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---------

Co-authored-by: ahmedlone127 <ahmedlone127@gmail.com>
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---
layout: model
title: English sent_tiny_biobert BertSentenceEmbeddings from nlpie
author: John Snow Labs
name: sent_tiny_biobert
date: 2024-09-04
tags: [en, open_source, onnx, sentence_embeddings, bert]
task: Embeddings
language: en
edition: Spark NLP 5.5.0
spark_version: 3.0
supported: true
engine: onnx
annotator: BertSentenceEmbeddings
article_header:
type: cover
use_language_switcher: "Python-Scala-Java"
---

## Description

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

{:.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/sent_tiny_biobert_en_5.5.0_3.0_1725454293294.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/sent_tiny_biobert_en_5.5.0_3.0_1725454293294.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")

sentenceDL = SentenceDetectorDLModel.pretrained("sentence_detector_dl", "xx") \
.setInputCols(["document"]) \
.setOutputCol("sentence")

embeddings = BertSentenceEmbeddings.pretrained("sent_tiny_biobert","en") \
.setInputCols(["sentence"]) \
.setOutputCol("embeddings")

pipeline = Pipeline().setStages([documentAssembler, sentenceDL, embeddings])
data = spark.createDataFrame([["I love spark-nlp"]]).toDF("text")
pipelineModel = pipeline.fit(data)
pipelineDF = pipelineModel.transform(data)

```
```scala

val documentAssembler = new DocumentAssembler()
.setInputCol("text")
.setOutputCol("document")

val sentenceDL = SentenceDetectorDLModel.pretrained("sentence_detector_dl", "xx")
.setInputCols(Array("document"))
.setOutputCol("sentence")

val embeddings = BertSentenceEmbeddings.pretrained("sent_tiny_biobert","en")
.setInputCols(Array("sentence"))
.setOutputCol("embeddings")

val pipeline = new Pipeline().setStages(Array(documentAssembler, sentenceDL, embeddings))
val data = Seq("I love spark-nlp").toDF("text")
val pipelineModel = pipeline.fit(data)
val pipelineDF = pipelineModel.transform(data)

```
</div>

{:.model-param}
## Model Information

{:.table-model}
|---|---|
|Model Name:|sent_tiny_biobert|
|Compatibility:|Spark NLP 5.5.0+|
|License:|Open Source|
|Edition:|Official|
|Input Labels:|[sentence]|
|Output Labels:|[embeddings]|
|Language:|en|
|Size:|51.9 MB|

## References

https://huggingface.co/nlpie/tiny-biobert
94 changes: 94 additions & 0 deletions ...9-04-xlm_roberta_base_tweet_sentiment_portuguese_trimmed_portuguese_15000_en.md
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---
layout: model
title: English xlm_roberta_base_tweet_sentiment_portuguese_trimmed_portuguese_15000 XlmRoBertaForSequenceClassification from vocabtrimmer
author: John Snow Labs
name: xlm_roberta_base_tweet_sentiment_portuguese_trimmed_portuguese_15000
date: 2024-09-04
tags: [en, open_source, onnx, sequence_classification, xlm_roberta]
task: Text Classification
language: en
edition: Spark NLP 5.5.0
spark_version: 3.0
supported: true
engine: onnx
annotator: XlmRoBertaForSequenceClassification
article_header:
type: cover
use_language_switcher: "Python-Scala-Java"
---

## Description

Pretrained XlmRoBertaForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`xlm_roberta_base_tweet_sentiment_portuguese_trimmed_portuguese_15000` is a English model originally trained by vocabtrimmer.

{:.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_base_tweet_sentiment_portuguese_trimmed_portuguese_15000_en_5.5.0_3.0_1725410232175.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/xlm_roberta_base_tweet_sentiment_portuguese_trimmed_portuguese_15000_en_5.5.0_3.0_1725410232175.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')

sequenceClassifier = XlmRoBertaForSequenceClassification.pretrained("xlm_roberta_base_tweet_sentiment_portuguese_trimmed_portuguese_15000","en") \
.setInputCols(["documents","token"]) \
.setOutputCol("class")

pipeline = Pipeline().setStages([documentAssembler, tokenizer, sequenceClassifier])
data = spark.createDataFrame([["I love spark-nlp"]]).toDF("text")
pipelineModel = pipeline.fit(data)
pipelineDF = pipelineModel.transform(data)

```
```scala

val documentAssembler = new DocumentAssembler()
.setInputCols("text")
.setOutputCols("document")

val tokenizer = new Tokenizer()
.setInputCols(Array("document"))
.setOutputCol("token")

val sequenceClassifier = XlmRoBertaForSequenceClassification.pretrained("xlm_roberta_base_tweet_sentiment_portuguese_trimmed_portuguese_15000", "en")
.setInputCols(Array("documents","token"))
.setOutputCol("class")

val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier))
val data = Seq("I love spark-nlp").toDS.toDF("text")
val pipelineModel = pipeline.fit(data)
val pipelineDF = pipelineModel.transform(data)

