Improve dataset card: Add metadata, HF paper link, and sample usage

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by nielsr HF Staff - opened
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  1. README.md +41 -9
README.md CHANGED
@@ -1,20 +1,28 @@
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  ---
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  license: apache-2.0
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  pretty_name: ImitSAT KeyTrace
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- # Show only PREVIEW files in the Viewer (full buckets remain downloadable)
 
 
 
 
 
 
 
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  configs:
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  - config_name: preview
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  default: true
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  data_files:
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  - split: train
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  path:
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- - preview/sat_5_15_preview.jsonl
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- - preview/sat_16_30_preview.jsonl
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- - preview/sat_31_60_preview.jsonl
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- - preview/sat_50_50_preview.jsonl
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- - preview/sat_61_100_preview.jsonl
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- - preview/sat_100_100_preview.jsonl
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  ---
 
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  <p align="center">
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  <h1 align="center"><em>ImitSAT‑KeyTrace</em> (Download & Preview)</h1>
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  <div align="center">
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  <img src="https://img.shields.io/badge/arXiv-Preprint-brightgreen.svg" alt="arXiv Preprint">
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  </a>
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  &nbsp;
 
 
 
 
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  <a href="https://github.com/zewei-Zhang/ImitSAT">
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  <img src="https://img.shields.io/badge/GitHub-Repo-black?logo=github" alt="GitHub repository">
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  </a>
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  </div>
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  </p>
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- **Summary.** This repository hosts the bucketed training data for **ImitSAT**, a learned branching policy for CDCL SAT solving.
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  Each bucket is a gzip‑compressed JSON Lines file (`*.jsonl.gz`) of **KeyTrace** training examples for random 3‑SAT with planted assignments.
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  > ⚠️ **Use our code to load & preprocess.** We intentionally **do not** provide a `datasets.load_dataset` builder here.
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  - [sat_100_100.jsonl.gz](https://huggingface.co/datasets/zeweizhang/ImitSAT-KeyTrace/resolve/main/sat_100_100.jsonl.gz)
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  ## Preview
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  The Dataset Viewer shows only small, human‑readable files under preview/ (100 examples each)
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  primaryClass={cs.AI},
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  url={https://arxiv.org/abs/2509.25411},
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  }
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- ```
 
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  ---
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  license: apache-2.0
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  pretty_name: ImitSAT KeyTrace
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+ language:
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+ - en
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+ task_categories:
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+ - reinforcement-learning
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+ tags:
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+ - sat
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+ - boolean-satisfiability
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+ - imitation-learning
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  configs:
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  - config_name: preview
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  default: true
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  data_files:
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  - split: train
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  path:
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+ - preview/sat_5_15_preview.jsonl
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+ - preview/sat_16_30_preview.jsonl
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+ - preview/sat_31_60_preview.jsonl
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+ - preview/sat_50_50_preview.jsonl
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+ - preview/sat_61_100_preview.jsonl
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+ - preview/sat_100_100_preview.jsonl
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  ---
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+
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  <p align="center">
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  <h1 align="center"><em>ImitSAT‑KeyTrace</em> (Download & Preview)</h1>
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  <div align="center">
 
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  <img src="https://img.shields.io/badge/arXiv-Preprint-brightgreen.svg" alt="arXiv Preprint">
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  </a>
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  &nbsp;
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+ <a href="https://huggingface.co/papers/2509.25411">
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+ <img src="https://img.shields.io/badge/HF-Paper-blue" alt="Hugging Face Paper">
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+ </a>
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+ &nbsp;
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  <a href="https://github.com/zewei-Zhang/ImitSAT">
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  <img src="https://img.shields.io/badge/GitHub-Repo-black?logo=github" alt="GitHub repository">
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  </a>
 
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  </div>
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  </p>
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+ **Summary.** This repository hosts the bucketed training data for **ImitSAT**, a learned branching policy for CDCL SAT solving, presented in the paper [Boolean Satisfiability via Imitation Learning](https://huggingface.co/papers/2509.25411).
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  Each bucket is a gzip‑compressed JSON Lines file (`*.jsonl.gz`) of **KeyTrace** training examples for random 3‑SAT with planted assignments.
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  > ⚠️ **Use our code to load & preprocess.** We intentionally **do not** provide a `datasets.load_dataset` builder here.
 
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  - [sat_100_100.jsonl.gz](https://huggingface.co/datasets/zeweizhang/ImitSAT-KeyTrace/resolve/main/sat_100_100.jsonl.gz)
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+ ## Sample Usage
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+
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+ This dataset is designed to be used with the ImitSAT code repository for training. The official loader for this dataset is `ImitSAT/ImitSAT_dataset.py` within the [ImitSAT GitHub repository](https://github.com/zewei-Zhang/ImitSAT).
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+
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+ ### Environment Setup
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+ First, set up the required Python environment as specified in the GitHub repository:
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+ ```bash
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+ conda create -n imitsat python=3.10 -y
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+ conda activate imitsat
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+ pip install -r requirements.txt
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+ ```
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+
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+ ### Training ImitSAT
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+ After setting up the environment and downloading the `ImitSAT-KeyTrace` dataset (as shown in the Download section above), you can train the ImitSAT model by pointing your training configuration's `dataset_path` to the downloaded folder. An example training command from the ImitSAT repository is:
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+ ```bash
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+ python train_ImitSAT.py --config ./model_config/ImitSAT_config.json
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+ # (Optional) resume from a folder containing ImitSAT.npz and tokenizer/
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+ python train_ImitSAT.py --config ./model_config/ImitSAT_config.json --resume_dir ./model_ckpt/
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+ ```
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+
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  ## Preview
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  The Dataset Viewer shows only small, human‑readable files under preview/ (100 examples each)
 
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  primaryClass={cs.AI},
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  url={https://arxiv.org/abs/2509.25411},
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  }
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+ ```