Description
Pretrained RoBertaForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. Clasificacion_sentimientos is a English model originally trained by alexhf90.
Predicted Entities
Comentario_Positivo, Comentario_Negativo
How to use
documentAssembler = DocumentAssembler() \
.setInputCol("text") \
.setOutputCol("document")
tokenizer = Tokenizer() \
.setInputCols("document") \
.setOutputCol("token")
seq_classifier = RoBertaForSequenceClassification.pretrained("roberta_classifier_clasificacion_sentimientos","es") \
.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 = RoBertaForSequenceClassification.pretrained("roberta_classifier_clasificacion_sentimientos","es")
.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("es.classify.roberta.by_alexhf90").predict("""PUT YOUR STRING HERE""")
Model Information
| Model Name: | roberta_classifier_clasificacion_sentimientos |
| Compatibility: | Spark NLP 4.1.0+ |
| License: | Open Source |
| Edition: | Official |
| Input Labels: | [document, token] |
| Output Labels: | [class] |
| Language: | es |
| Size: | 454.7 MB |
| Case sensitive: | true |
| Max sentence length: | 256 |
References
- https://huggingface.co/alexhf90/Clasificacion_sentimientos
- https://www.filmaffinity.com/es/main.html$
- https://www.kaggle.com/ricardomoya/criticas-peliculas-filmaffinity-en-espaniol/code