---
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=""
```