AventIQ-AI / QA-Squad-Model

🧠 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

📊 Evaluation Metrics

🚀 Usage

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.