Peng Ding
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Browse files- README.md +73 -0
- direct_download_data.png +3 -0
README.md
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# data
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If you are looking for our intermediate labeling version, please refer to [mango-ttic/data-intermediate](https://huggingface.co/datasets/mango-ttic/data-intermediate)
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Find more about us at [mango.ttic.edu](https://mango.ttic.edu)
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## Folder Structure
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Each folder inside `data` contains the cleaned up files used during LLM inference and results evaluations. Here is the tree structure from game `data/night` .
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```bash
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data/night/
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├── night.actions.json # list of mentioned actions
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├── night.all2all.json # all simple paths between any 2 locations
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├── night.all_pairs.json # all connectivity between any 2 locations
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├── night.edges.json # list of all edges
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├── night.locations.json # list of all locations
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└── night.walkthrough # enriched walkthrough exported from Jericho simulator
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```
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## Variations
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<!-- ### 70-step vs all-step version
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In our paper, we benchmark using the first 70 steps of the walkthrough from each game. We also provide all-step versions of both `data` and `data` collection. -->
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### Word-only & Word+ID
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* **Word-only** `data.tar.zst`: Nodes are annotated by additional descriptive text to distinguish different locations with similar names.
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* **Word + Object ID** `data-objid.tar.zst`: variation of the word-only version, where nodes are labeled using minimaly fixed names with object id from Jericho simulator.
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* **Word + Random ID** `data-randid.tar.zst`: variation of the Jericho ID version, where the Jericho object id replaced with randomly generated integer.
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We primarily rely on the **word-only** version as benchmark, yet providing word+ID version for diverse benchmark settings.
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## How to use
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We use `data.tar.zst` as an example here.
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### 1. download from Huggingface
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#### by directly download
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![](direct_download_data.png)
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#### by git
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Make sure you have [git-lfs](https://git-lfs.com) installed
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```bash
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git lfs install
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git clone https://huggingface.co/datasets/mango-ttic/data
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# or, use hf-mirror if your connection to huggingface.co is slow
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# git clone https://hf-mirror.com/datasets/mango-ttic/data
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```
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### 2. decompress
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Because some json files are huge, we use tar.zst to package the data efficiently.
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silently decompress
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```bash
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tar -I 'zstd -d' -xf data.tar.zst
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```
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or, verbosely decompress
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```bash
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zstd -d -c data.tar.zst | tar -xvf -
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```
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direct_download_data.png
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Git LFS Details
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