Create README.md

README.md

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+**🧠 Q&AMODEL-SQUAD**
+
+A roberta-base-squad2 extractive Question Answering model fine-tuned on the SQuAD v2.0 dataset to predict precise answers from context passages, including handling unanswerable questions.
+
+---
+
+✨ **Model Highlights**
+
+- 📌 Based on roberta-base-squad2
+- 🔍 Fine-tuned on SQuAD v2.0 (or your custom QA dataset)
+- ⚡ Supports extractive question answering  finds precise answers from context passages
+- 💾 Suitable for real-time inference with minimal latency on both CPU and GPU
+-🛠️ Easily integrable into web apps, enterprise tools, and virtual assistants
+-🔒 Handles unanswerable questions gracefully with no-answer detection (if trained on SQuAD v2)
+
+---
+
+🧠 Intended Uses
+
+- ✅Customer support bots that extract answers from product manuals or FAQs
+- ✅ Educational tools that answer student queries based on textbooks or syllabus
+- ✅ Legal, financial, or technical document analysis
+- ✅ Search engines with context-aware question answering
+- ✅ Chatbots that require contextual comprehension for precise responses
+  
+---
+
+- 🚫 Limitations
+
+- ❌Trained primarily on formal text performance may degrade on informal or slang-heavy input
+- ❌Does not support multi-hop questions requiring reasoning across multiple paragraphs
+- ❌ May struggle with ambiguous questions or context with multiple possible answers
+- ❌ Not designed for very long documents (performance may drop for inputs >512 tokens)
+
+---
+
+🏋️‍♂️ Training Details
+
+| Field          | Value                          |
+| -------------- | ------------------------------ |
+| **Base Model** | `roberta-base-squad2`          |
+| **Dataset**    |  SQuAD v2.0                    |
+| **Framework**  | PyTorch with Transformers      |
+| **Epochs**     | 3                              |
+| **Batch Size** | 16                             |
+| **Optimizer**  | AdamW                          |
+| **Loss**       | CrossEntropyLoss (token-level) |
+| **Device**     | Trained on CUDA-enabled GPU    |
+
+---
+
+📊 Evaluation Metrics
+
+| Metric                                          | Score |
+| ----------------------------------------------- | ----- |
+| Accuracy                                        | 0.80  |
+| F1-Score                                        | 0.78  |
+| Precision                                       | 0.79  |
+| Recall                                          | 0.78  |
+
+---
+
+🚀 Usage
+```python
+from transformers import BertTokenizerFast, BertForTokenClassification
+from transformers import pipeline
+import torch
+
+model_name = "AventIQ-AI/QA-Squad-Model"
+tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)
+model = AutoModelForQuestionAnswering.from_pretrained(model_checkpoint)
+model.eval()
+
+
+
+#Inference
+
+
+qa_pipeline = pipeline("question-answering", model="./qa_model", tokenizer="./qa_model")
+
+# Provide a context and a question
+context = """
+The Amazon rainforest, also known as Amazonia, is a moist broadleaf tropical rainforest in the Amazon biome 
+that covers most of the Amazon basin of South America. This region includes territory belonging to nine nations.
+"""
+question = "What is the Amazon rainforest also known as?"
+
+# Run inference
+result = qa_pipeline(question=question, context=context)
+
+# Print the result
+print(f"Question: {question}")
+print(f"Answer: {result['answer']}")
+print(f"Score: {result['score']:.4f}")
+```
+---
+
+- 🧩 Quantization
+- Post-training static quantization applied using PyTorch to reduce model size and accelerate inference on edge devices.
+
+----
+
+🗂 Repository Structure
+```
+.
+├── model/               # Quantized model files
+├── tokenizer_config/    # Tokenizer and vocab files
+├── model.safensors/     # Fine-tuned model in safetensors format
+├── README.md            # Model card
+
+```
+---
+🤝 Contributing
+
+Open to improvements and feedback! Feel free to submit a pull request or open an issue if you find any bugs or want to enhance the model.
+
+