Add dataset card for ELF pre-tokenized XSum

README.md

@@ -0,0 +1,52 @@
+---
+license: mit
+task_categories:
+- summarization
+language:
+- en
+tags:
+- diffusion
+- flow-matching
+- language-modeling
+- elf
+---
+
+This repository contains the pre-tokenized XSum dataset used in the paper [ELF: Embedded Language Flows](https://huggingface.co/papers/2605.10938).
+
+The dataset is tokenized using the T5 tokenizer and is prepared for use with ELF, a class of continuous-time Flow Matching models that operate in the continuous embedding space of a frozen T5 encoder.
+
+- **GitHub Repository:** [https://github.com/lillian039/ELF](https://github.com/lillian039/ELF)
+- **Paper:** [ELF: Embedded Language Flows](https://huggingface.co/papers/2605.10938)
+
+### Dataset Summary
+
+For the summarization task (XSum), the data is structured for conditional generation:
+- `condition_input_ids`: Tokenized source text (the article).
+- `input_ids`: Tokenized target text (the summary).
+
+The ELF model prepends the condition IDs to the input IDs and applies specific attention masks during training and inference.
+
+### Sample Usage
+
+You can load this dataset directly using the `datasets` library:
+
+```python
+from datasets import load_dataset
+
+# Load the training split
+dataset = load_dataset("embedded-language-flows/xsum_train_t5")
+
+# Example of an item in the dataset
+print(dataset["train"][0])
+```
+
+### 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}
+}
+```