Description
DistilBERT 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.
distilbert_base_token_classifier_conll03 is a fine-tuned DistilBERT 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 TFDistilBertForTokenClassification to train this model and used DistilBertForTokenClassification 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 = DistilBertForTokenClassification \
.pretrained('distilbert_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 = DistilBertForTokenClassification.pretrained("distilbert_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.ner.distil_bert.conll.base").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: | distilbert_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
Dev:
precision recall f1-score support
B-LOC 0.95 0.88 0.91 1837
B-MISC 0.85 0.86 0.85 922
B-ORG 0.86 0.90 0.88 1341
B-PER 0.96 0.96 0.96 1842
I-LOC 0.87 0.91 0.89 257
I-MISC 0.80 0.80 0.80 346
I-ORG 0.87 0.92 0.90 751
I-PER 0.98 0.97 0.98 1307
O 1.00 1.00 1.00 42759
accuracy 0.98 51362
macro avg 0.90 0.91 0.91 51362
weighted avg 0.98 0.98 0.98 51362
processed 51362 tokens with 5942 phrases; found: 5973 phrases; correct: 5306.
accuracy: 91.36%; (non-O)
accuracy: 98.20%; precision: 88.83%; recall: 89.30%; FB1: 89.06
LOC: precision: 94.11%; recall: 87.81%; FB1: 90.85 1714
MISC: precision: 78.38%; recall: 81.78%; FB1: 80.04 962
ORG: precision: 82.01%; recall: 88.07%; FB1: 84.93 1440
PER: precision: 94.67%; recall: 95.44%; FB1: 95.05 1857
Test:
precision recall f1-score support
B-LOC 0.93 0.85 0.89 1668
B-MISC 0.77 0.78 0.78 702
B-ORG 0.81 0.89 0.85 1661
B-PER 0.95 0.93 0.94 1617
I-LOC 0.80 0.76 0.78 257
I-MISC 0.60 0.69 0.64 216
I-ORG 0.80 0.92 0.86 835
I-PER 0.98 0.98 0.98 1156
O 0.99 0.99 0.99 38323
accuracy 0.97 46435
macro avg 0.85 0.87 0.86 46435
weighted avg 0.97 0.97 0.97 46435
processed 46435 tokens with 5648 phrases; found: 5738 phrases; correct: 4864.
accuracy: 88.52%; (non-O)
accuracy: 97.24%; precision: 84.77%; recall: 86.12%; FB1: 85.44
LOC: precision: 91.36%; recall: 84.29%; FB1: 87.68 1539
MISC: precision: 70.60%; recall: 75.93%; FB1: 73.16 755
ORG: precision: 77.29%; recall: 86.27%; FB1: 81.54 1854
PER: precision: 93.84%; recall: 92.27%; FB1: 93.05 1590