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_iid.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
  - text2text-generation
  - sentence-similarity
pretty_name: weblinx

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

Xing Han Lù*, Zdeněk Kasner*, Siva Reddy

Quickstart

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

from datasets import load_dataset
from huggingface_hub import snapshot_download

# Load the validation split
valid = load_dataset("McGill-NLP/weblinx", split="validation")

# Download the input templates and use the LLaMA one
snapshot_download(
    "McGill-NLP/WebLINX", repo_type="dataset", allow_patterns="templates/*", local_dir="."
)
with open('templates/llama.txt') as f:
    template = f.read()

# To get the input text, simply pass a turn from the valid split to the template
turn = valid[0]
turn_text = template.format(**turn)

You can now use turn_text as an input to LLaMA-style models. For example, you can use Sheared-LLaMA:

from transformers import pipeline

action_model = pipeline(
    model="McGill-NLP/Sheared-LLaMA-2.7B-weblinx", device=0, torch_dtype='auto'
)
out = action_model(turn_text, return_full_text=False, max_new_tokens=64, truncation=True)
pred = out[0]['generated_text']

print("Ref:", turn["action"])
print("Pred:", pred)

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.

Citation

If you use our dataset, please cite our work as follows:

@misc{lu-2024-weblinx,
      title={WebLINX: Real-World Website Navigation with Multi-Turn Dialogue}, 
      author={Xing Han Lù and Zdeněk Kasner and Siva Reddy},
      year={2024},
      eprint={2402.05930},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}