Improve dataset card: Add paper/code links, task categories, tags, and sample usage

README.md

@@ -19,4 +19,45 @@ configs:
     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=""
+```