| | --- |
| | 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}] |
| | |
