---
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
task_categories:
- image-to-text
- text-generation
- conversational
- text2text-generation
- sentence-similarity
pretty_name: weblinx
---

<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: 120%;">
  <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><a href="https://twitter.com/sivareddyg/status/1755799365031965140">🐦Tweets</a></div>
  <div><a href="https://huggingface.co/collections/McGill-NLP/weblinx-models-65c57d4afeeb282d1dcf8434">🤖Models</a></div>
</div>

<video width="100%" controls autoplay muted loop>
    <source src="https://huggingface.co/datasets/McGill-NLP/WebLINX/resolve/main/WeblinxWebsiteDemo.mp4?download=false" type="video/mp4">
    Your browser does not support the video tag.
</video>


## 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", split="train")
valid = load_dataset("McGill-NLP/weblinx", split="validation")
test = load_dataset("McGill-NLP/weblinx", split="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", split="test_web")

# Download and read template
snapshot_download(
  repo_id="McGill-NLP/WebLINX", repo_type="dataset", allow_patterns="template.txt", local_dir="./"
)
with open('template.txt') as f:
    template = f.read()

# Get the input text for a model by formatting the template with the correct
turn_data = valid[0]
input_text = template.format(**turn_data)

# input_text can be given to a model
```

## 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).