| dataset_info: | |
| features: | |
| - name: text_trg | |
| dtype: string | |
| splits: | |
| - name: train | |
| num_bytes: 9033045649 | |
| num_examples: 16086245 | |
| - name: test | |
| num_bytes: 28125706 | |
| num_examples: 50000 | |
| download_size: 6107844688 | |
| dataset_size: 9061171355 | |
| configs: | |
| - config_name: default | |
| data_files: | |
| - split: train | |
| path: data/train-* | |
| - split: test | |
| path: data/test-* | |
| task_categories: | |
| - text-generation | |
| tags: | |
| - diffusion-models | |
| - latent-space | |
| # COSMOS Dataset | |
| This repository contains the `rocstories` dataset, one of the pre-processed datasets used in the paper "[Compressed and Smooth Latent Space for Text Diffusion Modeling](https://huggingface.co/papers/2506.21170)". | |
| The paper introduces COSMOS, a novel approach to text generation that operates entirely in a compressed, smooth latent space tailored specifically for diffusion. This space is learned using an autoencoder trained for token-level reconstruction and alignment with frozen activations from a pretrained language encoder. The datasets are integral for training and evaluating text diffusion models across various generative tasks, including story generation, question generation, summarization, and detoxification. | |
| ## Paper | |
| [Compressed and Smooth Latent Space for Text Diffusion Modeling](https://huggingface.co/papers/2506.21170) | |
| ## Code | |
| The official implementation can be found on GitHub: [https://github.com/MeshchaninovViacheslav/cosmos](https://github.com/MeshchaninovViacheslav/cosmos) | |
| ## Sample Usage | |
| After training the autoencoder and diffusion models as described in the [GitHub repository](https://github.com/MeshchaninovViacheslav/cosmos), you can generate new text samples using the `generate.py` script. The following command is an example for generating text using a diffusion model trained with a dataset like `rocstories`: | |
| ```bash | |
| CUDA_LAUNCH_BLOCKING=1 \ | |
| HYDRA_FULL_ERROR=1 \ | |
| uv run \ | |
| torchrun --nproc_per_node=4 --master_port=12345 \ | |
| generate.py \ | |
| dataset=rocstories \ | |
| diffusion.dynamic.N=200 \ | |
| diffusion.dynamic.d=5 \ | |
| diffusion.training.batch_size=512 \ | |
| encoder.latent.num_latents=16 \ | |
| encoder.embedding.max_position_embeddings=128 \ | |
| decoder.latent.num_latents=16 \ | |
| decoder.embedding.max_position_embeddings=128 \ | |
| autoencoder.model.load_checkpoint='"autoencoder-num_latents=16-wikipedia-final-128/100000.pth"' \ | |
| diffusion.model.load_checkpoint='\"diffusion-rocstories-16-d=5-final/180000.pth\"' \ | |
| diffusion.generation.num_gen_texts=2000 \ | |
| training="" | |
| ``` |