Datasets:
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 ReddyQuickstart
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="./wl_data")
# You can download specific demos, for example
demo_names = ['saabwsg', 'ygprzve', 'iqaazif'] # 3 random demo from valid
patterns = [f"demonstrations/{name}/*" for name in demo_names]
snapshot_download(
repo_id="McGill-NLP/WebLINX-full", repo_type="dataset", local_dir="./wl_data", allow_patterns=patterns
)
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}
}