Create README.md

README.md

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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]()
+
+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)