).transform(data)
+```
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("fr.embed.camembert.generic.by_seyfullah").predict("""J'adore Spark NLP""")
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|camembert_embeddings_seyfullah_generic_model|
+|Compatibility:|Spark NLP 5.0.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[sentence, token]|
+|Output Labels:|[embeddings]|
+|Language:|fr|
+|Size:|264.0 MB|
+|Case sensitive:|true|
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_tnagata_generic_model_fr.md b/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_tnagata_generic_model_fr.md
new file mode 100644
index 00000000000000..be15fa1d102c14
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_tnagata_generic_model_fr.md
@@ -0,0 +1,99 @@
+---
+layout: model
+title: French CamemBert Embeddings (from tnagata)
+author: John Snow Labs
+name: camembert_embeddings_tnagata_generic_model
+date: 2023-07-31
+tags: [fr, open_source, camembert, embeddings, onnx]
+task: Embeddings
+language: fr
+edition: Spark NLP 5.0.2
+spark_version: 3.0
+supported: true
+engine: onnx
+annotator: CamemBertEmbeddings
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained CamemBert Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `dummy-model` is a French model orginally trained by `tnagata`.
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_embeddings_tnagata_generic_model_fr_5.0.2_3.0_1690841148034.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_embeddings_tnagata_generic_model_fr_5.0.2_3.0_1690841148034.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+documentAssembler = DocumentAssembler() \
+    .setInputCol("text") \
+    .setOutputCol("document")
+
+tokenizer = Tokenizer() \
+    .setInputCols("document") \
+    .setOutputCol("token")
+  
+embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_tnagata_generic_model","fr") \
+    .setInputCols(["document", "token"]) \
+    .setOutputCol("embeddings")
+    
+pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings])
+
+data = spark.createDataFrame([["J'adore Spark NLP"]]).toDF("text")
+
+result = pipeline.fit(data).transform(data)
+```
+```scala
+val documentAssembler = new DocumentAssembler() 
+      .setInputCol("text") 
+      .setOutputCol("document")
+ 
+val tokenizer = new Tokenizer() 
+    .setInputCols(Array("document"))
+    .setOutputCol("token")
+
+val embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_tnagata_generic_model","fr") 
+    .setInputCols(Array("document", "token")) 
+    .setOutputCol("embeddings")
+
+val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings))
+
+val data = Seq("J'adore Spark NLP").toDF("text")
+
+val result = pipeline.fit(data).transform(data)
+```
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("fr.embed.camembert.generic.by_tnagata").predict("""J'adore Spark NLP""")
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|camembert_embeddings_tnagata_generic_model|
+|Compatibility:|Spark NLP 5.0.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[sentence, token]|
+|Output Labels:|[embeddings]|
+|Language:|fr|
+|Size:|264.0 MB|
+|Case sensitive:|true|
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_tpanza_generic_model_fr.md b/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_tpanza_generic_model_fr.md
new file mode 100644
index 00000000000000..af70dc70467023
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_tpanza_generic_model_fr.md
@@ -0,0 +1,99 @@
+---
+layout: model
+title: French CamemBert Embeddings (from tpanza)
+author: John Snow Labs
+name: camembert_embeddings_tpanza_generic_model
+date: 2023-07-31
+tags: [fr, open_source, camembert, embeddings, onnx]
+task: Embeddings
+language: fr
+edition: Spark NLP 5.0.2
+spark_version: 3.0
+supported: true
+engine: onnx
+annotator: CamemBertEmbeddings
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained CamemBert Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `dummy-model` is a French model orginally trained by `tpanza`.
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_embeddings_tpanza_generic_model_fr_5.0.2_3.0_1690841821807.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_embeddings_tpanza_generic_model_fr_5.0.2_3.0_1690841821807.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+documentAssembler = DocumentAssembler() \
+    .setInputCol("text") \
+    .setOutputCol("document")
+
+tokenizer = Tokenizer() \
+    .setInputCols("document") \
+    .setOutputCol("token")
+  
+embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_tpanza_generic_model","fr") \
+    .setInputCols(["document", "token"]) \
+    .setOutputCol("embeddings")
+    
+pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings])
+
+data = spark.createDataFrame([["J'adore Spark NLP"]]).toDF("text")
+
+result = pipeline.fit(data).transform(data)
+```
+```scala
+val documentAssembler = new DocumentAssembler() 
+      .setInputCol("text") 
+      .setOutputCol("document")
+ 
+val tokenizer = new Tokenizer() 
+    .setInputCols(Array("document"))
+    .setOutputCol("token")
+
+val embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_tpanza_generic_model","fr") 
+    .setInputCols(Array("document", "token")) 
+    .setOutputCol("embeddings")
+
+val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings))
+
+val data = Seq("J'adore Spark NLP").toDF("text")
+
+val result = pipeline.fit(data).transform(data)
+```
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("fr.embed.camembert.generic.by_tpanza").predict("""J'adore Spark NLP""")
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|camembert_embeddings_tpanza_generic_model|
+|Compatibility:|Spark NLP 5.0.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[sentence, token]|
+|Output Labels:|[embeddings]|
+|Language:|fr|
+|Size:|264.0 MB|
+|Case sensitive:|true|
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_wangst_generic_model_fr.md b/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_wangst_generic_model_fr.md
new file mode 100644
index 00000000000000..2e9322c530190b
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_wangst_generic_model_fr.md
@@ -0,0 +1,99 @@
+---
+layout: model
+title: French CamemBert Embeddings (from wangst)
+author: John Snow Labs
+name: camembert_embeddings_wangst_generic_model
+date: 2023-07-31
+tags: [fr, open_source, camembert, embeddings, onnx]
+task: Embeddings
+language: fr
+edition: Spark NLP 5.0.2
+spark_version: 3.0
+supported: true
+engine: onnx
+annotator: CamemBertEmbeddings
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained CamemBert Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `dummy-model` is a French model orginally trained by `wangst`.
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_embeddings_wangst_generic_model_fr_5.0.2_3.0_1690840911109.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_embeddings_wangst_generic_model_fr_5.0.2_3.0_1690840911109.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+documentAssembler = DocumentAssembler() \
+    .setInputCol("text") \
+    .setOutputCol("document")
+
+tokenizer = Tokenizer() \
+    .setInputCols("document") \
+    .setOutputCol("token")
+  
+embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_wangst_generic_model","fr") \
+    .setInputCols(["document", "token"]) \
+    .setOutputCol("embeddings")
+    
+pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings])
+
+data = spark.createDataFrame([["J'adore Spark NLP"]]).toDF("text")
+
+result = pipeline.fit(data).transform(data)
+```
+```scala
+val documentAssembler = new DocumentAssembler() 
+      .setInputCol("text") 
+      .setOutputCol("document")
+ 
+val tokenizer = new Tokenizer() 
+    .setInputCols(Array("document"))
+    .setOutputCol("token")
+
+val embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_wangst_generic_model","fr") 
+    .setInputCols(Array("document", "token")) 
+    .setOutputCol("embeddings")
+
+val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings))
+
+val data = Seq("J'adore Spark NLP").toDF("text")
+
+val result = pipeline.fit(data).transform(data)
+```
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("fr.embed.camembert.generic.by_wangst").predict("""J'adore Spark NLP""")
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|camembert_embeddings_wangst_generic_model|
+|Compatibility:|Spark NLP 5.0.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[sentence, token]|
+|Output Labels:|[embeddings]|
+|Language:|fr|
+|Size:|264.0 MB|
+|Case sensitive:|true|
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_xkang_generic_model_fr.md b/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_xkang_generic_model_fr.md
new file mode 100644
index 00000000000000..b0465bddfdb95c
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_xkang_generic_model_fr.md
@@ -0,0 +1,99 @@
+---
+layout: model
+title: French CamemBert Embeddings (from xkang)
+author: John Snow Labs
+name: camembert_embeddings_xkang_generic_model
+date: 2023-07-31
+tags: [fr, open_source, camembert, embeddings, onnx]
+task: Embeddings
+language: fr
+edition: Spark NLP 5.0.2
+spark_version: 3.0
+supported: true
+engine: onnx
+annotator: CamemBertEmbeddings
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained CamemBert Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `dummy-model` is a French model orginally trained by `xkang`.
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_embeddings_xkang_generic_model_fr_5.0.2_3.0_1690840602500.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_embeddings_xkang_generic_model_fr_5.0.2_3.0_1690840602500.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+documentAssembler = DocumentAssembler() \
+    .setInputCol("text") \
+    .setOutputCol("document")
+
+tokenizer = Tokenizer() \
+    .setInputCols("document") \
+    .setOutputCol("token")
+  
+embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_xkang_generic_model","fr") \
+    .setInputCols(["document", "token"]) \
+    .setOutputCol("embeddings")
+    
+pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings])
+
+data = spark.createDataFrame([["J'adore Spark NLP"]]).toDF("text")
+
+result = pipeline.fit(data).transform(data)
+```
+```scala
+val documentAssembler = new DocumentAssembler() 
+      .setInputCol("text") 
+      .setOutputCol("document")
+ 
+val tokenizer = new Tokenizer() 
+    .setInputCols(Array("document"))
+    .setOutputCol("token")
+
+val embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_xkang_generic_model","fr") 
+    .setInputCols(Array("document", "token")) 
+    .setOutputCol("embeddings")
+
+val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings))
+
+val data = Seq("J'adore Spark NLP").toDF("text")
+
+val result = pipeline.fit(data).transform(data)
+```
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("fr.embed.camembert.generic.by_xkang").predict("""J'adore Spark NLP""")
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|camembert_embeddings_xkang_generic_model|
+|Compatibility:|Spark NLP 5.0.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[sentence, token]|
+|Output Labels:|[embeddings]|
+|Language:|fr|
+|Size:|264.0 MB|
+|Case sensitive:|true|
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_yancong_generic_model_fr.md b/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_yancong_generic_model_fr.md
new file mode 100644
index 00000000000000..c11c87a7dc9850
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_yancong_generic_model_fr.md
@@ -0,0 +1,99 @@
+---
+layout: model
+title: French CamemBert Embeddings (from yancong)
+author: John Snow Labs
+name: camembert_embeddings_yancong_generic_model
+date: 2023-07-31
+tags: [fr, open_source, camembert, embeddings, onnx]
+task: Embeddings
+language: fr
+edition: Spark NLP 5.0.2
+spark_version: 3.0
+supported: true
+engine: onnx
+annotator: CamemBertEmbeddings
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained CamemBert Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `dummy-model` is a French model orginally trained by `yancong`.
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_embeddings_yancong_generic_model_fr_5.0.2_3.0_1690841226605.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_embeddings_yancong_generic_model_fr_5.0.2_3.0_1690841226605.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+documentAssembler = DocumentAssembler() \
+    .setInputCol("text") \
+    .setOutputCol("document")
+
+tokenizer = Tokenizer() \
+    .setInputCols("document") \
+    .setOutputCol("token")
+  
+embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_yancong_generic_model","fr") \
+    .setInputCols(["document", "token"]) \
+    .setOutputCol("embeddings")
+    
+pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings])
+
+data = spark.createDataFrame([["J'adore Spark NLP"]]).toDF("text")
+
+result = pipeline.fit(data).transform(data)
+```
+```scala
+val documentAssembler = new DocumentAssembler() 
+      .setInputCol("text") 
+      .setOutputCol("document")
+ 
+val tokenizer = new Tokenizer() 
+    .setInputCols(Array("document"))
+    .setOutputCol("token")
+
+val embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_yancong_generic_model","fr") 
+    .setInputCols(Array("document", "token")) 
+    .setOutputCol("embeddings")
+
+val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings))
+
+val data = Seq("J'adore Spark NLP").toDF("text")
+
+val result = pipeline.fit(data).transform(data)
+```
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("fr.embed.camembert.generic.by_yancong").predict("""J'adore Spark NLP""")
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|camembert_embeddings_yancong_generic_model|
+|Compatibility:|Spark NLP 5.0.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[sentence, token]|
+|Output Labels:|[embeddings]|
+|Language:|fr|
+|Size:|264.0 MB|
+|Case sensitive:|true|
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_ysharma_generic_model_2_fr.md b/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_ysharma_generic_model_2_fr.md
new file mode 100644
index 00000000000000..d2bcaa21f544ef
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-07-31-camembert_embeddings_ysharma_generic_model_2_fr.md
@@ -0,0 +1,99 @@
+---
+layout: model
+title: French CamemBert Embeddings (from ysharma)
+author: John Snow Labs
+name: camembert_embeddings_ysharma_generic_model_2
+date: 2023-07-31
+tags: [fr, open_source, camembert, embeddings, onnx]
+task: Embeddings
+language: fr
+edition: Spark NLP 5.0.2
+spark_version: 3.0
+supported: true
+engine: onnx
+annotator: CamemBertEmbeddings
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained CamemBert Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `dummy-model-2` is a French model orginally trained by `ysharma`.
