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_ontonote 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: CARDINAL, DATE, EVENT, FAC, GPE, LANGUAGE, LAW, LOC, MONEY, NORP, ORDINAL, ORG, PERCENT, PERSON, PRODUCT, QUANTITY, TIME, and WORK_OF_ART.
We used TFBertForTokenClassification to train this model and used BertForTokenClassification annotator in Spark NLP 🚀 for prediction at scale!
Predicted Entities
CARDINAL, DATE, EVENT, FAC, GPE, LANGUAGE, LAW, LOC, MONEY, NORP, ORDINAL, ORG, PERCENT, PERSON, PRODUCT, QUANTITY, TIME, WORK_OF_ART
How to use
document_assembler = DocumentAssembler() \
.setInputCol('text') \
.setOutputCol('document')
tokenizer = Tokenizer() \
.setInputCols(['document']) \
.setOutputCol('token')
tokenClassifier = BertForTokenClassification \
.pretrained('bert_base_token_classifier_ontonote', '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_ontonote", "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.ontonote").predict("""My name is John!""")
Results
+------------------------------------------------------------------------------------+
|result |
+------------------------------------------------------------------------------------+
|[B-PERSON, I-PERSON, O, O, O, B-LOC, O, O, O, B-LOC, O, O, O, O, B-PERSON, O, O, O, O, B-LOC]|
+------------------------------------------------------------------------------------+
Model Information
| Model Name: | bert_base_token_classifier_ontonote |
| 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://catalog.ldc.upenn.edu/LDC2013T19
Benchmarking
Test:
precision recall f1-score support
B-CARDINAL 0.86 0.88 0.87 935
B-DATE 0.88 0.90 0.89 1602
B-EVENT 0.71 0.62 0.66 63
B-FAC 0.75 0.76 0.76 135
B-GPE 0.97 0.91 0.94 2240
B-LANGUAGE 0.79 0.68 0.73 22
B-LAW 0.76 0.65 0.70 40
B-LOC 0.78 0.83 0.80 179
B-MONEY 0.88 0.90 0.89 314
B-NORP 0.92 0.96 0.94 841
B-ORDINAL 0.81 0.93 0.87 195
B-ORG 0.87 0.89 0.88 1795
B-PERCENT 0.92 0.95 0.93 349
B-PERSON 0.96 0.95 0.95 1988
B-PRODUCT 0.75 0.78 0.76 76
B-QUANTITY 0.81 0.82 0.82 105
B-TIME 0.69 0.70 0.69 212
B-WORK_OF_ART 0.66 0.74 0.70 166
I-CARDINAL 0.83 0.88 0.86 331
I-DATE 0.89 0.92 0.90 2011
I-EVENT 0.70 0.71 0.70 130
I-FAC 0.82 0.85 0.84 213
I-GPE 0.96 0.89 0.93 628
I-LAW 0.85 0.65 0.74 106
I-LOC 0.85 0.84 0.84 180
I-MONEY 0.94 0.96 0.95 685
I-NORP 0.99 0.79 0.88 160
I-ORDINAL 0.00 0.00 0.00 4
I-ORG 0.91 0.93 0.92 2406
I-PERCENT 0.95 0.95 0.95 523
I-PERSON 0.96 0.96 0.96 1412
I-PRODUCT 0.81 0.80 0.80 69
I-QUANTITY 0.83 0.92 0.87 206
I-TIME 0.71 0.77 0.74 255
I-WORK_OF_ART 0.71 0.66 0.68 337
O 0.99 0.99 0.99 131815
accuracy 0.98 152728
macro avg 0.82 0.81 0.82 152728
weighted avg 0.98 0.98 0.98 152728
processed 152728 tokens with 11257 phrases; found: 11537 phrases; correct: 9906.
accuracy: 90.22%; (non-O)
accuracy: 98.00%; precision: 85.86%; recall: 88.00%; FB1: 86.92
CARDINAL: precision: 83.35%; recall: 86.20%; FB1: 84.75 967
DATE: precision: 82.23%; recall: 86.95%; FB1: 84.53 1694
EVENT: precision: 58.06%; recall: 57.14%; FB1: 57.60 62
FAC: precision: 68.67%; recall: 76.30%; FB1: 72.28 150
GPE: precision: 95.59%; recall: 90.00%; FB1: 92.71 2109
LANGUAGE: precision: 78.95%; recall: 68.18%; FB1: 73.17 19
LAW: precision: 63.16%; recall: 60.00%; FB1: 61.54 38
LOC: precision: 71.29%; recall: 80.45%; FB1: 75.59 202
MONEY: precision: 85.40%; recall: 87.58%; FB1: 86.48 322
NORP: precision: 89.82%; recall: 93.34%; FB1: 91.55 874
ORDINAL: precision: 81.17%; recall: 92.82%; FB1: 86.60 223
ORG: precision: 82.80%; recall: 86.35%; FB1: 84.54 1872
PERCENT: precision: 86.23%; recall: 89.68%; FB1: 87.92 363
PERSON: precision: 93.93%; recall: 94.22%; FB1: 94.07 1994
PRODUCT: precision: 70.37%; recall: 75.00%; FB1: 72.61 81
QUANTITY: precision: 73.28%; recall: 80.95%; FB1: 76.92 116
TIME: precision: 58.02%; recall: 66.51%; FB1: 61.98 243
WORK_OF_ART: precision: 52.40%; recall: 65.66%; FB1: 58.29 208