English RoBertaForSequenceClassification Cased model (from niksmer)

Description

Pretrained RoBertaForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. ManiBERT is a English model originally trained by niksmer.

Predicted Entities

Multiculturalism: Negative, Labour Groups: Positive, Nationalisation, European Community/Union or Latin America Integration: Positive, Economic Growth: Positive, Anti-Growth Economy and Sustainability, Education Limitation, Agriculture and Farmers, Technology and Infrastructure: Positive, Incentives: Positive, Governmental and Administrative Efficiency, Anti-Imperialism, Free Market Economy, Traditional Morality: Positive, Labour Groups: Negative, Constitutionalism: Negative, Peace, Welfare State Limitation, Traditional Morality: Negative, Centralisation: Positive, Environmental Protection, Economic Goals, Internationalism: Negative, Protectionism: Negative, Foreign Special Relationships: Negative, Welfare State Expansion, Controlled Economy, Market Regulation, Education Expansion, Culture: Positive, Law and Order, Protectionism: Positive, Corporatism/ Mixed Economy, Non-economic Demographic Groups, Constitutionalism: Positive, National Way of Life: Negative, Military: Positive, Freedom and Human Rights, European Community/Union or Latin America Integration: Negative, Decentralisation: Positive, Multiculturalism: Positive, Democracy, Economic Planning, Equality: Positive, Underprivileged Minority Groups, Foreign Special Relationships: Positive, Political Authority, Economic Orthodoxy, Military: Negative, Political Corruption, Keynesian Demand Management, Marxist Analysis: Positive, Civic Mindedness: Positive, Internationalism: Positive, Middle Class and Professional Groups, National Way of Life: Positive

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 = RoBertaForSequenceClassification.pretrained("roberta_classifier_manibert","en") \
    .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_manibert","en")
    .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("en.classify.manibert.by_niksmer").predict("""PUT YOUR STRING HERE""")

Model Information

References

References

• https://huggingface.co/niksmer/ManiBERT
• https://manifesto-project.wzb.eu/
• https://manifesto-project.wzb.eu/datasets
• https://manifesto-project.wzb.eu/down/tutorials/main-dataset.html#measuring-parties-left-right-positions