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_embeddings_ysharma_generic_model_2_fr_5.0.2_3.0_1690840552187.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_embeddings_ysharma_generic_model_2_fr_5.0.2_3.0_1690840552187.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+documentAssembler = DocumentAssembler() \
+    .setInputCol("text") \
+    .setOutputCol("document")
+
+tokenizer = Tokenizer() \
+    .setInputCols("document") \
+    .setOutputCol("token")
+  
+embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_ysharma_generic_model_2","fr") \
+    .setInputCols(["document", "token"]) \
+    .setOutputCol("embeddings")
+    
+pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings])
+
+data = spark.createDataFrame([["J'adore Spark NLP"]]).toDF("text")
+
+result = pipeline.fit(data).transform(data)
+```
+```scala
+val documentAssembler = new DocumentAssembler() 
+      .setInputCol("text") 
+      .setOutputCol("document")
+ 
+val tokenizer = new Tokenizer() 
+    .setInputCols(Array("document"))
+    .setOutputCol("token")
+
+val embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_ysharma_generic_model_2","fr") 
+    .setInputCols(Array("document", "token")) 
+    .setOutputCol("embeddings")
+
+val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings))
+
+val data = Seq("J'adore Spark NLP").toDF("text")
+
+val result = pipeline.fit(data).transform(data)
+```
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("fr.embed.camembert.generic.by_ysharma").predict("""J'adore Spark NLP""")
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|camembert_embeddings_ysharma_generic_model_2|
+|Compatibility:|Spark NLP 5.0.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[sentence, token]|
+|Output Labels:|[embeddings]|
+|Language:|fr|
+|Size:|264.0 MB|
+|Case sensitive:|true|
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-08-01-camembert_base_ccnet_4gb_fr.md b/docs/_posts/ahmedlone127/2023-08-01-camembert_base_ccnet_4gb_fr.md
new file mode 100644
index 00000000000000..eb59efb3511280
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-08-01-camembert_base_ccnet_4gb_fr.md
@@ -0,0 +1,86 @@
+---
+layout: model
+title: CamemBERT Subsample of CCNet
+author: John Snow Labs
+name: camembert_base_ccnet_4gb
+date: 2023-08-01
+tags: [fr, french, embeddings, camembert, ccnet, open_source, onnx]
+task: Embeddings
+language: fr
+edition: Spark NLP 5.0.2
+spark_version: 3.0
+supported: true
+engine: onnx
+annotator: CamemBertEmbeddings
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+[CamemBERT](https://arxiv.org/abs/1911.03894) is a state-of-the-art language model for French based on the RoBERTa model.
+For further information or requests, please go to [Camembert Website](https://camembert-model.fr/)
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_base_ccnet_4gb_fr_5.0.2_3.0_1690928369186.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_base_ccnet_4gb_fr_5.0.2_3.0_1690928369186.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+embeddings = CamemBertEmbeddings.pretrained("camembert_base_ccnet_4gb", "fr") \
+.setInputCols("sentence", "token") \
+.setOutputCol("embeddings")
+```
+```scala
+val embeddings = CamemBertEmbeddings.pretrained("camembert_base_ccnet_4gb", "fr")
+.setInputCols("sentence", "token")
+.setOutputCol("embeddings")
+```
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("fr.embed.camembert_ccnet4g").predict("""Put your text here.""")
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|camembert_base_ccnet_4gb|
+|Compatibility:|Spark NLP 5.0.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[token, sentence]|
+|Output Labels:|[embeddings]|
+|Language:|fr|
+|Size:|263.4 MB|
+|Case sensitive:|true|
+
+## Benchmarking
+
+```bash
+
+| Model                          | #params                        | Arch. | Training data                     |
+|--------------------------------|--------------------------------|-------|-----------------------------------|
+| `camembert-base` | 110M   | Base  | OSCAR (138 GB of text)            |
+| `camembert/camembert-large`              | 335M    | Large | CCNet (135 GB of text)            |
+| `camembert/camembert-base-ccnet`         | 110M    | Base  | CCNet (135 GB of text)            |
+| `camembert/camembert-base-wikipedia-4gb` | 110M    | Base  | Wikipedia (4 GB of text)          |
+| `camembert/camembert-base-oscar-4gb`     | 110M    | Base  | Subsample of OSCAR (4 GB of text) |
+| `camembert/camembert-base-ccnet-4gb`     | 110M    | Base  | Subsample of CCNet (4 GB of text) |
+```
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-08-01-camembert_base_ccnet_fr.md b/docs/_posts/ahmedlone127/2023-08-01-camembert_base_ccnet_fr.md
new file mode 100644
index 00000000000000..f6314446737cb8
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-08-01-camembert_base_ccnet_fr.md
@@ -0,0 +1,86 @@
+---
+layout: model
+title: CamemBERT Base CCNet
+author: John Snow Labs
+name: camembert_base_ccnet
+date: 2023-08-01
+tags: [fr, french, embeddings, camembert, ccnet, open_source, onnx]
+task: Embeddings
+language: fr
+edition: Spark NLP 5.0.2
+spark_version: 3.0
+supported: true
+engine: onnx
+annotator: CamemBertEmbeddings
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+[CamemBERT](https://arxiv.org/abs/1911.03894) is a state-of-the-art language model for French based on the RoBERTa model.
+For further information or requests, please go to [Camembert Website](https://camembert-model.fr/)
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_base_ccnet_fr_5.0.2_3.0_1690927305918.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_base_ccnet_fr_5.0.2_3.0_1690927305918.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+embeddings = CamemBertEmbeddings.pretrained("camembert_base_ccnet", "fr") \
+.setInputCols("sentence", "token") \
+.setOutputCol("embeddings")
+```
+```scala
+val embeddings = CamemBertEmbeddings.pretrained("camembert_base_ccnet", "fr")
+.setInputCols("sentence", "token")
+.setOutputCol("embeddings")
+```
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("fr.embed.camembert_base_ccnet").predict("""Put your text here.""")
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|camembert_base_ccnet|
+|Compatibility:|Spark NLP 5.0.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[token, sentence]|
+|Output Labels:|[embeddings]|
+|Language:|fr|
+|Size:|263.6 MB|
+|Case sensitive:|true|
+
+## Benchmarking
+
+```bash
+
+| Model                          | #params                        | Arch. | Training data                     |
+|--------------------------------|--------------------------------|-------|-----------------------------------|
+| `camembert-base` | 110M   | Base  | OSCAR (138 GB of text)            |
+| `camembert/camembert-large`              | 335M    | Large | CCNet (135 GB of text)            |
+| `camembert/camembert-base-ccnet`         | 110M    | Base  | CCNet (135 GB of text)            |
+| `camembert/camembert-base-wikipedia-4gb` | 110M    | Base  | Wikipedia (4 GB of text)          |
+| `camembert/camembert-base-oscar-4gb`     | 110M    | Base  | Subsample of OSCAR (4 GB of text) |
+| `camembert/camembert-base-ccnet-4gb`     | 110M    | Base  | Subsample of CCNet (4 GB of text) |
+```
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-08-01-camembert_base_fr.md b/docs/_posts/ahmedlone127/2023-08-01-camembert_base_fr.md
new file mode 100644
index 00000000000000..0a2792ccab32c0
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-08-01-camembert_base_fr.md
@@ -0,0 +1,86 @@
+---
+layout: model
+title: CamemBERT Base Model
+author: John Snow Labs
+name: camembert_base
+date: 2023-08-01
+tags: [fr, french, embeddings, camembert, base, open_source, onnx]
+task: Embeddings
+language: fr
+edition: Spark NLP 5.0.2
+spark_version: 3.0
+supported: true
+engine: onnx
+annotator: CamemBertEmbeddings
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+[CamemBERT](https://arxiv.org/abs/1911.03894) is a state-of-the-art language model for French based on the RoBERTa model.
+For further information or requests, please go to [Camembert Website](https://camembert-model.fr/)
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_base_fr_5.0.2_3.0_1690933576243.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_base_fr_5.0.2_3.0_1690933576243.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+embeddings = CamemBertEmbeddings.pretrained("camembert_base", "fr") \
+.setInputCols("sentence", "token") \
+.setOutputCol("embeddings")
+```
+```scala
+val embeddings = CamemBertEmbeddings.pretrained("camembert_base", "fr")
+.setInputCols("sentence", "token")
+.setOutputCol("embeddings")
+```
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("fr.embed.camembert_base").predict("""Put your text here.""")
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|camembert_base|
+|Compatibility:|Spark NLP 5.0.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[token, sentence]|
+|Output Labels:|[embeddings]|
+|Language:|fr|
+|Size:|264.0 MB|
+|Case sensitive:|true|
+
+## Benchmarking
+
+```bash
+
+| Model                          | #params                        | Arch. | Training data                     |
+|--------------------------------|--------------------------------|-------|-----------------------------------|
+| `camembert-base` | 110M   | Base  | OSCAR (138 GB of text)            |
+| `camembert/camembert-large`              | 335M    | Large | CCNet (135 GB of text)            |
+| `camembert/camembert-base-ccnet`         | 110M    | Base  | CCNet (135 GB of text)            |
+| `camembert/camembert-base-wikipedia-4gb` | 110M    | Base  | Wikipedia (4 GB of text)          |
+| `camembert/camembert-base-oscar-4gb`     | 110M    | Base  | Subsample of OSCAR (4 GB of text) |
+| `camembert/camembert-base-ccnet-4gb`     | 110M    | Base  | Subsample of CCNet (4 GB of text) |
+```
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-08-01-camembert_base_opt_fr.md b/docs/_posts/ahmedlone127/2023-08-01-camembert_base_opt_fr.md
new file mode 100644
index 00000000000000..0f0f64d821060b
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-08-01-camembert_base_opt_fr.md
@@ -0,0 +1,86 @@
+---
+layout: model
+title: CamemBERT Base Model
+author: John Snow Labs
+name: camembert_base_opt
+date: 2023-08-01
+tags: [fr, french, embeddings, camembert, base, open_source, onnx]
+task: Embeddings
+language: fr
+edition: Spark NLP 5.0.2
+spark_version: 3.0
+supported: true
+engine: onnx
+annotator: CamemBertEmbeddings
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+[CamemBERT](https://arxiv.org/abs/1911.03894) is a state-of-the-art language model for French based on the RoBERTa model.
+For further information or requests, please go to [Camembert Website](https://camembert-model.fr/)
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_base_opt_fr_5.0.2_3.0_1690933783384.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_base_opt_fr_5.0.2_3.0_1690933783384.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+embeddings = CamemBertEmbeddings.pretrained("camembert_base", "fr") \
+.setInputCols("sentence", "token") \
+.setOutputCol("embeddings")
+```
+```scala
+val embeddings = CamemBertEmbeddings.pretrained("camembert_base", "fr")
+.setInputCols("sentence", "token")
+.setOutputCol("embeddings")
+```
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("fr.embed.camembert_base").predict("""Put your text here.""")
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|camembert_base_opt|
+|Compatibility:|Spark NLP 5.0.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[token, sentence]|
+|Output Labels:|[embeddings]|
+|Language:|fr|
+|Size:|264.3 MB|
+|Case sensitive:|true|
+
+## Benchmarking
+
+```bash
+
+| Model                          | #params                        | Arch. | Training data                     |
+|--------------------------------|--------------------------------|-------|-----------------------------------|
+| `camembert-base` | 110M   | Base  | OSCAR (138 GB of text)            |
+| `camembert/camembert-large`              | 335M    | Large | CCNet (135 GB of text)            |
+| `camembert/camembert-base-ccnet`         | 110M    | Base  | CCNet (135 GB of text)            |
+| `camembert/camembert-base-wikipedia-4gb` | 110M    | Base  | Wikipedia (4 GB of text)          |
+| `camembert/camembert-base-oscar-4gb`     | 110M    | Base  | Subsample of OSCAR (4 GB of text) |
+| `camembert/camembert-base-ccnet-4gb`     | 110M    | Base  | Subsample of CCNet (4 GB of text) |
+```
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-08-01-camembert_base_oscar_4gb_fr.md b/docs/_posts/ahmedlone127/2023-08-01-camembert_base_oscar_4gb_fr.md
new file mode 100644
index 00000000000000..20af11cc2052cd
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-08-01-camembert_base_oscar_4gb_fr.md
@@ -0,0 +1,86 @@
+---
+layout: model
+title: CamemBERT Base OSCAR
+author: John Snow Labs
+name: camembert_base_oscar_4gb
+date: 2023-08-01
+tags: [fr, french, camembert, embeddings, oscar, open_source, onnx]
+task: Embeddings
+language: fr
+edition: Spark NLP 5.0.2
+spark_version: 3.0
+supported: true
+engine: onnx
+annotator: CamemBertEmbeddings
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+[CamemBERT](https://arxiv.org/abs/1911.03894) is a state-of-the-art language model for French based on the RoBERTa model.
