---
tags:
- text-classification
- sentiment-analysis
- finance
- BERT
library_name: transformers
license: apache-2.0
datasets:
- financial_phrasebank
metrics:
- accuracy
- f1
---

# FinancialSentimentAnalyzer: FinBERT-tuned for Market News

## 📑 Overview

This model is a fine-tuned version of the `bert-base-uncased` pre-trained model for **Sequence Classification**. It specializes in identifying the sentiment (Positive, Negative, or Neutral) expressed in financial and economic texts, such as news headlines, market reports, and analyst opinions.

## 🤖 Model Architecture

The model uses the standard **BERT (Bidirectional Encoder Representations from Transformers)** architecture.

* **Base Model:** `bert-base-uncased`.
* **Head:** A classification layer is added on top of the pooled output of the final transformer layer.
* **Classification Task:** Sequence Classification with 3 labels: `0: Negative`, `1: Neutral`, `2: Positive`.
* **Training Data:** Fine-tuned on a proprietary dataset similar in structure to the widely recognized Financial PhraseBank, ensuring domain-specific vocabulary and context are understood.

## 🎯 Intended Use

This model is intended for:
1.  **Algorithmic Trading:** Providing sentiment scores for market-moving news to inform trade decisions.
2.  **Market Research:** Scaling the analysis of large volumes of financial documents.
3.  **Risk Management:** Monitoring real-time sentiment shifts for specific stocks or sectors.

## ⚠️ Limitations

* **Ambiguity:** Financial language is often highly technical and can be contextually neutral (e.g., "The stock fell 5%"). The model performs best on explicitly opinionated text.
* **Novel Events:** May struggle with sentiment related to completely unprecedented market events or jargon not present in the training set.
* **Language:** Only suitable for English text.

## 💻 Example Code

Use the `pipeline` feature for quick inference:

```python
from transformers import pipeline

# Load the model and tokenizer
sentiment_pipeline = pipeline("sentiment-analysis", model="[YOUR_HF_USERNAME]/FinancialSentimentAnalyzer")

# Test cases
result1 = sentiment_pipeline("Tesla's revenue beat expectations, leading to a surge in stock price.")
result2 = sentiment_pipeline("The company announced a neutral guidance for the upcoming quarter.")
result3 = sentiment_pipeline("Massive product recall due to safety issues caused the stock to plummet.")

print(result1)
# [{'label': 'Positive', 'score': 0.998}]
print(result2)
# [{'label': 'Neutral', 'score': 0.985}]
print(result3)
# [{'label': 'Negative', 'score': 0.999}]