pmatorras/financial-sentiment-analysis

Financial-Sentiment-LLM (Multi-Task FinBERT)

A production-ready financial sentiment classifier fine-tuned on FinBERT. This model utilizes a Multi-Task Architecture (Classification + Regression) to achieve state-of-the-art performance across diverse financial text sources, including professional news, social media, and forum discussions.

• 🚀 Live Demo: Hugging Face Space
• 💻 Source Code: GitHub Repository
• 👨‍💻 Author: Pablo Matorras-Cuevas

Model Performance

This Multi-Task model achieves 85.4% overall accuracy, significantly outperforming standard baselines, particularly on noisy social media data.

Metric / Dataset	FinBERT (Multi-Task)	FinBERT (LoRA)
Overall Accuracy	85.4%	83.2%
Macro F1-Score	0.83	0.80
Financial PhraseBank (News)	95.9%	97.1%
Twitter Financial News	83.3%	80.5%
FiQA (Forums)	81.5%	72.6%

Note: For edge deployment or low-memory environments, check out the LoRA version which reduces storage by 99% (5MB vs 420MB).

Architecture

Unlike standard sentiment classifiers, this model shares a bert-base backbone with two task-specific heads:

1. Classification Head: Predicts Negative/Neutral/Positive (Optimized for News & Twitter).
2. Regression Head: Predicts a continuous sentiment score (Optimized for FiQA forum discussions).

This approach yielded a +6.1% accuracy boost on Twitter data compared to single-task training, proving that learning continuous sentiment intensity helps the model understand noisy social text better.

Usage

You can use this model directly with the Hugging Face pipeline or AutoModel:

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

# Load the model and tokenizer
model_name = "pmatorras/financial-sentiment-multi-task"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

# Inference
text = "The stock market rally is driven by strong tech earnings."
inputs = tokenizer(text, return_tensors="pt")

with torch.no_grad():
    outputs = model(**inputs)
    probabilities = torch.nn.functional.softmax(outputs.logits, dim=-1)

print(probabilities)

Training Details

• Base Model: ProsusAI/finbert
• Optimizer: AdamW
• Loss Function: Weighted sum of Cross-Entropy (Classification) and MSE (Regression).
• Compute: Trained on NVIDIA RTX 4050.