+For further information or requests, please go to [Camembert Website](https://camembert-model.fr/)
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_base_oscar_4gb_fr_5.0.2_3.0_1690926989664.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_base_oscar_4gb_fr_5.0.2_3.0_1690926989664.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+embeddings = CamemBertEmbeddings.pretrained("camembert_base_oscar_4gb", "fr") \
+.setInputCols("sentence", "token") \
+.setOutputCol("embeddings")
+```
+```scala
+val embeddings = CamemBertEmbeddings.pretrained("camembert_base_oscar_4gb", "fr")
+.setInputCols("sentence", "token")
+.setOutputCol("embeddings")
+```
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("fr.embed.camembert_oscar_4g").predict("""Put your text here.""")
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|camembert_base_oscar_4gb|
+|Compatibility:|Spark NLP 5.0.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[token, sentence]|
+|Output Labels:|[embeddings]|
+|Language:|fr|
+|Size:|263.6 MB|
+|Case sensitive:|true|
+
+## Benchmarking
+
+```bash
+
+| Model                          | #params                        | Arch. | Training data                     |
+|--------------------------------|--------------------------------|-------|-----------------------------------|
+| `camembert-base` | 110M   | Base  | OSCAR (138 GB of text)            |
+| `camembert/camembert-large`              | 335M    | Large | CCNet (135 GB of text)            |
+| `camembert/camembert-base-ccnet`         | 110M    | Base  | CCNet (135 GB of text)            |
+| `camembert/camembert-base-wikipedia-4gb` | 110M    | Base  | Wikipedia (4 GB of text)          |
+| `camembert/camembert-base-oscar-4gb`     | 110M    | Base  | Subsample of OSCAR (4 GB of text) |
+| `camembert/camembert-base-ccnet-4gb`     | 110M    | Base  | Subsample of CCNet (4 GB of text) |
+```
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-08-01-camembert_base_quantized_fr.md b/docs/_posts/ahmedlone127/2023-08-01-camembert_base_quantized_fr.md
new file mode 100644
index 00000000000000..64850ca0c2d212
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-08-01-camembert_base_quantized_fr.md
@@ -0,0 +1,86 @@
+---
+layout: model
+title: CamemBERT Base Model
+author: John Snow Labs
+name: camembert_base_quantized
+date: 2023-08-01
+tags: [fr, french, embeddings, camembert, base, open_source, onnx]
+task: Embeddings
+language: fr
+edition: Spark NLP 5.0.2
+spark_version: 3.0
+supported: true
+engine: onnx
+annotator: CamemBertEmbeddings
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+[CamemBERT](https://arxiv.org/abs/1911.03894) is a state-of-the-art language model for French based on the RoBERTa model.
+For further information or requests, please go to [Camembert Website](https://camembert-model.fr/)
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_base_quantized_fr_5.0.2_3.0_1690933869613.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_base_quantized_fr_5.0.2_3.0_1690933869613.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+embeddings = CamemBertEmbeddings.pretrained("camembert_base", "fr") \
+.setInputCols("sentence", "token") \
+.setOutputCol("embeddings")
+```
+```scala
+val embeddings = CamemBertEmbeddings.pretrained("camembert_base", "fr")
+.setInputCols("sentence", "token")
+.setOutputCol("embeddings")
+```
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("fr.embed.camembert_base").predict("""Put your text here.""")
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|camembert_base_quantized|
+|Compatibility:|Spark NLP 5.0.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[token, sentence]|
+|Output Labels:|[embeddings]|
+|Language:|fr|
+|Size:|107.9 MB|
+|Case sensitive:|true|
+
+## Benchmarking
+
+```bash
+
+| Model                          | #params                        | Arch. | Training data                     |
+|--------------------------------|--------------------------------|-------|-----------------------------------|
+| `camembert-base` | 110M   | Base  | OSCAR (138 GB of text)            |
+| `camembert/camembert-large`              | 335M    | Large | CCNet (135 GB of text)            |
+| `camembert/camembert-base-ccnet`         | 110M    | Base  | CCNet (135 GB of text)            |
+| `camembert/camembert-base-wikipedia-4gb` | 110M    | Base  | Wikipedia (4 GB of text)          |
+| `camembert/camembert-base-oscar-4gb`     | 110M    | Base  | Subsample of OSCAR (4 GB of text) |
+| `camembert/camembert-base-ccnet-4gb`     | 110M    | Base  | Subsample of CCNet (4 GB of text) |
+```
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-08-01-camembert_base_wikipedia_4gb_fr.md b/docs/_posts/ahmedlone127/2023-08-01-camembert_base_wikipedia_4gb_fr.md
new file mode 100644
index 00000000000000..ab8bfd83cf9b6f
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-08-01-camembert_base_wikipedia_4gb_fr.md
@@ -0,0 +1,86 @@
+---
+layout: model
+title: CamemBERT Base Wikipedia
+author: John Snow Labs
+name: camembert_base_wikipedia_4gb
+date: 2023-08-01
+tags: [fr, french, embeddings, camembert, wikipedia, open_source, onnx]
+task: Embeddings
+language: fr
+edition: Spark NLP 5.0.2
+spark_version: 3.0
+supported: true
+engine: onnx
+annotator: CamemBertEmbeddings
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+[CamemBERT](https://arxiv.org/abs/1911.03894) is a state-of-the-art language model for French based on the RoBERTa model.
+For further information or requests, please go to [Camembert Website](https://camembert-model.fr/)
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_base_wikipedia_4gb_fr_5.0.2_3.0_1690926794799.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_base_wikipedia_4gb_fr_5.0.2_3.0_1690926794799.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+embeddings = CamemBertEmbeddings.pretrained("camembert_base_wikipedia_4gb", "fr") \
+.setInputCols("sentence", "token") \
+.setOutputCol("embeddings")
+```
+```scala
+val embeddings = CamemBertEmbeddings.pretrained("camembert_base_wikipedia_4gb", "fr")
+.setInputCols("sentence", "token")
+.setOutputCol("embeddings")
+```
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("fr.embed.camembert_wiki_4g").predict("""Put your text here.""")
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|camembert_base_wikipedia_4gb|
+|Compatibility:|Spark NLP 5.0.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[token, sentence]|
+|Output Labels:|[embeddings]|
+|Language:|fr|
+|Size:|263.1 MB|
+|Case sensitive:|true|
+
+## Benchmarking
+
+```bash
+
+| Model                          | #params                        | Arch. | Training data                     |
+|--------------------------------|--------------------------------|-------|-----------------------------------|
+| `camembert-base` | 110M   | Base  | OSCAR (138 GB of text)            |
+| `camembert/camembert-large`              | 335M    | Large | CCNet (135 GB of text)            |
+| `camembert/camembert-base-ccnet`         | 110M    | Base  | CCNet (135 GB of text)            |
+| `camembert/camembert-base-wikipedia-4gb` | 110M    | Base  | Wikipedia (4 GB of text)          |
+| `camembert/camembert-base-oscar-4gb`     | 110M    | Base  | Subsample of OSCAR (4 GB of text) |
+| `camembert/camembert-base-ccnet-4gb`     | 110M    | Base  | Subsample of CCNet (4 GB of text) |
+```
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_DataikuNLP_camembert_base_fr.md b/docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_DataikuNLP_camembert_base_fr.md
new file mode 100644
index 00000000000000..2548f85d5d03da
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_DataikuNLP_camembert_base_fr.md
@@ -0,0 +1,93 @@
+---
+layout: model
+title: French CamemBert Embeddings (from DataikuNLP)
+author: John Snow Labs
+name: camembert_embeddings_DataikuNLP_camembert_base
+date: 2023-08-01
+tags: [fr, open_source, camembert, embeddings, onnx]
+task: Embeddings
+language: fr
+edition: Spark NLP 5.0.2
+spark_version: 3.0
+supported: true
+engine: onnx
+annotator: CamemBertEmbeddings
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained CamemBert Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `camembert-base` is a French model orginally trained by `DataikuNLP`.
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_embeddings_DataikuNLP_camembert_base_fr_5.0.2_3.0_1690926395140.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_embeddings_DataikuNLP_camembert_base_fr_5.0.2_3.0_1690926395140.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+documentAssembler = DocumentAssembler() \
+    .setInputCol("text") \
+    .setOutputCol("document")
+
+tokenizer = Tokenizer() \
+    .setInputCols("document") \
+    .setOutputCol("token")
+  
+embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_DataikuNLP_camembert_base","fr") \
+    .setInputCols(["document", "token"]) \
+    .setOutputCol("embeddings")
+    
+pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings])
+
+data = spark.createDataFrame([["J'adore Spark NLP"]]).toDF("text")
+
+result = pipeline.fit(data).transform(data)
+```
+```scala
+val documentAssembler = new DocumentAssembler() 
+      .setInputCol("text") 
+      .setOutputCol("document")
+ 
+val tokenizer = new Tokenizer() 
+    .setInputCols(Array("document"))
+    .setOutputCol("token")
+
+val embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_DataikuNLP_camembert_base","fr") 
+    .setInputCols(Array("document", "token")) 
+    .setOutputCol("embeddings")
+
+val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings))
+
+val data = Seq("J'adore Spark NLP").toDF("text")
+
+val result = pipeline.fit(data).transform(data)
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|camembert_embeddings_DataikuNLP_camembert_base|
+|Compatibility:|Spark NLP 5.0.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[sentence, token]|
+|Output Labels:|[embeddings]|
+|Language:|fr|
+|Size:|264.0 MB|
+|Case sensitive:|true|
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_bertweetfr_base_fr.md b/docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_bertweetfr_base_fr.md
new file mode 100644
index 00000000000000..4eee761df87a0e
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_bertweetfr_base_fr.md
@@ -0,0 +1,99 @@
+---
+layout: model
+title: French CamemBert Embeddings (from Yanzhu)
+author: John Snow Labs
+name: camembert_embeddings_bertweetfr_base
+date: 2023-08-01
+tags: [fr, open_source, camembert, embeddings, onnx]
+task: Embeddings
+language: fr
+edition: Spark NLP 5.0.2
+spark_version: 3.0
+supported: true
+engine: onnx
+annotator: CamemBertEmbeddings
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained CamemBert Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `bertweetfr-base` is a French model orginally trained by `Yanzhu`.
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_embeddings_bertweetfr_base_fr_5.0.2_3.0_1690927634160.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_embeddings_bertweetfr_base_fr_5.0.2_3.0_1690927634160.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+documentAssembler = DocumentAssembler() \
+    .setInputCol("text") \
+    .setOutputCol("document")
+
+tokenizer = Tokenizer() \
+    .setInputCols("document") \
+    .setOutputCol("token")
+  
+embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_bertweetfr_base","fr") \
+    .setInputCols(["document", "token"]) \
+    .setOutputCol("embeddings")
+    
+pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings])
+
+data = spark.createDataFrame([["J'adore Spark NLP"]]).toDF("text")
+
+result = pipeline.fit(data).transform(data)
+```
+```scala
+val documentAssembler = new DocumentAssembler() 
+      .setInputCol("text") 
+      .setOutputCol("document")
+ 
+val tokenizer = new Tokenizer() 
+    .setInputCols(Array("document"))
+    .setOutputCol("token")
+
+val embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_bertweetfr_base","fr") 
+    .setInputCols(Array("document", "token")) 
+    .setOutputCol("embeddings")
+
+val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings))
+
+val data = Seq("J'adore Spark NLP").toDF("text")
+
+val result = pipeline.fit(data).transform(data)
+```
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("fr.embed.camembert.tweet.base").predict("""J'adore Spark NLP""")
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|camembert_embeddings_bertweetfr_base|
+|Compatibility:|Spark NLP 5.0.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[sentence, token]|
+|Output Labels:|[embeddings]|
+|Language:|fr|
+|Size:|412.8 MB|
+|Case sensitive:|true|
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_distilcamembert_base_fr.md b/docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_distilcamembert_base_fr.md
new file mode 100644
index 00000000000000..a0bf1c37dde147
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_distilcamembert_base_fr.md
@@ -0,0 +1,99 @@
+---
+layout: model
+title: French CamemBert Embeddings (from cmarkea)
+author: John Snow Labs
+name: camembert_embeddings_distilcamembert_base
+date: 2023-08-01
+tags: [fr, open_source, camembert, embeddings, onnx]
+task: Embeddings
+language: fr
+edition: Spark NLP 5.0.2
+spark_version: 3.0
+supported: true
+engine: onnx
+annotator: CamemBertEmbeddings
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained CamemBert Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `distilcamembert-base` is a French model orginally trained by `cmarkea`.
