Description
Pretrained BertForTokenClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. EstBERT_NER_v2
is a Estonian model originally trained by tartuNLP
.
Predicted Entities
TIME
, ORG
, MONEY
, PER
, GPE
, DATE
, PERCENT
, TITLE
, LOC
, EVENT
, PROD
How to use
documentAssembler = DocumentAssembler() \
.setInputCol("text") \
.setOutputCol("document")
tokenizer = Tokenizer() \
.setInputCols("document") \
.setOutputCol("token")
tokenClassifier = BertForTokenClassification.pretrained("bert_token_classifier_est_ner_v2","et") \
.setInputCols(["document", "token"]) \
.setOutputCol("ner")
pipeline = Pipeline(stages=[documentAssembler, tokenizer, tokenClassifier])
data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text")
result = pipeline.fit(data).transform(data)
val documentAssembler = new DocumentAssembler()
.setInputCol("text")
.setOutputCol("document")
val tokenizer = new Tokenizer()
.setInputCols("document")
.setOutputCol("token")
val tokenClassifier = BertForTokenClassification.pretrained("bert_token_classifier_est_ner_v2","et")
.setInputCols(Array("document", "token"))
.setOutputCol("ner")
val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, tokenClassifier))
val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text")
val result = pipeline.fit(data).transform(data)
Model Information
Model Name: | bert_token_classifier_est_ner_v2 |
Compatibility: | Spark NLP 4.3.1+ |
License: | Open Source |
Edition: | Official |
Input Labels: | [document, token] |
Output Labels: | [ner] |
Language: | et |
Size: | 464.1 MB |
Case sensitive: | true |
Max sentence length: | 128 |
References
- https://huggingface.co/tartuNLP/EstBERT_NER_v2
- https://metashare.ut.ee/repository/browse/reannotated-estonian-ner-corpus/bd43f1f614a511eca6e4fa163e9d45477d086613d2894fd5af79bf13e3f13594/
- https://metashare.ut.ee/repository/browse/new-estonian-ner-corpus/98b6706c963c11eba6e4fa163e9d45470bcd0533b6994c93ab8b8c628516ffed/