| **🧠 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. | |