Multilingual BertForTokenClassification Base Cased model (from Sultannn)

Description

Pretrained BertForTokenClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. bert-base-ft-pos-xtreme is a Multilingual model originally trained by Sultannn.

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How to use

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

sentenceDetector = SentenceDetectorDLModel.pretrained("sentence_detector_dl", "xx")\
       .setInputCols(["document"])\
       .setOutputCol("sentence")

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

tokenClassifier = BertForTokenClassification.pretrained("bert_pos_bert_base_ft_pos_xtreme","xx") \
    .setInputCols(["sentence", "token"]) \
    .setOutputCol("pos")

pipeline = Pipeline(stages=[documentAssembler, sentenceDetector, 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 sentenceDetector = SentenceDetectorDLModel.pretrained("sentence_detector_dl", "xx")
       .setInputCols(Array("document"))
       .setOutputCol("sentence")

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

val tokenClassifier = BertForTokenClassification.pretrained("bert_pos_bert_base_ft_pos_xtreme","xx") 
    .setInputCols(Array("sentence", "token")) 
    .setOutputCol("pos")

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

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

val result = pipeline.fit(data).transform(data)
import nlu
nlu.load("xx.ner.pos.xtreme.base").predict("""PUT YOUR STRING HERE""")

Model Information

Model Name: bert_pos_bert_base_ft_pos_xtreme
Compatibility: Spark NLP 4.0.0+
License: Open Source
Edition: Official
Input Labels: [document, token]
Output Labels: [ner]
Language: xx
Size: 464.8 MB
Case sensitive: true
Max sentence length: 128

References

  • https://huggingface.co/Sultannn/bert-base-ft-pos-xtreme