Add dataset card, link to paper and Github

README.md

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+---
+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}
+}
+```