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…i_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>
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| --- | ||
| layout: model | ||
| title: DistilBERTZero-Shot Classification Base - MNLI(distilbert_base_zero_shot_classifier_uncased_mnli) | ||
| author: John Snow Labs | ||
| name: distilbert_base_zero_shot_classifier_uncased_mnli | ||
| date: 2023-04-20 | ||
| tags: [zero_shot, en, mnli, distilbert, english, base, open_source, tensorflow] | ||
| task: Zero-Shot Classification | ||
| language: en | ||
| 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" | ||
| --- | ||
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| ## Description | ||
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| This model is intended to be used for zero-shot text classification, especially in English. It is fine-tuned on MNLI by using DistilBERT Base Uncased model. | ||
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| 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. | ||
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| We used TFDistilBertForSequenceClassification to train this model and used DistilBertForZeroShotClassification annotator in Spark NLP 🚀 for prediction at scale! | ||
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| ## Predicted Entities | ||
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| {:.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_uncased_mnli_en_4.4.1_3.2_1682015669457.zip){:.button.button-orange} | ||
| [Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_zero_shot_classifier_uncased_mnli_en_4.4.1_3.2_1682015669457.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} | ||
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| ## How to use | ||
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| <div class="tabs-box" markdown="1"> | ||
| {% include programmingLanguageSelectScalaPythonNLU.html %} | ||
| ```python | ||
| document_assembler = DocumentAssembler() \ | ||
| .setInputCol('text') \ | ||
| .setOutputCol('document') | ||
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| tokenizer = Tokenizer() \ | ||
| .setInputCols(['document']) \ | ||
| .setOutputCol('token') | ||
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| zeroShotClassifier = DistilBertForZeroShotClassification \ | ||
| .pretrained('distilbert_base_zero_shot_classifier_uncased_mnli', 'en') \ | ||
| .setInputCols(['token', 'document']) \ | ||
| .setOutputCol('class') \ | ||
| .setCaseSensitive(True) \ | ||
| .setMaxSentenceLength(512) \ | ||
| .setCandidateLabels(["urgent", "mobile", "travel", "movie", "music", "sport", "weather", "technology"]) | ||
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| pipeline = Pipeline(stages=[ | ||
| document_assembler, | ||
| tokenizer, | ||
| zeroShotClassifier | ||
| ]) | ||
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| 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") | ||
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| val tokenizer = Tokenizer() | ||
| .setInputCols("document") | ||
| .setOutputCol("token") | ||
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| val zeroShotClassifier = DistilBertForZeroShotClassification.pretrained("distilbert_base_zero_shot_classifier_uncased_mnli", "en") | ||
| .setInputCols("document", "token") | ||
| .setOutputCol("class") | ||
| .setCaseSensitive(true) | ||
| .setMaxSentenceLength(512) | ||
| .setCandidateLabels(Array("urgent", "mobile", "travel", "movie", "music", "sport", "weather", "technology")) | ||
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| 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> | ||
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| {:.model-param} | ||
| ## Model Information | ||
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| {:.table-model} | ||
| |---|---| | ||
| |Model Name:|distilbert_base_zero_shot_classifier_uncased_mnli| | ||
| |Compatibility:|Spark NLP 4.4.1+| | ||
| |License:|Open Source| | ||
| |Edition:|Official| | ||
| |Input Labels:|[token, document]| | ||
| |Output Labels:|[multi_class]| | ||
| |Language:|en| | ||
| |Size:|249.7 MB| | ||
| |Case sensitive:|true| |