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screenlog_run_fusion_method_amazon.log
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[01;32mxgg@decs[00m:[01;34m~/pros/MLM_transfer[00m$ bash scripts/fusion_method/fine_tune_yelp_fusion_method.sh [6@requency_ratio/fine_tune_amazon_frequency_ratio[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[6Pusion_method/fine_tune_yelp_fusion_method[C[C[C[C[K[K[K[K[K[K[K[K[K[K[K[K[K[K[K[K[K[K[K[K[K[Kamazon_fusion_method.sh
+ PROJECTPATH=/home/xgg/pros/MLM_transfer
+ cp configs/bert_amazon_fusion_method.config run.config
+ PYTHONPATH=/home/xgg/pros/MLM_transfer
+ /home/xgg/.conda/envs/py36/bin//python fine_tune_bert.py
03/15/2019 21:55:47 - INFO - dataloader - device cuda n_gpu 1 distributed training False
03/15/2019 21:55:50 - INFO - dataloader - *** Example ***
03/15/2019 21:55:50 - INFO - dataloader - guid: train-1
03/15/2019 21:55:50 - INFO - dataloader - tokens: [CLS] this was from nu ##m _ nu ##m phones ago at least . [SEP]
03/15/2019 21:55:50 - INFO - dataloader - init_ids: 101 2023 2001 2013 16371 2213 1035 16371 2213 11640 3283 2012 2560 1012 102
03/15/2019 21:55:50 - INFO - dataloader - input_ids: 101 2023 2001 2013 16371 2213 103 16371 2213 11640 3283 2012 2560 1012 102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
03/15/2019 21:55:50 - INFO - dataloader - input_mask: 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 0
03/15/2019 21:55:50 - 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
03/15/2019 21:55:50 - INFO - dataloader - masked_lm_labels: -1 -1 -1 -1 -1 -1 1035 -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
03/15/2019 21:55:50 - INFO - dataloader - *** Example ***
03/15/2019 21:55:50 - INFO - dataloader - guid: train-2
03/15/2019 21:55:50 - INFO - dataloader - tokens: [CLS] i got them tried them on and thought these are what i have been waiting on . [SEP]
03/15/2019 21:55:50 - INFO - dataloader - init_ids: 101 1045 2288 2068 2699 2068 2006 1998 2245 2122 2024 2054 1045 2031 2042 3403 2006 1012 102
03/15/2019 21:55:50 - INFO - dataloader - input_ids: 101 1045 2288 2068 2699 2068 2006 103 103 103 2024 2054 1045 2031 2042 3403 2006 1012 102 0 0 0 0 0 0 0 0 0 0 0 0 0
03/15/2019 21:55:50 - INFO - dataloader - input_mask: 1 1 1 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
03/15/2019 21:55:50 - 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
03/15/2019 21:55:50 - INFO - dataloader - masked_lm_labels: -1 -1 -1 -1 -1 -1 -1 1998 2245 2122 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1
03/15/2019 21:55:50 - INFO - dataloader - *** Example ***
03/15/2019 21:55:50 - INFO - dataloader - guid: train-3
03/15/2019 21:55:50 - INFO - dataloader - tokens: [CLS] the unit is capable of very thin slices with meat , cheese ##s , and bread . [SEP]
03/15/2019 21:55:50 - INFO - dataloader - init_ids: 101 1996 3131 2003 5214 1997 2200 4857 25609 2007 6240 1010 8808 2015 1010 1998 7852 1012 102
03/15/2019 21:55:50 - INFO - dataloader - input_ids: 101 1996 3131 2003 5214 1997 2200 4857 103 2007 6240 1010 8808 2015 1010 1998 7852 1012 102 0 0 0 0 0 0 0 0 0 0 0 0 0
03/15/2019 21:55:50 - INFO - dataloader - input_mask: 1 1 1 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
03/15/2019 21:55:50 - 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
03/15/2019 21:55:50 - INFO - dataloader - masked_lm_labels: -1 -1 -1 -1 -1 -1 -1 -1 25609 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1
03/15/2019 21:55:50 - INFO - dataloader - *** Example ***
03/15/2019 21:55:50 - INFO - dataloader - guid: train-4
03/15/2019 21:55:50 - INFO - dataloader - tokens: [CLS] it has only one button and grind ##s evenly for coarse or thin . [SEP]
03/15/2019 21:55:50 - INFO - dataloader - init_ids: 101 2009 2038 2069 2028 6462 1998 23088 2015 18030 2005 20392 2030 4857 1012 102
03/15/2019 21:55:50 - INFO - dataloader - input_ids: 101 2009 2038 2069 2028 6462 1998 23088 103 18030 2005 20392 2030 4857 1012 102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
03/15/2019 21:55:50 - 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
03/15/2019 21:55:50 - 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
03/15/2019 21:55:50 - INFO - dataloader - masked_lm_labels: -1 -1 -1 -1 -1 -1 -1 -1 2015 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1
03/15/2019 21:55:50 - INFO - dataloader - *** Example ***
03/15/2019 21:55:50 - INFO - dataloader - guid: train-5
03/15/2019 21:55:50 - INFO - dataloader - tokens: [CLS] aside from the stone wheel , the sharpe ##ner is constructed entirely of plastic . [SEP]
03/15/2019 21:55:50 - INFO - dataloader - init_ids: 101 4998 2013 1996 2962 5217 1010 1996 22147 3678 2003 3833 4498 1997 6081 1012 102
03/15/2019 21:55:50 - INFO - dataloader - input_ids: 101 4998 2013 1996 2962 5217 1010 1996 103 3678 2003 3833 4498 1997 6081 1012 102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
03/15/2019 21:55:50 - INFO - dataloader - input_mask: 1 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
03/15/2019 21:55:50 - 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
03/15/2019 21:55:50 - INFO - dataloader - masked_lm_labels: -1 -1 -1 -1 -1 -1 -1 -1 22147 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1
03/15/2019 21:57:35 - INFO - dataloader - ***** Running training *****
03/15/2019 21:57:35 - INFO - dataloader - Num examples = 556995
03/15/2019 21:57:35 - INFO - dataloader - Batch size = 32
03/15/2019 21:57:35 - INFO - dataloader - Num steps = 174060
**********************************************************
Namespace(bert_model='/home/xgg/.pytorch_pretrained_bert/bert-base-uncased.tar.gz', data_dir='./processed_data_fusion_method/amazon/', 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/amazon_output/', seed=42, task_name=None, train_batch_size=32, warmup_proportion=0.1)
03/15/2019 21:57:36 - INFO - pytorch_pretrained_bert.modeling - loading archive file /home/xgg/.pytorch_pretrained_bert/bert-base-uncased.tar.gz
03/15/2019 21:57:36 - INFO - pytorch_pretrained_bert.modeling - extracting archive file /home/xgg/.pytorch_pretrained_bert/bert-base-uncased.tar.gz to temp dir /tmp/tmpgwgd3go9
03/15/2019 21:57:38 - 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
}
03/15/2019 21:57:40 - 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: 5.427108745574952
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