Description
BERT 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.
bert_large_token_classifier_conll03 is a fine-tuned BERT 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 TFBertForTokenClassification to train this model and used BertForTokenClassification annotator in Spark NLP 🚀 for prediction at scale!
Predicted Entities
PER, LOC, ORG, MISC
How to use
document_assembler = DocumentAssembler() \
.setInputCol('text') \
.setOutputCol('document')
tokenizer = Tokenizer() \
.setInputCols(['document']) \
.setOutputCol('token')
tokenClassifier = BertForTokenClassification \
.pretrained('bert_large_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 = BertForTokenClassification.pretrained("bert_large_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)
import nlu
nlu.load("en.classify.token_bert.large_conll03").predict("""My name is John!""")
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: | bert_large_token_classifier_conll03 |
| Compatibility: | Spark NLP 3.2.0+ |
| License: | Open Source |
| Edition: | Official |
| Input Labels: | [token, document] |
| Output Labels: | [ner] |
| Language: | en |
| Case sensitive: | true |
| Max sentense length: | 512 |
Data Source
https://www.clips.uantwerpen.be/conll2003/ner/
Benchmarking
Dev:
precision recall f1-score support
B-LOC 0.96 0.90 0.93 1837
I-ORG 0.93 0.95 0.94 751
I-MISC 0.91 0.87 0.89 346
I-LOC 0.91 0.94 0.93 257
I-PER 0.99 0.98 0.99 1307
B-MISC 0.94 0.90 0.92 922
B-ORG 0.88 0.95 0.91 1341
B-PER 0.98 0.98 0.98 1842
micro avg 0.95 0.94 0.94 8603
macro avg 0.94 0.93 0.93 8603
weighted avg 0.95 0.94 0.94 8603
processed 51362 tokens with 5942 phrases; found: 5915 phrases; correct: 5497.
accuracy: 93.99%; (non-O)
accuracy: 98.80%; precision: 92.93%; recall: 92.51%; FB1: 92.72
LOC: precision: 95.25%; recall: 89.49%; FB1: 92.28 1726
MISC: precision: 90.35%; recall: 88.39%; FB1: 89.36 902
ORG: precision: 86.75%; recall: 93.21%; FB1: 89.86 1441
PER: precision: 96.86%; recall: 97.07%; FB1: 96.96 1846
Test:
precision recall f1-score support
B-LOC 0.93 0.89 0.91 1668
I-ORG 0.86 0.94 0.90 835
I-MISC 0.68 0.75 0.71 216
I-LOC 0.87 0.86 0.87 257
I-PER 0.98 0.98 0.98 1156
B-MISC 0.84 0.82 0.83 702
B-ORG 0.87 0.92 0.90 1661
B-PER 0.97 0.96 0.97 1617
micro avg 0.91 0.92 0.91 8112
macro avg 0.88 0.89 0.88 8112
weighted avg 0.91 0.92 0.92 8112
processed 46435 tokens with 5648 phrases; found: 5682 phrases; correct: 5104.
accuracy: 92.01%; (non-O)
accuracy: 98.09%; precision: 89.83%; recall: 90.37%; FB1: 90.10
LOC: precision: 92.39%; recall: 88.85%; FB1: 90.59 1604
MISC: precision: 79.75%; recall: 80.20%; FB1: 79.97 706
ORG: precision: 85.50%; recall: 91.27%; FB1: 88.29 1773
PER: precision: 96.50%; recall: 95.42%; FB1: 95.96 1599