up readme

README.md

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+---
+license: apache-2.0
+tags:
+- question-answering
+- squad
+- transformers
+- pytorch
+- evaluation
+- hf-course
+- fine-tuned
+datasets:
+- squad
+metrics:
+- exact_match
+- f1
+model-index:
+  - name: QA-SQuAD-BERT
+    results:
+      - task:
+          type: question-answering
+          name: Question Answering
+        dataset:
+          name: SQuAD v1.1
+          type: squad
+        metrics:
+          - name: Exact Match
+            type: exact_match
+            value: 82.7
+          - name: F1
+            type: f1
+            value: 87.0039
+---
+
+# QA-SQuAD-BERT
+
+A BERT-based model fine-tuned on SQuAD v1.1 for extractive QA
+
+## Model Description
+
+This model is based on bert-base-uncased and was fine-tuned on the **SQuAD v1.1** dataset for extractive question answering. It takes a question and a context passage as input and predicts the span of text in the passage that most likely answers the question.
+
+The model was trained using the Hugging Face 🤗 Transformers library.
+
+## Intended Uses & Limitations
+
+### Intended Uses
+
+- Extractive question answering on Wikipedia-style passages.
+- As a downstream component in information retrieval pipelines.
+- Educational purposes or experimentation with fine-tuning on QA tasks.
+
+### Limitations
+
+- The model may not generalize well to out-of-domain datasets.
+- It does not handle unanswerable questions (not trained on SQuAD v2.0).
+- It may produce incorrect or misleading answers if context is ambiguous.
+
+## Training Details
+
+- **Base model**: bert-base-uncased
+- **Dataset**: [SQuAD v1.1](https://huggingface.co/datasets/squad)
+- **Epochs**: 3
+- **Batch size**: 8
+- **Learning rate**: 2e-5
+- **Optimizer**: AdamW
+- **Max length**: 384
+- **Hardware used**: Colab/GPU T4
+
+## Evaluation Results
+
+The model was evaluated on the SQuAD v1.1 development set using the standard metrics: Exact Match (EM) and F1.
+
+| Metric       | Score |
+|--------------|-------|
+| Exact Match  | 82.7 |
+| F1           | 87.0039 |
+
+## How to Use
+
+You can load this model using the `pipeline` API:
+
+```python
+from transformers import pipeline
+
+qa_pipeline = pipeline("question-answering", model="<!-- TODO: model repo name -->")
+result = qa_pipeline({
+    "context": "Hugging Face is creating a tool that democratizes AI.",
+    "question": "What is Hugging Face creating?"
+})
+print(result)