---
task_categories:
- text-generation
language:
- en
tags:
- story-generation
- question-generation
- summarization
- detoxification
dataset_info:
  features:
  - name: target
    dtype: string
  splits:
  - name: train
    num_bytes: 20493594
    num_examples: 88161
  - name: test
    num_bytes: 2310690
    num_examples: 10000
  download_size: 14376849
  dataset_size: 22804284
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: test
    path: data/test-*
---

# COSMOS Dataset

This repository hosts pre-processed datasets used with **COSMOS: Compressed and Smooth Latent Space for Text Diffusion Modeling**, as presented in the paper [Compressed and Smooth Latent Space for Text Diffusion Modeling](https://huggingface.co/papers/2506.21170).

COSMOS introduces a novel approach to text generation that operates entirely in a compressed, smooth latent space tailored specifically for diffusion. This method enables parallel generation and flexible control, achieving comparable or superior quality in tasks such as story generation, question generation, summarization, and detoxification.

The official code implementation can be found on GitHub: [MeshchaninovViacheslav/cosmos](https://github.com/MeshchaninovViacheslav/cosmos).

## Sample Usage

After training the autoencoder and diffusion model as described in the [GitHub repository](https://github.com/MeshchaninovViacheslav/cosmos), you can generate new text samples using the following command:

```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=""
```