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WebLINX is a benchmark for building web navigation agents with conversational capabilities

weblinx 최신버젼 다운로드

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# WebLINX

| [**🤗Dataset**](https://huggingface.co/datasets/McGill-NLP/WebLINX) | [**📄Paper**](https://arxiv.org/abs/2402.05930) | [**🌐Website**](https://mcgill-nlp.github.io/weblinx) | [**📓Colab**](https://colab.research.google.com/github/McGill-NLP/weblinx/blob/main/examples/WebLINX_Colab_Notebook.ipynb) |
| :--: | :--: | :--: | :--: |
| [**🤖Models**](https://huggingface.co/collections/McGill-NLP/weblinx-models-65c57d4afeeb282d1dcf8434) | [**💻Explorer**](https://huggingface.co/spaces/McGill-NLP/weblinx-explorer) | [**🐦Tweets**](https://twitter.com/sivareddyg/status/1755799365031965140) | [**🏆Leaderboard**](https://paperswithcode.com/sota/conversational-web-navigation-on-weblinx) |

<br>

**[WebLINX: Real-World Website Navigation with Multi-Turn Dialogue](https://mcgill-nlp.github.io/weblinx)**\
*[Xing Han Lù*](https://xinghanlu.com), [Zdeněk Kasner*](https://kasnerz.github.io/), [Siva Reddy](https://sivareddy.in)*\
_\*Equal contribution_\
**ICML 2024 (Spotlight)**

</div>

> [!IMPORTANT] 
> WebLINX is now available as a benchmark through [BrowserGym](https://github.com/ServiceNow/BrowserGym), allowing you to access demonstration steps in the same way you would access a web agent environment like [WebArena](https://webarena.dev/) or [MiniWoB](https://miniwob.farama.org/index.html). This also allows you to run agents from the [Agentlab](https://github.com/ServiceNow/AgentLab) library, including agents that achieve SOTA performance through Claude-3.5-Sonnet. To enable this integration, we are releasing the `weblinx-browsergym` extension for BrowserGym on PyPi, as well as a [new dataset, WebLINX 1.1, derived from WebLINX on Huggingface](https://huggingface.co/datasets/McGill-NLP/weblinx-browsergym). In WebLINX 1.1, a small number of demonstrations were removed after processing, but no new demonstration was added. There are substantial changes to the steps being evaluated, with the inclusion of tab actions. Please report your results as "WebLINX-1.1", "WebLINX-BrowserGym" or "WebLINX-BG" in your work, to differentiate from the [initial release of weblinx (1.0)](https://huggingface.co/datasets/McGill-NLP/WebLINX/tree/v1.0).

## Intro

Welcome to `WebLINX`'s official repository! In addition to providing code used to train [the models](https://huggingface.co/collections/McGill-NLP/weblinx-models-65c57d4afeeb282d1dcf8434) reported in our [WebLINX paper](https://arxiv.org/abs/2402.05930), we also provide a comprehensive Python library (aka API) to help you work with the [WebLINX dataset](https://huggingface.co/datasets/McGill-NLP/WebLINX).

If you want to get started with `weblinx`, please check out the following places:

| | | |
| :---: | :---: | --- |
| 🌐 | [Website](https://mcgill-nlp.github.io/weblinx) | If you want a quick overview of the project, this is the best place to start.|
| 📓 | [Colab](https://colab.research.google.com/github/McGill-NLP/weblinx/blob/main/examples/WebLINX_Colab_Notebook.ipynb) | Eager to try it out? Start by running this colab notebook!|
| 🗄️ | [Docs](https://mcgill-nlp.github.io/weblinx/docs/) | You can find quickstart instructions, the official user guide, and all relevant API specifications in the docs. |
| 📄 | [Paper](https://arxiv.org/abs/2402.05930) | If you want to get more in-depth, please read our paper, which provides comprehensive description of the project and report relevant results.|
| 🤗 | [Dataset](https://huggingface.co/datasets/McGill-NLP/WebLINX) | The official dataset page, you can download preprocessed dataset and follow instructions to get started.|
| | | |

*If you want to learn more about the codebase itself, please keep on reading!*

## Installation

```bash
# Install the base package
pip install weblinx

# Install all dependencies
pip install weblinx[all]

# Install specific dependencies for...
# ...processing HTML 🖥️
pip install weblinx[processing]
# ...video processing 📽️
pip install weblinx[video]
# ...evaluating models 🔬
pip install weblinx[eval]
# ...development of this library 🛠️
pip install weblinx[dev]
```

## Structure

This repository is structured in the following way:

| Module | Description |
| --- | --- |
| `weblinx` | The `__init__.py` provides many useful abstractions to provide a Pythonic experience when working with the dataset. For example, you can use `weblinx.Demonstration` to manipulate a demonstration at a high-level, `weblinx.Replay` to focus on more finegrained details of the demonstration, including iterating over turns, or `weblinx.Turn` to focus on a specific turn. All relevant information is included in the documentations! |
| `weblinx.eval` | Code for evaluating action models trained with WebLINX, it has both `import`able functions/metrics, but can also be accessed via command line |
| `weblinx.processing` | Code for processing various inputs or outputs used by the models, it is extensively used in the models' processing code |
| `weblinx.utils` | Miscellaneous utility functions used across the codebase. |

## Modeling

Our `modeling/` repo-level directory has code for processing, training and evaluating the models reported in the paper (DMR, LLaMA, MindAct, Pix2Act, Flan-T5). It is separate from the `weblinx` library, which focuses on data processing and evaluation. You can use it by cloning this repository, and it is recommended to edit the files in `modeling/` directly for your own needs. Our modeling code is separate from the `weblinx` library, but requires it as a dependency. You can install the modeling code by running:

```bash
# First, install the base package
pip install weblinx

# Then, clone this repo
git clone https://github.com/McGill-NLP/weblinx
cd weblinx/modeling
```

For the rest of the instructions, please take a look at the [modeling README](./modeling/README.md).

## Evaluation

To install packages necessary for evaluation, run:

```bash
pip install weblinx[eval]
```

You can now access the evaluation module by importing in Python:

```python
import weblinx.eval
```

Use `weblinx.eval.metrics` for evaluation metrics, `weblinx.eval.__init__` for useful evaluation-related functions. You may also find it useful to take a look at `weblinx.processing.outputs` to get an idea of how to use the outputs of the model for evaluation.

To run the automatic evaluation, you can use the following command:

```bash
python -m weblinx.eval --help
```

For more examples on how to use `weblinx.eval`, take a look at the [modeling README](./modeling/README.md).

> Note: We are still working on the code for `weblinx.eval` and `weblinx.processing.outputs`. If you have any questions or would like to contribute docs, please feel free to open an issue or a pull request.

## Citations

If you use this library, please cite our work using the following:
```bibtex
@misc{lù2024weblinx,
      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}
}
```

## License

This project's license can be found at [LICENSE](./LICENSE). Please note that the license of the data in `tests/data` follow the license from the official dataset, not the license of this repository. The official dataset's license can be found in the [official dataset page](https://huggingface.co/datasets/McGill-NLP/WebLINX). The license of the models trained using this repo might also differ - please find them in the respective model cards.

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