English BertForSequenceClassification Cased model (from course5i)

Description

Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. SEAD-L-6_H-384_A-12-qqp is a English model originally trained by course5i.

Predicted Entities

0, 1

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_sead_l_6_h_384_a_12_qqp","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_sead_l_6_h_384_a_12_qqp","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.glue_qqp.6l_384d_a12a.by_course5i").predict("""PUT YOUR STRING HERE""")

Model Information

Model Name: bert_classifier_sead_l_6_h_384_a_12_qqp
Compatibility: Spark NLP 4.1.0+
License: Open Source
Edition: Official
Input Labels: [document, token]
Output Labels: [class]
Language: en
Size: 84.7 MB
Case sensitive: true
Max sentence length: 256

References

  • https://huggingface.co/course5i/SEAD-L-6_H-384_A-12-qqp
  • https://arxiv.org/abs/1910.01108
  • https://arxiv.org/abs/1909.10351
  • https://arxiv.org/abs/2002.10957
  • https://arxiv.org/abs/1810.04805
  • https://arxiv.org/abs/1804.07461
  • https://arxiv.org/abs/1905.00537
  • https://www.adasci.org/journals/lattice-35309407/?volumes=true&open=621a3b18edc4364e8a96cb63