Models hub (#13793)

* Add model 2023-04-13-CyberbullyingDetection_ClassifierDL_tfhub_en (#13757) Co-authored-by: Naveen-004 <chinna.nk4@gmail.com> * 2023-04-20-distilbert_base_uncased_mnli_en (#13761) * Add model 2023-04-20-distilbert_base_uncased_mnli_en * Add model 2023-04-20-distilbert_base_turkish_cased_allnli_tr * Add model 2023-04-20-distilbert_base_turkish_cased_snli_tr * Add model 2023-04-20-distilbert_base_turkish_cased_multinli_tr * Update and rename 2023-04-20-distilbert_base_turkish_cased_allnli_tr.md to 2023-04-20-distilbert_base_zero_shot_classifier_turkish_cased_allnli_tr.md * Update and rename 2023-04-20-distilbert_base_turkish_cased_multinli_tr.md to 2023-04-20-distilbert_base_zero_shot_classifier_turkish_cased_multinli_tr.md * Update and rename 2023-04-20-distilbert_base_turkish_cased_snli_tr.md to 2023-04-20-distilbert_base_zero_shot_classifier_turkish_cased_snli_tr.md * Update and rename 2023-04-20-distilbert_base_uncased_mnli_en.md to distilbert_base_zero_shot_classifier_turkish_cased_snli * Rename distilbert_base_zero_shot_classifier_turkish_cased_snli to distilbert_base_zero_shot_classifier_turkish_cased_snli_en.md * Update 2023-04-20-distilbert_base_zero_shot_classifier_turkish_cased_snli_tr.md * Update 2023-04-20-distilbert_base_zero_shot_classifier_turkish_cased_multinli_tr.md * Update 2023-04-20-distilbert_base_zero_shot_classifier_turkish_cased_allnli_tr.md --------- Co-authored-by: ahmedlone127 <ahmedlone127@gmail.com> * 2023-04-20-distilbert_base_zero_shot_classifier_turkish_cased_multinli_tr (#13763) * Add model 2023-04-20-distilbert_base_zero_shot_classifier_turkish_cased_multinli_tr * Add model 2023-04-20-distilbert_base_zero_shot_classifier_uncased_mnli_en * Add model 2023-04-20-distilbert_base_zero_shot_classifier_turkish_cased_snli_tr * Add model 2023-04-20-distilbert_base_zero_shot_classifier_turkish_cased_allnli_tr * Update 2023-04-20-distilbert_base_zero_shot_classifier_turkish_cased_multinli_tr.md * Update 2023-04-20-distilbert_base_zero_shot_classifier_turkish_cased_snli_tr.md --------- Co-authored-by: ahmedlone127 <ahmedlone127@gmail.com> * 2023-05-04-roberta_base_zero_shot_classifier_nli_en (#13781) * Add model 2023-05-04-roberta_base_zero_shot_classifier_nli_en * Fix Spark version to 3.0 --------- Co-authored-by: ahmedlone127 <ahmedlone127@gmail.com> Co-authored-by: Maziyar Panahi <maziyar.panahi@iscpif.fr> * 2023-05-09-distilbart_xsum_6_6_en (#13788) * Add model 2023-05-09-distilbart_xsum_6_6_en * Add model 2023-05-09-distilbart_xsum_12_6_en * Add model 2023-05-09-distilbart_cnn_12_6_en * Add model 2023-05-09-distilbart_cnn_6_6_en * Add model 2023-05-09-bart_large_cnn_en * Update 2023-05-09-bart_large_cnn_en.md * Update 2023-05-09-distilbart_cnn_12_6_en.md * Update 2023-05-09-distilbart_cnn_6_6_en.md * Update 2023-05-09-distilbart_xsum_12_6_en.md * Update 2023-05-09-distilbart_xsum_6_6_en.md --------- Co-authored-by: prabod <prabod@rathnayaka.me> Co-authored-by: Maziyar Panahi <maziyar.panahi@iscpif.fr> --------- Co-authored-by: jsl-models <74001263+jsl-models@users.noreply.github.com> Co-authored-by: Naveen-004 <chinna.nk4@gmail.com> Co-authored-by: ahmedlone127 <ahmedlone127@gmail.com> Co-authored-by: prabod <prabod@rathnayaka.me>
JohnSnowLabs · May 10, 2023 · b262eed · b262eed
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diff --git a/docs/_posts/ahmedlone127/2023-05-04-roberta_base_zero_shot_classifier_nli_en.md b/docs/_posts/ahmedlone127/2023-05-04-roberta_base_zero_shot_classifier_nli_en.md
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+---
+layout: model
+title: RoBertaZero-Shot Classification Base roberta_base_zero_shot_classifier_nli
+author: John Snow Labs
+name: roberta_base_zero_shot_classifier_nli
+date: 2023-05-04
+tags: [en, open_source, tensorflow]
+task: Zero-Shot Classification
+language: en
+edition: Spark NLP 4.4.2
+spark_version: [3.0]
+supported: true
+engine: tensorflow
+annotator: RoBertaForZeroShotClassification
+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 Roberta Base  model.
