Description
ner_ontonotes_roberta_large is a Named Entity Recognition (or NER) model trained on OntoNotes 5.0. It can extract up to 18 entities such as people, places, organizations, money, time, date, etc.
This model uses the pretrained roberta_large model from the RoBertaEmbeddings annotator as an input.
Predicted Entities
CARDINAL, DATE, EVENT, FAC, GPE, LANGUAGE, LAW, LOC, MONEY, NORP, ORDINAL, ORG, PERCENT, PERSON, PRODUCT, QUANTITY, TIME, WORK_OF_ART
Live Demo Open in Colab Download Copy S3 URI
How to use
document_assembler = DocumentAssembler() \
.setInputCol('text') \
.setOutputCol('document')
tokenizer = Tokenizer() \
.setInputCols(['document']) \
.setOutputCol('token')
embeddings = RoBertaEmbeddings\
.pretrained('roberta_large', 'en')\
.setInputCols(["token", "document"])\
.setOutputCol("embeddings")
ner_model = NerDLModel.pretrained('ner_ontonotes_roberta_large', 'en') \
.setInputCols(['document', 'token', 'embeddings']) \
.setOutputCol('ner')
ner_converter = NerConverter() \
.setInputCols(['document', 'token', 'ner']) \
.setOutputCol('entities')
pipeline = Pipeline(stages=[
document_assembler,
tokenizer,
embeddings,
ner_model,
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 embeddings = RoBertaEmbeddings.pretrained("roberta_large", "en")
.setInputCols("document", "token")
.setOutputCol("embeddings")
val ner_model = NerDLModel.pretrained("ner_ontonotes_roberta_large", "en")
.setInputCols("document"', "token", "embeddings")
.setOutputCol("ner")
val ner_converter = NerConverter()
.setInputCols("document", "token", "ner")
.setOutputCol("entities")
val pipeline = new Pipeline().setStages(Array(document_assembler, tokenizer, embeddings, ner_model, ner_converter))
val example = Seq.empty["My name is John!"].toDS.toDF("text")
val result = pipeline.fit(example).transform(example)
import nlu
text = ["My name is John!"]
ner_df = nlu.load('en.ner.ner_ontonotes_roberta_large').predict(text, output_level='token')
Model Information
| Model Name: | ner_ontonotes_roberta_large |
| Type: | ner |
| Compatibility: | Spark NLP 3.2.0+ |
| License: | Open Source |
| Edition: | Official |
| Input Labels: | [sentence, token, embeddings] |
| Output Labels: | [ner] |
| Language: | en |
Data Source
https://catalog.ldc.upenn.edu/LDC2013T19
Benchmarking
precision recall f1-score support
B-CARDINAL 0.86 0.84 0.85 935
B-DATE 0.86 0.90 0.88 1602
B-EVENT 0.68 0.67 0.67 63
B-FAC 0.67 0.68 0.67 135
B-GPE 0.96 0.96 0.96 2240
B-LANGUAGE 0.92 0.50 0.65 22
B-LAW 0.68 0.62 0.65 40
B-LOC 0.88 0.75 0.81 179
B-MONEY 0.88 0.91 0.90 314
B-NORP 0.93 0.93 0.93 841
B-ORDINAL 0.82 0.90 0.86 195
B-ORG 0.90 0.91 0.91 1795
B-PERCENT 0.94 0.92 0.93 349
B-PERSON 0.95 0.94 0.94 1988
B-PRODUCT 0.83 0.63 0.72 76
B-QUANTITY 0.77 0.80 0.79 105
B-TIME 0.68 0.71 0.69 212
B-WORK_OF_ART 0.60 0.62 0.61 166
I-CARDINAL 0.81 0.81 0.81 331
I-DATE 0.86 0.94 0.90 2011
I-EVENT 0.78 0.81 0.80 130
I-FAC 0.68 0.83 0.74 213
I-GPE 0.94 0.89 0.91 628
I-LAW 0.87 0.64 0.74 106
I-LOC 0.93 0.71 0.81 180
I-MONEY 0.92 0.97 0.95 685
I-NORP 0.98 0.72 0.83 160
I-ORDINAL 0.00 0.00 0.00 4
I-ORG 0.91 0.94 0.92 2406
I-PERCENT 0.95 0.95 0.95 523
I-PERSON 0.96 0.94 0.95 1412
I-PRODUCT 0.89 0.74 0.81 69
I-QUANTITY 0.84 0.90 0.87 206
I-TIME 0.68 0.75 0.72 255
I-WORK_OF_ART 0.66 0.64 0.65 337
O 0.99 0.99 0.99 131815
accuracy 0.98 152728
macro avg 0.82 0.79 0.80 152728
weighted avg 0.98 0.98 0.98 152728
processed 152728 tokens with 11257 phrases; found: 11320 phrases; correct: 9995.
accuracy: 90.20%; (non-O)
accuracy: 97.94%; precision: 88.30%; recall: 88.79%; FB1: 88.54
CARDINAL: precision: 84.54%; recall: 82.46%; FB1: 83.49 912
DATE: precision: 84.12%; recall: 87.95%; FB1: 85.99 1675
EVENT: precision: 66.13%; recall: 65.08%; FB1: 65.60 62
FAC: precision: 65.94%; recall: 67.41%; FB1: 66.67 138
GPE: precision: 95.70%; recall: 95.40%; FB1: 95.55 2233
LANGUAGE: precision: 91.67%; recall: 50.00%; FB1: 64.71 12
LAW: precision: 64.86%; recall: 60.00%; FB1: 62.34 37
LOC: precision: 86.84%; recall: 73.74%; FB1: 79.76 152
MONEY: precision: 87.38%; recall: 90.45%; FB1: 88.89 325
NORP: precision: 92.18%; recall: 92.51%; FB1: 92.34 844
ORDINAL: precision: 81.86%; recall: 90.26%; FB1: 85.85 215
ORG: precision: 87.58%; recall: 89.19%; FB1: 88.38 1828
PERCENT: precision: 90.67%; recall: 89.11%; FB1: 89.88 343
PERSON: precision: 93.90%; recall: 93.61%; FB1: 93.75 1982
PRODUCT: precision: 82.76%; recall: 63.16%; FB1: 71.64 58
QUANTITY: precision: 76.15%; recall: 79.05%; FB1: 77.57 109
TIME: precision: 63.68%; recall: 66.98%; FB1: 65.29 223
WORK_OF_ART: precision: 55.23%; recall: 57.23%; FB1: 56.21 172