Migrate model card from transformers-repo
Browse filesRead announcement at /static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Ft%2Fannouncement-all-model-cards-will-be-migrated-to-hf-co-model-repos%2F2755%3Cbr%2F%3EOriginal file history: https://github.com/huggingface/transformers/commits/master/model_cards/microsoft/prophetnet-large-uncased/README.md
README.md
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---
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language: en
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---
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## prophetnet-large-uncased
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Pretrained weights for [ProphetNet](https://arxiv.org/abs/2001.04063).
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ProphetNet is a new pre-trained language model for sequence-to-sequence learning with a novel self-supervised objective called future n-gram prediction.
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ProphetNet is able to predict more future tokens with a n-stream decoder. The original implementation is Fairseq version at [github repo](https://github.com/microsoft/ProphetNet).
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### Usage
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This pre-trained model can be fine-tuned on *sequence-to-sequence* tasks. The model could *e.g.* be trained on headline generation as follows:
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```python
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from transformers import ProphetNetForConditionalGeneration, ProphetNetTokenizer
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model = ProphetNetForConditionalGeneration.from_pretrained("microsoft/prophetnet-large-uncased")
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tokenizer = ProphetNetTokenizer.from_pretrained("microsoft/prophetnet-large-uncased")
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input_str = "the us state department said wednesday it had received no formal word from bolivia that it was expelling the us ambassador there but said the charges made against him are `` baseless ."
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target_str = "us rejects charges against its ambassador in bolivia"
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input_ids = tokenizer(input_str, return_tensors="pt").input_ids
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labels = tokenizer(target_str, return_tensors="pt").input_ids
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loss = model(input_ids, labels=labels).loss
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```
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### Citation
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```bibtex
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@article{yan2020prophetnet,
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title={Prophetnet: Predicting future n-gram for sequence-to-sequence pre-training},
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author={Yan, Yu and Qi, Weizhen and Gong, Yeyun and Liu, Dayiheng and Duan, Nan and Chen, Jiusheng and Zhang, Ruofei and Zhou, Ming},
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journal={arXiv preprint arXiv:2001.04063},
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year={2020}
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}
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```
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