File size: 1,464 Bytes
6163039 c77ead7 6163039 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 | ---
tags:
- sentiment-analysis
- finance
- stock-market
- english
pipeline_tag: text-classification
license: mit
language: en
---
# 🧠 FinancialBERT Sentiment Analysis (FinNews Binary)
This is a fine-tuned BERT model for **binary sentiment classification** of financial news headlines, adapted for real-time **stock market sentiment prediction**.
## 🔍 Model Details
- **Architecture**: BERT (12-layer, 768-hidden, 12-heads)
- **Base model**: `ahmedrachid/FinancialBERT-Sentiment-Analysis`
- **Fine-tuned task**: Binary classification – `Positive` or `Negative`
- **Problem type**: `single_label_classification`
- **Special tokens**: `[CLS]`, `[SEP]`, `[PAD]`, `[MASK]`, `[UNK]`
Neutral headlines are **mapped to Positive** to simplify binary output.
## 🧾 Training Summary
- **Dataset**: 5,000+ manually labeled financial news headlines
- **Tokenizer**: Custom WordPiece tokenizer
- **Max sequence length**: 128
- **Framework**: Transformers v4.51.3 (PyTorch backend)
- **Output labels**:
- `LABEL_0 = Negative`
- `LABEL_1 = Positive`
## 📈 Intended Use
Ideal for:
- Real-time market sentiment dashboard
- Trading signal pipelines
- Event-driven NLP analysis
## 🚀 Usage (Example)
```python
from transformers import pipeline
classifier = pipeline("text-classification", model="your-username/your-model-name")
classifier("Apple's Q4 earnings beat expectations amid strong iPhone sales")
# Output: [{'label': 'LABEL_1', 'score': 0.98}]
|