English RobertaForSequenceClassification Large Cased model

Description

Pretrained RobertaForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. roberta-large-mnli is a English model originally trained by HuggingFace.

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 = RoBertaForSequenceClassification.pretrained("roberta_classifier_large_mnli","en") \
    .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 = RoBertaForSequenceClassification.pretrained("roberta_classifier_large_mnli","en")
    .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("en.classify.roberta.large.by_uploaded by huggingface").predict("""PUT YOUR STRING HERE""")

Model Information

References

References

• https://huggingface.co/roberta-large-mnli
• https://github.com/facebookresearch/fairseq/tree/main/examples/roberta
• https://arxiv.org/abs/1907.11692
• https://github.com/facebookresearch/fairseq/tree/main/examples/roberta
• https://github.com/facebookresearch/fairseq/tree/main/examples/roberta
• https://aclanthology.org/2021.acl-long.330.pdf
• https://dl.acm.org/doi/pdf/10.1145/3442188.3445922
• https://cims.nyu.edu/~sbowman/multinli/
• https://yknzhu.wixsite.com/mbweb
• https://en.wikipedia.org/wiki/English_Wikipedia
• https://commoncrawl.org/2016/10/news-dataset-available/
• https://github.com/jcpeterson/openwebtext
• https://arxiv.org/abs/1806.02847
• https://github.com/facebookresearch/fairseq/tree/main/examples/roberta
• https://arxiv.org/pdf/1804.07461.pdf
• https://cims.nyu.edu/~sbowman/multinli/
• https://arxiv.org/pdf/1804.07461.pdf
• https://arxiv.org/pdf/1804.07461.pdf
• https://arxiv.org/abs/1704.05426
• https://arxiv.org/abs/1508.05326
• https://arxiv.org/pdf/1809.05053.pdf
• https://cims.nyu.edu/~sbowman/multinli/
• https://arxiv.org/pdf/1809.05053.pdf
• https://mlco2.github.io/impact#compute
• https://arxiv.org/abs/1910.09700
• https://arxiv.org/pdf/1907.11692.pdf
• https://arxiv.org/pdf/1907.11692.pdf