+
+RoBertaForZeroShotClassificationusing a ModelForSequenceClassification trained on NLI (natural language inference) tasks. Equivalent of RoBertaForZeroShotClassification 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 TFRobertaForSequenceClassification to train this model and used RoBertaForZeroShotClassification 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/roberta_base_zero_shot_classifier_nli_en_4.4.2_3.0_1683228241365.zip){:.button.button-orange}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/roberta_base_zero_shot_classifier_nli_en_4.4.2_3.0_1683228241365.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 = RobertaForSequenceClassification \
+.pretrained('roberta_base_zero_shot_classifier_nli', 'en') \
+.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 = RobertaForSequenceClassification.pretrained("roberta_base_zero_shot_classifier_nli", "en")
+.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:|roberta_base_zero_shot_classifier_nli|
+|Compatibility:|Spark NLP 4.4.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[token, document]|
+|Output Labels:|[multi_class]|
+|Language:|en|
+|Size:|466.4 MB|
+|Case sensitive:|true|
diff --git a/docs/_posts/prabod/2023-05-09-bart_large_cnn_en.md b/docs/_posts/prabod/2023-05-09-bart_large_cnn_en.md
@@ -0,0 +1,74 @@
+---
+layout: model
+title: BART (large-sized model), fine-tuned on CNN Daily Mail
+author: John Snow Labs
+name: bart_large_cnn
+date: 2023-05-09
+tags: [bart, summarization, cnn, text_to_text, en, open_source, tensorflow]
+task: Summarization
+language: en
+edition: Spark NLP 4.4.2
+spark_version: 3.0
+supported: true
+engine: tensorflow
+annotator: BartTransformer
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+BART model pre-trained on English language, and fine-tuned on [CNN Daily Mail](https://huggingface.co/datasets/cnn_dailymail). It was introduced in the paper [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://arxiv.org/abs/1910.13461) by Lewis et al. and first released in [this repository (https://github.com/pytorch/fairseq/tree/master/examples/bart). 
+
+Disclaimer: The team releasing BART did not write a model card for this model so this model card has been written by the Hugging Face team.
+
+### Model description
+
+BART is a transformer encoder-encoder (seq2seq) model with a bidirectional (BERT-like) encoder and an autoregressive (GPT-like) decoder. BART is pre-trained by (1) corrupting text with an arbitrary noising function, and (2) learning a model to reconstruct the original text.
+
+BART is particularly effective when fine-tuned for text generation (e.g. summarization, translation) but also works well for comprehension tasks (e.g. text classification, question answering). This particular checkpoint has been fine-tuned on CNN Daily Mail, a large collection of text-summary pairs.
+
+## 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/bart_large_cnn_en_4.4.2_3.0_1683645394389.zip){:.button.button-orange}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bart_large_cnn_en_4.4.2_3.0_1683645394389.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+You can use this model for text summarization.
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+bart = BartTransformer.pretrained("bart_large_cnn") \
+            .setTask("summarize:") \
+            .setMaxOutputLength(200) \
+            .setInputCols(["documents"]) \
+            .setOutputCol("summaries")
+```
+```scala
+val bart = BartTransformer.pretrained("bart_large_cnn")
+            .setTask("summarize:")
+            .setMaxOutputLength(200)
+            .setInputCols("documents")
+            .setOutputCol("summaries")
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|bart_large_cnn|
+|Compatibility:|Spark NLP 4.4.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|en|
+|Size:|975.3 MB|
diff --git a/docs/_posts/prabod/2023-05-09-distilbart_cnn_12_6_en.md b/docs/_posts/prabod/2023-05-09-distilbart_cnn_12_6_en.md
@@ -0,0 +1,86 @@
+---
+layout: model
+title: Abstractive Summarization by BART - DistilBART CNN
+author: John Snow Labs
+name: distilbart_cnn_12_6
+date: 2023-05-09
+tags: [bart, summarization, cnn, distill, text_to_text, en, open_source, tensorflow]
+task: Summarization
+language: en
+edition: Spark NLP 4.4.2
+spark_version: 3.0
+supported: true
+engine: tensorflow
+annotator: BartTransformer
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+"BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension Transformer" The Facebook BART (Bidirectional and Auto-Regressive Transformer) model is a state-of-the-art language generation model that was introduced by Facebook AI in 2019. It is based on the transformer architecture and is designed to handle a wide range of natural language processing tasks such as text generation, summarization, and machine translation.
+
+This pre-trained model is DistilBART fine-tuned on the Extreme Summarization (CNN) Dataset.
