Update README.md

README.md

@@ -6,4 +6,62 @@ metrics:
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 pipeline_tag: zero-shot-classification
 library_name: transformers
----
+---
+
+
+bart-large-mnli
+This is the checkpoint for bart-large after being trained on the MultiNLI (MNLI) dataset.
+
+Additional information about this model:
+
+The bart-large model page
+BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension
+BART fairseq implementation
+NLI-based Zero Shot Text Classification
+Yin et al. 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 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:
+
+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.
+
+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:
+
+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
+# pose sequence as a NLI premise and label as a hypothesis
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+nli_model = AutoModelForSequenceClassification.from_pretrained('facebook/bart-large-mnli')
+tokenizer = AutoTokenizer.from_pretrained('facebook/bart-large-mnli')
+
+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]