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)
💾 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
1from transformers import BertTokenizerFast, BertForTokenClassification
2from transformers import pipeline
3import torch
45model_name ="AventIQ-AI/QA-Squad-Model"6tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)7model = AutoModelForQuestionAnswering.from_pretrained(model_checkpoint)8model.eval()9101112#Inference131415qa_pipeline = pipeline("question-answering", model="./qa_model", tokenizer="./qa_model")1617# Provide a context and a question18context ="""
19The Amazon rainforest, also known as Amazonia, is a moist broadleaf tropical rainforest in the Amazon biome
20that covers most of the Amazon basin of South America. This region includes territory belonging to nine nations.
21"""22question ="What is the Amazon rainforest also known as?"2324# Run inference25result = qa_pipeline(question=question, context=context)2627# Print the result28print(f"Question: {question}")29print(f"Answer: {result['answer']}")30print(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.
