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.
distilroberta_base_token_classifier_ontonotes 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: CARDINAL, DATE, EVENT, FAC, GPE, LANGUAGE, LAW, LOC, MONEY, NORP, ORDINAL, ORG, PERCENT, PERSON, PRODUCT, QUANTITY, TIME, and WORK_OF_ART.
We used TFRobertaForTokenClassification to train this model and used RoBertaForTokenClassification 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 = RoBertaForTokenClassification \
.pretrained('distilroberta_base_token_classifier_ontonotes', '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("distilroberta_base_token_classifier_ontonotes", "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_distilroberta_base_token_classifier_ontonotes").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: | distilroberta_base_token_classifier_ontonotes |
| Compatibility: | Spark NLP 3.3.0+ |
| 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
B-CARDINAL 0.82 0.86 0.84 935
B-DATE 0.86 0.85 0.85 1602
B-EVENT 0.69 0.56 0.61 63
B-FAC 0.66 0.59 0.62 135
B-GPE 0.94 0.89 0.91 2240
B-LANGUAGE 0.91 0.45 0.61 22
B-LAW 0.91 0.53 0.67 40
B-LOC 0.69 0.71 0.70 179
B-MONEY 0.86 0.89 0.87 314
B-NORP 0.84 0.87 0.85 841
B-ORDINAL 0.81 0.89 0.85 195
B-ORG 0.85 0.83 0.84 1795
B-PERCENT 0.92 0.92 0.92 349
B-PERSON 0.92 0.93 0.93 1988
B-PRODUCT 0.64 0.64 0.64 76
B-QUANTITY 0.73 0.76 0.74 105
B-TIME 0.71 0.54 0.61 212
B-WORK_OF_ART 0.72 0.52 0.61 166
I-CARDINAL 0.82 0.77 0.80 331
I-DATE 0.87 0.88 0.88 2011
I-EVENT 0.69 0.79 0.74 130
I-FAC 0.72 0.73 0.73 213
I-GPE 0.90 0.77 0.83 628
I-LAW 0.98 0.60 0.75 106
I-LOC 0.79 0.68 0.73 180
I-MONEY 0.92 0.96 0.94 685
I-NORP 0.86 0.57 0.68 160
I-ORDINAL 0.00 0.00 0.00 4
I-ORG 0.89 0.92 0.90 2406
I-PERCENT 0.93 0.96 0.94 523
I-PERSON 0.94 0.92 0.93 1412
I-PRODUCT 0.69 0.71 0.70 69
I-QUANTITY 0.79 0.87 0.82 206
I-TIME 0.73 0.78 0.75 255
I-WORK_OF_ART 0.70 0.57 0.63 337
O 0.99 0.99 0.99 131815
accuracy 0.97 152728
macro avg 0.80 0.74 0.76 152728
weighted avg 0.97 0.97 0.97 152728
processed 152728 tokens with 11257 phrases; found: 11382 phrases; correct: 9305.
accuracy: 85.75%; (non-O)
accuracy: 97.36%; precision: 81.75%; recall: 82.66%; FB1: 82.20
CARDINAL: precision: 79.09%; recall: 83.74%; FB1: 81.35 990
DATE: precision: 78.48%; recall: 81.02%; FB1: 79.73 1654
EVENT: precision: 57.14%; recall: 57.14%; FB1: 57.14 63
FAC: precision: 58.52%; recall: 58.52%; FB1: 58.52 135
GPE: precision: 90.96%; recall: 86.21%; FB1: 88.52 2123
LANGUAGE: precision: 90.91%; recall: 45.45%; FB1: 60.61 11
LAW: precision: 68.97%; recall: 50.00%; FB1: 57.97 29
LOC: precision: 63.30%; recall: 66.48%; FB1: 64.85 188
MONEY: precision: 78.90%; recall: 86.94%; FB1: 82.73 346
NORP: precision: 82.29%; recall: 86.21%; FB1: 84.20 881
ORDINAL: precision: 80.84%; recall: 88.72%; FB1: 84.60 214
ORG: precision: 78.98%; recall: 79.55%; FB1: 79.27 1808
PERCENT: precision: 85.99%; recall: 87.97%; FB1: 86.97 357
PERSON: precision: 88.43%; recall: 90.74%; FB1: 89.57 2040
PRODUCT: precision: 58.97%; recall: 60.53%; FB1: 59.74 78
QUANTITY: precision: 57.14%; recall: 72.38%; FB1: 63.87 133
TIME: precision: 59.44%; recall: 50.47%; FB1: 54.59 180
WORK_OF_ART: precision: 59.21%; recall: 54.22%; FB1: 56.60 152