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--- |
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license: mit |
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thumbnail: https://huggingface.co/front/thumbnails/facebook.png |
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pipeline_tag: zero-shot-classification |
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datasets: |
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- multi_nli |
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--- |
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# bart-large-mnli |
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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. |
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Additional information about this model: |
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- The [bart-large](https://huggingface.co/facebook/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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](https://arxiv.org/abs/1910.13461) |
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- [BART fairseq implementation](https://github.com/pytorch/fairseq/tree/master/fairseq/models/bart) |
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## NLI-based Zero Shot Text Classification |
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[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. |
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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](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. |
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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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```python |
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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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``` |
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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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```python |
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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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``` |
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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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```python |
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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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``` |
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#### With manual PyTorch |
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```python |
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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('joeddav/xlm-roberta-large-xnli') |
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tokenizer = AutoTokenizer.from_pretrained('joeddav/xlm-roberta-large-xnli') |
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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] |
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``` |
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