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+---
+license: mit
+thumbnail: https://huggingface.co/front/thumbnails/facebook.png
+pipeline_tag: zero-shot-classification
+datasets:
+- multi_nli
+---
+
+# bart-large-mnli
+
+This is the checkpoint for [bart-large](https://huggingface.co/facebook/bart-large) after being trained on the [MultiNLI (MNLI)](https://huggingface.co/datasets/multi_nli) dataset.
+
+Additional information about this model:
+- The [bart-large](https://huggingface.co/facebook/bart-large) model page
+- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension
+](https://arxiv.org/abs/1910.13461)
+- [BART fairseq implementation](https://github.com/pytorch/fairseq/tree/master/fairseq/models/bart)
+
+## NLI-based Zero Shot Text Classification
+
+[Yin et al.](https://arxiv.org/abs/1909.00161) proposed a method for using pre-trained NLI models as a ready-made zero-shot sequence classifiers. The method works by posing the sequence to be classified as the NLI premise and to construct a hypothesis from each candidate label. For example, if we want to evaluate whether a sequence belongs to the class "politics", we could construct a hypothesis of `This text is about politics.`. The probabilities for entailment and contradiction are then converted to label probabilities.
+
+This method is surprisingly effective in many cases, particularly when used with larger pre-trained models like BART and Roberta. See [this blog post](https://joeddav.github.io/blog/2020/05/29/ZSL.html) for a more expansive introduction to this and other zero shot methods, and see the code snippets below for examples of using this model for zero-shot classification both with Hugging Face's built-in pipeline and with native Transformers/PyTorch code.
+
+#### With the zero-shot classification pipeline
+
+The model can be loaded with the `zero-shot-classification` pipeline like so:
+
+```python
+from transformers import pipeline
+classifier = pipeline("zero-shot-classification",
+                      model="facebook/bart-large-mnli")
+```
+
+You can then use this pipeline to classify sequences into any of the class names you specify.
+
+```python
+sequence_to_classify = "one day I will see the world"
+candidate_labels = ['travel', 'cooking', 'dancing']
+classifier(sequence_to_classify, candidate_labels)
+#{'labels': ['travel', 'dancing', 'cooking'],
+# 'scores': [0.9938651323318481, 0.0032737774308770895, 0.002861034357920289],
+# 'sequence': 'one day I will see the world'}
+```
+
+If more than one candidate label can be correct, pass `multi_class=True` to calculate each class independently:
+
+```python
+candidate_labels = ['travel', 'cooking', 'dancing', 'exploration']
+classifier(sequence_to_classify, candidate_labels, multi_class=True)
+#{'labels': ['travel', 'exploration', 'dancing', 'cooking'],
+# 'scores': [0.9945111274719238,
+#  0.9383890628814697,
+#  0.0057061901316046715,
+#  0.0018193122232332826],
+# 'sequence': 'one day I will see the world'}
+```
+
+
+#### With manual PyTorch
+
+```python
+# pose sequence as a NLI premise and label as a hypothesis
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+nli_model = AutoModelForSequenceClassification.from_pretrained('joeddav/xlm-roberta-large-xnli')
+tokenizer = AutoTokenizer.from_pretrained('joeddav/xlm-roberta-large-xnli')
+
+premise = sequence
+hypothesis = f'This example is {label}.'
+
+# run through model pre-trained on MNLI
+x = tokenizer.encode(premise, hypothesis, return_tensors='pt',
+                     truncation_strategy='only_first')
+logits = nli_model(x.to(device))[0]
+
+# we throw away "neutral" (dim 1) and take the probability of
+# "entailment" (2) as the probability of the label being true 
+entail_contradiction_logits = logits[:,[0,2]]
+probs = entail_contradiction_logits.softmax(dim=1)
+prob_label_is_true = probs[:,1]
+```