2023-10-27-bert_classifier_tiny_sst2_1_mobile_2_distillation_en

docs/_posts/ahmedlone127/2023-10-27-bert_base_cased_best_en.md

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+---
+layout: model
+title: English bert_base_cased_best BertForSequenceClassification from edwardgowsmith
+author: John Snow Labs
+name: bert_base_cased_best
+date: 2023-10-27
+tags: [bert, en, open_source, sequence_classification, onnx]
+task: Text Classification
+language: en
+edition: Spark NLP 5.1.4
+spark_version: 3.4
+supported: true
+engine: onnx
+annotator: BertForSequenceClassification
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_cased_best` is a English model originally trained by edwardgowsmith.
+
+{:.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/bert_base_cased_best_en_5.1.4_3.4_1698397074640.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_cased_best_en_5.1.4_3.4_1698397074640.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")  
+
+sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_cased_best","en")\
+            .setInputCols(["document","token"])\
+            .setOutputCol("class")
+
+pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier])
+
+data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text")
+
+result = pipeline.fit(data).transform(data)
+
+```
+```scala
+
+val document_assembler = new DocumentAssembler()
+    .setInputCol("text")
+    .setOutputCol("document")
+
+val tokenizer = new Tokenizer()
+    .setInputCols("document") 
+    .setOutputCol("token")  
+
+val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_cased_best","en")
+            .setInputCols(Array("document","token"))
+            .setOutputCol("class")
+
+val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier))
+
+val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text")
+
+val result = pipeline.fit(data).transform(data)
+
+
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|bert_base_cased_best|
+|Compatibility:|Spark NLP 5.1.4+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[documents, token]|
+|Output Labels:|[class]|
+|Language:|en|
+|Size:|405.9 MB|
+
+## References
+
+https://huggingface.co/edwardgowsmith/bert-base-cased-best

docs/_posts/ahmedlone127/2023-10-27-bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_f1_v3_en.md

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+---
+layout: model
+title: English bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_f1_v3 BertForSequenceClassification from hw2942
+author: John Snow Labs
+name: bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_f1_v3
+date: 2023-10-27
+tags: [bert, en, open_source, sequence_classification, onnx]
+task: Text Classification
+language: en
+edition: Spark NLP 5.1.4
+spark_version: 3.4
+supported: true
+engine: onnx
+annotator: BertForSequenceClassification
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_f1_v3` is a English model originally trained by hw2942.
+
+{:.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/bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_f1_v3_en_5.1.4_3.4_1698395887581.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_f1_v3_en_5.1.4_3.4_1698395887581.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")  
+
+sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_f1_v3","en")\
+            .setInputCols(["document","token"])\
+            .setOutputCol("class")
+
+pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier])
+
+data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text")
+
+result = pipeline.fit(data).transform(data)
+
+```
+```scala
+
+val document_assembler = new DocumentAssembler()
+    .setInputCol("text")
+    .setOutputCol("document")
+
+val tokenizer = new Tokenizer()
+    .setInputCols("document") 
+    .setOutputCol("token")  
+
+val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_f1_v3","en")
+            .setInputCols(Array("document","token"))
+            .setOutputCol("class")
+
+val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier))
+
+val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text")
+
+val result = pipeline.fit(data).transform(data)
+
+
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_f1_v3|
+|Compatibility:|Spark NLP 5.1.4+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[documents, token]|
+|Output Labels:|[class]|
+|Language:|en|
+|Size:|383.3 MB|
+
+## References
+
+https://huggingface.co/hw2942/bert-base-chinese-wallstreetcn-morning-news-market-overview-SSEC-f1-v3

docs/_posts/ahmedlone127/2023-10-27-bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_f1_v4_en.md

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+---
+layout: model
+title: English bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_f1_v4 BertForSequenceClassification from hw2942
+author: John Snow Labs
+name: bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_f1_v4
+date: 2023-10-27
+tags: [bert, en, open_source, sequence_classification, onnx]
+task: Text Classification
+language: en
+edition: Spark NLP 5.1.4
+spark_version: 3.4
+supported: true
+engine: onnx
+annotator: BertForSequenceClassification
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_f1_v4` is a English model originally trained by hw2942.
+
+{:.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/bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_f1_v4_en_5.1.4_3.4_1698396592548.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_f1_v4_en_5.1.4_3.4_1698396592548.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")  
+
+sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_f1_v4","en")\
+            .setInputCols(["document","token"])\
+            .setOutputCol("class")
+
+pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier])
+
+data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text")
+
+result = pipeline.fit(data).transform(data)
+
+```
+```scala
+
+val document_assembler = new DocumentAssembler()
+    .setInputCol("text")
+    .setOutputCol("document")
+
+val tokenizer = new Tokenizer()
+    .setInputCols("document") 
+    .setOutputCol("token")  
+
+val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_f1_v4","en")
+            .setInputCols(Array("document","token"))
+            .setOutputCol("class")
+
+val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier))
+
+val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text")
+
+val result = pipeline.fit(data).transform(data)
+
+
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_f1_v4|
+|Compatibility:|Spark NLP 5.1.4+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[documents, token]|
+|Output Labels:|[class]|
+|Language:|en|
+|Size:|383.3 MB|
+
+## References
+
+https://huggingface.co/hw2942/bert-base-chinese-wallstreetcn-morning-news-market-overview-SSEC-f1-v4

docs/_posts/ahmedlone127/2023-10-27-bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_f1_v5_en.md

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+---
+layout: model
+title: English bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_f1_v5 BertForSequenceClassification from hw2942
+author: John Snow Labs
+name: bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_f1_v5
+date: 2023-10-27
+tags: [bert, en, open_source, sequence_classification, onnx]
+task: Text Classification
+language: en
+edition: Spark NLP 5.1.4
+spark_version: 3.4
+supported: true
+engine: onnx
+annotator: BertForSequenceClassification
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_f1_v5` is a English model originally trained by hw2942.
+
+{:.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/bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_f1_v5_en_5.1.4_3.4_1698397380576.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_f1_v5_en_5.1.4_3.4_1698397380576.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")  
+
+sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_f1_v5","en")\
+            .setInputCols(["document","token"])\
+            .setOutputCol("class")
+
+pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier])
+
+data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text")
+
+result = pipeline.fit(data).transform(data)
+
+```
+```scala
+
+val document_assembler = new DocumentAssembler()
+    .setInputCol("text")
+    .setOutputCol("document")
+
+val tokenizer = new Tokenizer()
+    .setInputCols("document") 
+    .setOutputCol("token")  
+
+val sequenceClassifier = BertForSequenceClassification.pretrained("bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_f1_v5","en")
+            .setInputCols(Array("document","token"))
+            .setOutputCol("class")
+
+val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier))
+
+val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text")
+
+val result = pipeline.fit(data).transform(data)
+
+
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|bert_base_chinese_wallstreetcn_morning_news_market_overview_ssec_f1_v5|
+|Compatibility:|Spark NLP 5.1.4+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[documents, token]|
+|Output Labels:|[class]|
+|Language:|en|
+|Size:|383.3 MB|
+
+## References
+
+https://huggingface.co/hw2942/bert-base-chinese-wallstreetcn-morning-news-market-overview-SSEC-f1-v5