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bert-base-uncased on SQuAD v1.1
for extractive question answering.| Metric | Score |
|---|---|
| Exact Match (EM) | 61.20 |
| F1 Score | 76.25 |
1from transformers import pipeline
2
3qa = pipeline("question-answering", model="argha9177/bert-squad-qa")
4
5result = qa(
6 question="What is the capital of France?",
7 context="France is a country in Western Europe. Its capital city is Paris."
8)
9print(result)
10# {'answer': 'Paris', 'score': 0.98, 'start': 58, 'end': 63}| Parameter | Value |
|---|---|
| Base model | bert-base-uncased |
| Dataset | rajpurkar/squad (v1.1) |
| Training samples | 8000 |
| Epochs | 2 |
| Batch size | 16 |
| Learning rate | 3e-05 |
| Max length | 384 |
| Doc stride | 128 |
| Warmup ratio | 0.1 |
| Optimizer | AdamW with linear LR decay |