English BertForTokenClassification Cased model (from rsuwaileh)

Description

Pretrained BertForTokenClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. IDRISI-LMR-HD-TB-partition is a English model originally trained by rsuwaileh.

Predicted Entities

L-ST, CTRY, L-HPOI, CNTY, U-OTHR, L-ISL, OTHR, U-HPOI, L-CNTY, CITY, L-CTRY, L-DIST, U-STAT, CONT, NBHD, L-NBHD, L-STAT, U-ST, L-CITY, NPOI, ST, ISL, U-NBHD, STAT, L-NPOI, HPOI, U-CNTY, L-OTHR, U-ISL, U-CTRY, L-CONT, U-CONT, U-NPOI, U-DIST, U-CITY, DIST

Live Demo Open in Colab 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_IDRISI_LMR_HD_TB_partition","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_IDRISI_LMR_HD_TB_partition","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)

Model Information

References

• https://huggingface.co/rsuwaileh/IDRISI-LMR-HD-TB-partition
• https://github.com/rsuwaileh/IDRISI
• https://github.com/rsuwaileh/TLLMR4CM/
• https://github.com/rsuwaileh/IDRISI/tree/main/data/LMR/EN/gold-random-bilou/hurricane_dorian_2019