mobadara/finbert-finetuned

FinBERT Sentiment Analyzer (Fine-Tuned)

Model Description

This is a fine-tuned version of ProsusAI/finbert designed specifically for classifying the sentiment of financial news headlines into three distinct categories: Positive, Negative, and Neutral.

This model serves as the core inference engine for the FinBERT Sentiment Analyzer FastAPI backend.

Dataset & Class Imbalance Strategy

The model was trained on a heavily cleaned and preprocessed version of the Financial PhraseBank dataset. During exploratory data analysis, a severe class imbalance was identified, with the Neutral class representing roughly 61% of the data.

To prevent the model from collapsing into a majority-class predictor, we implemented a custom MLOps training strategy:

1. Dynamic Class Weights: Penalty weights were calculated using the balanced heuristic ($N / (C \times n_i)$).
2. Custom Loss Function: A custom Hugging Face Trainer subclass was built to inject these weights directly into a PyTorch CrossEntropyLoss function during gradient descent, heavily penalizing misclassifications of the minority (Positive/Negative) classes.

Evaluation Results

The model was evaluated on a strictly segregated test set (1,000 samples) pulled directly from the Hugging Face Hub to ensure zero data leakage.

• Macro F1-Score: 0.9394
• Accuracy: 0.9600
• Validation Loss: 0.1891

(Note: Macro F1-Score was prioritized over standard accuracy to validate true performance across the minority classes).

Intended Use

This model is intended to be loaded into a FastAPI application for real-time financial sentiment inference. The heavy weight files (.safetensors) are hosted on the Hugging Face Hub under the repository name finbert-finetuned, while the tokenizer configurations and application logic reside in the associated GitHub repository.

Developer

Muyiwa J. Obadara
Data Scientist & AI Engineer