maverick-coref-de 1.0.6
co-reference resolution for German using the Maverick system
Requires Python: >=3.10

<h1 align="center">
  German Maverick Coref
</h1>
<div align="center">


[![License: CC BY-NC 4.0](https://img.shields.io/badge/License-CC%20BY--NC--SA%204.0-green.svg)](https://creativecommons.org/licenses/by-nc-sa/4.0/)
[![Pip Package](https://img.shields.io/badge/🐍%20Python%20package-blue)](https://pypi.org/project/maverick-coref-de/)
[![git](https://img.shields.io/badge/Git%20Repo%20-yellow.svg)](https://github.com/uhh-lt/maverick-coref-de)
</div>


# Python Package
The `maverick-coref-de` Python package provides an easy API to use German Maverick models, enabling efficient and accurate coreference resolution with few lines of code.

Install the library from [PyPI](https://pypi.org/project/maverick-coref-de/)

```bash
pip install maverick-coref-de
```
or from source 

```bash
git clone https://github.com/uhh-lt/maverick-coref-de.git
cd maverick-coref-de
pip install -e .
```

## Loading a Pretrained Model
Maverick models can be loaded using huggingface_id or local path:
```bash
from maverick_de import Maverick
model = Maverick(
  hf_name_or_path = "maverick_hf_name" | "maverick_ckpt_path", default = "fynnos/maverick-mes-de10"
  device = "cpu" | "cuda", default = "cuda:0"
)
```
## Inference

### Predict
You can use model.predict() to obtain coreference predictions.
For a sample input, the model will a dictionary containing:
- `tokens`, word tokenized version of the input.
- `clusters_token_offsets`, a list of clusters containing mentions' token offsets.
- `clusters_text_mentions`, a list of clusters containing mentions in plain text.


# Training

Create a Python venv and install from source.

```bash
git clone https://github.com/uhh-lt/maverick-coref-de.git
cd maverick-coref-de
pip install -e .
```

* Obtain data in `.conll` format split into train/dev/test
* Run the `minimize.py` script from `data` for the correct dataset
* Adjust `conf/data/<your dataset>.yaml` for your dataset
* Adjust `conf/model/mes/<your encoder model>.yaml` to 
* Adjust `conf/root.yaml` to use the your dataset and your encoder model
* Run `CUDA_VISIBLE_DEVICES=X python maverick_de/train.py`



# Citation
If you use this software, please consider citing our paper published at KONVENS 2025:

```bibtex
@inproceedings{petersenfrey-etal-2025-efficient,
    title = "Efficient and effective coreference resolution for German",
    author = "Petersen-Frey, Fynn and Hatzel, Hans Ole and Biemann, Chris",
    booktitle = "Proceedings of the 21st Conference on Natural Language Processing (KONVENS 2025). Volume 1: Long and Short Papers",
    month = "9",
    year = "2025",
    address = "Hildesheim, Germany",
    publisher = "KONVENS 2025 Organizers"
}
```

The software in this repository is based on the on the work "Maverick: Efficient and Accurate Coreference Resolution Defying Recent Trends" by Giuliano Martinelli, Edoardo Barba, and Roberto Navigli published at [ACL 2024 main conference](https://aclanthology.org/2024.acl-long.722.pdf).
It uses their implementation forked from the [original repository](https://github.com/SapienzaNLP/maverick-coref) with some adaptions to a) make it compatible with German and b) try additional model variants.
For English, refer to the [original python package](https://pypi.org/project/maverick-coref/).


## License
The data and software are licensed under [Creative Commons Attribution-NonCommercial-ShareAlike 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/).