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--- |
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license: apache-2.0 |
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language: |
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- en |
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pipeline_tag: text-generation |
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tags: |
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- non-autoregressive text generation |
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- generative model |
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- flow matching |
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--- |
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# Flow Matching for Conditional Text Generation in a Few Sampling Steps (EACL2024) |
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This model represents the official checkpoint of the paper titled "Flow Matching for Conditional Text Generation in a Few Sampling Steps (EACL2024)". |
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[Website](https://taohu.me/project_flowseq) |
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[![Paper](https://img.shields.io/badge/arXiv-PDF-b31b1b)](https://aclanthology.org/2024.eacl-short.33.pdf) |
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[![Hugging Face Model](https://img.shields.io/badge/🤗%20Hugging%20Face-Model-green)](https://huggingface.co./taohu/flowseq) |
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[![License](https://img.shields.io/badge/License-Apache--2.0-929292)](https://www.apache.org/licenses/LICENSE-2.0) |
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[Vincent Tao Hu](http://taohu.me), |
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[Di Wu](), |
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[Yuki M Asano](), |
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[Pascal Mettes](), |
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[Basura Fernando](), |
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[Björn Ommer]() |
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[Cees G.M. Snoek]() |
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![aaa](method.png) |
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Diffusion models are a promising tool for highquality text generation. However, current models face multiple drawbacks including slow |
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sampling, noise schedule sensitivity, and misalignment between the training and sampling |
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stages. In this paper, we introduce FlowSeq, |
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which bypasses all current drawbacks by leveraging flow matching for conditional text generation. FlowSeq can generate text in a few |
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steps by training with a novel anchor loss, alleviating the need for expensive hyperparameter |
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optimization of the noise schedule prevalent in |
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diffusion models. We extensively evaluate our |
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proposed method and show competitive performance in tasks such as question generation, |
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open-domain dialogue, and paraphrasing. |
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## 🎓 Citation |
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```bibtex |
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@inproceedings{HuEACL2024, |
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title = {Flow Matching for Conditional Text Generation in a Few Sampling Steps}, |
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author = {Vincent Tao Hu and Di Wu and Yuki M Asano and Pascal Mettes and Basura Fernando and Björn Ommer and Cees G M Snoek}, |
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year = {2024}, |
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date = {2024-03-27}, |
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booktitle = {EACL}, |
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tppubtype = {inproceedings} |
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} |
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``` |
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## 🎫 License |
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This work is licensed under the Apache License, Version 2.0 (as defined in the [LICENSE](LICENSE.txt)). |
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By downloading and using the code and model you agree to the terms in the [LICENSE](LICENSE.txt). |
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[![License](https://img.shields.io/badge/License-Apache--2.0-929292)](https://www.apache.org/licenses/LICENSE-2.0) |
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