E-MIMIC/inclusively-classification
text classificationtransformerstransformerspytorchsafetensorsberttext-classificationlicense:cc-by-nc-sa-4.0cc-by-nc-sa-4.0
HuggingFace290.0K
Inclusively Classification Model
This model is an Italian classification model fine-tuned from the Italian BERT model 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 using the following hyperparameters:
max_length: 128batch_size: 128learning_rate: 5e-5warmup_steps: 500epochs: 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:
@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:
@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"
}