Description
Pretrained RoBertaForTokenClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.nominal_groups_recognition_roberta_clinical_wl_spanish is a Castilian, Spanish model originally trained by bastianchinchon.
How to use
documentAssembler = DocumentAssembler() \
.setInputCol("text") \
.setOutputCol("document")
tokenizer = Tokenizer() \
.setInputCols(["document"]) \
.setOutputCol("token")
tokenClassifier = RoBertaForTokenClassification.pretrained("nominal_groups_recognition_roberta_clinical_wl_spanish","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("nominal_groups_recognition_roberta_clinical_wl_spanish", "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: | nominal_groups_recognition_roberta_clinical_wl_spanish |
| Compatibility: | Spark NLP 5.2.1+ |
| License: | Open Source |
| Edition: | Official |
| Input Labels: | [documents, token] |
| Output Labels: | [ner] |
| Language: | es |
| Size: | 469.7 MB |
References
https://huggingface.co/bastianchinchon/nominal-groups-recognition-roberta-clinical-wl-es