English BertForTokenClassification Base Cased model (from QCRI)

Description

Pretrained BertForTokenClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. bert-base-cased-sem is a English model originally trained by QCRI.

Predicted Entities

ALT, CON, ENT, EXN, MOR, RLI, EMP, ROL, DEF, FUT, DOM, EXS, UNK, UOM, EQA, EPG, EXG, ART, LES, NAT, DEC, EPT, QUE, TOP, MOY, NEC, QUA, PRO, PST, DIS, COO, DST, IMP, ORG, REF, COM, SUB, PER, ETV, EPS, EXC, DOW, APP, INT, PRX, BUT, NOT, EXT, NOW, POS, LOC, AND, HAS, EFS, ENS, REL, NIL, HAP, YOC, IST, GPE, ITJ, SCO, EXV, ENG, ETG, TIM

Download Copy S3 URI

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_ner_bert_base_cased_sem","en") \
    .setInputCols(["sentence", "token"]) \
    .setOutputCol("ner")

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_ner_bert_base_cased_sem","en") 
    .setInputCols(Array("sentence", "token")) 
    .setOutputCol("ner")

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("en.ner.bert.cased_base.by_qcri").predict("""PUT YOUR STRING HERE""")

Model Information

Model Name:	bert_ner_bert_base_cased_sem
Compatibility:	Spark NLP 4.0.0+
License:	Open Source
Edition:	Official
Input Labels:	[document, token]
Output Labels:	[ner]
Language:	en
Size:	404.4 MB
Case sensitive:	true
Max sentence length:	128

References

https://huggingface.co/QCRI/bert-base-cased-sem

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