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_base_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_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 = BertForTokenClassification.pretrained("bert_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)
import nlu
nlu.load("en.classify.token_bert.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_base_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
Test:
precision recall f1-score support
B-LOC 0.94 0.90 0.92 1668
I-ORG 0.85 0.93 0.88 835
I-MISC 0.63 0.80 0.71 216
I-LOC 0.87 0.84 0.86 257
I-PER 0.98 0.98 0.98 1156
B-MISC 0.78 0.82 0.80 702
B-ORG 0.88 0.91 0.89 1661
B-PER 0.96 0.94 0.95 1617
micro avg 0.90 0.91 0.91 8112
macro avg 0.86 0.89 0.87 8112
weighted avg 0.90 0.91 0.91 8112
processed 46435 tokens with 5648 phrases; found: 5730 phrases; correct: 5050.
accuracy: 91.33%; (non-O)
accuracy: 97.83%; precision: 88.13%; recall: 89.41%; FB1: 88.77
LOC: precision: 92.57%; recall: 89.69%; FB1: 91.11 1616
MISC: precision: 71.92%; recall: 79.91%; FB1: 75.71 780
ORG: precision: 84.89%; recall: 88.92%; FB1: 86.86 1740
PER: precision: 95.11%; recall: 93.75%; FB1: 94.43 1594
Dev:
precision recall f1-score support
B-LOC 0.96 0.91 0.93 1837
I-ORG 0.90 0.94 0.92 751
I-MISC 0.83 0.84 0.84 346
I-LOC 0.92 0.93 0.93 257
I-PER 0.99 0.98 0.98 1307
B-MISC 0.88 0.90 0.89 922
B-ORG 0.90 0.92 0.91 1341
B-PER 0.97 0.97 0.97 1842
micro avg 0.94 0.93 0.93 8603
macro avg 0.92 0.92 0.92 8603
weighted avg 0.94 0.93 0.93 8603
processed 51362 tokens with 5942 phrases; found: 5961 phrases; correct: 5457.
accuracy: 93.33%; (non-O)
accuracy: 98.64%; precision: 91.55%; recall: 91.84%; FB1: 91.69
LOC: precision: 95.09%; recall: 90.64%; FB1: 92.81 1751
MISC: precision: 83.45%; recall: 87.53%; FB1: 85.44 967
ORG: precision: 86.43%; recall: 90.75%; FB1: 88.54 1408
PER: precision: 96.35%; recall: 95.98%; FB1: 96.17 1835