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_embeddings_distilcamembert_base_fr_5.0.2_3.0_1690926517410.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_embeddings_distilcamembert_base_fr_5.0.2_3.0_1690926517410.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+documentAssembler = DocumentAssembler() \
+    .setInputCol("text") \
+    .setOutputCol("document")
+
+tokenizer = Tokenizer() \
+    .setInputCols("document") \
+    .setOutputCol("token")
+  
+embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_distilcamembert_base","fr") \
+    .setInputCols(["document", "token"]) \
+    .setOutputCol("embeddings")
+    
+pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings])
+
+data = spark.createDataFrame([["J'adore Spark NLP"]]).toDF("text")
+
+result = pipeline.fit(data).transform(data)
+```
+```scala
+val documentAssembler = new DocumentAssembler() 
+      .setInputCol("text") 
+      .setOutputCol("document")
+ 
+val tokenizer = new Tokenizer() 
+    .setInputCols(Array("document"))
+    .setOutputCol("token")
+
+val embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_distilcamembert_base","fr") 
+    .setInputCols(Array("document", "token")) 
+    .setOutputCol("embeddings")
+
+val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings))
+
+val data = Seq("J'adore Spark NLP").toDF("text")
+
+val result = pipeline.fit(data).transform(data)
+```
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("fr.embed.camembert.distilled_base").predict("""J'adore Spark NLP""")
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|camembert_embeddings_distilcamembert_base|
+|Compatibility:|Spark NLP 5.0.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[sentence, token]|
+|Output Labels:|[embeddings]|
+|Language:|fr|
+|Size:|253.5 MB|
+|Case sensitive:|true|
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_generic_model_test_fr.md b/docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_generic_model_test_fr.md
new file mode 100644
index 00000000000000..f1276b1133a22d
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_generic_model_test_fr.md
@@ -0,0 +1,99 @@
+---
+layout: model
+title: French CamemBert Embeddings (from osanseviero)
+author: John Snow Labs
+name: camembert_embeddings_generic_model_test
+date: 2023-08-01
+tags: [fr, open_source, camembert, embeddings, onnx]
+task: Embeddings
+language: fr
+edition: Spark NLP 5.0.2
+spark_version: 3.0
+supported: true
+engine: onnx
+annotator: CamemBertEmbeddings
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained CamemBert Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `dummy-model-test` is a French model orginally trained by `osanseviero`.
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_embeddings_generic_model_test_fr_5.0.2_3.0_1690926134164.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_embeddings_generic_model_test_fr_5.0.2_3.0_1690926134164.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+documentAssembler = DocumentAssembler() \
+    .setInputCol("text") \
+    .setOutputCol("document")
+
+tokenizer = Tokenizer() \
+    .setInputCols("document") \
+    .setOutputCol("token")
+  
+embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_generic_model_test","fr") \
+    .setInputCols(["document", "token"]) \
+    .setOutputCol("embeddings")
+    
+pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings])
+
+data = spark.createDataFrame([["J'adore Spark NLP"]]).toDF("text")
+
+result = pipeline.fit(data).transform(data)
+```
+```scala
+val documentAssembler = new DocumentAssembler() 
+      .setInputCol("text") 
+      .setOutputCol("document")
+ 
+val tokenizer = new Tokenizer() 
+    .setInputCols(Array("document"))
+    .setOutputCol("token")
+
+val embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_generic_model_test","fr") 
+    .setInputCols(Array("document", "token")) 
+    .setOutputCol("embeddings")
+
+val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings))
+
+val data = Seq("J'adore Spark NLP").toDF("text")
+
+val result = pipeline.fit(data).transform(data)
+```
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("fr.embed.camembert.generic.by_osanseviero").predict("""J'adore Spark NLP""")
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|camembert_embeddings_generic_model_test|
+|Compatibility:|Spark NLP 5.0.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[sentence, token]|
+|Output Labels:|[embeddings]|
+|Language:|fr|
+|Size:|264.0 MB|
+|Case sensitive:|true|
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_h4d35_generic_model_fr.md b/docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_h4d35_generic_model_fr.md
new file mode 100644
index 00000000000000..6041bc4bb5d976
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_h4d35_generic_model_fr.md
@@ -0,0 +1,99 @@
+---
+layout: model
+title: French CamemBert Embeddings (from h4d35)
+author: John Snow Labs
+name: camembert_embeddings_h4d35_generic_model
+date: 2023-08-01
+tags: [fr, open_source, camembert, embeddings, onnx]
+task: Embeddings
+language: fr
+edition: Spark NLP 5.0.2
+spark_version: 3.0
+supported: true
+engine: onnx
+annotator: CamemBertEmbeddings
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained CamemBert Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `dummy-model` is a French model orginally trained by `h4d35`.
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_embeddings_h4d35_generic_model_fr_5.0.2_3.0_1690927379924.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_embeddings_h4d35_generic_model_fr_5.0.2_3.0_1690927379924.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+documentAssembler = DocumentAssembler() \
+    .setInputCol("text") \
+    .setOutputCol("document")
+
+tokenizer = Tokenizer() \
+    .setInputCols("document") \
+    .setOutputCol("token")
+  
+embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_h4d35_generic_model","fr") \
+    .setInputCols(["document", "token"]) \
+    .setOutputCol("embeddings")
+    
+pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings])
+
+data = spark.createDataFrame([["J'adore Spark NLP"]]).toDF("text")
+
+result = pipeline.fit(data).transform(data)
+```
+```scala
+val documentAssembler = new DocumentAssembler() 
+      .setInputCol("text") 
+      .setOutputCol("document")
+ 
+val tokenizer = new Tokenizer() 
+    .setInputCols(Array("document"))
+    .setOutputCol("token")
+
+val embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_h4d35_generic_model","fr") 
+    .setInputCols(Array("document", "token")) 
+    .setOutputCol("embeddings")
+
+val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings))
+
+val data = Seq("J'adore Spark NLP").toDF("text")
+
+val result = pipeline.fit(data).transform(data)
+```
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("fr.embed.camembert.generic.by_h4d35").predict("""J'adore Spark NLP""")
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|camembert_embeddings_h4d35_generic_model|
+|Compatibility:|Spark NLP 5.0.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[sentence, token]|
+|Output Labels:|[embeddings]|
+|Language:|fr|
+|Size:|264.0 MB|
+|Case sensitive:|true|
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_hackertec_generic_fr.md b/docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_hackertec_generic_fr.md
new file mode 100644
index 00000000000000..8bf610174a3f4d
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_hackertec_generic_fr.md
@@ -0,0 +1,99 @@
+---
+layout: model
+title: French CamemBert Embeddings (from hackertec)
+author: John Snow Labs
+name: camembert_embeddings_hackertec_generic
+date: 2023-08-01
+tags: [fr, open_source, camembert, embeddings, onnx]
+task: Embeddings
+language: fr
+edition: Spark NLP 5.0.2
+spark_version: 3.0
+supported: true
+engine: onnx
+annotator: CamemBertEmbeddings
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained CamemBert Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `dummy` is a French model orginally trained by `hackertec`.
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_embeddings_hackertec_generic_fr_5.0.2_3.0_1690927057947.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_embeddings_hackertec_generic_fr_5.0.2_3.0_1690927057947.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+documentAssembler = DocumentAssembler() \
+    .setInputCol("text") \
+    .setOutputCol("document")
+
+tokenizer = Tokenizer() \
+    .setInputCols("document") \
+    .setOutputCol("token")
+  
+embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_hackertec_generic","fr") \
+    .setInputCols(["document", "token"]) \
+    .setOutputCol("embeddings")
+    
+pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings])
+
+data = spark.createDataFrame([["J'adore Spark NLP"]]).toDF("text")
+
+result = pipeline.fit(data).transform(data)
+```
+```scala
+val documentAssembler = new DocumentAssembler() 
+      .setInputCol("text") 
+      .setOutputCol("document")
+ 
+val tokenizer = new Tokenizer() 
+    .setInputCols(Array("document"))
+    .setOutputCol("token")
+
+val embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_hackertec_generic","fr") 
+    .setInputCols(Array("document", "token")) 
+    .setOutputCol("embeddings")
+
+val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings))
+
+val data = Seq("J'adore Spark NLP").toDF("text")
+
+val result = pipeline.fit(data).transform(data)
+```
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("fr.embed.camembert.generic_v2.by_hackertec").predict("""J'adore Spark NLP""")
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|camembert_embeddings_hackertec_generic|
+|Compatibility:|Spark NLP 5.0.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[sentence, token]|
+|Output Labels:|[embeddings]|
+|Language:|fr|
+|Size:|264.0 MB|
+|Case sensitive:|true|
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_jcai1_generic_model_fr.md b/docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_jcai1_generic_model_fr.md
new file mode 100644
index 00000000000000..cba25095d6389f
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_jcai1_generic_model_fr.md
@@ -0,0 +1,99 @@
+---
+layout: model
+title: French CamemBert Embeddings (from jcai1)
+author: John Snow Labs
+name: camembert_embeddings_jcai1_generic_model
+date: 2023-08-01
+tags: [fr, open_source, camembert, embeddings, onnx]
+task: Embeddings
+language: fr
+edition: Spark NLP 5.0.2
+spark_version: 3.0
+supported: true
+engine: onnx
+annotator: CamemBertEmbeddings
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained CamemBert Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `dummy-model` is a French model orginally trained by `jcai1`.
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_embeddings_jcai1_generic_model_fr_5.0.2_3.0_1690926370690.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_embeddings_jcai1_generic_model_fr_5.0.2_3.0_1690926370690.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+documentAssembler = DocumentAssembler() \
+    .setInputCol("text") \
+    .setOutputCol("document")
+
+tokenizer = Tokenizer() \
+    .setInputCols("document") \
+    .setOutputCol("token")
+  
+embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_jcai1_generic_model","fr") \
+    .setInputCols(["document", "token"]) \
+    .setOutputCol("embeddings")
+    
+pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings])
+
+data = spark.createDataFrame([["J'adore Spark NLP"]]).toDF("text")
+
+result = pipeline.fit(data).transform(data)
+```
+```scala
+val documentAssembler = new DocumentAssembler() 
+      .setInputCol("text") 
+      .setOutputCol("document")
+ 
+val tokenizer = new Tokenizer() 
+    .setInputCols(Array("document"))
+    .setOutputCol("token")
+
+val embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_jcai1_generic_model","fr") 
+    .setInputCols(Array("document", "token")) 
+    .setOutputCol("embeddings")
+
+val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings))
+
+val data = Seq("J'adore Spark NLP").toDF("text")
+
+val result = pipeline.fit(data).transform(data)
+```
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("fr.embed.camembert.generic.by_jcai1").predict("""J'adore Spark NLP""")
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|camembert_embeddings_jcai1_generic_model|
+|Compatibility:|Spark NLP 5.0.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[sentence, token]|
+|Output Labels:|[embeddings]|
+|Language:|fr|
+|Size:|264.0 MB|
+|Case sensitive:|true|
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_joe8zhang_generic_model_fr.md b/docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_joe8zhang_generic_model_fr.md
new file mode 100644
index 00000000000000..0177700d202a9c
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_joe8zhang_generic_model_fr.md
@@ -0,0 +1,99 @@
+---
+layout: model
+title: French CamemBert Embeddings (from joe8zhang)
+author: John Snow Labs
+name: camembert_embeddings_joe8zhang_generic_model
+date: 2023-08-01
+tags: [fr, open_source, camembert, embeddings, onnx]
+task: Embeddings
+language: fr
+edition: Spark NLP 5.0.2
+spark_version: 3.0
+supported: true
+engine: onnx
+annotator: CamemBertEmbeddings
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained CamemBert Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `dummy-model` is a French model orginally trained by `joe8zhang`.
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_embeddings_joe8zhang_generic_model_fr_5.0.2_3.0_1690926063630.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_embeddings_joe8zhang_generic_model_fr_5.0.2_3.0_1690926063630.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+documentAssembler = DocumentAssembler() \
+    .setInputCol("text") \
+    .setOutputCol("document")
+
+tokenizer = Tokenizer() \
+    .setInputCols("document") \
+    .setOutputCol("token")
+  
+embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_joe8zhang_generic_model","fr") \
+    .setInputCols(["document", "token"]) \
+    .setOutputCol("embeddings")
+    
+pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings])
+
+data = spark.createDataFrame([["J'adore Spark NLP"]]).toDF("text")
+
+result = pipeline.fit(data).transform(data)
+```
+```scala
+val documentAssembler = new DocumentAssembler() 
+      .setInputCol("text") 
+      .setOutputCol("document")
+ 
+val tokenizer = new Tokenizer() 
+    .setInputCols(Array("document"))
+    .setOutputCol("token")
+
+val embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_joe8zhang_generic_model","fr") 
+    .setInputCols(Array("document", "token")) 
+    .setOutputCol("embeddings")
+
+val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings))
+
+val data = Seq("J'adore Spark NLP").toDF("text")
+
+val result = pipeline.fit(data).transform(data)
+```
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("fr.embed.camembert.generic.by_joe8zhang").predict("""J'adore Spark NLP""")
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|camembert_embeddings_joe8zhang_generic_model|
+|Compatibility:|Spark NLP 5.0.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[sentence, token]|
+|Output Labels:|[embeddings]|
+|Language:|fr|
+|Size:|264.0 MB|
+|Case sensitive:|true|
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_katrin_kc_generic_model_fr.md b/docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_katrin_kc_generic_model_fr.md
new file mode 100644
index 00000000000000..82d12b7109e107
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_katrin_kc_generic_model_fr.md
@@ -0,0 +1,99 @@
+---
+layout: model
+title: French CamemBert Embeddings (from katrin-kc)
+author: John Snow Labs
+name: camembert_embeddings_katrin_kc_generic_model
+date: 2023-08-01
+tags: [fr, open_source, camembert, embeddings, onnx]
+task: Embeddings
+language: fr
+edition: Spark NLP 5.0.2
+spark_version: 3.0
+supported: true
+engine: onnx
+annotator: CamemBertEmbeddings
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained CamemBert Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `dummy-model` is a French model orginally trained by `katrin-kc`.
