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## BERT-large finetuned on squad v2. |
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F1 on dev (from paper)[https://arxiv.org/pdf/1810.04805v2.pdf] is 81.9, we reach 81.58. |
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``` |
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{'exact': 78.6321906847469, |
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'f1': 81.5816656803201, |
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'total': 11873, |
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'HasAns_exact': 73.73481781376518, |
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'HasAns_f1': 79.64222615088413, |
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'HasAns_total': 5928, |
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'NoAns_exact': 83.51555929352396, |
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'NoAns_f1': 83.51555929352396, |
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'NoAns_total': 5945, |
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'best_exact': 78.6321906847469, |
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'best_exact_thresh': 0.0, |
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'best_f1': 81.58166568032006, |
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'best_f1_thresh': 0.0, |
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'epoch': 1.59} |
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``` |
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``` |
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python run_qa.py \ |
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--model_name_or_path bert-large-uncased \ |
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--dataset_name squad_v2 \ |
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--do_train \ |
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--do_eval \ |
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--save_steps 2500 \ |
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--eval_steps 2500 \ |
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--evaluation_strategy steps \ |
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--per_device_train_batch_size 12 \ |
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--learning_rate 3e-5 \ |
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--num_train_epochs 2 \ |
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--max_seq_length 384 \ |
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--doc_stride 128 \ |
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--output_dir bert-large-uncased-squadv2 \ |
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--version_2_with_negative 1 |
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``` |