Description
Pretrained RoBertaForTokenClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.bsc_bio_ehr_spanish_pharmaconer
is a Castilian, Spanish model originally trained by PlanTL-GOB-ES.
How to use
documentAssembler = DocumentAssembler() \
.setInputCol("text") \
.setOutputCol("document")
tokenizer = Tokenizer() \
.setInputCols(["document"]) \
.setOutputCol("token")
tokenClassifier = RoBertaForTokenClassification.pretrained("bsc_bio_ehr_spanish_pharmaconer","es") \
.setInputCols(["document","token"]) \
.setOutputCol("ner")
pipeline = Pipeline().setStages([documentAssembler, tokenizer, tokenClassifier])
pipelineModel = pipeline.fit(data)
pipelineDF = pipelineModel.transform(data)
val documentAssembler = new DocumentAssembler()
.setInputCol("text")
.setOutputCol("document")
val tokenizer = Tokenizer() \
.setInputCols(Array("document")) \
.setOutputCol("token")
val tokenClassifier = RoBertaForTokenClassification
.pretrained("bsc_bio_ehr_spanish_pharmaconer", "es")
.setInputCols(Array("document","token"))
.setOutputCol("ner")
val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, tokenClassifier))
val pipelineModel = pipeline.fit(data)
val pipelineDF = pipelineModel.transform(data)
Model Information
Model Name: | bsc_bio_ehr_spanish_pharmaconer |
Compatibility: | Spark NLP 5.2.1+ |
License: | Open Source |
Edition: | Official |
Input Labels: | [documents, token] |
Output Labels: | [ner] |
Language: | es |
Size: | 434.1 MB |
References
https://huggingface.co/PlanTL-GOB-ES/bsc-bio-ehr-es-pharmaconer