README.md

---
language:
- en
size_categories:
- 10K<n<100K
config_names:
- chat
configs:
- config_name: chat
  default: true
  data_files:
  - split: train
    path: data/train.csv
  - split: validation
    path: data/valid.csv
  - split: test
    path: data/test.csv
  - split: test_geo
    path: data/test_geo.csv
  - split: test_vis
    path: data/test_vis.csv
  - split: test_cat
    path: data/test_cat.csv
  - split: test_web
    path: data/test_web.csv
tags:
- conversational
- image-to-text
- vision
- convAI
---

<div align="center">
  <h1 style="margin-bottom: 0.5em;">WebLINX: Real-World Website Navigation with Multi-Turn Dialogue</h1>
  <em>Xing Han Lù*, Zdeněk Kasner*, Siva Reddy</em>
</div>

<div style="margin-bottom: 2em"></div>

<div style="display: flex; justify-content: space-around; align-items: center; font-size: 110%;">
  <div><a href="https://arxiv.org/abs/2402.05930">📄Paper</a></div>
  <div><a href="https://mcgill-nlp.github.io/weblinx">🌐Website</a></div>
  <div><a href="https://huggingface.co/spaces/McGill-NLP/weblinx-explorer">💻Explorer</a></div>
  <div><a href="https://github.com/McGill-NLP/WebLINX">💻Code</a></div>
</div>



## Quickstart

To get started, simply install `datasets` with `pip install datasets` and load the chat data splits:

```python
from datasets import load_dataset

# Load the training, validation and test (IID) splits
train = load_dataset("McGill-NLP/weblinx", "train")
valid = load_dataset("McGill-NLP/weblinx", "validation")
test = load_dataset("McGill-NLP/weblinx", "test")

# Load one of the 4 out-of-domain splits (test_web, test_vis, test_geo, test_cat)
test_web = load_dataset("McGill-NLP/weblinx", "test_web")
```

## Raw Data

To use the raw data, you will need to use the `huggingface_hub`:

```python
from huggingface_hub import snapshot_download

snapshot_download(repo_id="McGill-NLP/WebLINX-full", repo_type="dataset", local_dir="./data/weblinx")
```

For more information on how to use this data using our [official library](https://github.com/McGill-NLP/WebLINX), please refer to the [WebLINX documentation](https://mcgill-nlp.github.io/WebLINX/docs).