English BertForTokenClassification Cased model (from icelab)

Description

Pretrained BertForTokenClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. spacebert_CR is a English model originally trained by icelab.

Predicted Entities

Space Environment, Propulsion, Project Scope, Structure & Mechanism, Parameter, Safety / Risk (Control), System engineering, Thermal, OBDH, Power, Materials / EEEs, Project Organisation / Documentation, Cleanliness, Nonconformity, GN&C, Quality control, Telecom., Measurement

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_spacebert_CR","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_spacebert_CR","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/icelab/spacebert_CR
• https://colab.research.google.com/drive/1EGh9bdxq6RqIzbvKuptAWvmIBG2EQJzJ?usp=sharing
• https://github.com/strath-ace/smart-nlp/blob/master/SpaceTransformers/CR/CR_ECSS_dataset.json