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---
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.
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