---
license: apache-2.0
datasets:
- squad
language:
- en
metrics:
- exact_match
- f1
library_name: transformers
pipeline_tag: question-answering
---
# Model Card for Model ID

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### Info to format
Evaluation Dataset:
Dataset({
    features: ['id', 'title', 'context', 'question', 'answers'],
    num_rows: 10570
})
Max Tokens Length:
512
Evaluation Metrics:
{'exact': 66.00660066006601, 'f1': 78.28040573606134, 'total': 909, 'HasAns_exact': 66.00660066006601, 'HasAns_f1': 78.28040573606134, 'HasAns_total': 909, 'best_exact': 66.00660066006601, 'best_exact_thresh': 0.0, 'best_f1': 78.28040573606134, 'best_f1_thresh': 0.0}

Train Dataset({
    features: ['id', 'title', 'context', 'question', 'answers'],
    num_rows: 8207
})

Eval dataset:
Dataset({
    features: ['id', 'title', 'context', 'question', 'answers'],
    num_rows: 637
})

Model: Roberta-base for question answering

Dataset:
squad = load_dataset("squad")
squad['train'] = squad['train'].select(range(30000))
squad['test'] = squad['validation']
squad['validation'] = squad['validation'].select(range(2000))