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_embeddings_katrin_kc_generic_model_fr_5.0.2_3.0_1690925694402.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_embeddings_katrin_kc_generic_model_fr_5.0.2_3.0_1690925694402.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+documentAssembler = DocumentAssembler() \
+    .setInputCol("text") \
+    .setOutputCol("document")
+
+tokenizer = Tokenizer() \
+    .setInputCols("document") \
+    .setOutputCol("token")
+  
+embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_katrin_kc_generic_model","fr") \
+    .setInputCols(["document", "token"]) \
+    .setOutputCol("embeddings")
+    
+pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings])
+
+data = spark.createDataFrame([["J'adore Spark NLP"]]).toDF("text")
+
+result = pipeline.fit(data).transform(data)
+```
+```scala
+val documentAssembler = new DocumentAssembler() 
+      .setInputCol("text") 
+      .setOutputCol("document")
+ 
+val tokenizer = new Tokenizer() 
+    .setInputCols(Array("document"))
+    .setOutputCol("token")
+
+val embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_katrin_kc_generic_model","fr") 
+    .setInputCols(Array("document", "token")) 
+    .setOutputCol("embeddings")
+
+val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings))
+
+val data = Seq("J'adore Spark NLP").toDF("text")
+
+val result = pipeline.fit(data).transform(data)
+```
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("fr.embed.camembert.generic.by_katrin_kc").predict("""J'adore Spark NLP""")
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|camembert_embeddings_katrin_kc_generic_model|
+|Compatibility:|Spark NLP 5.0.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[sentence, token]|
+|Output Labels:|[embeddings]|
+|Language:|fr|
+|Size:|264.0 MB|
+|Case sensitive:|true|
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_kaushikacharya_generic_model_fr.md b/docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_kaushikacharya_generic_model_fr.md
new file mode 100644
index 00000000000000..2c98576373f2cb
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_kaushikacharya_generic_model_fr.md
@@ -0,0 +1,99 @@
+---
+layout: model
+title: French CamemBert Embeddings (from kaushikacharya)
+author: John Snow Labs
+name: camembert_embeddings_kaushikacharya_generic_model
+date: 2023-08-01
+tags: [fr, open_source, camembert, embeddings, onnx]
+task: Embeddings
+language: fr
+edition: Spark NLP 5.0.2
+spark_version: 3.0
+supported: true
+engine: onnx
+annotator: CamemBertEmbeddings
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained CamemBert Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `dummy-model` is a French model orginally trained by `kaushikacharya`.
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_embeddings_kaushikacharya_generic_model_fr_5.0.2_3.0_1690925377512.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_embeddings_kaushikacharya_generic_model_fr_5.0.2_3.0_1690925377512.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+documentAssembler = DocumentAssembler() \
+    .setInputCol("text") \
+    .setOutputCol("document")
+
+tokenizer = Tokenizer() \
+    .setInputCols("document") \
+    .setOutputCol("token")
+  
+embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_kaushikacharya_generic_model","fr") \
+    .setInputCols(["document", "token"]) \
+    .setOutputCol("embeddings")
+    
+pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings])
+
+data = spark.createDataFrame([["J'adore Spark NLP"]]).toDF("text")
+
+result = pipeline.fit(data).transform(data)
+```
+```scala
+val documentAssembler = new DocumentAssembler() 
+      .setInputCol("text") 
+      .setOutputCol("document")
+ 
+val tokenizer = new Tokenizer() 
+    .setInputCols(Array("document"))
+    .setOutputCol("token")
+
+val embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_kaushikacharya_generic_model","fr") 
+    .setInputCols(Array("document", "token")) 
+    .setOutputCol("embeddings")
+
+val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings))
+
+val data = Seq("J'adore Spark NLP").toDF("text")
+
+val result = pipeline.fit(data).transform(data)
+```
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("fr.embed.camembert.generic.by_kaushikacharya").predict("""J'adore Spark NLP""")
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|camembert_embeddings_kaushikacharya_generic_model|
+|Compatibility:|Spark NLP 5.0.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[sentence, token]|
+|Output Labels:|[embeddings]|
+|Language:|fr|
+|Size:|264.0 MB|
+|Case sensitive:|true|
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_lewtun_generic_model_fr.md b/docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_lewtun_generic_model_fr.md
new file mode 100644
index 00000000000000..bc03b7b850c19a
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_lewtun_generic_model_fr.md
@@ -0,0 +1,99 @@
+---
+layout: model
+title: French CamemBert Embeddings (from lewtun)
+author: John Snow Labs
+name: camembert_embeddings_lewtun_generic_model
+date: 2023-08-01
+tags: [fr, open_source, camembert, embeddings, onnx]
+task: Embeddings
+language: fr
+edition: Spark NLP 5.0.2
+spark_version: 3.0
+supported: true
+engine: onnx
+annotator: CamemBertEmbeddings
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained CamemBert Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `dummy-model` is a French model orginally trained by `lewtun`.
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_embeddings_lewtun_generic_model_fr_5.0.2_3.0_1690925963643.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_embeddings_lewtun_generic_model_fr_5.0.2_3.0_1690925963643.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+documentAssembler = DocumentAssembler() \
+    .setInputCol("text") \
+    .setOutputCol("document")
+
+tokenizer = Tokenizer() \
+    .setInputCols("document") \
+    .setOutputCol("token")
+  
+embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_lewtun_generic_model","fr") \
+    .setInputCols(["document", "token"]) \
+    .setOutputCol("embeddings")
+    
+pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings])
+
+data = spark.createDataFrame([["J'adore Spark NLP"]]).toDF("text")
+
+result = pipeline.fit(data).transform(data)
+```
+```scala
+val documentAssembler = new DocumentAssembler() 
+      .setInputCol("text") 
+      .setOutputCol("document")
+ 
+val tokenizer = new Tokenizer() 
+    .setInputCols(Array("document"))
+    .setOutputCol("token")
+
+val embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_lewtun_generic_model","fr") 
+    .setInputCols(Array("document", "token")) 
+    .setOutputCol("embeddings")
+
+val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings))
+
+val data = Seq("J'adore Spark NLP").toDF("text")
+
+val result = pipeline.fit(data).transform(data)
+```
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("fr.embed.camembert.generic.by_lewtun").predict("""J'adore Spark NLP""")
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|camembert_embeddings_lewtun_generic_model|
+|Compatibility:|Spark NLP 5.0.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[sentence, token]|
+|Output Labels:|[embeddings]|
+|Language:|fr|
+|Size:|264.0 MB|
+|Case sensitive:|true|
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_lijingxin_generic_model_2_fr.md b/docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_lijingxin_generic_model_2_fr.md
new file mode 100644
index 00000000000000..fe2ca4486b6513
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_lijingxin_generic_model_2_fr.md
@@ -0,0 +1,99 @@
+---
+layout: model
+title: French CamemBert Embeddings (from lijingxin)
+author: John Snow Labs
+name: camembert_embeddings_lijingxin_generic_model_2
+date: 2023-08-01
+tags: [fr, open_source, camembert, embeddings, onnx]
+task: Embeddings
+language: fr
+edition: Spark NLP 5.0.2
+spark_version: 3.0
+supported: true
+engine: onnx
+annotator: CamemBertEmbeddings
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained CamemBert Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `dummy-model-2` is a French model orginally trained by `lijingxin`.
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_embeddings_lijingxin_generic_model_2_fr_5.0.2_3.0_1690925509326.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_embeddings_lijingxin_generic_model_2_fr_5.0.2_3.0_1690925509326.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+documentAssembler = DocumentAssembler() \
+    .setInputCol("text") \
+    .setOutputCol("document")
+
+tokenizer = Tokenizer() \
+    .setInputCols("document") \
+    .setOutputCol("token")
+  
+embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_lijingxin_generic_model_2","fr") \
+    .setInputCols(["document", "token"]) \
+    .setOutputCol("embeddings")
+    
+pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings])
+
+data = spark.createDataFrame([["J'adore Spark NLP"]]).toDF("text")
+
+result = pipeline.fit(data).transform(data)
+```
+```scala
+val documentAssembler = new DocumentAssembler() 
+      .setInputCol("text") 
+      .setOutputCol("document")
+ 
+val tokenizer = new Tokenizer() 
+    .setInputCols(Array("document"))
+    .setOutputCol("token")
+
+val embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_lijingxin_generic_model_2","fr") 
+    .setInputCols(Array("document", "token")) 
+    .setOutputCol("embeddings")
+
+val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings))
+
+val data = Seq("J'adore Spark NLP").toDF("text")
+
+val result = pipeline.fit(data).transform(data)
+```
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("fr.embed.camembert.generic_v2.by_lijingxin").predict("""J'adore Spark NLP""")
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|camembert_embeddings_lijingxin_generic_model_2|
+|Compatibility:|Spark NLP 5.0.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[sentence, token]|
+|Output Labels:|[embeddings]|
+|Language:|fr|
+|Size:|264.0 MB|
+|Case sensitive:|true|
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_linyi_generic_model_fr.md b/docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_linyi_generic_model_fr.md
new file mode 100644
index 00000000000000..d08e2fbe1cdacf
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_linyi_generic_model_fr.md
@@ -0,0 +1,99 @@
+---
+layout: model
+title: French CamemBert Embeddings (from linyi)
+author: John Snow Labs
+name: camembert_embeddings_linyi_generic_model
+date: 2023-08-01
+tags: [fr, open_source, camembert, embeddings, onnx]
+task: Embeddings
+language: fr
+edition: Spark NLP 5.0.2
+spark_version: 3.0
+supported: true
+engine: onnx
+annotator: CamemBertEmbeddings
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained CamemBert Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `dummy-model` is a French model orginally trained by `linyi`.
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_embeddings_linyi_generic_model_fr_5.0.2_3.0_1690925802007.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_embeddings_linyi_generic_model_fr_5.0.2_3.0_1690925802007.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+documentAssembler = DocumentAssembler() \
+    .setInputCol("text") \
+    .setOutputCol("document")
+
+tokenizer = Tokenizer() \
+    .setInputCols("document") \
+    .setOutputCol("token")
+  
+embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_linyi_generic_model","fr") \
+    .setInputCols(["document", "token"]) \
+    .setOutputCol("embeddings")
+    
+pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings])
+
+data = spark.createDataFrame([["J'adore Spark NLP"]]).toDF("text")
+
+result = pipeline.fit(data).transform(data)
+```
+```scala
+val documentAssembler = new DocumentAssembler() 
+      .setInputCol("text") 
+      .setOutputCol("document")
+ 
+val tokenizer = new Tokenizer() 
+    .setInputCols(Array("document"))
+    .setOutputCol("token")
+
+val embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_linyi_generic_model","fr") 
+    .setInputCols(Array("document", "token")) 
+    .setOutputCol("embeddings")
+
+val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings))
+
+val data = Seq("J'adore Spark NLP").toDF("text")
+
+val result = pipeline.fit(data).transform(data)
+```
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("fr.embed.camembert.generic.by_linyi").predict("""J'adore Spark NLP""")
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|camembert_embeddings_linyi_generic_model|
+|Compatibility:|Spark NLP 5.0.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[sentence, token]|
+|Output Labels:|[embeddings]|
+|Language:|fr|
+|Size:|264.0 MB|
+|Case sensitive:|true|
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_mbateman_generic_model_fr.md b/docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_mbateman_generic_model_fr.md
new file mode 100644
index 00000000000000..6e92ecc54ac197
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_mbateman_generic_model_fr.md
@@ -0,0 +1,99 @@
+---
+layout: model
+title: French CamemBert Embeddings (from mbateman)
+author: John Snow Labs
+name: camembert_embeddings_mbateman_generic_model
+date: 2023-08-01
+tags: [fr, open_source, camembert, embeddings, onnx]
+task: Embeddings
+language: fr
+edition: Spark NLP 5.0.2
+spark_version: 3.0
+supported: true
+engine: onnx
+annotator: CamemBertEmbeddings
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained CamemBert Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `dummy-model` is a French model orginally trained by `mbateman`.
