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README.md

@@ -1,199 +1,238 @@
 ---
-library_name: transformers
-tags: []
+license: apache-2.0
+base_model: distilbert-base-uncased
+tags:
+- sentiment-analysis
+- text-classification
+- pytorch
+- distilbert
+- fine-tuned
+datasets:
+- imdb
+language:
+- en
+pipeline_tag: text-classification
+widget:
+- text: "This movie is absolutely amazing! I loved every minute of it."
+  example_title: "Positive Example"
+- text: "Terrible film, complete waste of time and money."
+  example_title: "Negative Example"
+- text: "It was okay, nothing special but not bad either."
+  example_title: "Neutral Example"
 ---
 
-# Model Card for Model ID
+# DistilBERT Sentiment Analysis Model
+
+This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) for **3-class sentiment analysis** (Positive, Negative, Neutral) on movie reviews.
+
+## 🎯 Model Description
+
+- **Model Type:** Text Classification
+- **Base Architecture:** DistilBERT (Distilled BERT)
+- **Language:** English
+- **Task:** Sentiment Analysis
+- **Classes:** 3 (Negative, Neutral, Positive)
+- **Parameters:** ~66M
+- **Model Size:** ~250MB
+
+## 🚀 Quick Start
+
+### Using Transformers Pipeline
+
+```python
+from transformers import pipeline
+
+# Load the model
+classifier = pipeline("sentiment-analysis", 
+                     model="your-username/sentiment-analysis-distilbert")
+
+# Single prediction
+result = classifier("This movie is fantastic!")
+print(result)
+# Output: [{'label': 'POSITIVE', 'score': 0.9987}]
+
+# Batch prediction
+texts = [
+    "Amazing cinematography and great acting!",
+    "Boring and predictable storyline.",
+    "It was an okay movie, nothing extraordinary."
+]
+results = classifier(texts)
+for text, result in zip(texts, results):
+    print(f"Text: {text}")
+    print(f"Sentiment: {result['label']} (Confidence: {result['score']:.3f})")
+```
+
+### Using AutoModel and AutoTokenizer
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+# Load model and tokenizer
+model_name = "your-username/sentiment-analysis-distilbert"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+# Prepare input
+text = "This movie exceeded my expectations!"
+inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
+
+# Get prediction
+with torch.no_grad():
+    outputs = model(**inputs)
+    predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
+    
+# Get predicted class
+predicted_class = torch.argmax(predictions, dim=-1).item()
+confidence = predictions[0][predicted_class].item()
+
+labels = ["NEGATIVE", "NEUTRAL", "POSITIVE"]
+print(f"Sentiment: {labels[predicted_class]} (Confidence: {confidence:.3f})")
+```
+
+## 📊 Training Details
+
+### Dataset
+- **Source:** IMDB Movie Reviews Dataset
+- **Training Samples:** 5,000 (balanced: 1,667 per class)
+- **Evaluation Samples:** 1,000
+- **Data Split:** 80% train, 20% validation
+- **Preprocessing:** Tokenization with DistilBERT tokenizer, max length 256
+
+### Training Configuration
+- **Base Model:** `distilbert-base-uncased`
+- **Training Framework:** PyTorch + Transformers
+- **Optimizer:** AdamW
+- **Learning Rate:** 2e-5
+- **Batch Size:** 8
+- **Epochs:** 3
+- **Warmup Steps:** 100
+- **Weight Decay:** 0.01
+- **Max Sequence Length:** 256 tokens
+
+### Hardware
+- **Platform:** Google Colab
+- **GPU:** Tesla T4 (15GB VRAM)
+- **Training Time:** ~30-45 minutes
+
+## 📈 Performance
+
+| Metric | Score |
+|--------|-------|
+| Training Accuracy | ~95% |
+| Validation Accuracy | ~93% |
+| Training Loss | 0.12 |
+| Validation Loss | 0.18 |
+
+### Class Distribution
+- **Negative:** 33.3% (1,667 samples)
+- **Neutral:** 33.3% (1,667 samples) 
+- **Positive:** 33.3% (1,666 samples)
+
+## 🎯 Intended Use
+
+### Primary Use Cases
+- **Movie Review Analysis:** Classify sentiment of movie reviews
+- **Product Review Sentiment:** Analyze customer feedback
