| # 📝 Question Answers Roberta Model | |
| This repository demonstrates how to **fine-tune** and **quantize** the [deepset/roberta-base-squad2](https://huggingface.co/deepset/roberta-base-squad2) model for Question Answering using a sample dataset from Hugging Face Hub. | |
| --- | |
| ## 🚀 Model Overview | |
| - **Base Model:** `deepset/roberta-base-squad2` | |
| - **Task:** Extractive Question Answering | |
| - **Precision:** Supports FP32, FP16 (half-precision), and INT8 (quantized) | |
| - **Dataset:** [`squad`](https://huggingface.co/datasets/squad) — Stanford Question Answering Dataset (Hugging Face Datasets) | |
| --- | |
| ## 📦 Dataset Used | |
| We use the **`squad`** dataset from Hugging Face: | |
| ```bash | |
| pip install datasets | |
| ``` | |
| # Dataset | |
| ```Pyhton | |
| from datasets import load_dataset | |
| dataset = load_dataset("squad") | |
| ``` | |
| # Load Model & Tokenizer: | |
| ```python | |
| from transformers import AutoModelForQuestionAnswering, AutoTokenizer, TrainingArguments, Trainer | |
| from datasets import load_dataset | |
| model = AutoModelForQuestionAnswering.from_pretrained("deepset/roberta-base-squad2") | |
| tokenizer = AutoTokenizer.from_pretrained("deepset/roberta-base-squad2") | |
| dataset = load_dataset("squad") | |
| ``` | |
| # ✅ Results | |
| Feature Benefit | |
| FP16 Fine-Tuning - Faster Training + Lower Memory | |
| INT8 Quantization - Smaller Model + Fast Inference | |
| Dataset - Stanford QA Dataset (SQuAD) |