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[01;32mxgg@decs[00m:[01;34m~/pros/MLM_transfer[00m$ bash scrii[Kpts/frequency_ratio/r[Kfine_tune_yelp_frequency_ratio.sh
+ PROJECTPATH=/home/xgg/pros/MLM_transfer
+ cp configs/bert_yelp_frequency_ratio.config run.config
+ PYTHONPATH=/home/xgg/pros/MLM_transfer
+ /home/xgg/.conda/envs/py36/bin//python fine_tune_bert.py
03/12/2019 08:12:20 - INFO - dataloader - device cuda n_gpu 1 distributed training False
03/12/2019 08:12:21 - INFO - dataloader - *** Example ***
03/12/2019 08:12:21 - INFO - dataloader - guid: train-1
03/12/2019 08:12:21 - INFO - dataloader - tokens: [CLS] always a great place to stop by in the mall . [SEP]
03/12/2019 08:12:21 - INFO - dataloader - init_ids: 101 2467 1037 2307 2173 2000 2644 2011 1999 1996 6670 1012 102
03/12/2019 08:12:21 - INFO - dataloader - input_ids: 101 103 103 103 103 103 103 2011 1999 1996 6670 1012 102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
03/12/2019 08:12:21 - INFO - dataloader - input_mask: 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
03/12/2019 08:12:21 - INFO - dataloader - segment_ids: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
03/12/2019 08:12:21 - INFO - dataloader - masked_lm_labels: -1 2467 1037 2307 2173 2000 2644 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1
03/12/2019 08:12:21 - INFO - dataloader - *** Example ***
03/12/2019 08:12:21 - INFO - dataloader - guid: train-2
03/12/2019 08:12:21 - INFO - dataloader - tokens: [CLS] the food is good for inexpensive chinese food . [SEP]
03/12/2019 08:12:21 - INFO - dataloader - init_ids: 101 1996 2833 2003 2204 2005 23766 2822 2833 1012 102
03/12/2019 08:12:21 - INFO - dataloader - input_ids: 101 103 103 103 103 2005 23766 2822 2833 1012 102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
03/12/2019 08:12:21 - INFO - dataloader - input_mask: 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
03/12/2019 08:12:21 - INFO - dataloader - segment_ids: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
03/12/2019 08:12:21 - INFO - dataloader - masked_lm_labels: -1 1996 2833 2003 2204 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1
03/12/2019 08:12:21 - INFO - dataloader - *** Example ***
03/12/2019 08:12:21 - INFO - dataloader - guid: train-3
03/12/2019 08:12:21 - INFO - dataloader - tokens: [CLS] i would highly recommend taking your car here . [SEP]
03/12/2019 08:12:21 - INFO - dataloader - init_ids: 101 1045 2052 3811 16755 2635 2115 2482 2182 1012 102
03/12/2019 08:12:21 - INFO - dataloader - input_ids: 101 103 103 103 103 2635 2115 2482 2182 1012 102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
03/12/2019 08:12:21 - INFO - dataloader - input_mask: 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
03/12/2019 08:12:21 - INFO - dataloader - segment_ids: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
03/12/2019 08:12:21 - INFO - dataloader - masked_lm_labels: -1 1045 2052 3811 16755 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1
03/12/2019 08:12:21 - INFO - dataloader - *** Example ***
03/12/2019 08:12:21 - INFO - dataloader - guid: train-4
03/12/2019 08:12:21 - INFO - dataloader - tokens: [CLS] my husband had the chile re ##llen ##o and thought it was amazing . [SEP]
03/12/2019 08:12:21 - INFO - dataloader - init_ids: 101 2026 3129 2018 1996 7029 2128 12179 2080 1998 2245 2009 2001 6429 1012 102
03/12/2019 08:12:21 - INFO - dataloader - input_ids: 101 2026 3129 2018 1996 7029 2128 12179 2080 103 103 103 103 6429 1012 102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
03/12/2019 08:12:21 - INFO - dataloader - input_mask: 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
03/12/2019 08:12:21 - INFO - dataloader - segment_ids: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
03/12/2019 08:12:21 - INFO - dataloader - masked_lm_labels: -1 -1 -1 -1 -1 -1 -1 -1 -1 1998 2245 2009 2001 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1
