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[01;32mxgg@decs[00m:[01;34m~/pros/MLM_transfer[00m$ bash scripts/frequency_ratio/fine_tune_yelp_frequency_ratio.sh [K[K[K[K[K[K[K[K[K[K[K[K[K[K[K[K[K[K[K[K[K[K[K[Kamazon_frequency_ratio.sh
+ PROJECTPATH=/home/xgg/pros/MLM_transfer
+ cp configs/bert_amazon_frequency_ratio.config run.config
+ PYTHONPATH=/home/xgg/pros/MLM_transfer
+ /home/xgg/.conda/envs/py36/bin//python fine_tune_bert.py
03/13/2019 18:08:56 - INFO - dataloader - device cuda n_gpu 1 distributed training False
03/13/2019 18:08:57 - INFO - dataloader - *** Example ***
03/13/2019 18:08:57 - INFO - dataloader - guid: train-1
03/13/2019 18:08:57 - INFO - dataloader - tokens: [CLS] save your money and buy a toy that has actual notes . [SEP]
03/13/2019 18:08:57 - INFO - dataloader - init_ids: 101 3828 2115 2769 1998 4965 1037 9121 2008 2038 5025 3964 1012 102
03/13/2019 18:08:57 - INFO - dataloader - input_ids: 101 103 103 2769 103 103 103 9121 2008 2038 5025 3964 1012 102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
03/13/2019 18:08:57 - INFO - dataloader - input_mask: 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 0
03/13/2019 18:08:57 - 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
03/13/2019 18:08:57 - INFO - dataloader - masked_lm_labels: -1 3828 2115 -1 1998 4965 1037 -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
03/13/2019 18:08:57 - INFO - dataloader - *** Example ***
03/13/2019 18:08:57 - INFO - dataloader - guid: train-2
03/13/2019 18:08:57 - INFO - dataloader - tokens: [CLS] heat it up slowly so it won t warp and it should last a lifetime . [SEP]
03/13/2019 18:08:57 - INFO - dataloader - init_ids: 101 3684 2009 2039 3254 2061 2009 2180 1056 24136 1998 2009 2323 2197 1037 6480 1012 102
03/13/2019 18:08:57 - INFO - dataloader - input_ids: 101 3684 2009 2039 3254 2061 2009 2180 1056 24136 103 103 103 103 103 6480 1012 102 0 0 0 0 0 0 0 0 0 0 0 0 0 0
03/13/2019 18:08:57 - INFO - dataloader - input_mask: 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 0
03/13/2019 18:08:57 - 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
03/13/2019 18:08:57 - INFO - dataloader - masked_lm_labels: -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1998 2009 2323 2197 1037 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1
03/13/2019 18:08:57 - INFO - dataloader - *** Example ***
03/13/2019 18:08:57 - INFO - dataloader - guid: train-3
03/13/2019 18:08:57 - INFO - dataloader - tokens: [CLS] this game would be alright , had i never played the original . [SEP]
03/13/2019 18:08:57 - INFO - dataloader - init_ids: 101 2023 2208 2052 2022 10303 1010 2018 1045 2196 2209 1996 2434 1012 102
03/13/2019 18:08:57 - INFO - dataloader - input_ids: 101 2023 2208 2052 2022 10303 1010 2018 1045 103 103 103 103 1012 102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
03/13/2019 18:08:57 - 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
03/13/2019 18:08:57 - 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
03/13/2019 18:08:57 - INFO - dataloader - masked_lm_labels: -1 -1 -1 -1 -1 -1 -1 -1 -1 2196 2209 1996 2434 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1
03/13/2019 18:08:57 - INFO - dataloader - *** Example ***
03/13/2019 18:08:57 - INFO - dataloader - guid: train-4
03/13/2019 18:08:57 - INFO - dataloader - tokens: [CLS] anyway ##s . . . many of the noodles were broken , so be aware of that if ordering vs . [SEP]
03/13/2019 18:08:57 - INFO - dataloader - init_ids: 101 4312 2015 1012 1012 1012 2116 1997 1996 27130 2020 3714 1010 2061 2022 5204 1997 2008 2065 13063 5443 1012 102
