2021-11-21-distilbert_base_sequence_classifier_imdb_ur

docs/_posts/maziyarpanahi/2021-11-21-distilbert_base_sequence_classifier_imdb_ur.md

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+---
+layout: model
+title: DistilBERT Sequence Classification - Urdu IMDB (distilbert_base_sequence_classifier_imdb)
+author: John Snow Labs
+name: distilbert_base_sequence_classifier_imdb
+date: 2021-11-21
+tags: [ur, urdu, sentiment, imdb, sequence_classification, open_source, distilbert]
+task: Text Classification
+language: ur
+edition: Spark NLP 3.3.3
+spark_version: 3.0
+supported: true
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+DistilBERT Model with sequence classification/regression head on top (a linear layer on top of the pooled output) e.g. for multi-class document classification tasks.
+
+`distilbert_base_sequence_classifier_imdb ` is a fine-tuned DistilBERT model that is ready to be used for Sequence Classification tasks such as sentiment analysis or multi-class text classification and it achieves state-of-the-art performance. 
+
+We used TFDistilBertForSequenceClassification to train this model and used BertForSequenceClassification annotator in Spark NLP 🚀 for prediction at scale!
+
+## Predicted Entities
+
+`negative`, `positive`
+
+{:.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_sequence_classifier_imdb_ur_3.3.3_3.0_1637503332156.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+
+## 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('distilbert_base_sequence_classifier_imdb', 'ur') \
+      .setInputCols(['token', 'document']) \
+      .setOutputCol('class') \
+      .setCaseSensitive(True) \
+      .setMaxSentenceLength(512)
+
+pipeline = Pipeline(stages=[
+    document_assembler, 
+    tokenizer,
+    sequenceClassifier    
+])
+
+example = spark.createDataFrame([['یہ فلم واقعی اچھی تھی!']]).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 tokenClassifier = DistilBertForSequenceClassification.pretrained("distilbert_base_sequence_classifier_imdb", "ur")
+      .setInputCols("document", "token")
+      .setOutputCol("class")
+      .setCaseSensitive(true)
+      .setMaxSentenceLength(512)
+
+val pipeline = new Pipeline().setStages(Array(document_assembler, tokenizer, sequenceClassifier))
+
+val example = Seq("یہ فلم واقعی اچھی تھی!").toDS.toDF("text")
+
+val result = pipeline.fit(example).transform(example)
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|distilbert_base_sequence_classifier_imdb|
+|Compatibility:|Spark NLP 3.3.3+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[token, document]|
+|Output Labels:|[class]|
+|Language:|ur|
+|Case sensitive:|true|
+|Max sentense length:|512|
+
+## Data Source
+
+[https://huggingface.co/datasets/imdb_urdu_reviews](https://huggingface.co/datasets/imdb_urdu_reviews)
+
+## Benchmarking
+
+```bash
+          precision    recall  f1-score   support
+
+    negative       0.94      0.96      0.95     24285
+    positive       0.96      0.95      0.95     25715
+
+    accuracy                           0.95     50000
+   macro avg       0.95      0.95      0.95     50000
+weighted avg       0.95      0.95      0.95     50000
+```