alisawuffles
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README.md
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This is an off-the-shelf roberta-large model finetuned on WANLI, the Worker-AI Collaborative NLI dataset ([Liu et al., 2022](https://arxiv.org/abs/2201.05955)). It outperforms the `roberta-large-mnli` model on seven out-of-domain test sets, including by 11% on HANS and 9% on Adversarial NLI.
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```
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@misc{liu-etal-2022-wanli,
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title = "WANLI: Worker and AI Collaboration for Natural Language Inference Dataset Creation",
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- text: "I believe I will get into UW. </s></s> I will get into UW."
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This is an off-the-shelf roberta-large model finetuned on WANLI, the Worker-AI Collaborative NLI dataset ([Liu et al., 2022](https://arxiv.org/abs/2201.05955)). It outperforms the `roberta-large-mnli` model on seven out-of-domain test sets, including by 11% on HANS and 9% on Adversarial NLI.
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### How to use
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```
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model = RobertaForSequenceClassification.from_pretrained('alisawuffles/roberta-large-wanli')
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tokenizer = RobertaTokenizer.from_pretrained('alisawuffles/roberta-large-wanli')
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x = tokenizer("I believe I will get into UW.", "I will get into UW.", hypothesis, return_tensors='pt', max_length=128, truncation=True)
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logits = model(**x).logits
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probs = logits.softmax(dim=1).squeeze(0)
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label_id = torch.argmax(probs).item()
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prediction = nli_model.config.id2label[label_id]
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```
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### Citation
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```
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@misc{liu-etal-2022-wanli,
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title = "WANLI: Worker and AI Collaboration for Natural Language Inference Dataset Creation",
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