---
license: mit
datasets:
- zeroshot/twitter-financial-news-sentiment
---

# Financial Sentiment Analysis with FinBERT

This repository contains a financial sentiment analysis model fine-tuned on `ProsusAI/finbert`. The model classifies financial text (like tweets or news headlines) into three categories: **Bullish**, **Bearish**, or **Neutral**.

The project includes scripts for data preprocessing, model training with hyperparameter optimization, and a Streamlit web application for interactive predictions.

## Model Card

### Model Description

This model is a `BertForSequenceClassification` based on the `ProsusAI/finbert` architecture. It has been fine-tuned to predict the sentiment of financial text. The model was trained on a dataset of financial tweets and headlines, and it outputs one of three labels: `Bullish`, `Bearish`, or `Neutral`.

```python
from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification

MODEL_PATH = "path to your model"

tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH)
model = AutoModelForSequenceClassification.from_pretrained(MODEL_PATH)

pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)

# Analyze sentiment
results = pipe("Adobe price target raised to $350 vs. $320 at Canaccord")
print(results)
# [{'label': 'Bullish', 'score': 0.9...}]
```

### Training Data

The model was trained on the [Twitter Financial News Sentiment](https://huggingface.co/datasets/zeroshot/twitter-financial-news-sentiment) dataset. The text data undergoes a comprehensive cleaning process (`data_preprocessing.py`) which includes: