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Euanyu

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combined_bionlp_task_dataset_model_cards

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ERROR: type should be string, got "\nhttps://huggingface.co/facebook/bart-large-mnli with ONNX weights to be compatible with Transformers PHP\n\n\n# bart-large-mnli\n\nThis 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.\n\nAdditional information about this model:\n- The [bart-large](https://huggingface.co/facebook/bart-large) model page\n- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension\n](https://arxiv.org/abs/1910.13461)\n- [BART fairseq implementation](https://github.com/pytorch/fairseq/tree/master/fairseq/models/bart)\n\n## NLI-based Zero Shot Text Classification\n\n[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.\n\nThis 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.\n\n#### With the zero-shot classification pipeline\n\nThe model can be loaded with the `zero-shot-classification` pipeline like so:\n\n```python\nfrom transformers import pipeline\nclassifier = pipeline(\"zero-shot-classification\",\n model=\"facebook/bart-large-mnli\")\n```\n\nYou can then use this pipeline to classify sequences into any of the class names you specify.\n\n```python\nsequence_to_classify = \"one day I will see the world\"\ncandidate_labels = ['travel', 'cooking', 'dancing']\nclassifier(sequence_to_classify, candidate_labels)\n#{'labels': ['travel', 'dancing', 'cooking'],\n# 'scores': [0.9938651323318481, 0.0032737774308770895, 0.002861034357920289],\n# 'sequence': 'one day I will see the world'}\n```\n\nIf more than one candidate label can be correct, pass `multi_label=True` to calculate each class independently:\n\n```python\ncandidate_labels = ['travel', 'cooking', 'dancing', 'exploration']\nclassifier(sequence_to_classify, candidate_labels, multi_label=True)\n#{'labels': ['travel', 'exploration', 'dancing', 'cooking'],\n# 'scores': [0.9945111274719238,\n# 0.9383890628814697,\n# 0.0057061901316046715,\n# 0.0018193122232332826],\n# 'sequence': 'one day I will see the world'}\n```\n\n\n#### With manual PyTorch\n\n```python\n# pose sequence as a NLI premise and label as a hypothesis\nfrom transformers import AutoModelForSequenceClassification, AutoTokenizer\nnli_model = AutoModelForSequenceClassification.from_pretrained('facebook/bart-large-mnli')\ntokenizer = AutoTokenizer.from_pretrained('facebook/bart-large-mnli')\n\npremise = sequence\nhypothesis = f'This example is {label}.'\n\n# run through model pre-trained on MNLI\nx = tokenizer.encode(premise, hypothesis, return_tensors='pt',\n truncation_strategy='only_first')\nlogits = nli_model(x.to(device))[0]\n\n# we throw away \"neutral\" (dim 1) and take the probability of\n# \"entailment\" (2) as the probability of the label being true \nentail_contradiction_logits = logits[:,[0,2]]\nprobs = entail_contradiction_logits.softmax(dim=1)\nprob_label_is_true = probs[:,1]\n```\n\n---\n\nNote: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using [🤗 Optimum](https://huggingface.co/docs/optimum/index) and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`).\n"