Danish BertForSequenceClassification Cased model (from DaNLP)

Description

Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. da-bert-tone-sentiment-polarity is a Danish model originally trained by DaNLP.

Predicted Entities

positive, neutral, negative

Live Demo Open in Colab Download Copy S3 URI

How to use

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

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

seq_classifier = BertForSequenceClassification.pretrained("bert_classifier_da_tone_sentiment_polarity","da") \
    .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 = BertForSequenceClassification.pretrained("bert_classifier_da_tone_sentiment_polarity","da")
    .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("da.classify.bert.sentiment.by_danlp").predict("""PUT YOUR STRING HERE""")

Model Information

References

• https://huggingface.co/DaNLP/da-bert-tone-sentiment-polarity
• https://github.com/certainlyio/nordic_bert
• https://danlp-alexandra.readthedocs.io/en/latest/docs/tasks/sentiment_analysis.html#bert-tone
• https://danlp-alexandra.readthedocs.io/en/latest/docs/datasets.html#twitsent
• https://danlp-alexandra.readthedocs.io/en/latest/docs/datasets.html#europarl-sentiment2