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This model is [Distilbert base uncased](https://huggingface.co/distilbert-base-uncased) trained on SQuAD v2 as: |
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``` |
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export SQUAD_DIR=../../squad2 |
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python3 run_squad.py |
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--model_type distilbert |
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--model_name_or_path distilbert-base-uncased |
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--do_train |
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--do_eval |
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--overwrite_cache |
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--do_lower_case |
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--version_2_with_negative |
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--save_steps 100000 |
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--train_file $SQUAD_DIR/train-v2.0.json |
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--predict_file $SQUAD_DIR/dev-v2.0.json |
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--per_gpu_train_batch_size 8 |
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--num_train_epochs 3 |
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--learning_rate 3e-5 |
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--max_seq_length 384 |
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--doc_stride 128 |
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--output_dir ./tmp/distilbert_fine_tuned/ |
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``` |
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Performance on a dev subset is close to the original paper: |
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``` |
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Results: |
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{ |
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'exact': 64.88976637051661, |
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'f1': 68.1776176526635, |
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'total': 6078, |
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'HasAns_exact': 69.7594501718213, |
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'HasAns_f1': 76.62665295288285, |
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'HasAns_total': 2910, |
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'NoAns_exact': 60.416666666666664, |
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'NoAns_f1': 60.416666666666664, |
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'NoAns_total': 3168, |
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'best_exact': 64.88976637051661, |
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'best_exact_thresh': 0.0, |
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'best_f1': 68.17761765266337, |
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'best_f1_thresh': 0.0 |
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} |
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``` |
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We are hopeful this might save you time, energy, and compute. Cheers! |