| # FinancialNewsSentimentClassifier_DistilBERT | |
| ## 📰 Overview | |
| This is a fine-tuned **DistilBERT** model optimized for **Sequence Classification** to analyze the sentiment of financial news headlines and short articles. It categorizes the text into three classes: **Bullish**, **Neutral**, and **Bearish**, providing a quantifiable measure of market outlook derived from textual data. The model was trained on a comprehensive dataset of news articles from major financial publications, labeled by human experts. | |
| ## 🧠 Model Architecture | |
| This model is built upon the **DistilBERT base uncased** architecture, a smaller, faster, and lighter version of BERT. | |
| * **Base Model:** `distilbert-base-uncased` | |
| * **Task:** Sequence Classification (`DistilBertForSequenceClassification`) | |
| * **Input:** Tokenized financial news headlines or short-form texts (max sequence length 512). | |
| * **Output:** Logits for three classes: | |
| * `0`: Bullish (Positive market sentiment) | |
| * `1`: Neutral (No significant market impact) | |
| * `2`: Bearish (Negative market sentiment) | |
| * **Training Details:** Fine-tuned for 3 epochs with a batch size of 16 and AdamW optimizer. Achieved an F1-score of 0.89 on the validation set. | |
| ## 💡 Intended Use | |
| * **Quantitative Finance:** Generating sentiment scores for stocks, sectors, or the entire market based on real-time news feeds. | |
| * **Algorithmic Trading:** Using the sentiment output as an input feature for high-frequency trading models. | |
| * **Market Research:** Tracking historical shifts in market sentiment towards specific companies or topics. | |
| * **News Filtering:** Prioritizing news articles based on their potential market impact. | |
| ### How to use | |
| ```python | |
| from transformers import pipeline | |
| classifier = pipeline( | |
| "sentiment-analysis", | |
| model="[YOUR_HF_USERNAME]/FinancialNewsSentimentClassifier_DistilBERT", | |
| tokenizer="distilbert-base-uncased" | |
| ) | |
| # Example usage | |
| result = classifier("Tesla stock surges 5% on better-than-expected Q4 earnings and new China factory plans.") | |
| print(result) | |
| # Expected output: [{'label': 'Bullish', 'score': 0.98...}] |