README.md

---
license: cc-by-nc-sa-4.0
---

# Inclusively Classification Model

This model is an Italian classification model fine-tuned from the [Italian BERT model](https://huggingface.co/dbmdz/bert-base-italian-xxl-cased) for the classification of inclusive language in Italian.

It has been trained to detect three classes:
- `inclusive`: the sentence is inclusive (e.g. "Il personale docente e non docente")
- `not_inclusive`: the sentence is not inclusive (e.g. "I professori")
- `not_pertinent`: the sentence is not pertinent to the task (e.g. "La scuola è chiusa")

## Training data

The model has been trained on a dataset containing:
- 8580 training sentences
- 1073 validation sentences
- 1072 test sentences

The data collection has been manually annotated by experts in the field of inclusive language (dataset is not publicly available yet).

## Training procedure

The model has been fine-tuned from the [Italian BERT model](https://huggingface.co/dbmdz/bert-base-italian-xxl-cased) using the following hyperparameters:
- `max_length`: 128
- `batch_size`: 128
- `learning_rate`: 5e-5
- `warmup_steps`: 500
- `epochs`: 10 (best model is selected based on validation accuracy)
- `optimizer`: AdamW

## Evaluation results

The model has been evaluated on the test set and obtained the following results:

| Model | Accuracy | Inclusive F1 | Not inclusive F1 | Not pertinent F1 |
|-------|----------|--------------|------------------|------------------|
| TF-IDF + MLP | 0.68 | 0.63 | 0.69 | 0.66 |
| TF-IDF + SVM | 0.61 | 0.53 | 0.60 | 0.78 |
| TF-IDF + GB  | 0.74 | 0.74 | 0.76 | 0.72 |
| multilingual | 0.86 | 0.88 | 0.89 | 0.83 |
| **This**     | 0.89 | 0.88 | 0.92 | 0.85 |

The model has been compared with a multilingual model trained on the same data and obtained better results.

## Citation

If you use this model, please make sure to cite the following papers:

**Main paper**:

```bibtex
@article{10.1145/3729237,
author = {Greco, Salvatore and La Quatra, Moreno and Cagliero, Luca and Cerquitelli, Tania},
title = {Towards AI-Assisted Inclusive Language Writing in Italian Formal Communications},
year = {2025},
issue_date = {August 2025},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {16},
number = {4},
issn = {2157-6904},
url = {https://doi.org/10.1145/3729237},
doi = {10.1145/3729237},
journal = {ACM Trans. Intell. Syst. Technol.},
month = jun,
articleno = {79},
numpages = {24},
keywords = {inclusive language, natural language processing, text classification, text generation}
}
```

**Demo paper**:

```bibtex
@InProceedings{PKDD23_inclusively,
author="La Quatra, Moreno
and Greco, Salvatore
and Cagliero, Luca
and Cerquitelli, Tania",
title="Inclusively: An AI-Based Assistant for Inclusive Writing",
booktitle="Machine Learning and Knowledge Discovery in Databases: Applied Data Science and Demo Track",
year="2023",
publisher="Springer Nature Switzerland",
address="Cham",
pages="361--365",
isbn="978-3-031-43430-3",
doi="10.1007/978-3-031-43430-3_31"
}
```