---
language: en
license: mit
tags:
- financial-qa
- distilgpt2
- fine-tuned
datasets:
- financial-qa
metrics:
- perplexity
---

# Financial QA Fine-Tuned Model

This model is a fine-tuned version of `distilgpt2` on financial question-answering data from Allstate's financial reports.

## Model description

The model was fine-tuned to answer questions about Allstate's financial reports and performance.

## Intended uses & limitations

This model is intended to be used for answering factual questions about Allstate's financial reports for 2022-2023.
It should not be used for financial advice or decision-making without verification from original sources.

## Training data

The model was trained on a custom dataset of financial QA pairs derived from Allstate's 10-K reports.

## Training procedure

The model was fine-tuned using the `Trainer` class from Hugging Face's Transformers library with the following parameters:
- Learning rate: default
- Batch size: 2
- Number of epochs: 3

## Evaluation results

The model achieved a final training loss of 0.44 and validation loss of 0.43.

## Limitations and bias

This model has limited knowledge only of Allstate's financial data and cannot answer questions about other companies or financial topics outside its training data.