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".
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
Code
The official implementation can be found on GitHub: https://github.com/MeshchaninovViacheslav/cosmos
Sample Usage
After training the autoencoder and diffusion models as described in the GitHub repository, 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:
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=""