metadata

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

WebLINX: Real-World Website Navigation with Multi-Turn Dialogue

Xing Han Lù, Zdeněk Kasner, Siva Reddy

Link to the website

Quickstart

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

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", "valid")
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:

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, please refer to the WebLINX documentation.