English BertForTokenClassification Base Cased model (from Jzuluaga)

Description

Pretrained BertForTokenClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. bert-base-ner-atc-en-atco2-1h is a English model originally trained by Jzuluaga.

Predicted Entities

command, value, callsign, O

Download Copy S3 URI

How to use

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

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

tokenClassifier = BertForTokenClassification.pretrained("bert_token_classifier_base_ner_atc_en_atco2_1h","en") \
    .setInputCols(["document", "token"]) \
    .setOutputCol("ner")

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

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("document")
    .setOutputCol("token")

val tokenClassifier = BertForTokenClassification.pretrained("bert_token_classifier_base_ner_atc_en_atco2_1h","en")
    .setInputCols(Array("document", "token"))
    .setOutputCol("ner")

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

val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text")

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

Model Information

Model Name: bert_token_classifier_base_ner_atc_en_atco2_1h
Compatibility: Spark NLP 4.3.1+
License: Open Source
Edition: Official
Input Labels: [document, token]
Output Labels: [ner]
Language: en
Size: 407.8 MB
Case sensitive: true
Max sentence length: 128

References

  • https://huggingface.co/Jzuluaga/bert-base-ner-atc-en-atco2-1h
  • https://github.com/idiap/atco2-corpus
  • https://arxiv.org/abs/2211.04054
  • https://www.atco2.org/data
  • https://github.com/idiap/atco2-corpus
  • https://arxiv.org/abs/2211.04054
  • https://www.atco2.org/data
  • https://github.com/idiap/atco2-corpus/tree/main/data/databases/atco2_test_set_1h/data_prepare_atco2_corpus_other.sh
  • https://paperswithcode.com/sota?task=ner&dataset=ATCO2+corpus+%28Air+Traffic+Control+Communications%29