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--- |
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license: mit |
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language: |
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- en |
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metrics: |
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- character |
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pipeline_tag: zero-shot-classification |
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library_name: transformers |
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--- |
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bart-large-mnli |
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This is the checkpoint for bart-large after being trained on the MultiNLI (MNLI) dataset. |
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Additional information about this model: |
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The bart-large model page |
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BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension |
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BART fairseq implementation |
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NLI-based Zero Shot Text Classification |
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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. |
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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. |
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With the zero-shot classification pipeline |
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The model can be loaded with the zero-shot-classification pipeline like so: |
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from transformers import pipeline |
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classifier = pipeline("zero-shot-classification", |
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model="facebook/bart-large-mnli") |
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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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sequence_to_classify = "one day I will see the world" |
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candidate_labels = ['travel', 'cooking', 'dancing'] |
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classifier(sequence_to_classify, candidate_labels) |
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#{'labels': ['travel', 'dancing', 'cooking'], |
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# 'scores': [0.9938651323318481, 0.0032737774308770895, 0.002861034357920289], |
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# 'sequence': 'one day I will see the world'} |
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If more than one candidate label can be correct, pass multi_class=True to calculate each class independently: |
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candidate_labels = ['travel', 'cooking', 'dancing', 'exploration'] |
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classifier(sequence_to_classify, candidate_labels, multi_class=True) |
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#{'labels': ['travel', 'exploration', 'dancing', 'cooking'], |
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# 'scores': [0.9945111274719238, |
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# 0.9383890628814697, |
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# 0.0057061901316046715, |
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# 0.0018193122232332826], |
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# 'sequence': 'one day I will see the world'} |
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With manual PyTorch |
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# pose sequence as a NLI premise and label as a hypothesis |
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from transformers import AutoModelForSequenceClassification, AutoTokenizer |
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nli_model = AutoModelForSequenceClassification.from_pretrained('facebook/bart-large-mnli') |
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tokenizer = AutoTokenizer.from_pretrained('facebook/bart-large-mnli') |
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premise = sequence |
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hypothesis = f'This example is {label}.' |
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# run through model pre-trained on MNLI |
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x = tokenizer.encode(premise, hypothesis, return_tensors='pt', |
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truncation_strategy='only_first') |
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logits = nli_model(x.to(device))[0] |
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# we throw away "neutral" (dim 1) and take the probability of |
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# "entailment" (2) as the probability of the label being true |
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entail_contradiction_logits = logits[:,[0,2]] |
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probs = entail_contradiction_logits.softmax(dim=1) |
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prob_label_is_true = probs[:,1] |
