MAMKit#

MAMKit is an open-source, publicly available PyTorch toolkit designed to access and develop datasets, models, and benchmarks for Multimodal Argument Mining (MAM). It provides a flexible interface for accessing and integrating datasets, models, and preprocessing strategies through composition or custom definition. MAMKit is designed to be extendible, ensure replicability, and provide a shared interface as a common foundation for experimentation in the field.

Currently, MAMKit offers 4 datasets, 4 tasks and 6 distinct model architectures, along with audio and text processing capabilities, organized in 5 main components.

Structure#

The toolkit is organized into five main components: configs, data, models, modules and utility. In addition to that, the toolkit provides a demos directory for running all the experiments presented in the paper. The figure below illustrates the toolkit’s structure.

Prerequisites#

Before installing MAMKit, ensure you have the following:

Python 3.10 (MAMKit is tested with this version)

FFmpeg (Required for audio processing)

You can install it via:

sudo apt install ffmpeg  # Debian/Ubuntu
brew install ffmpeg      # macOS
choco install ffmpeg     # Windows (using Chocolatey)

For other installation methods, refer to the FFmpeg official website.

Install via PyPi#

Install MAMKit using PyPi:
```
pip install mamkit
```
Access MAMKit in your Python code:
```
import mamkit
```

Install from GitHub#

This installation is recommended for users who wish to conduct experiments and customize the toolkit according to their needs.

Clone the repository and install the requirements:

git clone git@github.com:nlp-unibo/mamkit.git
cd mamkit
pip install -r requirements.txt

Access MAMKit in your Python code:
```
import mamkit
```

Contribute#

Feel free to submit a pull request! We welcome new datasets, models, and any other contribution that can improve the toolkit!

MAMKit is meant to be a community project :)

Contact#

Don’t hesitate to contact: - Eleonora Mancini - Federico Ruggeri

for questions/doubts/issues!

Citing#

If you use MAMKit in your research, please cite the following paper:

@inproceedings{TBAmamkit,
  title={MAMKit: A Comprehensive Multimodal Argument Mining Toolkit},
  author={TBA},
  booktitle={TBA},
  year={TBA}
}