-
Notifications
You must be signed in to change notification settings - Fork 717
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
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 <[email protected]>
- Loading branch information
1 parent
afb700e
commit bb9a155
Showing
4 changed files
with
429 additions
and
0 deletions.
There are no files selected for viewing
107 changes: 107 additions & 0 deletions
107
...e127/2023-04-20-distilbert_base_zero_shot_classifier_turkish_cased_allnli_tr.md
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -0,0 +1,107 @@ | ||
--- | ||
layout: model | ||
title: DistilBERTZero-Shot Classification Base - distilbert_base_zero_shot_classifier_turkish_cased_allnli | ||
author: John Snow Labs | ||
name: distilbert_base_zero_shot_classifier_turkish_cased_allnli | ||
date: 2023-04-20 | ||
tags: [zero_shot, distilbert, base, tr, turkish, cased, open_source, tensorflow] | ||
task: Zero-Shot Classification | ||
language: tr | ||
edition: Spark NLP 4.4.1 | ||
spark_version: [3.2, 3.0] | ||
supported: true | ||
engine: tensorflow | ||
annotator: DistilBertForZeroShotClassification | ||
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 Trukish. It is fine-tuned on MNLI by using DistilBERT Base Uncased model. | ||
|
||
DistilBertForZeroShotClassification using a ModelForSequenceClassification trained on NLI (natural language inference) tasks. Equivalent of DistilBertForSequenceClassification 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 TFDistilBertForSequenceClassification to train this model and used DistilBertForZeroShotClassification 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/distilbert_base_zero_shot_classifier_turkish_cased_allnli_4.4.1_3.2_1681950583033.zip){:.button.button-orange} | ||
[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_zero_shot_classifier_turkish_cased_allnli_tr_4.4.1_3.2_1681950583033.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 = DistilBertForZeroShotClassification \ | ||
.pretrained('distilbert_base_zero_shot_classifier_turkish_cased_allnli', 'en') \ | ||
.setInputCols(['token', 'document']) \ | ||
.setOutputCol('class') \ | ||
.setCaseSensitive(True) \ | ||
.setMaxSentenceLength(512) \ | ||
.setCandidateLabels(["olumsuz", "olumlu"]) | ||
|
||
pipeline = Pipeline(stages=[ | ||
document_assembler, | ||
tokenizer, | ||
zeroShotClassifier | ||
]) | ||
|
||
example = spark.createDataFrame([['Senaryo çok saçmaydı, beğendim diyemem.']]).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 = DistilBertForZeroShotClassification.pretrained("distilbert_base_zero_shot_classifier_turkish_cased_allnli", "en") | ||
.setInputCols("document", "token") | ||
.setOutputCol("class") | ||
.setCaseSensitive(true) | ||
.setMaxSentenceLength(512) | ||
.setCandidateLabels(Array("olumsuz", "olumlu")) | ||
|
||
val pipeline = new Pipeline().setStages(Array(document_assembler, tokenizer, zeroShotClassifier)) | ||
|
||
val example = Seq("Senaryo çok saçmaydı, beğendim diyemem.").toDS.toDF("text") | ||
|
||
val result = pipeline.fit(example).transform(example) | ||
``` | ||
</div> | ||
|
||
{:.model-param} | ||
## Model Information | ||
|
||
{:.table-model} | ||
|---|---| | ||
|Model Name:|distilbert_base_zero_shot_classifier_turkish_cased_allnli| | ||
|Compatibility:|Spark NLP 4.4.1+| | ||
|License:|Open Source| | ||
|Edition:|Official| | ||
|Input Labels:|[token, document]| | ||
|Output Labels:|[multi_class]| | ||
|Language:|tr| | ||
|Size:|254.3 MB| | ||
|Case sensitive:|true| |
108 changes: 108 additions & 0 deletions
108
...27/2023-04-20-distilbert_base_zero_shot_classifier_turkish_cased_multinli_tr.md
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -0,0 +1,108 @@ | ||
--- | ||
layout: model | ||
title: DistilBERTZero-Shot Classification Base - distilbert_base_zero_shot_classifier_turkish_cased_multinli | ||
author: John Snow Labs | ||
name: distilbert_base_zero_shot_classifier_turkish_cased_multinli | ||
date: 2023-04-20 | ||
tags: [zero_shot, tr, turkish, distilbert, base, cased, open_source, tensorflow] | ||
task: Zero-Shot Classification | ||
language: tr | ||
edition: Spark NLP 4.4.1 | ||
spark_version: [3.2, 3.0] | ||
supported: true | ||
engine: tensorflow | ||
annotator: DistilBertForZeroShotClassification | ||
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 Trukish. It is fine-tuned on MNLI by using DistilBERT Base Uncased model. | ||
|
||
DistilBertForZeroShotClassification using a ModelForSequenceClassification trained on NLI (natural language inference) tasks. Equivalent of DistilBertForSequenceClassification 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 TFDistilBertForSequenceClassification to train this model and used DistilBertForZeroShotClassification 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/distilbert_base_zero_shot_classifier_turkish_cased_multinli_tr_4.4.1_3.2_1681952299918.zip){:.button.button-orange} | ||
