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flowseq

Text Generation
English
non-autoregressive text generation
generative model
flow matching
Model card Files Community
flowseq
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---
license: apache-2.0
language:
- en
pipeline_tag: text-generation
tags:
- non-autoregressive text generation
- generative model
- flow matching


---
# Flow Matching for Conditional Text Generation in a Few Sampling Steps (EACL2024)

This model represents the official checkpoint of the paper titled "Flow Matching for Conditional Text Generation in a Few Sampling Steps (EACL2024)".


[Website](https://taohu.me/project_flowseq)
[![Paper](https://img.shields.io/badge/arXiv-PDF-b31b1b)](https://aclanthology.org/2024.eacl-short.33.pdf)
[![Hugging Face Model](https://img.shields.io/badge/🤗%20Hugging%20Face-Model-green)](https://huggingface.co./taohu/flowseq)
[![License](https://img.shields.io/badge/License-Apache--2.0-929292)](https://www.apache.org/licenses/LICENSE-2.0)


[Vincent Tao Hu](http://taohu.me),
[Di Wu](),
[Yuki M Asano](),
[Pascal Mettes](),
[Basura Fernando](),
[Björn Ommer]()
[Cees G.M. Snoek]()

![aaa](method.png)


Diffusion models are a promising tool for highquality text generation. However, current models face multiple drawbacks including slow
sampling, noise schedule sensitivity, and misalignment between the training and sampling
stages. In this paper, we introduce FlowSeq,
which bypasses all current drawbacks by leveraging flow matching for conditional text generation. FlowSeq can generate text in a few
steps by training with a novel anchor loss, alleviating the need for expensive hyperparameter
optimization of the noise schedule prevalent in
diffusion models. We extensively evaluate our
proposed method and show competitive performance in tasks such as question generation,
open-domain dialogue, and paraphrasing.


## 🎓 Citation

```bibtex
@inproceedings{HuEACL2024,
        title = {Flow Matching for Conditional Text Generation in a Few Sampling Steps},
        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},
        year = {2024},
        date = {2024-03-27},
        booktitle = {EACL},
        tppubtype = {inproceedings}
        }
```

## 🎫 License

This work is licensed under the Apache License, Version 2.0 (as defined in the [LICENSE](LICENSE.txt)).

By downloading and using the code and model you agree to the terms in the  [LICENSE](LICENSE.txt).

[![License](https://img.shields.io/badge/License-Apache--2.0-929292)](https://www.apache.org/licenses/LICENSE-2.0)