English DistilBertForSequenceClassification Base Cased model (from Wi)

Description

Pretrained DistilBertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. arxiv-topics-distilbert-base-cased is a English model originally trained by Wi.

Predicted Entities

Other, Statistics, Astrophysics, Quantum Physics, Nonlinear Sciences, Electrical Engineering and Systems Science, High Energy Physics - Lattice, Quantitative Biology, High Energy Physics - Theory, Nuclear Theory, High Energy Physics - Experiment, Condensed Matter, Nuclear Experiment, High Energy Physics - Phenomenology, Mathematics, Physics, Quantitative Finance, Mathematical Physics, Economics, General Relativity and Quantum Cosmology, Computer Science

Live Demo Open in Colab 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_arxiv_topics_distilbert_base_cased","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_arxiv_topics_distilbert_base_cased","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.cased_base").predict("""PUT YOUR STRING HERE""")

Model Information

References

References

• https://huggingface.co/Wi/arxiv-topics-distilbert-base-cased