README.md

---
license: mit
task_categories:
- text-generation
language:
- en
tags:
- diffusion-models
- flow-matching
---

# ELF: Embedded Language Flows

This repository contains pre-tokenized datasets used in the paper [ELF: Embedded Language Flows](https://huggingface.co/papers/2605.10938).

[**Github**](https://github.com/lillian039/ELF) | [**Paper**](https://huggingface.co/papers/2605.10938)

ELF is a class of diffusion models in continuous embedding space based on continuous-time Flow Matching. The datasets provided here are pre-tokenized using the T5 tokenizer and encoded using a frozen T5-small encoder as described in the paper.

## Dataset Details

The authors provide pre-tokenized splits for several benchmarks:
- **OpenWebText**: Used for unconditional generation.
- **WMT14 De-En**: Used for machine translation.
- **XSum**: Used for abstractive summarization.

## Usage

You can load the pre-tokenized datasets directly using the Hugging Face `datasets` library:

```python
from datasets import load_dataset

# Example: Load the OpenWebText pre-tokenized dataset
dataset = load_dataset("embedded-language-flows/openwebtext-t5")

# Example: Load the WMT14 De-En validation set
dataset_val = load_dataset("embedded-language-flows/wmt14_de-en_validation_t5")
```

## Citation

```bibtex
@article{elf2026,
  title={ELF: Embedded Language Flows},
  author={Hu, Keya and Qiu, Linlu and Lu, Yiyang and Zhao, Hanhong and Li, Tianhong and Kim, Yoon and Andreas, Jacob and He, Kaiming},
  journal={arXiv preprint arXiv:2605.10938},
  year={2026}
}
```