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llama-financial-sentiment-analysis's Introduction

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

llama-financial-sentiment-analysis's People

Contributors

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