Description
Pretrained BertForTokenClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. BC4CHEMD-Modified_PubMedBERT
is a English model originally trained by ghadeermobasher
.
Predicted Entities
Chemical
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("bert_ner_BC4CHEMD_Modified_PubMedBERT","en") \
.setInputCols(["sentence", "token"]) \
.setOutputCol("pos")
pipeline = Pipeline(stages=[documentAssembler, sentenceDetector, 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 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("bert_ner_BC4CHEMD_Modified_PubMedBERT","en")
.setInputCols(Array("sentence", "token"))
.setOutputCol("pos")
val pipeline = new Pipeline().setStages(Array(documentAssembler,sentenceDetector, tokenizer, tokenClassifier))
val data = Seq("PUT YOUR STRING HERE").toDF("text")
val result = pipeline.fit(data).transform(data)
Model Information
Model Name: | bert_ner_BC4CHEMD_Modified_PubMedBERT |
Compatibility: | Spark NLP 4.0.0+ |
License: | Open Source |
Edition: | Official |
Input Labels: | [document, token] |
Output Labels: | [ner] |
Language: | en |
Size: | 408.7 MB |
Case sensitive: | true |
Max sentence length: | 128 |
References
- https://huggingface.co/ghadeermobasher/BC4CHEMD-Modified_PubMedBERT