README.md

---
license: bigscience-bloom-rail-1.0
language:
- it
---
# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->

This model is obtained by fine-tuning the BLOOM model over two Italian classification task prompts without language adaptation. To deal with this step, we decided to 
use data from two well-known EVALITA tasks: AMI2020 (misogyny detection) and HASPEEDE-v2-2020 (hate-speech detection).

## Model Details

### Model Description

The BLOOM model is directly fine-tuned over two Italian classification task prompts using two well-known EVALITA tasks: AMI2020 (misogyny detection)
and HASPEEDE-v2-2020 (hate-speech detection).

We transformed the training data of the two tasks into an LLM prompt following a template. For the AMI task, we used the following template:

*instruction: Nel testo seguente si esprime odio contro le donne? Rispondi sì o no., input: \<text\>, output: \<sì/no\>.*

Similarly, for HASPEEDE we used:

*instruction: “Il testo seguente incita all’odio? Rispondi sì o no., input: \<text\>, output: \<sì/no\>.*

To fill these templates, we mapped the label "1" with the word "sì" and the label "0" with the word "no", \<text\> is just the sentence from the
dataset to classify.

To fine-tune the model, we use the script available here: https://github.com/hyintell/BLOOM-fine-tuning/tree/main

- **Developed by:** Pierpaolo Basile, Pierluigi Cassotti, Marco Polignano, Lucia Siciliani, Giovanni Semeraro. Department of Computer Science, University of Bari Aldo Moro, Italy
- **Model type:** BLOOM
- **Language(s) (NLP):** Italian
- **License:** BigScience BLOOM RAIL 1.0

## Citation

Pierpaolo Basile, Pierluigi Cassotti, Marco Polignano, Lucia Siciliani, Giovanni Semeraro. On the impact of Language Adaptation for Large Language Models: A
case study for the Italian language using only open resources. Proceedings of the Ninth Italian Conference on Computational Linguistics (CLiC-it 2023).