Catalan RobertaForSequenceClassification Base Cased model (from projecte-aina)

Description

Pretrained RobertaForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. roberta-base-ca-v2-cased-te is a Catalan model originally trained by projecte-aina.

Predicted Entities

ENTAILMENT, NEUTRAL, CONTRADICTION

Download Copy S3 URI

How to use

documentAssembler = DocumentAssembler() \
    .setInputCol("text") \
    .setOutputCol("document")

tokenizer = Tokenizer() \
    .setInputCols("document") \
    .setOutputCol("token")

seq_classifier = RoBertaForSequenceClassification.pretrained("roberta_classifier_base_ca_v2_te_cased","ca") \
    .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_base_ca_v2_te_cased","ca")
    .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("ca.classify.roberta.cased_v2_base.te.by_projecte_aina").predict("""PUT YOUR STRING HERE""")

Model Information

Model Name:	roberta_classifier_base_ca_v2_te_cased
Compatibility:	Spark NLP 4.1.0+
License:	Open Source
Edition:	Official
Input Labels:	[document, token]
Output Labels:	[class]
Language:	ca
Size:	446.6 MB
Case sensitive:	true
Max sentence length:	256

References

https://huggingface.co/projecte-aina/roberta-base-ca-v2-cased-te
https://arxiv.org/abs/1907.11692
https://github.com/projecte-aina/club
https://www.apache.org/licenses/LICENSE-2.0
https://politiquesdigitals.gencat.cat/ca/inici/index.html#googtrans(ca%7Cen
https://politiquesdigitals.gencat.cat/ca/economia/catalonia-ai/aina
https://paperswithcode.com/sota?task=text-classification&dataset=TECA

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