Description
Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. dehatebert-mono-spanish-finetuned-sentiments_reviews_politicos
is a Spanish model originally trained by anthonny
.
Predicted Entities
NON_HATE
, HATE
How to use
documentAssembler = DocumentAssembler() \
.setInputCol("text") \
.setOutputCol("document")
tokenizer = Tokenizer() \
.setInputCols("document") \
.setOutputCol("token")
sequenceClassifier_loaded = BertForSequenceClassification.pretrained("bert_classifier_dehate_mono_spanish_finetuned_sentiments_reviews_politicos","es") \
.setInputCols(["document", "token"]) \
.setOutputCol("class")
pipeline = Pipeline(stages=[documentAssembler, tokenizer,sequenceClassifier_loaded])
data = spark.createDataFrame([["Amo Spark NLP"]]).toDF("text")
result = pipeline.fit(data).transform(data)
val documentAssembler = new DocumentAssembler()
.setInputCol("text")
.setOutputCol("document")
val tokenizer = new Tokenizer()
.setInputCols(Array("document"))
.setOutputCol("token")
val sequenceClassifier_loaded = BertForSequenceClassification.pretrained("bert_classifier_dehate_mono_spanish_finetuned_sentiments_reviews_politicos","es")
.setInputCols(Array("document", "token"))
.setOutputCol("class")
val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer,sequenceClassifier_loaded))
val data = Seq("Amo Spark NLP").toDF("text")
val result = pipeline.fit(data).transform(data)
import nlu
nlu.load("es.classify.bert.sentiment_hate.finetuned").predict("""Amo Spark NLP""")
Model Information
Model Name: | bert_classifier_dehate_mono_spanish_finetuned_sentiments_reviews_politicos |
Compatibility: | Spark NLP 4.2.0+ |
License: | Open Source |
Edition: | Official |
Input Labels: | [document, token] |
Output Labels: | [class] |
Language: | es |
Size: | 628.3 MB |
Case sensitive: | true |
Max sentence length: | 256 |
References
- https://huggingface.co/anthonny/dehatebert-mono-spanish-finetuned-sentiments_reviews_politicos