Description
Pretrained RobertaForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. autotrain-neurips_chanllenge-1287149282 is a English model originally trained by jawadhussein462.
Predicted Entities
1, 0
How to use
documentAssembler = DocumentAssembler() \
.setInputCol("text") \
.setOutputCol("document")
tokenizer = Tokenizer() \
.setInputCols("document") \
.setOutputCol("token")
roberta_classifier = RoBertaForSequenceClassification.pretrained("roberta_classifier_autotrain_neurips_chanllenge_1287149282","en") \
.setInputCols(["document", "token"]) \
.setOutputCol("class")
pipeline = Pipeline(stages=[documentAssembler, tokenizer, roberta_classifier])
data = spark.createDataFrame([["I love you!"], ["I feel lucky to be here."]]).toDF("text")
result = pipeline.fit(data).transform(data)
val documentAssembler = new DocumentAssembler()
.setInputCols("text")
.setOutputCols("document")
val tokenizer = new Tokenizer()
.setInputCols("document")
.setOutputCol("token")
val roberta_classifier = RoBertaForSequenceClassification.pretrained("roberta_classifier_autotrain_neurips_chanllenge_1287149282","en")
.setInputCols(Array("document", "token"))
.setOutputCol("class")
val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, roberta_classifier))
val data = Seq("I love you!").toDS.toDF("text")
val result = pipeline.fit(data).transform(data)
import nlu
nlu.load("en.classify.roberta.128d").predict("""I feel lucky to be here.""")
Model Information
| Model Name: | roberta_classifier_autotrain_neurips_chanllenge_1287149282 |
| Compatibility: | Spark NLP 4.2.4+ |
| License: | Open Source |
| Edition: | Official |
| Input Labels: | [document, token] |
| Output Labels: | [class] |
| Language: | en |
| Size: | 1.3 GB |
| Case sensitive: | true |
| Max sentence length: | 128 |
References
- https://huggingface.co/jawadhussein462/autotrain-neurips_chanllenge-1287149282