Description
Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. rubertconv_toxic_clf
is a Russian model originally trained by IlyaGusev
.
Predicted Entities
toxic
, neutral
How to use
documentAssembler = DocumentAssembler() \
.setInputCol("text") \
.setOutputCol("document")
tokenizer = Tokenizer() \
.setInputCols("document") \
.setOutputCol("token")
seq_classifier = BertForSequenceClassification.pretrained("bert_classifier_rubertconv_toxic_clf","ru") \
.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_rubertconv_toxic_clf","ru")
.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("ru.classify.bert").predict("""PUT YOUR STRING HERE""")
Model Information
Model Name: | bert_classifier_rubertconv_toxic_clf |
Compatibility: | Spark NLP 4.1.0+ |
License: | Open Source |
Edition: | Official |
Input Labels: | [document, token] |
Output Labels: | [class] |
Language: | ru |
Size: | 665.1 MB |
Case sensitive: | true |
Max sentence length: | 256 |
References
- https://huggingface.co/IlyaGusev/rubertconv_toxic_clf
- https://www.kaggle.com/blackmoon/russian-language-toxic-comments
- https://www.kaggle.com/alexandersemiletov/toxic-russian-comments
- https://toloka.ai/ru/datasets
- https://github.com/Koziev/NLP_Datasets/blob/master/Conversations/Data