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2023-12-29-finetuning_sentiment_model_3000_samples_zhaohui_en #14115

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Add model 2023-12-31-finetuning_sentiment_model_3000_samples_chabuno_en
ahmedlone127 Dec 31, 2023
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Add model 2023-12-31-sms_class_test2_en
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Add model 2023-12-31-finetuning_sentiment_model_3000_samples_richardl…
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Add model 2023-12-31-distilbert_base_uncased_dsc_nepal_bhasa_cllbck_en
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Add model 2023-12-31-finetuning_sentiment_model_3000_samples_yhwng_en
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Add model 2023-12-31-intent_classifier_incentive_for_connecting_en
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Add model 2023-12-31-distilbert_base_uncased_finetuned_emotion_tobrun_en
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Add model 2023-12-31-model_demo_pingyaol_en
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ahmedlone127 Dec 31, 2023
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ahmedlone127 Jan 1, 2024
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Add model 2024-01-01-imdbreviews_classification_distilbert_v02_laly_en
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Add model 2024-01-01-finetuning_sentiment_model_3000_samples_danexl_en
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Add model 2024-01-01-iotnation_classification_model_0_6_5k_dataset_an…
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Add model 2024-01-01-imdbreviews_classification_distilbert_v02_clf_fi…
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97 changes: 97 additions & 0 deletions docs/_posts/ahmedlone127/2023-12-29-1030_en.md
Original file line number Diff line number Diff line change
@@ -0,0 +1,97 @@
---
layout: model
title: English 1030 DistilBertForSequenceClassification from tingchih
author: John Snow Labs
name: 1030
date: 2023-12-29
tags: [bert, en, open_source, sequence_classification, onnx]
task: Text Classification
language: en
edition: Spark NLP 5.2.2
spark_version: 3.0
supported: true
engine: onnx
annotator: DistilBertForSequenceClassification
article_header:
type: cover
use_language_switcher: "Python-Scala-Java"
---

## Description

Pretrained DistilBertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`1030` is a English model originally trained by tingchih.

{:.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/1030_en_5.2.2_3.0_1703893284059.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/1030_en_5.2.2_3.0_1703893284059.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 = DistilBertForSequenceClassification.pretrained("1030","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 = DistilBertForSequenceClassification.pretrained("1030","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:|1030|
|Compatibility:|Spark NLP 5.2.2+|
|License:|Open Source|
|Edition:|Official|
|Input Labels:|[documents, token]|
|Output Labels:|[class]|
|Language:|en|
|Size:|249.5 MB|

## References

https://huggingface.co/tingchih/1030
97 changes: 97 additions & 0 deletions docs/_posts/ahmedlone127/2023-12-29-22s_dl_sentiment_1_en.md
Original file line number Diff line number Diff line change
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---
layout: model
title: English 22s_dl_sentiment_1 DistilBertForSequenceClassification from DLochmelis33
author: John Snow Labs
name: 22s_dl_sentiment_1
date: 2023-12-29
tags: [bert, en, open_source, sequence_classification, onnx]
task: Text Classification
language: en
edition: Spark NLP 5.2.2
spark_version: 3.0
supported: true
engine: onnx
annotator: DistilBertForSequenceClassification
article_header:
type: cover
use_language_switcher: "Python-Scala-Java"
---

## Description

Pretrained DistilBertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`22s_dl_sentiment_1` is a English model originally trained by DLochmelis33.

{:.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/22s_dl_sentiment_1_en_5.2.2_3.0_1703889238506.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/22s_dl_sentiment_1_en_5.2.2_3.0_1703889238506.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 = DistilBertForSequenceClassification.pretrained("22s_dl_sentiment_1","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 = DistilBertForSequenceClassification.pretrained("22s_dl_sentiment_1","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:|22s_dl_sentiment_1|
|Compatibility:|Spark NLP 5.2.2+|
|License:|Open Source|
|Edition:|Official|
|Input Labels:|[documents, token]|
|Output Labels:|[class]|
|Language:|en|
|Size:|249.5 MB|

## References

https://huggingface.co/DLochmelis33/22s-dl-sentiment-1
97 changes: 97 additions & 0 deletions docs/_posts/ahmedlone127/2023-12-29-607_demo_model_en.md
Original file line number Diff line number Diff line change
@@ -0,0 +1,97 @@
---
layout: model
title: English 607_demo_model DistilBertForSequenceClassification from mgreenbe
author: John Snow Labs
name: 607_demo_model
date: 2023-12-29
tags: [bert, en, open_source, sequence_classification, onnx]
task: Text Classification
language: en
edition: Spark NLP 5.2.2
spark_version: 3.0
supported: true
engine: onnx
annotator: DistilBertForSequenceClassification
article_header:
type: cover
use_language_switcher: "Python-Scala-Java"
---

## Description

Pretrained DistilBertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`607_demo_model` is a English model originally trained by mgreenbe.

{:.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/607_demo_model_en_5.2.2_3.0_1703873477575.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/607_demo_model_en_5.2.2_3.0_1703873477575.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 = DistilBertForSequenceClassification.pretrained("607_demo_model","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 = DistilBertForSequenceClassification.pretrained("607_demo_model","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:|607_demo_model|
|Compatibility:|Spark NLP 5.2.2+|
|License:|Open Source|
|Edition:|Official|
|Input Labels:|[documents, token]|
|Output Labels:|[class]|
|Language:|en|
|Size:|249.4 MB|

## References

https://huggingface.co/mgreenbe/607-demo-model
Original file line number Diff line number Diff line change
@@ -0,0 +1,97 @@
---
layout: model
title: English 607_live_demo_yelp_polarity DistilBertForSequenceClassification from mgreenbe
author: John Snow Labs
name: 607_live_demo_yelp_polarity
date: 2023-12-29
tags: [bert, en, open_source, sequence_classification, onnx]
task: Text Classification
language: en
edition: Spark NLP 5.2.2
spark_version: 3.0
supported: true
engine: onnx
annotator: DistilBertForSequenceClassification
article_header:
type: cover
use_language_switcher: "Python-Scala-Java"
---

## Description

Pretrained DistilBertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`607_live_demo_yelp_polarity` is a English model originally trained by mgreenbe.

{:.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/607_live_demo_yelp_polarity_en_5.2.2_3.0_1703878692618.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/607_live_demo_yelp_polarity_en_5.2.2_3.0_1703878692618.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 = DistilBertForSequenceClassification.pretrained("607_live_demo_yelp_polarity","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 = DistilBertForSequenceClassification.pretrained("607_live_demo_yelp_polarity","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:|607_live_demo_yelp_polarity|
|Compatibility:|Spark NLP 5.2.2+|
|License:|Open Source|
|Edition:|Official|
|Input Labels:|[documents, token]|
|Output Labels:|[class]|
|Language:|en|
|Size:|249.5 MB|

## References

https://huggingface.co/mgreenbe/607-live-demo-yelp-polarity
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