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transformers/docs/source/en/model_doc/xlm-prophetnet.md

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+*This model was released on 2020-01-13 and added to Hugging Face Transformers on 2023-06-20.*
+
+# XLM-ProphetNet
+
+<div class="flex flex-wrap space-x-1">
+<img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
+</div>
+
+<Tip warning={true}>
+
+This model is in maintenance mode only, we don't accept any new PRs changing its code.
+If you run into any issues running this model, please reinstall the last version that supported this model: v4.40.2.
+You can do so by running the following command: `pip install -U transformers==4.40.2`.
+
+</Tip>
+
+<div class="flex flex-wrap space-x-1">
+<a href="https://huggingface.co/models?filter=xprophetnet">
+<img alt="Models" src="https://img.shields.io/badge/All_model_pages-xprophetnet-blueviolet">
+</a>
+<a href="https://huggingface.co/spaces/docs-demos/xprophetnet-large-wiki100-cased-xglue-ntg">
+<img alt="Spaces" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue">
+</a>
+</div>
+
+**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://huggingface.co/papers/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