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--- |
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language: |
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- en |
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size_categories: |
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- 10K<n<100K |
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config_names: |
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- chat |
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configs: |
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- config_name: chat |
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default: true |
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data_files: |
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- split: train |
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path: data/train.csv |
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- split: validation |
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path: data/valid.csv |
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- split: test |
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path: data/test_iid.csv |
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- split: test_geo |
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path: data/test_geo.csv |
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- split: test_vis |
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path: data/test_vis.csv |
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- split: test_cat |
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path: data/test_cat.csv |
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- split: test_web |
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path: data/test_web.csv |
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tags: |
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- conversational |
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- image-to-text |
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- vision |
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- convAI |
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task_categories: |
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- image-to-text |
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- text-generation |
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- text2text-generation |
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- sentence-similarity |
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pretty_name: weblinx |
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--- |
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<div align="center"> |
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<h1 style="margin-bottom: 0.5em;">WebLINX: Real-World Website Navigation with Multi-Turn Dialogue</h1> |
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<em>Xing Han Lù*, Zdeněk Kasner*, Siva Reddy</em> |
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</div> |
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<div style="margin-bottom: 2em"></div> |
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<div style="display: flex; justify-content: space-around; align-items: center; font-size: 120%;"> |
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<div><a href="https://arxiv.org/abs/2402.05930">📄Paper</a></div> |
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<div><a href="https://mcgill-nlp.github.io/weblinx">🌐Website</a></div> |
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<div><a href="https://huggingface.co/spaces/McGill-NLP/weblinx-explorer">💻Explorer</a></div> |
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<div><a href="https://github.com/McGill-NLP/WebLINX">💾Code</a></div> |
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<div><a href="https://twitter.com/sivareddyg/status/1755799365031965140">🐦Tweets</a></div> |
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<div><a href="https://huggingface.co/collections/McGill-NLP/weblinx-models-65c57d4afeeb282d1dcf8434">🤖Models</a></div> |
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</div> |
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<video width="100%" controls autoplay muted loop> |
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<source src="https://huggingface.co/datasets/McGill-NLP/WebLINX/resolve/main/WeblinxWebsiteDemo.mp4?download=false" type="video/mp4"> |
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Your browser does not support the video tag. |
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</video> |
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## Quickstart |
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To get started, simply install `datasets` with `pip install datasets` and load the chat data splits: |
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```python |
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from datasets import load_dataset |
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from huggingface_hub import snapshot_download |
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# Load the validation split |
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valid = load_dataset("McGill-NLP/weblinx", split="validation") |
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|
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# Download the input templates and use the LLaMA one |
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snapshot_download( |
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"McGill-NLP/WebLINX", repo_type="dataset", allow_patterns="templates/*", local_dir="." |
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) |
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with open('templates/llama.txt') as f: |
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template = f.read() |
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# To get the input text, simply pass a turn from the valid split to the template |
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turn = valid[0] |
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turn_text = template.format(**turn) |
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``` |
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You can now use `turn_text` as an input to LLaMA-style models. For example, you can use Sheared-LLaMA: |
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```python |
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from transformers import pipeline |
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action_model = pipeline( |
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model="McGill-NLP/Sheared-LLaMA-2.7B-weblinx", device=0, torch_dtype='auto' |
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) |
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out = action_model(turn_text, return_full_text=False, max_new_tokens=64, truncation=True) |
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pred = out[0]['generated_text'] |
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print("Ref:", turn["action"]) |
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print("Pred:", pred) |
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``` |
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## Raw Data |
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To use the raw data, you will need to use the `huggingface_hub`: |
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```python |
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from huggingface_hub import snapshot_download |
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snapshot_download(repo_id="McGill-NLP/WebLINX-full", repo_type="dataset", local_dir="./wl_data") |
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|
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# You can download specific demos, for example |
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demo_names = ['saabwsg', 'ygprzve', 'iqaazif'] # 3 random demo from valid |
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patterns = [f"demonstrations/{name}/*" for name in demo_names] |
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snapshot_download( |
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repo_id="McGill-NLP/WebLINX-full", repo_type="dataset", local_dir="./wl_data", allow_patterns=patterns |
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) |
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``` |
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For more information on how to use this data using our [official library](https://github.com/McGill-NLP/WebLINX), please refer to the [WebLINX documentation](https://mcgill-nlp.github.io/weblinx/docs). |
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## Citation |
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If you use our dataset, please cite our work as follows: |
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```bibtex |
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@misc{lu-2024-weblinx, |
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title={WebLINX: Real-World Website Navigation with Multi-Turn Dialogue}, |
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author={Xing Han Lù and Zdeněk Kasner and Siva Reddy}, |
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year={2024}, |
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eprint={2402.05930}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
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``` |