Multilingual XlmRobertaForSequenceClassification Base Cased model (from symanto)

Description

Pretrained XlmRobertaForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. xlm-roberta-base-snli-mnli-anli-xnli is a Multilingual model originally trained by symanto.

Predicted Entities

ENTAILMENT, NEUTRAL, CONTRADICTION

Live Demo Open in Colab Download Copy S3 URI

How to use

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

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

seq_classifier = XlmRoBertaForSequenceClassification.pretrained("xlmroberta_classifier_base_snli_mnli_anli_xnli","xx") \
    .setInputCols(["document", "token"]) \
    .setOutputCol("class")

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

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

result = pipeline.fit(data).transform(data)

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

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

val seq_classifier = XlmRoBertaForSequenceClassification.pretrained("xlmroberta_classifier_base_snli_mnli_anli_xnli","xx")
    .setInputCols(Array("document", "token"))
    .setOutputCol("class")

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

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

val result = pipeline.fit(data).transform(data)

import nlu
nlu.load("xx.classify.xlmr_roberta.xnli.base").predict("""PUT YOUR STRING HERE""")

Model Information

References

References

• https://huggingface.co/symanto/xlm-roberta-base-snli-mnli-anli-xnli
• https://nlp.stanford.edu/projects/snli/
• https://cims.nyu.edu/~sbowman/multinli/
• https://github.com/facebookresearch/anli
• https://github.com/facebookresearch/XNLI