# LLaMA-Financial-sentiment-analysis

**Repository Path**: dufefintech/LLaMA-Financial-sentiment-analysis

## Basic Information

- **Project Name**: LLaMA-Financial-sentiment-analysis
- **Description**: No description available
- **Primary Language**: Python
- **License**: Not specified
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2024-04-27
- **Last Updated**: 2024-04-27

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# First author: Wei Luo(罗威)
# Pre-trained Large Language Models for Financial Sentiment Analysis.
This project contains codes for the research paper title "Pre-trained Large Language Models for Financial Sentiment Analysis". Authors: Wei Luo, Dihong Gong.

## Environment setup
- `hostfile.txt` should contain the IP addresses for distributed training, one line per IP.
- `env_a800.sh` should contain all the additional custom environment variables for running the codes.

## Data preprocessing
- Split the train, val and test sets for the PhraseBank dataset: `process_financial_phrasebank.py`

## Training
- May need to tune some parameters accordingly.
- Execute the training script with `./train.sh`

## Testing
- Execute the testing script with `torchrun test.py`

## Results
Methods | Accuracy
--- | ---
LSTM | 0.71
LSTM with ELMo | 0.75
ULMFit | 0.83
LPS | 0.71
HSC | 0.71
FinBERT | 0.86
`Ours (Few-shots)` | `0.68`
`Ours (SFT)` | `0.90`
`Ours (3-class classification)` | `0.90`