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This is a fine-tuned Gemma-2-9b model. Fine tuning was carried out using a synthetic dataset of 1,800 questions and answers in Spanish language divided in three levels of complexity.
The synthetic dataset was generated using Python and, the highest level of complexity, I used the V1 LLM of DeepSeek.
The original dataset included the complete electoral results of both 2019 general elections (April and November), along with some socio-economic data.
The electoral results were collected at 'section level', around an average of 700-800 electors. There are around 36,500 sections in Spain, therefore the dataset had 72,500 rows and 95 columns.
I finetuned the model using the transformer library, obtaining a Validation Loss of 0.32 after 3 epochs.
Model Details
Model Description
The model answers questions about the election results, and their relations with socioeconomic data, such as income per capita.
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by: Guillermo Barrio [More Information Needed]
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