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_embeddings_mbateman_generic_model_fr_5.0.2_3.0_1690925436014.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_embeddings_mbateman_generic_model_fr_5.0.2_3.0_1690925436014.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+documentAssembler = DocumentAssembler() \
+    .setInputCol("text") \
+    .setOutputCol("document")
+
+tokenizer = Tokenizer() \
+    .setInputCols("document") \
+    .setOutputCol("token")
+  
+embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_mbateman_generic_model","fr") \
+    .setInputCols(["document", "token"]) \
+    .setOutputCol("embeddings")
+    
+pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings])
+
+data = spark.createDataFrame([["J'adore Spark NLP"]]).toDF("text")
+
+result = pipeline.fit(data).transform(data)
+```
+```scala
+val documentAssembler = new DocumentAssembler() 
+      .setInputCol("text") 
+      .setOutputCol("document")
+ 
+val tokenizer = new Tokenizer() 
+    .setInputCols(Array("document"))
+    .setOutputCol("token")
+
+val embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_mbateman_generic_model","fr") 
+    .setInputCols(Array("document", "token")) 
+    .setOutputCol("embeddings")
+
+val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings))
+
+val data = Seq("J'adore Spark NLP").toDF("text")
+
+val result = pipeline.fit(data).transform(data)
+```
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("fr.embed.camembert.generic.by_mbateman").predict("""J'adore Spark NLP""")
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|camembert_embeddings_mbateman_generic_model|
+|Compatibility:|Spark NLP 5.0.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[sentence, token]|
+|Output Labels:|[embeddings]|
+|Language:|fr|
+|Size:|264.0 MB|
+|Case sensitive:|true|
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_new_generic_model_fr.md b/docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_new_generic_model_fr.md
new file mode 100644
index 00000000000000..ba067640a36231
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_new_generic_model_fr.md
@@ -0,0 +1,99 @@
+---
+layout: model
+title: French CamemBert Embeddings (from gulabpatel)
+author: John Snow Labs
+name: camembert_embeddings_new_generic_model
+date: 2023-08-01
+tags: [fr, open_source, camembert, embeddings, onnx]
+task: Embeddings
+language: fr
+edition: Spark NLP 5.0.2
+spark_version: 3.0
+supported: true
+engine: onnx
+annotator: CamemBertEmbeddings
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained CamemBert Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `new-dummy-model` is a French model orginally trained by `gulabpatel`.
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_embeddings_new_generic_model_fr_5.0.2_3.0_1690925425451.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_embeddings_new_generic_model_fr_5.0.2_3.0_1690925425451.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+documentAssembler = DocumentAssembler() \
+    .setInputCol("text") \
+    .setOutputCol("document")
+
+tokenizer = Tokenizer() \
+    .setInputCols("document") \
+    .setOutputCol("token")
+  
+embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_new_generic_model","fr") \
+    .setInputCols(["document", "token"]) \
+    .setOutputCol("embeddings")
+    
+pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings])
+
+data = spark.createDataFrame([["J'adore Spark NLP"]]).toDF("text")
+
+result = pipeline.fit(data).transform(data)
+```
+```scala
+val documentAssembler = new DocumentAssembler() 
+      .setInputCol("text") 
+      .setOutputCol("document")
+ 
+val tokenizer = new Tokenizer() 
+    .setInputCols(Array("document"))
+    .setOutputCol("token")
+
+val embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_new_generic_model","fr") 
+    .setInputCols(Array("document", "token")) 
+    .setOutputCol("embeddings")
+
+val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings))
+
+val data = Seq("J'adore Spark NLP").toDF("text")
+
+val result = pipeline.fit(data).transform(data)
+```
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("fr.embed.camembert.generic.by_gulabpatel").predict("""J'adore Spark NLP""")
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|camembert_embeddings_new_generic_model|
+|Compatibility:|Spark NLP 5.0.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[sentence, token]|
+|Output Labels:|[embeddings]|
+|Language:|fr|
+|Size:|264.0 MB|
+|Case sensitive:|true|
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_sloberta_sl.md b/docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_sloberta_sl.md
new file mode 100644
index 00000000000000..029dce1090a83b
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_sloberta_sl.md
@@ -0,0 +1,99 @@
+---
+layout: model
+title: Slovenian CamemBert Embeddings (from EMBEDDIA)
+author: John Snow Labs
+name: camembert_embeddings_sloberta
+date: 2023-08-01
+tags: [sl, open_source, camembert, embeddings, onnx]
+task: Embeddings
+language: sl
+edition: Spark NLP 5.0.2
+spark_version: 3.0
+supported: true
+engine: onnx
+annotator: CamemBertEmbeddings
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained CamemBert Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `sloberta` is a Slovenian model orginally trained by `EMBEDDIA`.
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_embeddings_sloberta_sl_5.0.2_3.0_1690926104653.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_embeddings_sloberta_sl_5.0.2_3.0_1690926104653.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+documentAssembler = DocumentAssembler() \
+    .setInputCol("text") \
+    .setOutputCol("document")
+
+tokenizer = Tokenizer() \
+    .setInputCols("document") \
+    .setOutputCol("token")
+
+embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_sloberta","sl") \
+    .setInputCols(["document", "token"]) \
+    .setOutputCol("embeddings")
+
+pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings])
+
+data = spark.createDataFrame([["Obožujem Spark NLP"]]).toDF("text")
+
+result = pipeline.fit(data).transform(data)
+```
+```scala
+val documentAssembler = new DocumentAssembler()
+      .setInputCol("text")
+      .setOutputCol("document")
+
+val tokenizer = new Tokenizer()
+    .setInputCols(Array("document"))
+    .setOutputCol("token")
+
+val embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_sloberta","sl")
+    .setInputCols(Array("document", "token"))
+    .setOutputCol("embeddings")
+
+val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings))
+
+val data = Seq("Obožujem Spark NLP").toDF("text")
+
+val result = pipeline.fit(data).transform(data)
+```
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("sl.embed.camembert").predict("""Obožujem Spark NLP""")
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|camembert_embeddings_sloberta|
+|Compatibility:|Spark NLP 5.0.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[sentence, token]|
+|Output Labels:|[embeddings]|
+|Language:|sl|
+|Size:|263.5 MB|
+|Case sensitive:|true|
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_umberto_commoncrawl_cased_v1_it.md b/docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_umberto_commoncrawl_cased_v1_it.md
new file mode 100644
index 00000000000000..0c3c83cb8e961b
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_umberto_commoncrawl_cased_v1_it.md
@@ -0,0 +1,99 @@
+---
+layout: model
+title: Italian CamemBert Embeddings (from Musixmatch)
+author: John Snow Labs
+name: camembert_embeddings_umberto_commoncrawl_cased_v1
+date: 2023-08-01
+tags: [it, open_source, camembert, embeddings, onnx]
+task: Embeddings
+language: it
+edition: Spark NLP 5.0.2
+spark_version: 3.0
+supported: true
+engine: onnx
+annotator: CamemBertEmbeddings
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained CamemBert Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `umberto-commoncrawl-cased-v1` is a Italian model orginally trained by `Musixmatch`.
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_embeddings_umberto_commoncrawl_cased_v1_it_5.0.2_3.0_1690926373074.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_embeddings_umberto_commoncrawl_cased_v1_it_5.0.2_3.0_1690926373074.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+documentAssembler = DocumentAssembler() \
+    .setInputCol("text") \
+    .setOutputCol("document")
+
+tokenizer = Tokenizer() \
+    .setInputCols("document") \
+    .setOutputCol("token")
+  
+embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_umberto_commoncrawl_cased_v1","it") \
+    .setInputCols(["document", "token"]) \
+    .setOutputCol("embeddings")
+    
+pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings])
+
+data = spark.createDataFrame([["Adoro Spark NLP"]]).toDF("text")
+
+result = pipeline.fit(data).transform(data)
+```
+```scala
+val documentAssembler = new DocumentAssembler() 
+      .setInputCol("text") 
+      .setOutputCol("document")
+ 
+val tokenizer = new Tokenizer() 
+    .setInputCols(Array("document"))
+    .setOutputCol("token")
+
+val embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_umberto_commoncrawl_cased_v1","it") 
+    .setInputCols(Array("document", "token")) 
+    .setOutputCol("embeddings")
+
+val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings))
+
+val data = Seq("Adoro Spark NLP").toDF("text")
+
+val result = pipeline.fit(data).transform(data)
+```
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("it.embed.camembert.cased").predict("""Adoro Spark NLP""")
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|camembert_embeddings_umberto_commoncrawl_cased_v1|
+|Compatibility:|Spark NLP 5.0.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[sentence, token]|
+|Output Labels:|[embeddings]|
+|Language:|it|
+|Size:|263.1 MB|
+|Case sensitive:|true|
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_umberto_wikipedia_uncased_v1_it.md b/docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_umberto_wikipedia_uncased_v1_it.md
new file mode 100644
index 00000000000000..f810799f3b227d
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-08-01-camembert_embeddings_umberto_wikipedia_uncased_v1_it.md
@@ -0,0 +1,99 @@
+---
+layout: model
+title: Italian CamemBert Embeddings (from Musixmatch)
+author: John Snow Labs
+name: camembert_embeddings_umberto_wikipedia_uncased_v1
+date: 2023-08-01
+tags: [it, open_source, camembert, embeddings, onnx]
+task: Embeddings
+language: it
+edition: Spark NLP 5.0.2
+spark_version: 3.0
+supported: true
+engine: onnx
+annotator: CamemBertEmbeddings
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained CamemBert Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `umberto-wikipedia-uncased-v1` is a Italian model orginally trained by `Musixmatch`.
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/camembert_embeddings_umberto_wikipedia_uncased_v1_it_5.0.2_3.0_1690926515076.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_embeddings_umberto_wikipedia_uncased_v1_it_5.0.2_3.0_1690926515076.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+documentAssembler = DocumentAssembler() \
+    .setInputCol("text") \
+    .setOutputCol("document")
+
+tokenizer = Tokenizer() \
+    .setInputCols("document") \
+    .setOutputCol("token")
+  
+embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_umberto_wikipedia_uncased_v1","it") \
+    .setInputCols(["document", "token"]) \
+    .setOutputCol("embeddings")
+    
+pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings])
+
+data = spark.createDataFrame([["Adoro Spark NLP"]]).toDF("text")
+
+result = pipeline.fit(data).transform(data)
+```
+```scala
+val documentAssembler = new DocumentAssembler() 
+      .setInputCol("text") 
+      .setOutputCol("document")
+ 
+val tokenizer = new Tokenizer() 
+    .setInputCols(Array("document"))
+    .setOutputCol("token")
+
+val embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_umberto_wikipedia_uncased_v1","it") 
+    .setInputCols(Array("document", "token")) 
+    .setOutputCol("embeddings")
+
+val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings))
+
+val data = Seq("Adoro Spark NLP").toDF("text")
+
+val result = pipeline.fit(data).transform(data)
+```
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("it.embed.camembert.uncased").predict("""Adoro Spark NLP""")
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|camembert_embeddings_umberto_wikipedia_uncased_v1|
+|Compatibility:|Spark NLP 5.0.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[sentence, token]|
+|Output Labels:|[embeddings]|
+|Language:|it|
+|Size:|262.7 MB|
+|Case sensitive:|false|
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-08-01-xlmroberta_embeddings_fairlex_cail_minilm_zh.md b/docs/_posts/ahmedlone127/2023-08-01-xlmroberta_embeddings_fairlex_cail_minilm_zh.md
new file mode 100644
index 00000000000000..0f58c4e10d68a0
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-08-01-xlmroberta_embeddings_fairlex_cail_minilm_zh.md
@@ -0,0 +1,99 @@
+---
+layout: model
+title: Chinese XLMRoBerta Embeddings (from coastalcph)
+author: John Snow Labs
+name: xlmroberta_embeddings_fairlex_cail_minilm
+date: 2023-08-01
+tags: [zh, open_source, xlm_roberta, embeddings, fairlex, onnx]
+task: Embeddings
+language: zh
+edition: Spark NLP 5.0.2
+spark_version: 3.0
+supported: true
+engine: onnx
+annotator: XlmRoBertaEmbeddings
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained XLMRoBERTa Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `fairlex-cail-minilm` is a Chinese model orginally trained by `coastalcph`.
