Create README.md

README.md

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+# Talent-Match-AI: Resume and Job Description Matching
+
+## 📌 Overview
+
+This repository hosts the quantized version of the **BERT-base-uncased** model for **Resume and Job Description Matching**. The model is designed to determine whether a resume aligns well with a given job description. If they are a strong match, the model outputs "Good Fit" with a confidence score; otherwise, it categorizes them as "Potential Fit" or "Not a Good Fit." The model has been optimized for efficient deployment while maintaining reasonable accuracy, making it suitable for real-time applications.
+
+## 🏰 Model Details
+
+- **Model Architecture:** BERT-base-uncased  
+- **Task:** Resume and Job Description Matching  
+- **Dataset:** `facehuggerapoorv/resume-jd-match`  
+- **Quantization:** Float16 (FP16) for optimized inference  
+- **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/bert-talentmatchai"
+model = BertForSequenceClassification.from_pretrained(model_name).to(device)
+tokenizer = BertTokenizer.from_pretrained(model_name)
+```
+
+### Resume Matching Inference
+
+```python
+import torch
+
+# Set device (use GPU if available)
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model.to(device)
+
+# Define label mapping
+label_mapping = {0: "Not a Good Fit", 1: "Potential Fit", 2: "Good Fit"}
+
+# Sample resume text for testing
+test_resume = ["I have worked in different industries and have a lot of experience. I am a hard worker and can learn anything."]
+
+# Tokenize test data
+test_tokens = tokenizer(test_resume, padding="max_length", truncation=True, return_tensors="pt").to(device)  # Move input to same device as model
+
+# Make predictions
+with torch.no_grad():  # Disable gradient computation for inference
+    output = model(**test_tokens)
+
+# Get predicted label
+predicted_label = output.logits.argmax(dim=1).item()
+
+# Print result
+print(f"Predicted Category: {predicted_label} ({label_mapping[predicted_label]})")
+
+label_mapping = {0: "No Fit", 1: "Low Fit", 2: "Potential Fit", 3: "Good Fit"}
+print(f"Predicted Category: {label_mapping[predictions]}")
+
+```
+
+## 📊 Quantized Model Evaluation Results
+
+### 🔥 Evaluation Metrics 🔥
+
+- ✅ **Accuracy:**  0.9224  
+- ✅ **Precision:** 0.9212  
+- ✅ **Recall:**    0.8450  
+- ✅ **F1-score:**  0.7718  
+
+## ⚡ Quantization Details
+
+Post-training quantization was applied using PyTorch's built-in quantization framework. The model was quantized to Float16 (FP16) to reduce model size and improve inference efficiency while balancing accuracy.
+
+## 💽 Repository Structure
+
+```
+.
+├── model/               # Contains the quantized model files
+├── tokenizer_config/    # Tokenizer configuration and vocabulary files
+├── model.safetensors/   # Quantized Model
+├── README.md            # Model documentation
+```
+
+## ⚠️ Limitations
+
+- The model may struggle with resumes and job descriptions that use non-standard terminology.
+- Quantization may lead to slight degradation in accuracy compared to full-precision models.
+- Performance may vary across different industries and job levels.
+
+## 🤝 Contributing
+
+Contributions are welcome! Feel free to open an issue or submit a pull request if you have suggestions or improvements.