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luke_pytorch_train.log
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luke_pytorch_train.log
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[2022-01-14 02:03:05,197] [INFO] Creating features from the dataset... (load_examples@main.py:195)
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Selected optimization level O2: FP16 training with FP32 batchnorm and FP32 master weights.
Defaults for this optimization level are:
enabled : True
opt_level : O2
cast_model_type : torch.float16
patch_torch_functions : False
keep_batchnorm_fp32 : True
master_weights : True
loss_scale : dynamic
Processing user overrides (additional kwargs that are not None)...
After processing overrides, optimization options are:
enabled : True
opt_level : O2
cast_model_type : torch.float16
patch_torch_functions : False
keep_batchnorm_fp32 : True
master_weights : True
loss_scale : dynamic
Warning: multi_tensor_applier fused unscale kernel is unavailable, possibly because apex was installed without --cuda_ext --cpp_ext. Using Python fallback. Original ImportError was: ModuleNotFoundError("No module named 'amp_C'")
0%| | 0/1497 [00:00<?, ?it/s]/usr/local/lib/python3.7/dist-packages/torch/optim/lr_scheduler.py:125: UserWarning: Seems like `optimizer.step()` has been overridden after learning rate scheduler initialization. Please, make sure to call `optimizer.step()` before `lr_scheduler.step()`. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate
"https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate", UserWarning)
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(load_examples@main.py:195)
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(load_examples@main.py:195)
dev: {'precision': 0.7930839002267573, 'recall': 0.7294056308654849, 'f1': 0.7599130907115698}
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(load_examples@main.py:195)
dev: {'precision': 0.8024830699774267, 'recall': 0.7413972888425443, 'f1': 0.7707317073170731}
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[2022-01-14 02:19:38,936] [INFO] global_step = 1497, average loss = 0.13534044343468024 (train@trainer.py:149)
[2022-01-14 02:19:38,936] [INFO] Saving the model checkpoint to /content/luke/outputs (run@main.py:97)
[2022-01-14 02:20:17,729] [INFO] Creating features from the dataset... (load_examples@main.py:195)
dev: {'precision': 0.8120842572062085, 'recall': 0.7638164754953076, 'f1': 0.7872111767866737}
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[2022-01-14 02:20:51,558] [INFO] Creating features from the dataset... (load_examples@main.py:195)
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[2022-01-14 02:21:24,835] [INFO] Results: {
"best_epoch": 2,
"dev_f1": 0.7872111767866737,
"dev_f1_epoch0": 0.7599130907115698,
"dev_f1_epoch1": 0.7707317073170731,
"dev_f1_epoch2": 0.7872111767866737,
"dev_precision": 0.8120842572062085,
"dev_precision_epoch0": 0.7930839002267573,
"dev_precision_epoch1": 0.8024830699774267,
"dev_precision_epoch2": 0.8120842572062085,
"dev_recall": 0.7638164754953076,
"dev_recall_epoch0": 0.7294056308654849,
"dev_recall_epoch1": 0.7413972888425443,
"dev_recall_epoch2": 0.7638164754953076,
"test_f1": 0.7750883872722328,
"test_precision": 0.8037225042301185,
"test_recall": 0.7484243697478992
} (run@main.py:118)