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.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 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
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.