# data

If you are looking for our intermediate labeling version, please refer to [mango-ttic/data-intermediate](https://huggingface.co/datasets/mango-ttic/data-intermediate)

Find more about us at [mango.ttic.edu](https://mango.ttic.edu)

## Folder Structure

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` .

```bash
data/night/
├── night.actions.json      # list of mentioned actions
├── night.all2all.jsonl      # all simple paths between any 2 locations
├── night.all_pairs.jsonl    # all connectivity between any 2 locations
├── night.edges.json        # list of all edges
├── night.locations.json    # list of all locations
└── night.walkthrough       # enriched walkthrough exported from Jericho simulator
```

## Variations

### 70-step vs all-step version

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-intermediate` collection.

* **70-step** `data-70steps.tar.zst`: contains the first 70 steps of each walkthrough. If the complete walkthrough is shorter than 70 steps, then all steps are used.

* **All-step** `data.tar.zst`: contains all steps of each walkthrough.

### Word-only & Word+ID

* **Word-only** `data.tar.zst`: Nodes are annotated by additional descriptive text to distinguish different locations with similar names.

* **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.

* **Word + Random ID** `data-randid.tar.zst`: variation of the Jericho ID version, where the Jericho object id replaced with randomly generated integer.

We primarily rely on the **word-only** version as benchmark, yet providing word+ID version for diverse benchmark settings.

## How to use

We use `data.tar.zst` as an example here.

### 1. download from Huggingface

#### by directly download

You can selectively download certain variation of your choice.
![](direct_download_data.png)

#### by git

Make sure you have [git-lfs](https://git-lfs.com) installed

```bash
git lfs install
git clone https://huggingface.co/datasets/mango-ttic/data

# or, use hf-mirror if your connection to huggingface.co is slow
# git clone https://hf-mirror.com/datasets/mango-ttic/data
```

If you want to clone without large files - just their pointers

```bash
GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/datasets/mango-ttic/data

# or, use hf-mirror if your connection to huggingface.co is slow
# GIT_LFS_SKIP_SMUDGE=1 git clone https://hf-mirror.com/datasets/mango-ttic/data
```

### 2. decompress

Because some json files are huge, we use tar.zst to package the data efficiently.

silently decompress

```bash
tar -I 'zstd -d' -xf data.tar.zst
```

or, verbosely decompress

```bash
zstd -d -c data.tar.zst | tar -xvf -
```