English RobertaForTokenClassification Large Cased model (from Lexemo)

Description

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

Predicted Entities

another_act_sequence_start, another_act_equal_previous_act, article_relevant_previous_act, abbreviation_relevant_following_act, article_relevant_following_act, article_current, service_label, another_act_abbreviation, abbreviation_relevant_previous_act, article_relevant_previous_act_range_start, another_article_equal_previous_article, article_relevant_previous_act_range_end, article_relevant_current_act, article_relevant_current_act_range_start, article_relevant_current_act_range_end, article_relevant_following_act_range_end, article_relevant_following_act_range_start, another_act_sequence_end, another_act, current_act, treaty_name, treaty_abbreviation

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("roberta_ner_roberta_large_legal_act_extraction","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("roberta_ner_roberta_large_legal_act_extraction","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.roberta.large_legal").predict("""PUT YOUR STRING HERE""")

Model Information

References

References

• https://huggingface.co/Lexemo/roberta_large_legal_act_extraction