**🧠 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.

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✨ **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)

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🧠 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
  
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- 🚫 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)

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🏋️‍♂️ 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    |

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📊 Evaluation Metrics

| Metric                                          | Score |
| ----------------------------------------------- | ----- |
| Accuracy                                        | 0.80  |
| F1-Score                                        | 0.78  |
| Precision                                       | 0.79  |
| Recall                                          | 0.78  |

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🚀 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}")
```
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- 🧩 Quantization
- Post-training static quantization applied using PyTorch to reduce model size and accelerate inference on edge devices.

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🗂 Repository Structure
```
.
├── model/               # Quantized model files
├── tokenizer_config/    # Tokenizer and vocab files
├── model.safensors/     # Fine-tuned model in safetensors format
├── README.md            # Model card

```
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🤝 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.