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
task_categories:
  - image-to-text
  - text-generation
  - conversational
  - 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

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

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

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.