Edit on GitHub
  • John Snow Labs
  • Dec 02, 2022

English BertForMaskedLM Base Uncased model (from ayansinha)

  • en
  • open_source
  • bert_embeddings
  • bertformaskedlm

Description

Pretrained BertForMaskedLM model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. false-positives-scancode-bert-base-uncased-L8-1 is a English model originally trained by ayansinha.

Download Copy S3 URI

How to use

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

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

bert_loaded = BertEmbeddings.pretrained("bert_embeddings_false_positives_scancode_base_uncased_l8_1","en") \
    .setInputCols(["document", "token"]) \
    .setOutputCol("embeddings") \
    .setCaseSensitive(True)

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

data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text")

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

val documentAssembler = new DocumentAssembler()
    .setInputCol("text")
    .setOutputCol("document")

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

val bert_loaded = BertEmbeddings.pretrained("bert_embeddings_false_positives_scancode_base_uncased_l8_1","en")
    .setInputCols(Array("document", "token"))
    .setOutputCol("embeddings")
    .setCaseSensitive(True)

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

val data = Seq("I love Spark NLP").toDS.toDF("text")

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

import nlu
nlu.load("en.embed.pos.uncased_base").predict("""I love Spark NLP""")

Model Information

Model Name: bert_embeddings_false_positives_scancode_base_uncased_l8_1
Compatibility: Spark NLP 4.2.4+
License: Open Source
Edition: Official
Input Labels: [sentence, token]
Output Labels: [bert]
Language: en
Size: 409.9 MB
Case sensitive: true

References

  • https://huggingface.co/ayansinha/false-positives-scancode-bert-base-uncased-L8-1
  • https://github.com/nexB/scancode-results-analyzer
  • https://github.com/nexB/scancode-results-analyzer
  • https://github.com/nexB/scancode-results-analyzer#quickstart—local-machine
  • https://github.com/nexB/scancode-results-analyzer/blob/master/src/results_analyze/nlp_models.py
  • https://github.com/nexB/scancode-results-analyzer/blob/master/src/results_analyze/nlp_models.py