# XLM-ProphetNet

**DISCLAIMER:** If you see something strange, file a [Github Issue](https://github.com/huggingface/transformers/issues/new?assignees=&labels=&template=bug-report.md&title) and assign @patrickvonplaten ## Overview The XLM-ProphetNet model was proposed in [ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training,](https://arxiv.org/abs/2001.04063) by Yu Yan, Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang, Ming Zhou on 13 Jan, 2020. XLM-ProphetNet is an encoder-decoder model and can predict n-future tokens for "ngram" language modeling instead of just the next token. Its architecture is identical to ProhpetNet, but the model was trained on the multi-lingual "wiki100" Wikipedia dump. XLM-ProphetNet's model architecture and pretraining objective is same as ProphetNet, but XLM-ProphetNet was pre-trained on the cross-lingual dataset XGLUE. The abstract from the paper is the following: *In this paper, we present a new sequence-to-sequence pretraining model called ProphetNet, which introduces a novel self-supervised objective named future n-gram prediction and the proposed n-stream self-attention mechanism. Instead of the optimization of one-step ahead prediction in traditional sequence-to-sequence model, the ProphetNet is optimized by n-step ahead prediction which predicts the next n tokens simultaneously based on previous context tokens at each time step. The future n-gram prediction explicitly encourages the model to plan for the future tokens and prevent overfitting on strong local correlations. We pre-train ProphetNet using a base scale dataset (16GB) and a large scale dataset (160GB) respectively. Then we conduct experiments on CNN/DailyMail, Gigaword, and SQuAD 1.1 benchmarks for abstractive summarization and question generation tasks. Experimental results show that ProphetNet achieves new state-of-the-art results on all these datasets compared to the models using the same scale pretraining corpus.* The Authors' code can be found [here](https://github.com/microsoft/ProphetNet). ## Resources - [Causal language modeling task guide](../tasks/language_modeling) - [Translation task guide](../tasks/translation) - [Summarization task guide](../tasks/summarization) ## XLMProphetNetConfig [[autodoc]] XLMProphetNetConfig ## XLMProphetNetTokenizer [[autodoc]] XLMProphetNetTokenizer ## XLMProphetNetModel [[autodoc]] XLMProphetNetModel ## XLMProphetNetEncoder [[autodoc]] XLMProphetNetEncoder ## XLMProphetNetDecoder [[autodoc]] XLMProphetNetDecoder ## XLMProphetNetForConditionalGeneration [[autodoc]] XLMProphetNetForConditionalGeneration ## XLMProphetNetForCausalLM [[autodoc]] XLMProphetNetForCausalLM