03/12/2019 08:12:21 - INFO - dataloader - *** Example ***
03/12/2019 08:12:21 - INFO - dataloader - guid: train-5
03/12/2019 08:12:21 - INFO - dataloader - tokens: [CLS] my second bad experience at a t ##gi fridays . [SEP]
03/12/2019 08:12:21 - INFO - dataloader - init_ids: 101 2026 2117 2919 3325 2012 1037 1056 5856 26587 1012 102
03/12/2019 08:12:21 - INFO - dataloader - input_ids: 101 2026 2117 103 103 103 1037 1056 5856 26587 1012 102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
03/12/2019 08:12:21 - INFO - dataloader - input_mask: 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
03/12/2019 08:12:21 - INFO - dataloader - segment_ids: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
03/12/2019 08:12:21 - INFO - dataloader - masked_lm_labels: -1 -1 -1 2919 3325 2012 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1
03/12/2019 08:12:47 - INFO - dataloader - ***** Running training *****
03/12/2019 08:12:47 - INFO - dataloader - Num examples = 172670
03/12/2019 08:12:47 - INFO - dataloader - Batch size = 32
03/12/2019 08:12:47 - INFO - dataloader - Num steps = 53959
**********************************************************
Namespace(bert_model='/home/xgg/.pytorch_pretrained_bert/bert-base-uncased.tar.gz', data_dir='./processed_data_frequency_ratio/yelp/', do_lower_case=True, do_train=True, eval_batch_size=8, gradient_accumulation_steps=1, learning_rate=2e-05, local_rank=-1, loss_scale=128, max_seq_length=32, no_cuda=False, num_train_epochs=10.0, optimize_on_cpu=False, output_dir='/tmp/yelp_output/', seed=42, task_name=None, train_batch_size=32, warmup_proportion=0.1)
03/12/2019 08:12:48 - INFO - pytorch_pretrained_bert.modeling - loading archive file /home/xgg/.pytorch_pretrained_bert/bert-base-uncased.tar.gz
03/12/2019 08:12:48 - INFO - pytorch_pretrained_bert.modeling - extracting archive file /home/xgg/.pytorch_pretrained_bert/bert-base-uncased.tar.gz to temp dir /tmp/tmpp5kutnu1
03/12/2019 08:12:50 - INFO - pytorch_pretrained_bert.modeling - Model config {
"attention_probs_dropout_prob": 0.1,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 768,
"initializer_range": 0.02,
"intermediate_size": 3072,
"max_position_embeddings": 512,
"num_attention_heads": 12,
"num_hidden_layers": 12,
"type_vocab_size": 2,
"vocab_size": 30522
}
03/12/2019 08:12:52 - INFO - pytorch_pretrained_bert.modeling - Weights from pretrained model not used in BertForMaskedLM: ['cls.seq_relationship.weight', 'cls.seq_relationship.bias']
Epoch: 0%| | 0/10 [00:00<?, ?it/s]avg_loss: 5.942069072723388
avg_loss: 5.439849977493286
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avg_loss: 2.970157513618469
avg_loss: 2.970995454788208
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Epoch: 10%|███████████████▊ | 1/10 [14:03<2:06:27, 843.11s/it]avg_loss: 2.6710274600982666
avg_loss: 2.6368083429336546
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Epoch: 20%|███████████████████████████████▌ | 2/10 [28:06<1:52:26, 843.27s/it]avg_loss: 2.153592166900635
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Epoch: 30%|███████████████████████████████████████████████▍ | 3/10 [42:10<1:38:24, 843.46s/it]avg_loss: 1.8875628209114075
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Epoch: 40%|███████████████████████████████████████████████████████████████▏ | 4/10 [56:14<1:24:22, 843.72s/it]avg_loss: 1.6321519112586975
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Epoch: 50%|██████████████████████████████████████████████████████████████████████████████ | 5/10 [1:10:19<1:10:20, 844.06s/it]avg_loss: 1.4330149126052856
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Epoch: 60%|██████████████████████████████████████████████████████████████████████████████████████████████▊ | 6/10 [1:24:24<56:17, 844.29s/it]avg_loss: 1.3712639439105987
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