Description
Pretrained DistilBertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.distilbert_base_uncased_finetuned_requirement_classification is a English model originally trained by dadi.
How to use
document_assembler = DocumentAssembler()\
.setInputCol("text")\
.setOutputCol("document")
tokenizer = Tokenizer()\
.setInputCols("document")\
.setOutputCol("token")
sequenceClassifier = DistilBertForSequenceClassification.pretrained("distilbert_base_uncased_finetuned_requirement_classification","en")\
.setInputCols(["document","token"])\
.setOutputCol("class")
pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier])
data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text")
result = pipeline.fit(data).transform(data)
val document_assembler = new DocumentAssembler()
.setInputCol("text")
.setOutputCol("document")
val tokenizer = new Tokenizer()
.setInputCols("document")
.setOutputCol("token")
val sequenceClassifier = DistilBertForSequenceClassification.pretrained("distilbert_base_uncased_finetuned_requirement_classification","en")
.setInputCols(Array("document","token"))
.setOutputCol("class")
val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier))
val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text")
val result = pipeline.fit(data).transform(data)
Model Information
| Model Name: | distilbert_base_uncased_finetuned_requirement_classification |
| Compatibility: | Spark NLP 5.2.0+ |
| License: | Open Source |
| Edition: | Official |
| Input Labels: | [documents, token] |
| Output Labels: | [class] |
| Language: | en |
| Size: | 249.5 MB |
References
https://huggingface.co/dadi/distilbert-base-uncased-finetuned-requirement-classification