English BertForSequenceClassification Cased model (from owaiskha9654)

Description

Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. Multi-Label-Classification-of-PubMed-Articles is a English model originally trained by owaiskha9654.

Predicted Entities

Phenomena and Processes [G], Diseases [C], Health Care [N], Chemicals and Drugs [D], Psychiatry and Psychology [F], Anatomy [A], Information Science [L], Geographicals [Z], Organisms [B], Disciplines and Occupations [H], Named Groups [M]

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 = BertForSequenceClassification.pretrained("bert_classifier_multi_label_classification_of_pubmed_articles","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 = BertForSequenceClassification.pretrained("bert_classifier_multi_label_classification_of_pubmed_articles","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.bert.pubmed.").predict("""PUT YOUR STRING HERE""")

Model Information

References

References

• https://huggingface.co/owaiskha9654/Multi-Label-Classification-of-PubMed-Articles
• https://www.kaggle.com/datasets/owaiskhan9654/pubmed-multilabel-text-classification
• https://www.kaggle.com/code/owaiskhan9654/multi-label-classification-of-pubmed-articles
• https://www.kaggle.com/datasets/owaiskhan9654/pubmed-multilabel-text-classification
• https://arxiv.org/abs/1706.03762
• https://arxiv.org/abs/1810.04805
• https://github.com/google-research/bert
• https://pytorch.org/docs/stable/generated/torch.nn.BCEWithLogitsLoss.html#torch.nn.BCEWithLogitsLoss