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  • John Snow Labs
  • Sep 25, 2022

English CamembertForTokenClassification Base Cased model (from SergeiGKS)

  • camembert
  • ner
  • open_source
  • en

Description

Pretrained CamembertForTokenClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. camembert-base-wikipedia-4gb-finetuned-job-ner is a English model originally trained by SergeiGKS.

Predicted Entities

JOB, LOC, ORG, PER, MISC

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")

sequenceClassifier_loaded = CamemBertForTokenClassification.pretrained("camembert_classifier_base_wikipedia_4gb_finetuned_job_ner","en") \
    .setInputCols(["sentence", "token"]) \
    .setOutputCol("ner")

pipeline = Pipeline(stages=[documentAssembler,sentenceDetector,tokenizer,sequenceClassifier_loaded])

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 sequenceClassifier_loaded = CamemBertForTokenClassification.pretrained("camembert_classifier_base_wikipedia_4gb_finetuned_job_ner","en") 
    .setInputCols(Array("sentence", "token")) 
    .setOutputCol("ner")

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

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

val result = pipeline.fit(data).transform(data)

import nlu
nlu.load("en.ner.camembert.base_finetuned").predict("""PUT YOUR STRING HERE""")

Model Information

Model Name: camembert_classifier_base_wikipedia_4gb_finetuned_job_ner
Compatibility: Spark NLP 4.2.0+
License: Open Source
Edition: Official
Input Labels: [document, token]
Output Labels: [class]
Language: en
Size: 412.2 MB
Case sensitive: true
Max sentence length: 256

References

  • https://huggingface.co/SergeiGKS/camembert-base-wikipedia-4gb-finetuned-job-ner