Description
This model has been pulled from the HF Hub - https://huggingface.co/PlanTL-GOB-ES/roberta-base-biomedical-clinical-es
This is a result of reproducing the tutorial for bringing HF’s models into Spark NLP - https://medium.com/spark-nlp/importing-huggingface-models-into-sparknlp-8c63bdea671d
Predicted Entities
How to use
documentAssembler = DocumentAssembler()\
.setInputCol("term")\
.setOutputCol("document")
tokenizer = Tokenizer()\
.setInputCols("document")\
.setOutputCol("token")
roberta_embeddings = RoBertaEmbeddings.pretrained("roberta_base_biomedical", "es", "@ireneisdoomed")\
.setInputCols(["document", "token"])\
.setOutputCol("roberta_embeddings")
pipeline = Pipeline(stages = [
documentAssembler,
tokenizer,
roberta_embeddings])
import nlu
nlu.load("es.embed.roberta_base_biomedical").predict("""Put your text here.""")
Model Information
| Model Name: | roberta_base_biomedical |
| Compatibility: | Spark NLP 3.4.0+ |
| License: | Open Source |
| Edition: | Community |
| Input Labels: | [document, token] |
| Output Labels: | [embeddings] |
| Language: | es |
| Size: | 301.7 MB |