readme: ---
  datasets:
  - multi_nli
  language: en
  license: mit
  pipeline_tag: zero-shot-classification
  tags:
  - bart
  - zero-shot-classification
  ---
  # Bart large model for NLI-based Zero Shot Text Classification
  
  This model uses [bart-large](https://huggingface.co/facebook/bart-large).
  
  ## Training Data
  This model was trained on the [MultiNLI (MNLI)](https://huggingface.co/datasets/multi_nli) dataset in the manner originally described in [Yin et al. 2019](https://arxiv.org/abs/1909.00161).
  
  It can be used to predict whether a topic label can be assigned to a given sequence, whether or not the label has been seen before.
  
  ## Usage and Performance
  The trained model can be used like this:
  ```python
  from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
  
  # Load model & tokenizer
  bart_model = AutoModelForSequenceClassification.from_pretrained('navteca/bart-large-mnli')
  bart_tokenizer = AutoTokenizer.from_pretrained('navteca/bart-large-mnli')
  
  # Get predictions
  nlp = pipeline('zero-shot-classification', model=bart_model, tokenizer=bart_tokenizer)
  
  sequence = 'One day I will see the world.'
  candidate_labels = ['cooking', 'dancing', 'travel']
  
  result = nlp(sequence, candidate_labels, multi_label=True)
  
  print(result)
  
  #{
  #  "sequence": "One day I will see the world.",
  #  "labels": [
  #    "travel",
  #    "dancing",
  #    "cooking"
  #  ],
  #  "scores": [
  #    0.9941897988319397,
  #    0.0060537424869835,
  #    0.0020010927692056
  #  ]
  #}
  ```