XLM-RoBERTa Sequence Classification Base - Language Detection (xlm_roberta_base_sequence_classifier_language_detection)

Description

“ XLM-RoBERTa Model with sequence classification/regression head on top (a linear layer on top of the pooled output) e.g. for multi-class document classification tasks.

xlm_roberta_base_sequence_classifier_language_detection is a fine-tuned XLM-RoBERTa model that is ready to be used for Sequence Classification tasks such as sentiment analysis or multi-class text classification and it achieves state-of-the-art performance.

We used TFXLMRobertaForSequenceClassification to train this model and used XlmRoBertaForSequenceClassification annotator in Spark NLP 🚀 for prediction at scale!

Live Demo Open in Colab Download Copy S3 URI

How to use

.setInputCol('text') \
.setOutputCol('document')

tokenizer = Tokenizer() \
.setInputCols(['document']) \
.setOutputCol('token')

sequenceClassifier = XlmRoBertaForSequenceClassification \
.pretrained('xlm_roberta_base_sequence_classifier_language_detection', 'en') \
.setInputCols(['token', 'document']) \
.setOutputCol('class') \
.setCaseSensitive(True) \
.setMaxSentenceLength(512)

pipeline = Pipeline(stages=[
document_assembler,
tokenizer,
sequenceClassifier
])

example = spark.createDataFrame([['I really liked that movie!']]).toDF("text")
result = pipeline.fit(example).transform(example)

		val document_assembler = DocumentAssembler()
.setInputCol("text")
.setOutputCol("document")

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

val tokenClassifier = XlmRoBertaForSequenceClassification.pretrained("xlm_roberta_base_sequence_classifier_language_detection", "en")
.setInputCols("document", "token")
.setOutputCol("class")
.setCaseSensitive(true)
.setMaxSentenceLength(512)

val pipeline = new Pipeline().setStages(Array(document_assembler, tokenizer, sequenceClassifier))

val example = Seq("I really liked that movie!").toDS.toDF("text")

val result = pipeline.fit(example).transform(example)

Model Information