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  • John Snow Labs
  • Nov 03, 2021

BERT Sequence Classification - Spanish Sentiment Analysis (bert_sequence_classifier_beto_sentiment_analysis)

  • bert
  • beto
  • sentiment
  • spanish
  • es
  • sequence_classification
  • open_source

Description

Sentiment Analysis in Spanish

Model trained with TASS 2020 corpus (around ~5k tweets) of several dialects of Spanish. Base model is BETO, a BERT model trained in Spanish.

Uses POS, NEG, NEU labels.

Citation

this paper

@misc{perez2021pysentimiento,
      title={pysentimiento: A Python Toolkit for Sentiment Analysis and SocialNLP tasks},
      author={Juan Manuel Pérez and Juan Carlos Giudici and Franco Luque},
      year={2021},
      eprint={2106.09462},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

Predicted Entities

NEG, NEU, POS

Download Copy S3 URI

How to use

document_assembler = DocumentAssembler() \
    .setInputCol('text') \
    .setOutputCol('document')

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

sequenceClassifier = BertForSequenceClassification \
      .pretrained('bert_sequence_classifier_beto_sentiment_analysis', 'es') \
      .setInputCols(['token', 'document']) \
      .setOutputCol('class') \
      .setCaseSensitive(True) \
      .setMaxSentenceLength(512)

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

example = spark.createDataFrame([['¡Me siento muy bien!!']]).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 = BertForSequenceClassification.pretrained("bert_sequence_classifier_beto_sentiment_analysis", "es")
      .setInputCols("document", "token")
      .setOutputCol("class")
      .setCaseSensitive(true)
      .setMaxSentenceLength(512)

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

val example = Seq("¡Me siento muy bien!!").toDS.toDF("text")

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

import nlu
nlu.load("es.classify.beto_bert.sentiment_analysis").predict("""¡Me siento muy bien!!""")

Model Information

Model Name: bert_sequence_classifier_beto_sentiment_analysis
Compatibility: Spark NLP 3.3.2+
License: Open Source
Edition: Official
Input Labels: [token, document]
Output Labels: [class]
Language: es
Case sensitive: false
Max sentense length: 512

Data Source

https://huggingface.co/finiteautomata/beto-sentiment-analysis