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README.md

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+# FinBERT Sentiment Analysis on English/Quotes Dataset
+
+## 📌 Overview
+
+This repository hosts the FinBERT model fine-tuned for sentiment analysis using the English/Quotes dataset. The model classifies text into sentiment categories such as positive, negative, or neutral. 
+
+## 🏗 Model Details
+
+- **Model Architecture:** FinBERT (BERT-based model for sentiment analysis)
+- **Task:** Sentiment Analysis
+- **Dataset:** English/quotes dataset
+- **Fine-tuning Framework:** Hugging Face Transformers
+
+## 🚀 Usage
+
+### Installation
+
+```bash
+pip install transformers torch
+```
+
+### Loading the Model
+
+```python
+from transformers import BertTokenizer, BertForSequenceClassification
+import torch
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+model_name = "Aventiq-AI/finbert-english/quotes"
+model = BertForSequenceClassification.from_pretrained(model_name).to(device)
+tokenizer = BertTokenizer.from_pretrained(model_name)
+```
+
+### Sentiment Classification Inference
+
+```python
+def predict_sentiment(text):
+    inputs = tokenizer(text, padding="max_length", truncation=True, max_length=128, return_tensors="pt")
+    inputs = {key: val.to(device) for key, val in inputs.items()}  # Move inputs to device
+    with torch.no_grad():
+        outputs = model(**inputs)
+    logits = outputs.logits
+    prediction = torch.argmax(logits, dim=-1).item()
+    label_map = {0: "negative", 1: "neutral", 2: "positive"}
+    return label_map[prediction]
+ 
+# Test on the original 5 quotes
+original_quotes = [
+    "“Be yourself; everyone else is already taken.”",
+    "“I'm selfish, impatient and a little insecure. I make mistakes, I am out of control and at times hard to handle. But if you can't handle me at my worst, then you sure as hell don't deserve me at my best.”",
+    "“Two things are infinite: the universe and human stupidity; and I'm not sure about the universe.”",
+    "“So many books, so little time.”",
+    "“A room without books is like a body without a soul.”"
+]
+ 
+print("Predictions for original quotes:")
+for quote in original_quotes:
+    pred = predict_sentiment(quote)
+    print(f"Quote: {quote}\nPredicted Sentiment: {pred}\n")
+ 
+# Test on a new example
+new_quote = "Life is beautiful when you smile."
+print("Prediction for new quote:")
+print(f"Quote: {new_quote}\nPredicted Sentiment: {predict_sentiment(new_quote)}")
+```
+
+## 📊 Evaluation Metric: Accuracy & F1 Score
+
+For sentiment analysis, accuracy and F1-score are key evaluation metrics. The model achieves:
+- **Accuracy:** 88%
+- **F1 Score:** 0.85
+
+## 📂 Repository Structure
+
+```
+.
+├── model/               # Contains the fine-tuned model files
+├── tokenizer_config/    # Tokenizer configuration and vocabulary files
+├── model.safetensors/   # Model weights
+├── README.md            # Model documentation
+```
+
+## ⚠️ Limitations
+
+- The model may struggle with ambiguous phrases.
+- Performance might vary across different jurisdictions and terminologies.
+- The dataset primarily contains English text, making it less effective for multilingual applications.
+
+## 🤝 Contributing
+
+Contributions are welcome! Feel free to ope