+
+## 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/distilbart_cnn_12_6_en_4.4.2_3.0_1683644937231.zip){:.button.button-orange}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbart_cnn_12_6_en_4.4.2_3.0_1683644937231.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
+bart = BartTransformer.pretrained("distilbart_cnn_12_6") \
+            .setTask("summarize:") \
+            .setMaxOutputLength(200) \
+            .setInputCols(["documents"]) \
+            .setOutputCol("summaries")
+```
+```scala
+val bart = BartTransformer.pretrained("distilbart_cnn_12_6")
+            .setTask("summarize:")
+            .setMaxOutputLength(200)
+            .setInputCols("documents")
+            .setOutputCol("summaries")
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|distilbart_cnn_12_6|
+|Compatibility:|Spark NLP 4.4.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|en|
+|Size:|870.4 MB|
+
+## Benchmarking
+
+```bash
+### Metrics for DistilBART models
+| Model Name                 |   MM Params |   Inference Time (MS) |   Speedup |   Rouge 2 |   Rouge-L |
+|:---------------------------|------------:|----------------------:|----------:|----------:|----------:|
+| distilbart-xsum-12-1       |         222 |                    90 |      2.54 |     18.31 |     33.37 |
+| distilbart-xsum-6-6        |         230 |                   132 |      1.73 |     20.92 |     35.73 |
+| distilbart-xsum-12-3       |         255 |                   106 |      2.16 |     21.37 |     36.39 |
+| distilbart-xsum-9-6        |         268 |                   136 |      1.68 |     21.72 |     36.61 |
+| bart-large-xsum (baseline) |         406 |                   229 |      1    |     21.85 |     36.50 |
+| distilbart-xsum-12-6       |         306 |                   137 |      1.68 |     22.12 |     36.99 |
+| bart-large-cnn (baseline)  |         406 |                   381 |      1    |     21.06 |     30.63 |
+| distilbart-12-3-cnn        |         255 |                   214 |      1.78 |     20.57 |     30.00 |
+| distilbart-12-6-cnn        |         306 |                   307 |      1.24 |     21.26 |     30.59 |
+| distilbart-6-6-cnn         |         230 |                   182 |      2.09 |     20.17 |     29.70 |
+```
diff --git a/docs/_posts/prabod/2023-05-09-distilbart_cnn_6_6_en.md b/docs/_posts/prabod/2023-05-09-distilbart_cnn_6_6_en.md
@@ -0,0 +1,86 @@
+---
+layout: model
+title: Abstractive Summarization by BART - DistilBART CNN
+author: John Snow Labs
+name: distilbart_cnn_6_6
+date: 2023-05-09
+tags: [bart, summarization, cnn, distil, text_to_text, en, open_source, tensorflow]
+task: Summarization
+language: en
+edition: Spark NLP 4.4.2
+spark_version: 3.0
+supported: true
+engine: tensorflow
+annotator: BartTransformer
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+"BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension Transformer" The Facebook BART (Bidirectional and Auto-Regressive Transformer) model is a state-of-the-art language generation model that was introduced by Facebook AI in 2019. It is based on the transformer architecture and is designed to handle a wide range of natural language processing tasks such as text generation, summarization, and machine translation.
+
+This pre-trained model is DistilBART fine-tuned on the Extreme Summarization (CNN) Dataset.
+
+## 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/distilbart_cnn_6_6_en_4.4.2_3.0_1683645206157.zip){:.button.button-orange}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbart_cnn_6_6_en_4.4.2_3.0_1683645206157.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
+bart = BartTransformer.pretrained("distilbart_cnn_6_6") \
+            .setTask("summarize:") \
+            .setMaxOutputLength(200) \
+            .setInputCols(["documents"]) \
+            .setOutputCol("summaries")
+```
+```scala
+val bart = BartTransformer.pretrained("distilbart_cnn_6_6")
+            .setTask("summarize:")
+            .setMaxOutputLength(200)
+            .setInputCols("documents")
+            .setOutputCol("summaries")
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|distilbart_cnn_6_6|
+|Compatibility:|Spark NLP 4.4.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|en|
+|Size:|551.9 MB|
+
+## Benchmarking
+
+```bash
+### Metrics for DistilBART models
+| Model Name                 |   MM Params |   Inference Time (MS) |   Speedup |   Rouge 2 |   Rouge-L |
+|:---------------------------|------------:|----------------------:|----------:|----------:|----------:|
+| distilbart-xsum-12-1       |         222 |                    90 |      2.54 |     18.31 |     33.37 |
+| distilbart-xsum-6-6        |         230 |                   132 |      1.73 |     20.92 |     35.73 |
+| distilbart-xsum-12-3       |         255 |                   106 |      2.16 |     21.37 |     36.39 |
+| distilbart-xsum-9-6        |         268 |                   136 |      1.68 |     21.72 |     36.61 |
+| bart-large-xsum (baseline) |         406 |                   229 |      1    |     21.85 |     36.50 |
+| distilbart-xsum-12-6       |         306 |                   137 |      1.68 |     22.12 |     36.99 |
+| bart-large-cnn (baseline)  |         406 |                   381 |      1    |     21.06 |     30.63 |
+| distilbart-12-3-cnn        |         255 |                   214 |      1.78 |     20.57 |     30.00 |
+| distilbart-12-6-cnn        |         306 |                   307 |      1.24 |     21.26 |     30.59 |
+| distilbart-6-6-cnn         |         230 |                   182 |      2.09 |     20.17 |     29.70 |
+```