Update README.md

README.md

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----
-license: mit
----
+---
+language: en
+license: mit
+tags:
+- sentiment-analysis
+- customer-reviews
+- transformers
+- distilbert
+- text-classification
+datasets:
+- IberaSoft/ecommerce-reviews-sentiment
+metrics:
+- accuracy
+- f1
+model-index:
+- name: customer-sentiment-analyzer
+  results:
+  - task:
+      type: text-classification
+      name: Sentiment Analysis
+    dataset:
+      name: E-commerce Reviews
+      type: IberaSoft/ecommerce-reviews-sentiment
+    metrics:
+    - type: accuracy
+      value: 90.2
+      name: Accuracy
+    - type: f1
+      value: 0.89
+      name: F1 Score
+widget:
+- text: "This product exceeded my expectations! Fast shipping and great quality."
+  example_title: "Positive Review"
+- text: "Terrible experience. Product broke after one week and customer service was unhelpful."
+  example_title: "Negative Review"
+- text: "It's okay, nothing special. Does what it's supposed to do."
+  example_title: "Neutral Review"
+---
+
+# 🎯 Customer Sentiment Analyzer
+
+> Fine-tuned DistilBERT model for analyzing customer review sentiment in e-commerce and SaaS domains.
+
+[![Model on HF](https://huggingface.co/datasets/huggingface/badges/resolve/main/model-on-hf-sm.svg)](https://huggingface.co/IberaSoft/customer-sentiment-analyzer)
+[![Dataset](https://img.shields.io/badge/Dataset-HuggingFace-yellow)](https://huggingface.co/datasets/IberaSoft/ecommerce-reviews-sentiment)
+[![Demo](https://img.shields.io/badge/Demo-Spaces-orange)](https://huggingface.co/spaces/IberaSoft/sentiment-analyzer-demo)
+[![License](https://img.shields.io/badge/License-MIT-green.svg)](LICENSE)
+
+## 🌟 Model Description
+
+This model is a fine-tuned version of [`distilbert-base-uncased`](https://huggingface.co/distilbert-base-uncased) on a custom dataset of 20,000 customer reviews from e-commerce and SaaS platforms. It classifies text into three sentiment categories: **positive**, **negative**, and **neutral**.
+
+### Key Features
+
+- ✅ **Fast Inference**: ~35ms per prediction (CPU)
+- ✅ **High Accuracy**: 90.2% on test set
+- ✅ **Domain-Specific**: Trained on customer reviews
+- ✅ **Production-Ready**: Optimized for real-world deployment
+- ✅ **Multi-Class**: Handles positive, negative, and neutral sentiments
+
+## 🚀 Quick Start
+
+### Using Transformers Pipeline
+```python
+from transformers import pipeline
+
+# Load the model
+classifier = pipeline(
+    "sentiment-analysis",
+    model="IberaSoft/customer-sentiment-analyzer"
+)
+
+# Analyze sentiment
+result = classifier("This product is amazing! Highly recommend.")
+print(result)
+# [{'label': 'positive', 'score': 0.9823}]
+```
+
+### Using AutoModel
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+# Load model and tokenizer
+model_name = "IberaSoft/customer-sentiment-analyzer"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+# Prepare text
+text = "Great quality but shipping took forever"
+inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)
+
+# Get prediction
+with torch.no_grad():
+    outputs = model(**inputs)
+    predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
+
+# Map to labels
+labels = ['negative', 'neutral', 'positive']
+predicted_class = predictions.argmax().item()
+confidence = predictions[0][predicted_class].item()
+
+print(f"Sentiment: {labels[predicted_class]}")
+print(f"Confidence: {confidence:.2%}")
+```
+
+### Batch Processing
+```python
+from transformers import pipeline
+
+classifier = pipeline(
+    "sentiment-analysis",
+    model="IberaSoft/customer-sentiment-analyzer",
+    device=0  # Use GPU if available
+)
+
+reviews = [
+    "Excellent product, will buy again!",
+    "Disappointed with the quality.",
+    "It's okay, nothing special."
