---
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:

**Demo paper**:

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**Main paper**:

```bibtex

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