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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).

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