+]
+
+results = classifier(reviews)
+for review, result in zip(reviews, results):
+    print(f"{review[:30]}... → {result['label']} ({result['score']:.2f})")
+```
+
+## 📊 Model Performance
+
+### Evaluation Metrics
+
+| Metric | Score |
+|--------|-------|
+| **Accuracy** | 90.2% |
+| **F1 Score (Macro)** | 0.89 |
+| **Precision** | 0.90 |
+| **Recall** | 0.89 |
+
+### Per-Class Performance
+
+| Class | Precision | Recall | F1-Score | Support |
+|-------|-----------|--------|----------|---------|
+| **Positive** | 0.92 | 0.91 | 0.91 | 800 |
+| **Negative** | 0.89 | 0.90 | 0.89 | 700 |
+| **Neutral** | 0.88 | 0.86 | 0.87 | 500 |
+
+### Confusion Matrix
+```
+                Predicted
+              Pos  Neu  Neg
+Actual Pos  [ 728   45   27 ]
+       Neu  [  38  430   32 ]
+       Neg  [  22   48  630 ]
+```
+
+### Inference Speed
+
+| Batch Size | CPU (ms) | GPU (ms) |
+|------------|----------|----------|
+| 1 | 35 | 8 |
+| 8 | 180 | 25 |
+| 32 | 650 | 75 |
+
+*Tested on Intel i7-11700K (CPU) and NVIDIA RTX 3080 (GPU)*
+
+## 🎯 Intended Use
+
+### Primary Use Cases
+
+- **Customer Support**: Automatically triage support tickets by sentiment
+- **Product Reviews**: Analyze product feedback at scale
+- **Brand Monitoring**: Track customer sentiment over time
+- **Market Research**: Understand customer opinions
+- **Quality Assurance**: Flag negative feedback for review
+
+### Out-of-Scope Use
+
+❌ Medical or health-related sentiment analysis  
+❌ Financial advice or stock sentiment (not trained on financial data)  
+❌ Political sentiment analysis (potential bias)  
+❌ Languages other than English  
+❌ Detecting sarcasm or irony (limited capability)  
+
+## 📚 Training Details
+
+### Training Data
+
+The model was fine-tuned on **20,000 labeled customer reviews** consisting of:
+
+- **Amazon Customer Reviews**: 8,000 reviews
+- **Yelp Business Reviews**: 7,000 reviews
+- **SaaS Product Reviews**: 5,000 reviews (G2, Capterra, TrustRadius)
+
+**Dataset Distribution**:
+- Training: 15,000 (75%)
+- Validation: 3,000 (15%)
+- Test: 2,000 (10%)
+
+**Class Balance**:
+- Positive: 40% (8,000 reviews)
+- Negative: 35% (7,000 reviews)
+- Neutral: 25% (5,000 reviews)
+
+📦 **[View Dataset on HuggingFace](https://huggingface.co/datasets/IberaSoft/ecommerce-reviews-sentiment)**
+
+### Training Procedure
+
+**Base Model**: `distilbert-base-uncased` (66M parameters)
+
+**Hyperparameters**:
+```yaml
+learning_rate: 2e-5
+batch_size: 16
+epochs: 3
+warmup_steps: 500
+weight_decay: 0.01
+max_length: 512
+optimizer: AdamW
+scheduler: linear with warmup
+```
+
+**Training Environment**:
+- **Hardware**: NVIDIA Tesla V100 (16GB)
+- **Training Time**: ~2.5 hours
+- **Framework**: PyTorch 2.1, Transformers 4.36
+- **Mixed Precision**: FP16
+
+**Training Code**: [GitHub Repository](https://github.com/IberaSoft/sentiment-analysis-api)
+
+### Preprocessing
+
+Text preprocessing steps:
+1. Lowercase conversion
+2. URL removal
+3. Excessive whitespace normalization
+4. Emoji handling (converted to text)
+5. HTML tag removal
+6. Truncation to 512 tokens
+
+## ⚠️ Limitations and Bias
+
+### Known Limitations
+
+1. **English Only**: Trained exclusively on English text
+2. **Domain Specificity**: Best performance on e-commerce/SaaS reviews
+3. **Sarcasm**: May misclassify sarcastic reviews
+4. **Context Length**: Limited to 512 tokens (~350 words)
+5. **Informal Language**: May struggle with heavy slang or abbreviations
+
+### Potential Biases
+
+- **Product Category Bias**: Training data skewed toward electronics and software
+- **Platform Bias**: Amazon and Yelp reviews may have different characteristics
+- **Temporal Bias**: Reviews collected 2020-2023
