Description
Pretrained XlmRobertaForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. deoffxlmr-mono-malyalam is a Malayalam model originally trained by Hate-speech-CNERG.
Predicted Entities
Not_offensive, Off_target_group, Profanity, Off_target_ind, Not_in_intended_language
How to use
documentAssembler = DocumentAssembler() \
.setInputCol("text") \
.setOutputCol("document")
tokenizer = Tokenizer() \
.setInputCols("document") \
.setOutputCol("token")
seq_classifier = XlmRoBertaForSequenceClassification.pretrained("xlmroberta_classifier_deoffxlmr_mono_malyalam","ml") \
.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 = XlmRoBertaForSequenceClassification.pretrained("xlmroberta_classifier_deoffxlmr_mono_malyalam","ml")
.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("ml.classify.xlmr_roberta").predict("""PUT YOUR STRING HERE""")
Model Information
| Model Name: | xlmroberta_classifier_deoffxlmr_mono_malyalam |
| Compatibility: | Spark NLP 5.5.0+ |
| License: | Open Source |
| Edition: | Official |
| Input Labels: | [document, token] |
| Output Labels: | [class] |
| Language: | ml |
| Size: | 1.0 GB |
References
References
- https://huggingface.co/Hate-speech-CNERG/deoffxlmr-mono-malyalam
- https://www.aclweb.org/anthology/2021.dravidianlangtech-1.38/