|
--- |
|
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 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 an Italian translation of the dolly dataset and 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). |
|
|
|
## Model Details |
|
|
|
### Model Description |
|
|
|
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 |
|
|
|
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. |
|
|
|
Then, the adopted model is fine-tuned over an Italian translation of the dolly dataset and two 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. |
|
|
|
The dolly dataset is automatically translated into Italian using an open-source machine translation tool: https://pypi.org/project/argostranslate/ |
|
|
|
To fine-tune the adapted model, we use the script available here: https://github.com/hyintell/BLOOM-fine-tuning/tree/main |
|
|
|
**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.** |
|
|
|
- **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). |
|
|
|
|
|
|
