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--- |
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language: multilingual |
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license: mit |
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tags: |
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- zero-shot-classification |
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- nli |
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- pytorch |
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datasets: |
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- mnli |
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- xnli |
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- facebook/anli |
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pipeline_tag: zero-shot-classification |
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widget: |
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- text: De pugna erat fantastic. Nam Crixo decem quam dilexit et praeciderunt caput |
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aemulus. |
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candidate_labels: violent, peaceful |
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- text: La película empezaba bien pero terminó siendo un desastre. |
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candidate_labels: positivo, negativo, neutral |
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- text: La película empezó siendo un desastre pero en general fue bien. |
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candidate_labels: positivo, negativo, neutral |
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- text: ¿A quién vas a votar en 2020? |
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candidate_labels: Europa, elecciones, política, ciencia, deportes |
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--- |
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### XLM-RoBERTa-large-XNLI-ANLI |
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XLM-RoBERTa-large model finetunned over several NLI datasets, ready to use for zero-shot classification. |
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Here are the accuracies for several test datasets: |
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| | XNLI-es | XNLI-fr | ANLI-R1 | ANLI-R2 | ANLI-R3 | |
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|-----------------------------|---------|---------|---------|---------|---------| |
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| xlm-roberta-large-xnli-anli | 93.7% | 93.2% | 68.5% | 53.6% | 49.0% | |
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The model can be loaded with the zero-shot-classification pipeline like so: |
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``` |
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from transformers import pipeline |
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classifier = pipeline("zero-shot-classification", |
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model="vicgalle/xlm-roberta-large-xnli-anli") |
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``` |
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You can then use this pipeline to classify sequences into any of the class names you specify: |
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``` |
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sequence_to_classify = "Algún día iré a ver el mundo" |
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candidate_labels = ['viaje', 'cocina', 'danza'] |
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classifier(sequence_to_classify, candidate_labels) |
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#{'sequence': 'Algún día iré a ver el mundo', |
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#'labels': ['viaje', 'danza', 'cocina'], |
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#'scores': [0.9991760849952698, 0.0004178212257102132, 0.0004059972707182169]} |
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``` |
