Named Entity Recognition - CoNLL03 RoBERTa (ner_conll_roberta_base)

Description

ner_conll_roberta_base is a Named Entity Recognition (or NER) model, meaning it annotates text to find features like the names of people, places, and organizations. It was trained on the CoNLL 2003 text corpus. This NER model does not read words directly but instead reads word embeddings, which represent words as points such that more semantically similar words are closer together.ner_conll_roberta_base model is trained withroberta_base word embeddings, so be sure to use the same embeddings in the pipeline.

Predicted Entities

PER, LOC, ORG, MISC

Live Demo Open in Colab Download

How to use

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

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

embeddings = RoBertaEmbeddings\
      .pretrained('roberta_base', 'en')\
      .setInputCols(["token", "document"])\
      .setOutputCol("embeddings")

ner_model = NerDLModel.pretrained('ner_conll_roberta_base', 'en') \
    .setInputCols(['document', 'token', 'embeddings']) \
    .setOutputCol('ner')

ner_converter = NerConverter() \
    .setInputCols(['document', 'token', 'ner']) \
    .setOutputCol('entities')

pipeline = Pipeline(stages=[
    document_assembler, 
    tokenizer,
    embeddings,
    ner_model,
    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 embeddings = RoBertaEmbeddings.pretrained("roberta_base", "en")
    .setInputCols("document", "token") 
    .setOutputCol("embeddings")

val ner_model = NerDLModel.pretrained("ner_conll_roberta_base", "en") 
    .setInputCols("document"', "token", "embeddings") 
    .setOutputCol("ner")

val ner_converter = NerConverter() 
    .setInputCols("document", "token", "ner") 
    .setOutputCol("entities")

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

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

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

text = ["My name is John!"]

ner_df = nlu.load('en.ner.ner_conll_roberta_base').predict(text, output_level='token')

Model Information

Model Name: ner_conll_roberta_base
Type: ner
Compatibility: Spark NLP 3.2.0+
License: Open Source
Edition: Official
Input Labels: [sentence, token, embeddings]
Output Labels: [ner]
Language: en

Data Source

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

Benchmarking

Test:

          precision    recall  f1-score   support

       B-LOC       0.90      0.92      0.91      1668
       I-ORG       0.85      0.90      0.87       835
      I-MISC       0.61      0.73      0.67       216
       I-LOC       0.80      0.87      0.84       257
       I-PER       0.98      0.98      0.98      1156
      B-MISC       0.82      0.81      0.82       702
       B-ORG       0.87      0.88      0.87      1661
       B-PER       0.95      0.94      0.94      1617

   micro avg       0.89      0.90      0.90      8112
   macro avg       0.85      0.88      0.86      8112
weighted avg       0.89      0.90      0.90      8112

processed 46435 tokens with 5648 phrases; found: 5675 phrases; correct: 5027.
accuracy:  90.50%; (non-O)
accuracy:  97.69%; precision:  88.58%; recall:  89.00%; FB1:  88.79
              LOC: precision:  89.89%; recall:  91.13%; FB1:  90.50  1691
             MISC: precision:  78.86%; recall:  78.63%; FB1:  78.74  700
              ORG: precision:  85.65%; recall:  86.27%; FB1:  85.96  1673
              PER: precision:  94.48%; recall:  94.12%; FB1:  94.30  1611

Dev:

        precision    recall  f1-score   support

       B-LOC       0.94      0.96      0.95      1837
       I-ORG       0.93      0.91      0.92       751
      I-MISC       0.86      0.85      0.85       346
       I-LOC       0.94      0.92      0.93       257
       I-PER       0.98      0.97      0.98      1307
      B-MISC       0.92      0.88      0.90       922
       B-ORG       0.91      0.93      0.92      1341
       B-PER       0.96      0.97      0.97      1842

   micro avg       0.94      0.94      0.94      8603
   macro avg       0.93      0.92      0.93      8603
weighted avg       0.94      0.94      0.94      8603

processed 51362 tokens with 5942 phrases; found: 5959 phrases; correct: 5544.
accuracy:  93.94%; (non-O)
accuracy:  98.76%; precision:  93.04%; recall:  93.30%; FB1:  93.17
              LOC: precision:  94.35%; recall:  95.48%; FB1:  94.91  1859
             MISC: precision:  90.19%; recall:  86.77%; FB1:  88.45  887
              ORG: precision:  89.24%; recall:  90.90%; FB1:  90.06  1366
              PER: precision:  95.89%; recall:  96.15%; FB1:  96.02  1847