RoBERTa Token Classification Base - NER CoNLL (roberta_base_token_classifier_conll03)

Description

RoBERTa Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks.

roberta_base_token_classifier_conll03 is a fine-tuned RoBERTa model that is ready to use for Named Entity Recognition and achieves state-of-the-art performance for the NER task. This model has been trained to recognize four types of entities: location (LOC), organizations (ORG), person (PER), and Miscellaneous (MISC).

We used TFRobertaForTokenClassification to train this model and used RoBertaForTokenClassification annotator in Spark NLP 🚀 for prediction at scale!

Predicted Entities

Download

How to use

document_assembler = DocumentAssembler() \
    .setInputCol('text') \
    .setOutputCol('document')

tokenizer = Tokenizer() \
    .setInputCols(['document']) \
    .setOutputCol('token')

tokenClassifier = RoBertaForTokenClassification \
      .pretrained('roberta_base_token_classifier_conll03', 'en') \
      .setInputCols(['token', 'document']) \
      .setOutputCol('ner') \
      .setCaseSensitive(True) \
      .setMaxSentenceLength(512)

# since output column is IOB/IOB2 style, NerConverter can extract entities
ner_converter = NerConverter() \
    .setInputCols(['document', 'token', 'ner']) \
    .setOutputCol('entities')

pipeline = Pipeline(stages=[
    document_assembler, 
    tokenizer,
    tokenClassifier,
    ner_converter
])

example = spark.createDataFrame([['My name is John!']]).toDF("text")
result = pipeline.fit(example).transform(example)
val document_assembler = DocumentAssembler() 
    .setInputCol("text") 
    .setOutputCol("document")

val tokenizer = Tokenizer() 
    .setInputCols("document") 
    .setOutputCol("token")

val tokenClassifier = RoBertaForTokenClassification.pretrained("roberta_base_token_classifier_conll03", "en")
      .setInputCols("document", "token")
      .setOutputCol("ner")
      .setCaseSensitive(true)
      .setMaxSentenceLength(512)

// since output column is IOB/IOB2 style, NerConverter can extract entities
val ner_converter = NerConverter() 
    .setInputCols("document", "token", "ner") 
    .setOutputCol("entities")

val pipeline = new Pipeline().setStages(Array(document_assembler, tokenizer, tokenClassifier, ner_converter))

val example = Seq.empty["My name is John!"].toDS.toDF("text")

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

Results

+------------------------------------------------------------------------------------+
 |result                                                                              |
 +------------------------------------------------------------------------------------+
 |[B-PER, I-PER, O, O, O, B-LOC, O, O, O, B-LOC, O, O, O, O, B-PER, O, O, O, O, B-LOC]|
 +------------------------------------------------------------------------------------+

Model Information

Model Name: roberta_base_token_classifier_conll03
Compatibility: Spark NLP 3.3.0+
License: Open Source
Edition: Official
Input Labels: [token, document]
Output Labels: [ner]
Language: en
Case sensitive: false
Max sentense length: 512

Data Source

https://www.clips.uantwerpen.be/conll2003/ner/

Benchmarking

        precision    recall  f1-score   support

       B-LOC       0.94      0.86      0.90      1837
       I-ORG       0.86      0.91      0.88       751
      I-MISC       0.85      0.76      0.80       346
       I-LOC       0.85      0.77      0.81       257
       I-PER       0.98      0.97      0.98      1307
      B-MISC       0.88      0.84      0.86       922
       B-ORG       0.85      0.91      0.88      1341
       B-PER       0.95      0.95      0.95      1842

   micro avg       0.91      0.90      0.91      8603
   macro avg       0.90      0.87      0.88      8603
weighted avg       0.92      0.90      0.91      8603



processed 51362 tokens with 5942 phrases; found: 5928 phrases; correct: 5270.
accuracy:  90.04%; (non-O)
accuracy:  98.07%; precision:  88.90%; recall:  88.69%; FB1:  88.80
              LOC: precision:  92.54%; recall:  85.08%; FB1:  88.66  1689
             MISC: precision:  83.48%; recall:  81.67%; FB1:  82.57  902
              ORG: precision:  81.28%; recall:  90.01%; FB1:  85.42  1485
              PER: precision:  94.33%; recall:  94.84%; FB1:  94.59  1852