+- **Rating Correlation**: 5-star reviews assumed positive (may not always be true)
+
+### Recommendations
+
+- ✅ Test on your specific domain before production use
+- ✅ Implement human review for edge cases
+- ✅ Monitor performance on your data distribution
+- ✅ Consider retraining for specialized domains
+- ✅ Use confidence scores to flag uncertain predictions
+
+## 🔧 Optimization
+
+### Model Size Reduction
+
+**Standard Model**: 268 MB  
+**Quantized (INT8)**: 67 MB (4x smaller, <2% accuracy drop)
+```python
+from optimum.onnxruntime import ORTModelForSequenceClassification
+
+# Convert to ONNX with quantization
+model = ORTModelForSequenceClassification.from_pretrained(
+    "IberaSoft/customer-sentiment-analyzer",
+    export=True,
+    provider="CPUExecutionProvider"
+)
+
+# Save quantized model
+model.save_pretrained("./optimized_model")
+```
+
+### Performance Tips
+```python
+import torch
+
+# Use GPU if available
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model = model.to(device)
+
+# Enable inference mode
+model.eval()
+torch.set_grad_enabled(False)
+
+# Batch processing for better throughput
+classifier = pipeline(
+    "sentiment-analysis",
+    model=model,
+    tokenizer=tokenizer,
+    batch_size=32,
+    device=0 if device == "cuda" else -1
+)
+```
+
+## 🌐 Production Deployment
+
+### FastAPI Example
+```python
+from fastapi import FastAPI
+from transformers import pipeline
+from pydantic import BaseModel
+
+app = FastAPI()
+
+# Load model once at startup
+classifier = pipeline(
+    "sentiment-analysis",
+    model="IberaSoft/customer-sentiment-analyzer"
+)
+
+class ReviewRequest(BaseModel):
+    text: str
+
+@app.post("/predict")
+def predict_sentiment(request: ReviewRequest):
+    result = classifier(request.text)[0]
+    return {
+        "sentiment": result["label"],
+        "confidence": round(result["score"], 4)
+    }
+```
+
+### Docker Deployment
+```dockerfile
+FROM python:3.11-slim
+
+RUN pip install transformers torch fastapi uvicorn
+
+# Download model during build
+RUN python -c "from transformers import pipeline; \
+    pipeline('sentiment-analysis', \
+    model='IberaSoft/customer-sentiment-analyzer')"
+
+COPY app.py .
+
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "8000"]
+```
+
+**Full API**: [GitHub Repository](https://github.com/IberaSoft/sentiment-analysis-api)
+
+## 📖 Citation
+
+If you use this model in your research or application, please cite:
+```bibtex
+@misc{customer-sentiment-analyzer,
+  author = {Your Name},
+  title = {Customer Sentiment Analyzer: Fine-tuned DistilBERT for E-commerce Reviews},
+  year = {2026},
+  publisher = {HuggingFace},
+  howpublished = {\url{https://huggingface.co/IberaSoft/customer-sentiment-analyzer}},
+}
+```
+
+## 📝 License
+
+This model is licensed under the **MIT License**. See [LICENSE](LICENSE) for details.
+
+The base model `distilbert-base-uncased` is licensed under Apache 2.0.
+
+## 🤝 Contributing
+
+Found an issue or want to improve the model?
+
+- 🐛 [Report bugs](https://github.com/IberaSoft/sentiment-analysis-api/issues)
+- 💡 [Suggest features](https://github.com/IberaSoft/sentiment-analysis-api/issues)
+- 🔧 [Submit pull requests](https://github.com/IberaSoft/sentiment-analysis-api/pulls)
+
+## 🙏 Acknowledgments
+
+- **HuggingFace** for the Transformers library and model hub
+- **DistilBERT Authors** for the efficient base model
+- **Dataset Contributors** for publicly available reviews
+- **Community** for feedback and testing
+
+---
+
+<div align="center">
+
+### ⭐ Star this model if you find it useful!
+
+**Try the live demo**: [HuggingFace Spaces](https://huggingface.co/spaces/IberaSoft/sentiment-analyzer-demo)
+
+</div>