This is a fine-tuned sentiment analysis model based on intfloat/multilingual-e5-large-instruct for financial text, supporting both English and Thai languages.

Model Details

Base Model: intfloat/multilingual-e5-large-instruct
Fine-tuning Dataset: A custom dataset containing financial news headlines and tweets in English and Thai, labeled with sentiment (No Impact, Bullish, Bearish). The dataset was created by combining financial-sentiment.jsonl and validation.jsonl.
Task: Sentiment Classification
Labels:
- 0: No Impact
- 1: Bullish
- 2: Bearish

Training

The model was fine-tuned using the Hugging Face Transformers library.

Optimizer: AdamW
Learning Rate: 2e-5
Epochs: 3
Batch Size: 18
Evaluation Strategy: Evaluated at the end of each epoch.
Saving Strategy: Model checkpoints saved at the end of each epoch.
Metric for Best Model: Accuracy

Evaluation Results

The model was evaluated on a custom financial sentiment dataset for comparison with other models.

Model	Accuracy	Negative F1	Positive F1
ModernFinBERT	0.0000	0.0000	0.0000
FinBERT	0.6000	1.0000	0.5000
tabularisai/ModernFinBERT	0.8000	1.0000	0.8000

Usage

You can use this model with the Hugging Face Transformers library:

ZombitX64/Fin-E5-pro

Fin-E5-pro Financial Sentiment Analysis Model

Model Details

Training

Evaluation Results

Usage

ZombitX64/
Fin-E5-pro