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README.md

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 license: bigscience-bloom-rail-1.0
+language:
+- it
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+# 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).
+
+
+