[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_zero_shot_classifier_turkish_cased_multinli_tr_4.4.1_3.2_1681952299918.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 = DistilBertForZeroShotClassification \ | ||
.pretrained('distilbert_base_zero_shot_classifier_turkish_cased_multinli', 'en') \ | ||
.setInputCols(['token', 'document']) \ | ||
.setOutputCol('class') \ | ||
.setCaseSensitive(True) \ | ||
.setMaxSentenceLength(512) \ | ||
.setCandidateLabels(["ekonomi", "siyaset","spor"]) | ||
|
||
pipeline = Pipeline(stages=[ | ||
document_assembler, | ||
tokenizer, | ||
zeroShotClassifier | ||
]) | ||
|
||
example = spark.createDataFrame([['Dolar yükselmeye devam ediyor.']]).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 = DistilBertForZeroShotClassification.pretrained("distilbert_base_zero_shot_classifier_turkish_cased_multinli", "en") | ||
.setInputCols("document", "token") | ||
.setOutputCol("class") | ||
.setCaseSensitive(true) | ||
.setMaxSentenceLength(512) | ||
.setCandidateLabels(Array("ekonomi", "siyaset","spor")) | ||
|
||
val pipeline = new Pipeline().setStages(Array(document_assembler, tokenizer, zeroShotClassifier)) | ||
|
||
val example = Seq("Dolar yükselmeye devam ediyor.").toDS.toDF("text") | ||
|
||
val result = pipeline.fit(example).transform(example) | ||
``` | ||
</div> | ||
|
||
{:.model-param} | ||
## Model Information | ||
|
||
{:.table-model} | ||
|---|---| | ||
|Model Name:|distilbert_base_zero_shot_classifier_turkish_cased_multinli| | ||
|Compatibility:|Spark NLP 4.4.1+| | ||
|License:|Open Source| | ||
|Edition:|Official| | ||
|Input Labels:|[token, document]| | ||
|Output Labels:|[multi_class]| | ||
|Language:|tr| | ||
|Size:|254.3 MB| | ||
|Case sensitive:|true| |
107 changes: 107 additions & 0 deletions
107
...one127/2023-04-20-distilbert_base_zero_shot_classifier_turkish_cased_snli_tr.md
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -0,0 +1,107 @@ | ||
--- | ||
layout: model | ||
title: DistilBERTZero-Shot Classification Base - distilbert_base_zero_shot_classifier_turkish_cased_snli | ||
author: John Snow Labs | ||
name: distilbert_base_zero_shot_classifier_turkish_cased_snli | ||
date: 2023-04-20 | ||
tags: [zero_shot, tr, turkish, distilbert, base, cased, open_source, tensorflow] | ||
task: Zero-Shot Classification | ||
language: tr | ||
edition: Spark NLP 4.4.1 | ||
spark_version: [3.2, 3.0] | ||
supported: true | ||
engine: tensorflow | ||
annotator: DistilBertForZeroShotClassification | ||
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 Trukish. It is fine-tuned on MNLI by using DistilBERT Base Uncased model. | ||
|
||
DistilBertForZeroShotClassification using a ModelForSequenceClassification trained on NLI (natural language inference) tasks. Equivalent of DistilBertForSequenceClassification 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 TFDistilBertForSequenceClassification to train this model and used DistilBertForZeroShotClassification 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/distilbert_base_zero_shot_classifier_turkish_cased_snli_tr_4.4.1_3.2_1681951486863.zip){:.button.button-orange} | ||
[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_zero_shot_classifier_turkish_cased_snli_tr_4.4.1_3.2_1681951486863.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 = DistilBertForZeroShotClassification \ | ||
.pretrained('distilbert_base_zero_shot_classifier_turkish_cased_snli', 'en') \ | ||
.setInputCols(['token', 'document']) \ | ||
.setOutputCol('class') \ | ||
.setCaseSensitive(True) \ | ||
.setMaxSentenceLength(512) \ | ||
.setCandidateLabels(["olumsuz", "olumlu"]) | ||
|
||
pipeline = Pipeline(stages=[ | ||
document_assembler, | ||
tokenizer, | ||
zeroShotClassifier | ||
]) | ||
|
||
example = spark.createDataFrame([['Senaryo çok saçmaydı, beğendim diyemem.']]).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 = DistilBertForZeroShotClassification.pretrained("distilbert_base_zero_shot_classifier_turkish_cased_snli", "en") | ||
.setInputCols("document", "token") | ||
.setOutputCol("class") | ||
.setCaseSensitive(true) | ||
.setMaxSentenceLength(512) | ||
.setCandidateLabels(Array("olumsuz", "olumlu")) | ||
|
||
val pipeline = new Pipeline().setStages(Array(document_assembler, tokenizer, zeroShotClassifier)) | ||
|
||
val example = Seq("Senaryo çok saçmaydı, beğendim diyemem.").toDS.toDF("text") | ||
|
||
val result = pipeline.fit(example).transform(example) | ||
``` | ||
</div> | ||
|
||
{:.model-param} | ||
## Model Information | ||
|
||
{:.table-model} | ||
|---|---| | ||
|Model Name:|distilbert_base_zero_shot_classifier_turkish_cased_snli| | ||
|Compatibility:|Spark NLP 4.4.1+| | ||
|License:|Open Source| | ||
|Edition:|Official| | ||
|Input Labels:|[token, document]| | ||
|Output Labels:|[multi_class]| | ||
|Language:|tr| | ||
|Size:|254.3 MB| | ||
|Case sensitive:|true| |
Oops, something went wrong.