03/13/2019 18:08:57 - INFO - dataloader - input_ids: 101 4312 2015 1012 1012 1012 2116 1997 1996 27130 2020 3714 1010 2061 2022 5204 1997 103 103 13063 5443 1012 102 0 0 0 0 0 0 0 0 0
03/13/2019 18:08:57 - INFO - dataloader - input_mask: 1 1 1 1 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
03/13/2019 18:08:57 - 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
03/13/2019 18:08:57 - INFO - dataloader - masked_lm_labels: -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 2008 2065 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1
03/13/2019 18:08:57 - INFO - dataloader - *** Example ***
03/13/2019 18:08:57 - INFO - dataloader - guid: train-5
03/13/2019 18:08:57 - INFO - dataloader - tokens: [CLS] they are not safe to wear because they will come undone and you will loose them . [SEP]
03/13/2019 18:08:57 - INFO - dataloader - init_ids: 101 2027 2024 2025 3647 2000 4929 2138 2027 2097 2272 25757 1998 2017 2097 6065 2068 1012 102
03/13/2019 18:08:57 - INFO - dataloader - input_ids: 101 2027 2024 103 103 2000 4929 2138 2027 2097 2272 25757 1998 2017 2097 6065 2068 1012 102 0 0 0 0 0 0 0 0 0 0 0 0 0
03/13/2019 18:08:57 - 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
03/13/2019 18:08:57 - 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
03/13/2019 18:08:57 - INFO - dataloader - masked_lm_labels: -1 -1 -1 2025 3647 -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
03/13/2019 18:09:41 - INFO - dataloader - ***** Running training *****
03/13/2019 18:09:41 - INFO - dataloader - Num examples = 226224
03/13/2019 18:09:41 - INFO - dataloader - Batch size = 32
03/13/2019 18:09:41 - INFO - dataloader - Num steps = 70695
**********************************************************
Namespace(bert_model='/home/xgg/.pytorch_pretrained_bert/bert-base-uncased.tar.gz', data_dir='./processed_data_frequency_ratio/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)
03/13/2019 18:09:41 - INFO - pytorch_pretrained_bert.modeling - loading archive file /home/xgg/.pytorch_pretrained_bert/bert-base-uncased.tar.gz
03/13/2019 18:09:41 - INFO - pytorch_pretrained_bert.modeling - extracting archive file /home/xgg/.pytorch_pretrained_bert/bert-base-uncased.tar.gz to temp dir /tmp/tmpdxcrtmyy
03/13/2019 18:09:43 - 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
}
03/13/2019 18:09:46 - 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: 5.416446237564087
avg_loss: 5.136317539215088
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Epoch: 10%|███████████████▋ | 1/10 [18:30<2:46:31, 1110.12s/it]avg_loss: 2.6324873042106627
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Epoch: 20%|███████████████████████████████▍ | 2/10 [36:59<2:27:59, 1109.92s/it]avg_loss: 2.0770528721809387
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Epoch: 30%|███████████████████████████████████████████████ | 3/10 [55:29<2:09:28, 1109.85s/it]avg_loss: 1.8144258153438568
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Epoch: 40%|██████████████████████████████████████████████████████████████ | 4/10 [1:13:58<1:50:57, 1109.56s/it]avg_loss: 1.5076558315753936
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Epoch: 50%|█████████████████████████████████████████████████████████████████████████████▌ | 5/10 [1:32:27<1:32:26, 1109.39s/it]avg_loss: 1.4044195222854614
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Epoch: 60%|█████████████████████████████████████████████████████████████████████████████████████████████ | 6/10 [1:50:55<1:13:56, 1109.01s/it]avg_loss: 1.3356457006931306
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