Description
Pretrained RobertaForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. codebert-base-finetuned-detect-insecure-code is a English model originally trained by mrm8488.
Predicted Entities
How to use
documentAssembler = DocumentAssembler() \
.setInputCol("text") \
.setOutputCol("document")
tokenizer = Tokenizer() \
.setInputCols("document") \
.setOutputCol("token")
classifier = RoBertaForSequenceClassification.pretrained("roberta_sequence_classifier_codebert_base_finetuned_detect_insecure_cod","en") \
.setInputCols(["document", "token"]) \
.setOutputCol("class")
pipeline = Pipeline(stages=[documentAssembler, tokenizer, 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 classifer = RoBertaForSequenceClassification.pretrained("roberta_sequence_classifier_codebert_base_finetuned_detect_insecure_cod","en")
.setInputCols(Array("document", "token"))
.setOutputCol("class")
val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, classifier))
val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text")
val result = pipeline.fit(data).transform(data)
import nlu
nlu.load("en.classify.roberta.base_finetuned").predict("""PUT YOUR STRING HERE""")
Model Information
| Model Name: | roberta_sequence_classifier_codebert_base_finetuned_detect_insecure_cod |
| Compatibility: | Spark NLP 5.2.0+ |
| License: | Open Source |
| Edition: | Official |
| Input Labels: | [document, token] |
| Output Labels: | [class] |
| Language: | en |
| Size: | 468.3 MB |
| Case sensitive: | true |
| Max sentence length: | 256 |
References
References
- https://huggingface.co/mrm8488/codebert-base-finetuned-detect-insecure-code
- https://github.com/microsoft/CodeXGLUE/tree/main/Code-Code/Defect-detection
- https://arxiv.org/pdf/1907.11692.pdf
- https://arxiv.org/pdf/2002.08155.pdf