Description
Pretrained RobertaForTokenClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. Spanish_disease_finder is a Spanish model originally trained by chizhikchi.
Predicted Entities
DISEASE
How to use
documentAssembler = DocumentAssembler() \
.setInputCol("text") \
.setOutputCol("document")
sentenceDetector = SentenceDetectorDLModel.pretrained("sentence_detector_dl", "xx")\
.setInputCols(["document"])\
.setOutputCol("sentence")
tokenizer = Tokenizer() \
.setInputCols("sentence") \
.setOutputCol("token")
tokenClassifier = BertForTokenClassification.pretrained("roberta_ner_Spanish_disease_finder","es") \
.setInputCols(["sentence", "token"]) \
.setOutputCol("ner")
pipeline = Pipeline(stages=[documentAssembler, sentenceDetector, tokenizer, tokenClassifier])
data = spark.createDataFrame([["Amo Spark NLP"]]).toDF("text")
result = pipeline.fit(data).transform(data)
val documentAssembler = new DocumentAssembler()
.setInputCol("text")
.setOutputCol("document")
val sentenceDetector = SentenceDetectorDLModel.pretrained("sentence_detector_dl", "xx")
.setInputCols(Array("document"))
.setOutputCol("sentence")
val tokenizer = new Tokenizer()
.setInputCols(Array("sentence"))
.setOutputCol("token")
val tokenClassifier = BertForTokenClassification.pretrained("roberta_ner_Spanish_disease_finder","es")
.setInputCols(Array("sentence", "token"))
.setOutputCol("ner")
val pipeline = new Pipeline().setStages(Array(documentAssembler,sentenceDetector, tokenizer, tokenClassifier))
val data = Seq("Amo Spark NLP").toDF("text")
val result = pipeline.fit(data).transform(data)
Model Information
| Model Name: | roberta_ner_Spanish_disease_finder |
| Compatibility: | Spark NLP 4.1.0+ |
| License: | Open Source |
| Edition: | Official |
| Input Labels: | [document, token] |
| Output Labels: | [ner] |
| Language: | es |
| Size: | 434.1 MB |
| Case sensitive: | true |
| Max sentence length: | 128 |
References
- https://huggingface.co/chizhikchi/Spanish_disease_finder
- https://temu.bsc.es/distemist/
- http://participants-area.bioasq.org/results/DisTEMIST/
- http://www.dei.unipd.it/~ferro/CLEF-WN-Drafts/CLEF2022/paper-17.pdf