---
language: en
datasets:
- Jean-Baptiste/financial_news_sentiment_mixte_with_phrasebank_75
metrics:
- accuracy
- precision
- recall
- f1
model_name: Financial Sentiment Analysis Model
tags:
- sentiment-analysis
- finance
- distilbert
license: mit
---

# Financial Sentiment Analysis Model

## Model Description

This model is a fine-tuned version of the `distilbert-base-uncased` transformer model, specifically trained to perform sentiment analysis on financial news articles. The model classifies the sentiment of the given text into three categories: positive, negative, and neutral.

## Training Data

The model was trained on the [Financial News Sentiment Dataset](https://huggingface.co/datasets/Jean-Baptiste/financial_news_sentiment_mixte_with_phrasebank_75), which includes financial news articles annotated for sentiment analysis.

## Model Training

### Pre-trained Model
- Model: `distilbert-base-uncased`
- Tokenizer: `distilbert-base-uncased`

### Training Parameters
- Learning rates: `2e-5`, `3e-5`
- Number of epochs: `3`, `4`
- Batch size: `16`
- Weight decay: `0.01`

### Best Hyperparameters
The best hyperparameters found during hyperparameter tuning were:
- Learning rate: `2e-5`
- Number of epochs: `3`

### Performance
The model was evaluated using accuracy, precision, recall, and F1 score. The best accuracy achieved was `94.7%'.'

## Usage

You can use this model for sentiment analysis on financial news articles by loading it with the Hugging Face Transformers library.

## Model Limitations

- The model is specifically trained on financial news articles and may not generalize well to other domains.
- It might not capture the nuances of financial sentiment entirely due to the complexity of financial language.

## Acknowledgments

This model was trained using the Hugging Face Transformers library and the financial news sentiment dataset by Jean-Baptiste. Special thanks to the creators of the dataset and the Hugging Face team.