| --- |
| language: en |
| --- |
| |
| ## prophetnet-large-uncased |
| Pretrained weights for [ProphetNet](https://arxiv.org/abs/2001.04063). |
| ProphetNet is a new pre-trained language model for sequence-to-sequence learning with a novel self-supervised objective called future n-gram prediction. |
| 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). |
|
|
| ### Usage |
|
|
| 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: |
|
|
| ```python |
| from transformers import ProphetNetForConditionalGeneration, ProphetNetTokenizer |
| |
| model = ProphetNetForConditionalGeneration.from_pretrained("microsoft/prophetnet-large-uncased") |
| tokenizer = ProphetNetTokenizer.from_pretrained("microsoft/prophetnet-large-uncased") |
| |
| 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 ." |
| target_str = "us rejects charges against its ambassador in bolivia" |
| |
| input_ids = tokenizer(input_str, return_tensors="pt").input_ids |
| labels = tokenizer(target_str, return_tensors="pt").input_ids |
| |
| loss = model(input_ids, labels=labels).loss |
| ``` |
|
|
| ### Citation |
| ```bibtex |
| @article{yan2020prophetnet, |
| title={Prophetnet: Predicting future n-gram for sequence-to-sequence pre-training}, |
| author={Yan, Yu and Qi, Weizhen and Gong, Yeyun and Liu, Dayiheng and Duan, Nan and Chen, Jiusheng and Zhang, Ruofei and Zhou, Ming}, |
| journal={arXiv preprint arXiv:2001.04063}, |
| year={2020} |
| } |
| ``` |
|
|
