Description
Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. coronabert
is a English model originally trained by jakelever
.
Predicted Entities
Review
, Imaging
, Non-medical
, Medical Devices
, Transmission
, Misinformation
, Prevention
, Infection Reports
, Contact Tracing
, Effect on Medical Specialties
, Psychology
, Meta-analysis
, Drug Targets
, Model Systems & Tools
, Education
, Communication
, Forecasting & Modelling
, Diagnostics
, Healthcare Workers
, Comment/Editorial
, Recommendations
, Non-human
, Pediatrics
, Immunology
, Prevalence
, Molecular Biology
, Therapeutics
, Clinical Reports
, Health Policy
, Vaccines
, News
, Inequality
, Long Haul
, Surveillance
, Risk Factors
How to use
documentAssembler = DocumentAssembler() \
.setInputCol("text") \
.setOutputCol("document")
tokenizer = Tokenizer() \
.setInputCols("document") \
.setOutputCol("token")
seq_classifier = BertForSequenceClassification.pretrained("bert_classifier_coronabert","en") \
.setInputCols(["document", "token"]) \
.setOutputCol("class")
pipeline = Pipeline(stages=[documentAssembler, tokenizer, seq_classifier])
data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text")
result = pipeline.fit(data).transform(data)
val documentAssembler = new DocumentAssembler()
.setInputCols(Array("text"))
.setOutputCols(Array("document"))
val tokenizer = new Tokenizer()
.setInputCols("document")
.setOutputCol("token")
val seq_classifier = BertForSequenceClassification.pretrained("bert_classifier_coronabert","en")
.setInputCols(Array("document", "token"))
.setOutputCol("class")
val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, seq_classifier))
val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text")
val result = pipeline.fit(data).transform(data)
import nlu
nlu.load("en.classify.bert.cord19.").predict("""PUT YOUR STRING HERE""")
Model Information
Model Name: | bert_classifier_coronabert |
Compatibility: | Spark NLP 4.1.0+ |
License: | Open Source |
Edition: | Official |
Input Labels: | [document, token] |
Output Labels: | [class] |
Language: | en |
Size: | 411.1 MB |
Case sensitive: | true |
Max sentence length: | 256 |
References
- https://huggingface.co/jakelever/coronabert
- https://coronacentral.ai
- https://github.com/jakelever/corona-ml
- https://doi.org/10.1101/2020.12.21.423860