```
</div>

{:.model-param}
## Model Information

{:.table-model}
|---|---|
|Model Name:|xlm_roberta_base_tweet_sentiment_portuguese_trimmed_portuguese_15000|
|Compatibility:|Spark NLP 5.5.0+|
|License:|Open Source|
|Edition:|Official|
|Input Labels:|[document, token]|
|Output Labels:|[class]|
|Language:|en|
|Size:|358.2 MB|

## References

https://huggingface.co/vocabtrimmer/xlm-roberta-base-tweet-sentiment-pt-trimmed-pt-15000
94 changes: 94 additions & 0 deletions docs/_posts/ahmedlone127/2024-09-05-bert_finetuned_ner_july_en.md
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---
layout: model
title: English bert_finetuned_ner_july DistilBertForTokenClassification from Amhyr
author: John Snow Labs
name: bert_finetuned_ner_july
date: 2024-09-05
tags: [en, open_source, onnx, token_classification, distilbert, ner]
task: Named Entity Recognition
language: en
edition: Spark NLP 5.5.0
spark_version: 3.0
supported: true
engine: onnx
annotator: DistilBertForTokenClassification
article_header:
type: cover
use_language_switcher: "Python-Scala-Java"
---

## Description

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

{:.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_finetuned_ner_july_en_5.5.0_3.0_1725506530169.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_finetuned_ner_july_en_5.5.0_3.0_1725506530169.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')

tokenClassifier = DistilBertForTokenClassification.pretrained("bert_finetuned_ner_july","en") \
.setInputCols(["documents","token"]) \
.setOutputCol("ner")

pipeline = Pipeline().setStages([documentAssembler, tokenizer, tokenClassifier])
data = spark.createDataFrame([["I love spark-nlp"]]).toDF("text")
pipelineModel = pipeline.fit(data)
pipelineDF = pipelineModel.transform(data)

```
```scala

val documentAssembler = new DocumentAssembler()
.setInputCols("text")
.setOutputCols("document")

val tokenizer = new Tokenizer()
.setInputCols("document")
.setOutputCol("token")

val tokenClassifier = DistilBertForTokenClassification.pretrained("bert_finetuned_ner_july", "en")
.setInputCols(Array("documents","token"))
.setOutputCol("ner")

val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, tokenClassifier))
val data = Seq("I love spark-nlp").toDS.toDF("text")
val pipelineModel = pipeline.fit(data)
val pipelineDF = pipelineModel.transform(data)

```
</div>

{:.model-param}
## Model Information

{:.table-model}
|---|---|
|Model Name:|bert_finetuned_ner_july|
|Compatibility:|Spark NLP 5.5.0+|
|License:|Open Source|
|Edition:|Official|
|Input Labels:|[document, token]|
|Output Labels:|[ner]|
|Language:|en|
|Size:|505.4 MB|

## References

https://huggingface.co/Amhyr/bert-finetuned-ner_july
94 changes: 94 additions & 0 deletions docs/_posts/ahmedlone127/2024-09-05-darijabert_ar.md
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---
layout: model
title: Arabic darijabert BertEmbeddings from SI2M-Lab
author: John Snow Labs
name: darijabert
date: 2024-09-05
tags: [ar, open_source, onnx, embeddings, bert]
task: Embeddings
language: ar
edition: Spark NLP 5.5.0
spark_version: 3.0
supported: true
engine: onnx
annotator: BertEmbeddings
article_header:
type: cover
use_language_switcher: "Python-Scala-Java"
---

## Description

Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`darijabert` is a Arabic model originally trained by SI2M-Lab.

{:.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/darijabert_ar_5.5.0_3.0_1725520088704.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/darijabert_ar_5.5.0_3.0_1725520088704.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 = BertEmbeddings.pretrained("darijabert","ar") \
.setInputCols(["document", "token"]) \
.setOutputCol("embeddings")

pipeline = Pipeline().setStages([documentAssembler, tokenizer, embeddings])
data = spark.createDataFrame([["I love spark-nlp"]]).toDF("text")
pipelineModel = pipeline.fit(data)
pipelineDF = pipelineModel.transform(data)

```
```scala

val documentAssembler = new DocumentAssembler()
.setInputCol("text")
.setOutputCol("document")

val tokenizer = new Tokenizer()
.setInputCols(Array("document"))
.setOutputCol("token")

val embeddings = BertEmbeddings.pretrained("darijabert","ar")
.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 pipelineModel = pipeline.fit(data)
val pipelineDF = pipelineModel.transform(data)

```
</div>

{:.model-param}
## Model Information

{:.table-model}
|---|---|
|Model Name:|darijabert|
|Compatibility:|Spark NLP 5.5.0+|
|License:|Open Source|
|Edition:|Official|
|Input Labels:|[document, token]|
|Output Labels:|[bert]|
|Language:|ar|
|Size:|551.5 MB|

## References

https://huggingface.co/SI2M-Lab/DarijaBERT
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