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