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/xlmroberta_embeddings_fairlex_cail_minilm_zh_5.0.2_3.0_1690928905681.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/xlmroberta_embeddings_fairlex_cail_minilm_zh_5.0.2_3.0_1690928905681.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+documentAssembler = DocumentAssembler() \
+    .setInputCol("text") \
+    .setOutputCol("document")
+
+tokenizer = Tokenizer() \
+    .setInputCols("document") \
+    .setOutputCol("token")
+  
+embeddings = XlmRoBertaEmbeddings.pretrained("xlmroberta_embeddings_fairlex_cail_minilm","zh") \
+    .setInputCols(["document", "token"]) \
+    .setOutputCol("embeddings")
+    
+pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings])
+
+data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text")
+
+result = pipeline.fit(data).transform(data)
+```
+```scala
+val documentAssembler = new DocumentAssembler() 
+      .setInputCol("text") 
+      .setOutputCol("document")
+ 
+val tokenizer = new Tokenizer() 
+    .setInputCols(Array("document"))
+    .setOutputCol("token")
+
+val embeddings = XlmRoBertaEmbeddings.pretrained("xlmroberta_embeddings_fairlex_cail_minilm","zh") 
+    .setInputCols(Array("document", "token")) 
+    .setOutputCol("embeddings")
+
+val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings))
+
+val data = Seq("PUT YOUR STRING HERE").toDF("text")
+
+val result = pipeline.fit(data).transform(data)
+```
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("zh.embed.xlmr_roberta.mini_lm_mini").predict("""PUT YOUR STRING HERE""")
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|xlmroberta_embeddings_fairlex_cail_minilm|
+|Compatibility:|Spark NLP 5.0.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[sentence, token]|
+|Output Labels:|[embeddings]|
+|Language:|zh|
+|Size:|402.9 MB|
+|Case sensitive:|true|
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-08-01-xlmroberta_embeddings_fairlex_fscs_minilm_xx.md b/docs/_posts/ahmedlone127/2023-08-01-xlmroberta_embeddings_fairlex_fscs_minilm_xx.md
new file mode 100644
index 00000000000000..fc4285cef2e324
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-08-01-xlmroberta_embeddings_fairlex_fscs_minilm_xx.md
@@ -0,0 +1,99 @@
+---
+layout: model
+title: Multilingual XLMRoBerta Embeddings (from coastalcph)
+author: John Snow Labs
+name: xlmroberta_embeddings_fairlex_fscs_minilm
+date: 2023-08-01
+tags: [fr, de, it, open_source, xlm_roberta, embeddings, xx, fairlex, onnx]
+task: Embeddings
+language: xx
+edition: Spark NLP 5.0.2
+spark_version: 3.0
+supported: true
+engine: onnx
+annotator: XlmRoBertaEmbeddings
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained XLMRoBERTa Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `fairlex-fscs-minilm` is a Multilingual model orginally trained by `coastalcph`.
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/xlmroberta_embeddings_fairlex_fscs_minilm_xx_5.0.2_3.0_1690928849227.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/xlmroberta_embeddings_fairlex_fscs_minilm_xx_5.0.2_3.0_1690928849227.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+documentAssembler = DocumentAssembler() \
+    .setInputCol("text") \
+    .setOutputCol("document")
+
+tokenizer = Tokenizer() \
+    .setInputCols("document") \
+    .setOutputCol("token")
+  
+embeddings = XlmRoBertaEmbeddings.pretrained("xlmroberta_embeddings_fairlex_fscs_minilm","xx") \
+    .setInputCols(["document", "token"]) \
+    .setOutputCol("embeddings")
+    
+pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings])
+
+data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text")
+
+result = pipeline.fit(data).transform(data)
+```
+```scala
+val documentAssembler = new DocumentAssembler() 
+      .setInputCol("text") 
+      .setOutputCol("document")
+ 
+val tokenizer = new Tokenizer() 
+    .setInputCols(Array("document"))
+    .setOutputCol("token")
+
+val embeddings = XlmRoBertaEmbeddings.pretrained("xlmroberta_embeddings_fairlex_fscs_minilm","xx") 
+    .setInputCols(Array("document", "token")) 
+    .setOutputCol("embeddings")
+
+val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings))
+
+val data = Seq("PUT YOUR STRING HERE").toDF("text")
+
+val result = pipeline.fit(data).transform(data)
+```
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("xx.embed.xlmr_roberta.mini_lm_mini").predict("""PUT YOUR STRING HERE""")
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|xlmroberta_embeddings_fairlex_fscs_minilm|
+|Compatibility:|Spark NLP 5.0.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[sentence, token]|
+|Output Labels:|[embeddings]|
+|Language:|xx|
+|Size:|402.9 MB|
+|Case sensitive:|true|
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-08-02-albert_base_uncased_en.md b/docs/_posts/ahmedlone127/2023-08-02-albert_base_uncased_en.md
new file mode 100644
index 00000000000000..703963892f63d2
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-08-02-albert_base_uncased_en.md
@@ -0,0 +1,78 @@
+---
+layout: model
+title: ALBERT Embeddings (Base Uncase)
+author: John Snow Labs
+name: albert_base_uncased
+date: 2023-08-02
+tags: [open_source, en, english, embeddings, albert, onnx]
+task: Embeddings
+language: en
+edition: Spark NLP 5.0.2
+spark_version: 3.0
+supported: true
+engine: onnx
+annotator: AlbertEmbeddings
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+ALBERT is "A Lite" version of BERT, a popular unsupervised language representation learning algorithm. ALBERT uses parameter-reduction techniques that allow for large-scale configurations, overcome previous memory limitations, and achieve better behavior with respect to model degradation. The details are described in the paper "[ALBERT: A Lite BERT for Self-supervised Learning of Language Representations.](https://arxiv.org/abs/1909.11942)"
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_base_uncased_en_5.0.2_3.0_1690935260361.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_base_uncased_en_5.0.2_3.0_1690935260361.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+embeddings = AlbertEmbeddings.pretrained("albert_base_uncased", "en") \
+.setInputCols("sentence", "token") \
+.setOutputCol("embeddings")
+```
+```scala
+val embeddings = AlbertEmbeddings.pretrained("albert_base_uncased", "en")
+.setInputCols("sentence", "token")
+.setOutputCol("embeddings")
+```
+
+{:.nlu-block}
+```python
+import nlu
+
+text = ["I love NLP"]
+embeddings_df = nlu.load('en.embed.albert.base_uncased').predict(text, output_level='token')
+embeddings_df
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|albert_base_uncased|
+|Compatibility:|Spark NLP 5.0.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[token, sentence]|
+|Output Labels:|[embeddings]|
+|Language:|en|
+|Size:|42.0 MB|
+|Case sensitive:|false|
+
+## References
+
+[https://huggingface.co/albert-base-v2](https://huggingface.co/albert-base-v2)
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-08-02-albert_base_uncased_opt_en.md b/docs/_posts/ahmedlone127/2023-08-02-albert_base_uncased_opt_en.md
new file mode 100644
index 00000000000000..21794c5b8aa379
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-08-02-albert_base_uncased_opt_en.md
@@ -0,0 +1,78 @@
+---
+layout: model
+title: ALBERT Embeddings (Base Uncase)
+author: John Snow Labs
+name: albert_base_uncased_opt
+date: 2023-08-02
+tags: [open_source, en, english, embeddings, albert, onnx]
+task: Embeddings
+language: en
+edition: Spark NLP 5.0.2
+spark_version: 3.0
+supported: true
+engine: onnx
+annotator: AlbertEmbeddings
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+ALBERT is "A Lite" version of BERT, a popular unsupervised language representation learning algorithm. ALBERT uses parameter-reduction techniques that allow for large-scale configurations, overcome previous memory limitations, and achieve better behavior with respect to model degradation. The details are described in the paper "[ALBERT: A Lite BERT for Self-supervised Learning of Language Representations.](https://arxiv.org/abs/1909.11942)"
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_base_uncased_opt_en_5.0.2_3.0_1690935304465.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_base_uncased_opt_en_5.0.2_3.0_1690935304465.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+embeddings = AlbertEmbeddings.pretrained("albert_base_uncased", "en") \
+.setInputCols("sentence", "token") \
+.setOutputCol("embeddings")
+```
+```scala
+val embeddings = AlbertEmbeddings.pretrained("albert_base_uncased", "en")
+.setInputCols("sentence", "token")
+.setOutputCol("embeddings")
+```
+
+{:.nlu-block}
+```python
+import nlu
+
+text = ["I love NLP"]
+embeddings_df = nlu.load('en.embed.albert.base_uncased').predict(text, output_level='token')
+embeddings_df
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|albert_base_uncased_opt|
+|Compatibility:|Spark NLP 5.0.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[token, sentence]|
+|Output Labels:|[embeddings]|
+|Language:|en|
+|Size:|115.0 MB|
+|Case sensitive:|false|
+
+## References
+
+[https://huggingface.co/albert-base-v2](https://huggingface.co/albert-base-v2)
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-08-02-albert_base_uncased_quantized_en.md b/docs/_posts/ahmedlone127/2023-08-02-albert_base_uncased_quantized_en.md
new file mode 100644
index 00000000000000..1f373a267014d1
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-08-02-albert_base_uncased_quantized_en.md
@@ -0,0 +1,78 @@
+---
+layout: model
+title: ALBERT Embeddings (Base Uncase)
+author: John Snow Labs
+name: albert_base_uncased_quantized
+date: 2023-08-02
+tags: [open_source, en, english, embeddings, albert, onnx]
+task: Embeddings
+language: en
+edition: Spark NLP 5.0.2
+spark_version: 3.0
+supported: true
+engine: onnx
+annotator: AlbertEmbeddings
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+ALBERT is "A Lite" version of BERT, a popular unsupervised language representation learning algorithm. ALBERT uses parameter-reduction techniques that allow for large-scale configurations, overcome previous memory limitations, and achieve better behavior with respect to model degradation. The details are described in the paper "[ALBERT: A Lite BERT for Self-supervised Learning of Language Representations.](https://arxiv.org/abs/1909.11942)"
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_base_uncased_quantized_en_5.0.2_3.0_1690935326685.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_base_uncased_quantized_en_5.0.2_3.0_1690935326685.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+embeddings = AlbertEmbeddings.pretrained("albert_base_uncased", "en") \
+.setInputCols("sentence", "token") \
+.setOutputCol("embeddings")
+```
+```scala
+val embeddings = AlbertEmbeddings.pretrained("albert_base_uncased", "en")
+.setInputCols("sentence", "token")
+.setOutputCol("embeddings")
+```
+
+{:.nlu-block}
+```python
+import nlu
+
+text = ["I love NLP"]
+embeddings_df = nlu.load('en.embed.albert.base_uncased').predict(text, output_level='token')
+embeddings_df
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|albert_base_uncased_quantized|
+|Compatibility:|Spark NLP 5.0.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[token, sentence]|
+|Output Labels:|[embeddings]|
+|Language:|en|
+|Size:|46.0 MB|
+|Case sensitive:|false|
+
+## References
+
+[https://huggingface.co/albert-base-v2](https://huggingface.co/albert-base-v2)
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-08-02-albert_large_uncased_en.md b/docs/_posts/ahmedlone127/2023-08-02-albert_large_uncased_en.md
new file mode 100644
index 00000000000000..2868cd5390206c
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-08-02-albert_large_uncased_en.md
@@ -0,0 +1,78 @@
+---
+layout: model
+title: ALBERT Embeddings (Large Uncase)
+author: John Snow Labs
+name: albert_large_uncased
+date: 2023-08-02
+tags: [open_source, en, english, embeddings, albert, large, onnx]
+task: Embeddings
+language: en
+edition: Spark NLP 5.0.2
+spark_version: 3.0
+supported: true
+engine: onnx
+annotator: AlbertEmbeddings
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+ALBERT is "A Lite" version of BERT, a popular unsupervised language representation learning algorithm. ALBERT uses parameter-reduction techniques that allow for large-scale configurations, overcome previous memory limitations, and achieve better behavior with respect to model degradation. The details are described in the paper "[ALBERT: A Lite BERT for Self-supervised Learning of Language Representations.](https://arxiv.org/abs/1909.11942)"
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_large_uncased_en_5.0.2_3.0_1690935781574.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_large_uncased_en_5.0.2_3.0_1690935781574.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+embeddings = AlbertEmbeddings.pretrained("albert_large_uncased", "en") \
+.setInputCols("sentence", "token") \
+.setOutputCol("embeddings")
+```
+```scala
+val embeddings = AlbertEmbeddings.pretrained("albert_large_uncased", "en")
