Description
Pretrained BertForTokenClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. bert-base-swedish-cased-ner
is a Swedish model originally trained by KBLab
.
Predicted Entities
PER
, LOC
, TME
, WRK
, PRS/WRK
, LOC/ORG
, MSR
, ORG
, OBJ/ORG
, ORG/PRS
, OBJ
, LOC/PRS
, EVN
How to use
documentAssembler = DocumentAssembler() \
.setInputCol("text") \
.setOutputCol("document")
tokenizer = Tokenizer() \
.setInputCols("document") \
.setOutputCol("token")
tokenClassifier = BertForTokenClassification.pretrained("bert_token_classifier_base_swedish_cased_ner","sv") \
.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_base_swedish_cased_ner","sv")
.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_base_swedish_cased_ner |
Compatibility: | Spark NLP 4.3.1+ |
License: | Open Source |
Edition: | Official |
Input Labels: | [document, token] |
Output Labels: | [ner] |
Language: | sv |
Size: | 465.9 MB |
Case sensitive: | true |
Max sentence length: | 128 |
References
- https://huggingface.co/KBLab/bert-base-swedish-cased-ner