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| license: bigscience-bloom-rail-1.0 |
| language: |
| - it |
| --- |
| # Model Card for Model ID |
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| <!-- Provide a quick summary of what the model is/does. --> |
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| This model is obtained by adapting bloom-1b7 to the Italian language. Among the languages supported by the BLOOM model, there is no Italian, making its use |
| in that context challenging. We adapt the original BLOOM model using the MAD-X language adaptation strategy. |
| Then, the adapted model is fine-tuned over two classification task prompts. 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). |
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| ## Model Details |
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| ### Model Description |
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| We adapt the bloom-1b7 to the Italian language using the MAD-X language adaptation strategy. |
| To produce a valuable model, we follow the same procedure proposed in: https://arxiv.org/abs/2212.09535 |
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| We use default script parameters and select a sample of 100,000 examples in the Italian language. We decided to sample data from the Filtered Oscar Dataset for |
| the Italian Language released by Sarti. |
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| Then the adopted model is fine-tuned over two classification task prompts using two well-known EVALITA tasks: AMI2020 (misogyny detection) |
| and HASPEEDE-v2-2020 (hate-speech detection). |
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| 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: |
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| *instruction: Nel testo seguente si esprime odio contro le donne? Rispondi sì o no., input: \<text\>, output: \<sì/no\>.* |
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| Similarly, for HASPEEDE we used: |
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| *instruction: “Il testo seguente incita all’odio? Rispondi sì o no., input: \<text\>, output: \<sì/no\>.* |
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| 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. |
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| To fine-tune the adapted model, we use the script available here: https://github.com/hyintell/BLOOM-fine-tuning/tree/main |
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| **It is important to underline that when you use the adapted LLM or one of its fine-tuned models is necessary to use the tokenizer of the adapted model. |
| The BLOOM model adapted to the Italian language is available here: https://huggingface.co/basilepp19/bloom-1b7_it.** |
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| - **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 |
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| ## Citation |
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| 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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