English DistilBertForSequenceClassification Cased model (from martin-ha)

Description

Pretrained DistilBertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. toxic-comment-model is a English model originally trained by martin-ha.

Predicted Entities

toxic, non-toxic

Download Copy S3 URI

How to use

documentAssembler = DocumentAssembler() \
        .setInputCol("text") \
        .setOutputCol("document")

tokenizer = Tokenizer() \
    .setInputCols("document") \
    .setOutputCol("token")

sequenceClassifier_loaded = DistilBertForSequenceClassification.pretrained("distilbert_sequence_classifier_toxic_comment_model","en") \
    .setInputCols(["document", "token"]) \
    .setOutputCol("class")

pipeline = Pipeline(stages=[documentAssembler, tokenizer,sequenceClassifier_loaded])

data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text")

result = pipeline.fit(data).transform(data)
val documentAssembler = new DocumentAssembler() 
          .setInputCol("text") 
          .setOutputCol("document")

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

val sequenceClassifier_loaded = DistilBertForSequenceClassification.pretrained("distilbert_sequence_classifier_toxic_comment_model","en") 
    .setInputCols(Array("document", "token")) 
    .setOutputCol("class")

val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer,sequenceClassifier_loaded))

val data = Seq("PUT YOUR STRING HERE").toDF("text")

val result = pipeline.fit(data).transform(data)
import nlu
nlu.load("en.classify.distil_bert.by_martin_ha").predict("""PUT YOUR STRING HERE""")

Model Information

Model Name: distilbert_sequence_classifier_toxic_comment_model
Compatibility: Spark NLP 4.1.0+
License: Open Source
Edition: Official
Input Labels: [document, token]
Output Labels: [ner]
Language: en
Size: 249.7 MB
Case sensitive: true
Max sentence length: 128

References

  • https://huggingface.co/martin-ha/toxic-comment-model
  • https://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification/overview/evaluation
  • https://github.com/MSIA/wenyang_pan_nlp_project_2021
  • https://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification/data