+.setInputCols("sentence", "token")
+.setOutputCol("embeddings")
+```
+
+{:.nlu-block}
+```python
+import nlu
+
+text = ["I love NLP"]
+embeddings_df = nlu.load('en.embed.albert.large_uncased').predict(text, output_level='token')
+embeddings_df
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|albert_large_uncased|
+|Compatibility:|Spark NLP 5.0.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[token, sentence]|
+|Output Labels:|[embeddings]|
+|Language:|en|
+|Size:|62.7 MB|
+|Case sensitive:|false|
+
+## References
+
+[https://huggingface.co/albert-large-v2](https://huggingface.co/albert-large-v2)
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-08-02-albert_large_uncased_opt_en.md b/docs/_posts/ahmedlone127/2023-08-02-albert_large_uncased_opt_en.md
new file mode 100644
index 00000000000000..53d69c5d209c22
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-08-02-albert_large_uncased_opt_en.md
@@ -0,0 +1,78 @@
+---
+layout: model
+title: ALBERT Embeddings (Large Uncase)
+author: John Snow Labs
+name: albert_large_uncased_opt
+date: 2023-08-02
+tags: [open_source, en, english, embeddings, albert, large, onnx]
+task: Embeddings
+language: en
+edition: Spark NLP 5.0.2
+spark_version: 3.0
+supported: true
+engine: onnx
+annotator: AlbertEmbeddings
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+ALBERT is "A Lite" version of BERT, a popular unsupervised language representation learning algorithm. ALBERT uses parameter-reduction techniques that allow for large-scale configurations, overcome previous memory limitations, and achieve better behavior with respect to model degradation. The details are described in the paper "[ALBERT: A Lite BERT for Self-supervised Learning of Language Representations.](https://arxiv.org/abs/1909.11942)"
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_large_uncased_opt_en_5.0.2_3.0_1690935934328.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_large_uncased_opt_en_5.0.2_3.0_1690935934328.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+embeddings = AlbertEmbeddings.pretrained("albert_large_uncased", "en") \
+.setInputCols("sentence", "token") \
+.setOutputCol("embeddings")
+```
+```scala
+val embeddings = AlbertEmbeddings.pretrained("albert_large_uncased", "en")
+.setInputCols("sentence", "token")
+.setOutputCol("embeddings")
+```
+
+{:.nlu-block}
+```python
+import nlu
+
+text = ["I love NLP"]
+embeddings_df = nlu.load('en.embed.albert.large_uncased').predict(text, output_level='token')
+embeddings_df
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|albert_large_uncased_opt|
+|Compatibility:|Spark NLP 5.0.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[token, sentence]|
+|Output Labels:|[embeddings]|
+|Language:|en|
+|Size:|333.8 MB|
+|Case sensitive:|false|
+
+## References
+
+[https://huggingface.co/albert-large-v2](https://huggingface.co/albert-large-v2)
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-08-02-albert_large_uncased_quantized_en.md b/docs/_posts/ahmedlone127/2023-08-02-albert_large_uncased_quantized_en.md
new file mode 100644
index 00000000000000..0e7cdcec3cac9a
--- /dev/null
+++ b/docs/_posts/ahmedlone127/2023-08-02-albert_large_uncased_quantized_en.md
@@ -0,0 +1,78 @@
+---
+layout: model
+title: ALBERT Embeddings (Large Uncase)
+author: John Snow Labs
+name: albert_large_uncased_quantized
+date: 2023-08-02
+tags: [open_source, en, english, embeddings, albert, large, onnx]
+task: Embeddings
+language: en
+edition: Spark NLP 5.0.2
+spark_version: 3.0
+supported: true
+engine: onnx
+annotator: AlbertEmbeddings
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+ALBERT is "A Lite" version of BERT, a popular unsupervised language representation learning algorithm. ALBERT uses parameter-reduction techniques that allow for large-scale configurations, overcome previous memory limitations, and achieve better behavior with respect to model degradation. The details are described in the paper "[ALBERT: A Lite BERT for Self-supervised Learning of Language Representations.](https://arxiv.org/abs/1909.11942)"
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/albert_large_uncased_quantized_en_5.0.2_3.0_1690935970820.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_large_uncased_quantized_en_5.0.2_3.0_1690935970820.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+embeddings = AlbertEmbeddings.pretrained("albert_large_uncased", "en") \
+.setInputCols("sentence", "token") \
+.setOutputCol("embeddings")
+```
+```scala
+val embeddings = AlbertEmbeddings.pretrained("albert_large_uncased", "en")
+.setInputCols("sentence", "token")
+.setOutputCol("embeddings")
+```
+
+{:.nlu-block}
+```python
+import nlu
+
+text = ["I love NLP"]
+embeddings_df = nlu.load('en.embed.albert.large_uncased').predict(text, output_level='token')
+embeddings_df
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|albert_large_uncased_quantized|
+|Compatibility:|Spark NLP 5.0.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[token, sentence]|
+|Output Labels:|[embeddings]|
+|Language:|en|
+|Size:|71.4 MB|
+|Case sensitive:|false|
+
+## References
+
+[https://huggingface.co/albert-large-v2](https://huggingface.co/albert-large-v2)
\ No newline at end of file
diff --git a/docs/_posts/veerdhwaj/2023-07-28-twitter_xlm_roberta_base_sentiment_en.md b/docs/_posts/veerdhwaj/2023-07-28-twitter_xlm_roberta_base_sentiment_en.md
new file mode 100644
index 00000000000000..c9328186d943cb
--- /dev/null
+++ b/docs/_posts/veerdhwaj/2023-07-28-twitter_xlm_roberta_base_sentiment_en.md
@@ -0,0 +1,90 @@
+---
+layout: model
+title: twitter_xlm_roberta_base_sentiment(Cardiff nlp) (Veer)
+author: veerdhwaj
+name: twitter_xlm_roberta_base_sentiment
+date: 2023-07-28
+tags: [en, open_source, tensorflow]
+task: Text Classification
+language: en
+edition: Spark NLP 5.0.0
+spark_version: 3.2
+supported: false
+engine: tensorflow
+annotator: XlmRoBertaForSequenceClassification
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+This is a multilingual XLM-roBERTa-base model trained on ~198M tweets and finetuned for sentiment analysis. The sentiment fine-tuning was done on 8 languages (Ar, En, Fr, De, Hi, It, Sp, Pt) but it can be used for more languages (see paper for details).
+
+Paper: XLM-T: A Multilingual Language Model Toolkit for Twitter.
+Git Repo: XLM-T official repository.
+This model has been integrated into the TweetNLP library.
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/community.johnsnowlabs.com/veerdhwaj/twitter_xlm_roberta_base_sentiment_en_5.0.0_3.2_1690542160993.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://community.johnsnowlabs.com/veerdhwaj/twitter_xlm_roberta_base_sentiment_en_5.0.0_3.2_1690542160993.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+from pyspark.ml import Pipeline
+
+document_assembler = DocumentAssembler() \
+    .setInputCol('text') \
+    .setOutputCol('document')
+
+tokenizer = Tokenizer() \
+    .setInputCols(['document']) \
+    .setOutputCol('token')
+
+sequenceClassifier = XlmRoBertaForSequenceClassification.pretrained('twitter_xlm_roberta_base_sentiment')\
+  .setInputCols(["document",'token'])\
+  .setOutputCol("class")
+
+pipeline = Pipeline(stages=[
+    document_assembler, 
+    tokenizer,
+    sequenceClassifier    
+])
+
+# couple of simple examples
+example = spark.createDataFrame([['사랑해!'], ["T'estimo! ❤️"], ["I love you!"], ['Mahal kita!']]).toDF("text")
+
+result = pipeline.fit(example).transform(example)
+
+# result is a DataFrame
+result.select("text", "class.result").show()
+```
+
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|twitter_xlm_roberta_base_sentiment|
+|Compatibility:|Spark NLP 5.0.0+|
+|License:|Open Source|
+|Edition:|Community|
+|Input Labels:|[document, token]|
+|Output Labels:|[class]|
+|Language:|en|
+|Size:|1.0 GB|
+|Case sensitive:|true|
+|Max sentence length:|512|
\ No newline at end of file
diff --git a/docs/_posts/veerdhwaj/2023-07-31-sentiment_twitter_xlm_roBerta_pdc_en.md b/docs/_posts/veerdhwaj/2023-07-31-sentiment_twitter_xlm_roBerta_pdc_en.md
new file mode 100644
index 00000000000000..7878aeac4fea03
--- /dev/null
+++ b/docs/_posts/veerdhwaj/2023-07-31-sentiment_twitter_xlm_roBerta_pdc_en.md
@@ -0,0 +1,94 @@
+---
+layout: model
+title: Sentiment tags predictions trained on twitter
+author: veerdhwaj
+name: sentiment_twitter_xlm_roBerta_pdc
+date: 2023-07-31
+tags: [sentiment, en, open_source, tensorflow]
+task: Text Classification
+language: en
+edition: Spark NLP 3.3.2
+spark_version: 3.2
+supported: false
+engine: tensorflow
+annotator: XlmRoBertaForSequenceClassification
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+This is a multilingual XLM-roBERTa-base model trained on ~198M tweets and finetuned for sentiment analysis. The sentiment fine-tuning was done on 8 languages (Ar, En, Fr, De, Hi, It, Sp, Pt) but it can be used for more languages (see paper for details).
+
+Paper: XLM-T: A Multilingual Language Model Toolkit for Twitter.
+Git Repo: XLM-T official repository.
+This model has been integrated into the TweetNLP library.
+
+HF Model: https://huggingface.co/cardiffnlp/twitter-xlm-roberta-base-sentiment
+
+## Predicted Entities
+
+`class`
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/community.johnsnowlabs.com/veerdhwaj/sentiment_twitter_xlm_roBerta_pdc_en_3.3.2_3.2_1690780495131.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://community.johnsnowlabs.com/veerdhwaj/sentiment_twitter_xlm_roBerta_pdc_en_3.3.2_3.2_1690780495131.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+from pyspark.ml import Pipeline
+from sparknlp.annotator import *
+
+document_assembler = DocumentAssembler() \
+    .setInputCol('text') \
+    .setOutputCol('document')
+
+tokenizer = Tokenizer() \
+    .setInputCols(['document']) \
+    .setOutputCol('token')
+
+sequenceClassifier = XlmRoBertaForSequenceClassification.pretrained('sentiment_twitter_xlm_roBerta_pdc_en')\
+  .setInputCols(["document",'token'])\
+  .setOutputCol("class")
+
+pipeline = Pipeline(stages=[
+    document_assembler, 
+    tokenizer,
+    sequenceClassifier    
+])
+
+# couple of simple examples
+example = spark.createDataFrame([['사랑해!'], ["T'estimo! ❤️"], ["I love you!"], ['Mahal kita!']]).toDF("text")
+
+result = pipeline.fit(example).transform(example)
+
+# result is a DataFrame
+result.select("text", "class.result").show()
+```
+
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|sentiment_twitter_xlm_roBerta_pdc|
+|Compatibility:|Spark NLP 3.3.2+|
+|License:|Open Source|
+|Edition:|Community|
+|Input Labels:|[document, token]|
+|Output Labels:|[class]|
+|Language:|en|
+|Size:|1.0 GB|
+|Case sensitive:|true|
+|Max sentence length:|512|
+|Dependencies:|xlm_roBerta|
\ No newline at end of file
diff --git a/docs/_posts/veerdhwaj/2023-07-31-twitter_xlm_roberta_base_sentiment_pdc_en.md b/docs/_posts/veerdhwaj/2023-07-31-twitter_xlm_roberta_base_sentiment_pdc_en.md
new file mode 100644
index 00000000000000..45dd26651c0dba
--- /dev/null
+++ b/docs/_posts/veerdhwaj/2023-07-31-twitter_xlm_roberta_base_sentiment_pdc_en.md
@@ -0,0 +1,86 @@
+---
+layout: model
+title: twitter_xlm_roberta_base_sentiment_pdc(cardiff)
+author: veerdhwaj
+name: twitter_xlm_roberta_base_sentiment_pdc
+date: 2023-07-31
+tags: [en, open_source, tensorflow]
+task: Text Classification
+language: en
+edition: Spark NLP 5.0.0
+spark_version: 3.2
+supported: false
+engine: tensorflow
+annotator: XlmRoBertaForSequenceClassification
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Huggingface model: https://huggingface.co/cardiffnlp/twitter-xlm-roberta-base-sentiment
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/community.johnsnowlabs.com/veerdhwaj/twitter_xlm_roberta_base_sentiment_pdc_en_5.0.0_3.2_1690779049644.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://community.johnsnowlabs.com/veerdhwaj/twitter_xlm_roberta_base_sentiment_pdc_en_5.0.0_3.2_1690779049644.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+from pyspark.ml import Pipeline
+
+document_assembler = DocumentAssembler() \
+    .setInputCol('text') \
+    .setOutputCol('document')
+
+tokenizer = Tokenizer() \
+    .setInputCols(['document']) \
+    .setOutputCol('token')
+
+sequenceClassifier = XlmRoBertaForSequenceClassification.pretrained('twitter_xlm_roberta_base_sentiment_pdc')\
+  .setInputCols(["document",'token'])\
+  .setOutputCol("class")
+
+pipeline = Pipeline(stages=[
+    document_assembler, 
+    tokenizer,
+    sequenceClassifier    
+])
+
+# couple of simple examples
+example = spark.createDataFrame([['사랑해!'], ["T'estimo! ❤️"], ["I love you!"], ['Mahal kita!']]).toDF("text")
+
+result = pipeline.fit(example).transform(example)
+
+# result is a DataFrame
+result.select("text", "class.result").show()
+```
+
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|twitter_xlm_roberta_base_sentiment_pdc|
+|Compatibility:|Spark NLP 5.0.0+|
+|License:|Open Source|
+|Edition:|Community|
+|Input Labels:|[document, token]|
+|Output Labels:|[class]|
+|Language:|en|
+|Size:|1.0 GB|
+|Case sensitive:|true|
+|Max sentence length:|512|
\ No newline at end of file