Create README.md

README.md

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+# 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...}]