Create README.md

README.md

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+---
+license: mit
+datasets:
+- rajpurkar/squad
+language:
+- en
+base_model:
+- distilbert/distilbert-base-uncased
+pipeline_tag: question-answering
+library_name: transformers
+tags:
+- extractive-qa
+- span-prediction
+---
+
+# DistilBERT Fine-Tuned on SQuAD for Extractive QA
+
+## Model Description
+
+DistilBERT base uncased fine-tuned on a 5,000-sample subset of [SQuAD](https://huggingface.co/datasets/rajpurkar/squad) for extractive question answering.
+
+## Training Details
+
+- **Base model:** distilbert-base-uncased
+- **Dataset:** SQuAD (5,000 samples, 80/20 train/test split)
+- **Epochs:** 3
+- **Learning rate:** 2e-5
+- **Batch size:** 16
+- **Training loss:** 2.303
+- **Device:** Apple Silicon (MPS)
+
+## Usage
+
+```python
+from transformers import AutoTokenizer, AutoModelForQuestionAnswering
+import torch
+
+tokenizer = AutoTokenizer.from_pretrained("John-Machado/distilbert-squad-qa")
+model = AutoModelForQuestionAnswering.from_pretrained("John-Machado/distilbert-squad-qa")
+
+question = "How many official league titles has Juventus won?"
+context = "The club has won 36 official league titles, 14 Coppa Italia titles and nine Supercoppa Italiana titles."
+
+inputs = tokenizer(question, context, return_tensors="pt")
+with torch.no_grad():
+    outputs = model(**inputs)
+
+start = outputs.start_logits.argmax()
+end = outputs.end_logits.argmax()
+answer = tokenizer.decode(inputs.input_ids[0, start : end + 1])
+print(answer)  # "36"
+```
+
+## Limitations
+- Trained on only 5,000 SQuAD samples (out of 87,599) as a learning exercise
+- Answers must exist as a verbatim substring in the context
+- Cannot synthesize answers across multiple passages