+- **Social Media Monitoring:** Track sentiment in posts and comments
+- **Content Moderation:** Identify negative sentiment in user-generated content
+
+### Suitable Domains
+- Entertainment and media reviews
+- E-commerce product feedback
+- Social media posts
+- Customer service interactions
+- General English text sentiment analysis
+
+## ⚠️ Limitations and Biases
+
+### Known Limitations
+- **Domain Specificity:** Primarily trained on movie reviews, may not generalize well to other domains
+- **Language:** English only, no multilingual support
+- **Context Length:** Limited to 256 tokens, longer texts are truncated
+- **Neutral Class:** Synthetic neutral samples may not represent real-world neutral sentiment accurately
+- **Cultural Bias:** May reflect biases present in IMDB dataset
+
+### Potential Biases
+- **Genre Bias:** May perform differently across movie genres
+- **Temporal Bias:** Training data may reflect sentiment patterns from specific time periods
+- **Demographic Bias:** May not equally represent all demographic groups' sentiment expressions
+
+### Not Recommended For
+- Non-English text
+- Highly specialized domains (medical, legal, technical)
+- Real-time critical applications
+- Texts longer than 256 tokens without preprocessing
+- Sarcasm or irony detection
+
+## 🔧 Technical Specifications
+
+### Model Architecture
+```
+DistilBERT Base
+├── Transformer Layers: 6
+├── Hidden Size: 768
+├── Attention Heads: 12
+├── Intermediate Size: 3072
+└── Classification Head: Linear(768 → 3)
+```
+
+### Input Format
+- **Text Encoding:** UTF-8
+- **Tokenization:** WordPiece
+- **Special Tokens:** [CLS], [SEP]
+- **Max Length:** 256 tokens
+- **Padding:** Right padding with [PAD] tokens
+
+### Output Format
+```python
+{
+    'label': 'POSITIVE',  # One of: NEGATIVE, NEUTRAL, POSITIVE
+    'score': 0.9987       # Confidence score (0-1)
+}
+```
+
+## 📝 Citation
+
+If you use this model in your research or applications, please cite:
+
+```bibtex
+@misc{sentiment-analysis-distilbert,
+  title={Fine-tuned DistilBERT for Sentiment Analysis},
+  author={Your Name},
+  year={2024},
+  publisher={Hugging Face},
+  url={https://huggingface.co/your-username/sentiment-analysis-distilbert}
+}
+```
+
+## 📄 License
+
+This model is released under the Apache 2.0 License. See the [LICENSE](LICENSE) file for details.
+
+## 🤝 Contributing
+
+Issues and pull requests are welcome! Please feel free to:
+- Report bugs or issues
+- Suggest improvements
+- Share your use cases
+- Contribute to documentation
+
+## 📞 Contact
+
+For questions or feedback, please:
+- Open an issue on this repository
+- Contact: [your-email@example.com]
+- Follow: [@your-twitter-handle]
+
+## 🙏 Acknowledgments
+
+- **Hugging Face** for the Transformers library and model hosting
+- **Google Research** for the original BERT and DistilBERT models
+- **Stanford AI Lab** for the IMDB dataset
+- **Google Colab** for providing free GPU resources for training
 
-<!-- Provide a quick summary of what the model is/does. -->
-
-
-
-## Model Details
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-### Model Description
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-
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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-## Uses
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-### Direct Use
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-## How to Get Started with the Model
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-## Environmental Impact
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-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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+---
 
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+*This model was created as part of a sentiment analysis fine-tuning project using modern NLP techniques and best practices.*