MLM_transfer/screenlog_run_attention_based_amazon.log at master · 1024er/MLM_transfer · GitHub

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[01;32mxgg@1604[00m:[01;34m~/pros/MLM_transfer[00m$ bash scripts/attention_based/fine_tune_yelp_attention_based.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_attention_based.sh
+ PROJECTPATH=/home/xgg/pros/MLM_transfer
+ cp configs/bert_amazon_attention_based.config run.config
\+ PYTHONPATH=/home/xgg/pros/MLM_transfer
+ /home/xgg/anaconda3/bin/python fine_tune_bert.py
03/13/2019 17:31:51 - INFO - dataloader -   device cuda n_gpu 1 distributed training False
03/13/2019 17:31:53 - INFO - dataloader -   *** Example ***
03/13/2019 17:31:53 - INFO - dataloader -   guid: train-1
03/13/2019 17:31:53 - INFO - dataloader -   tokens: [CLS] this royal jelly had no effect whatsoever on me . [SEP]
03/13/2019 17:31:53 - INFO - dataloader -   init_ids: 101 2023 2548 20919 2018 2053 3466 18971 2006 2033 1012 102
03/13/2019 17:31:53 - INFO - dataloader -   input_ids: 101 2023 2548 20919 2018 103 3466 18971 2006 2033 1012 102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
03/13/2019 17:31:53 - 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
03/13/2019 17:31:53 - 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 17:31:53 - INFO - dataloader -   masked_lm_labels: -1 -1 -1 -1 -1 2053 -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 17:31:53 - INFO - dataloader -   *** Example ***
03/13/2019 17:31:53 - INFO - dataloader -   guid: train-2
03/13/2019 17:31:53 - INFO - dataloader -   tokens: [CLS] you always want as high an optical zoom because using pure digital zoom will make your pictures look fuzzy ! memory . [SEP]
03/13/2019 17:31:53 - INFO - dataloader -   init_ids: 101 2017 2467 2215 2004 2152 2019 9380 24095 2138 2478 5760 3617 24095 2097 2191 2115 4620 2298 18001 999 3638 1012 102
03/13/2019 17:31:53 - INFO - dataloader -   input_ids: 101 2017 2467 2215 2004 2152 2019 9380 103 2138 2478 5760 3617 24095 2097 2191 2115 4620 2298 18001 999 3638 1012 102 0 0 0 0 0 0 0 0
03/13/2019 17:31:53 - 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 1 0 0 0 0 0 0 0 0
03/13/2019 17:31:53 - 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 17:31:53 - INFO - dataloader -   masked_lm_labels: -1 -1 -1 -1 -1 -1 -1 -1 24095 -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 17:31:53 - INFO - dataloader -   *** Example ***
03/13/2019 17:31:53 - INFO - dataloader -   guid: train-3
03/13/2019 17:31:53 - INFO - dataloader -   tokens: [CLS] so we continue to let him explore with my real ds ##lr under very close supervision . [SEP]
03/13/2019 17:31:53 - INFO - dataloader -   init_ids: 101 2061 2057 3613 2000 2292 2032 8849 2007 2026 2613 16233 20974 2104 2200 2485 10429 1012 102
03/13/2019 17:31:53 - INFO - dataloader -   input_ids: 101 2061 2057 3613 2000 2292 2032 8849 2007 2026 2613 103 20974 2104 2200 2485 10429 1012 102 0 0 0 0 0 0 0 0 0 0 0 0 0
03/13/2019 17:31:53 - 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 17:31:53 - 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 17:31:53 - INFO - dataloader -   masked_lm_labels: -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 16233 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1
03/13/2019 17:31:53 - INFO - dataloader -   *** Example ***
03/13/2019 17:31:53 - INFO - dataloader -   guid: train-4
03/13/2019 17:31:53 - INFO - dataloader -   tokens: [CLS] for me it works great , but i do not demand a whole lot from it . [SEP]
03/13/2019 17:31:53 - INFO - dataloader -   init_ids: 101 2005 2033 2009 2573 2307 1010 2021 1045 2079 2025 5157 1037 2878 2843 2013 2009 1012 102
03/13/2019 17:31:53 - INFO - dataloader -   input_ids: 101 2005 2033 2009 103 2307 1010 2021 1045 2079 2025 5157 1037 2878 2843 2013 2009 1012 102 0 0 0 0 0 0 0 0 0 0 0 0 0
03/13/2019 17:31:53 - 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 17:31:53 - 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 17:31:53 - INFO - dataloader -   masked_lm_labels: -1 -1 -1 -1 2573 -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 17:31:53 - INFO - dataloader -   *** Example ***
03/13/2019 17:31:53 - INFO - dataloader -   guid: train-5
03/13/2019 17:31:53 - INFO - dataloader -   tokens: [CLS] some of the puzzles weren t too bad , but others were pretty far fetch ##ed . [SEP]
03/13/2019 17:31:53 - INFO - dataloader -   init_ids: 101 2070 1997 1996 19672 4694 1056 2205 2919 1010 2021 2500 2020 3492 2521 18584 2098 1012 102
03/13/2019 17:31:53 - INFO - dataloader -   input_ids: 101 2070 1997 1996 103 4694 1056 2205 2919 1010 2021 2500 2020 3492 2521 18584 2098 1012 102 0 0 0 0 0 0 0 0 0 0 0 0 0
03/13/2019 17:31:53 - 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 17:31:53 - 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 17:31:53 - INFO - dataloader -   masked_lm_labels: -1 -1 -1 -1 19672 -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 17:33:14 - INFO - dataloader -   ***** Running training *****
03/13/2019 17:33:14 - INFO - dataloader -     Num examples = 556995
03/13/2019 17:33:14 - INFO - dataloader -     Batch size = 32
03/13/2019 17:33:14 - INFO - dataloader -     Num steps = 174060
**********************************************************
Namespace(bert_model='/home/xgg/.pytorch_pretrained_bert/bert-base-uncased.tar.gz', data_dir='./processed_data_attention_based/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 17:33:15 - INFO - pytorch_pretrained_bert.modeling -   loading archive file /home/xgg/.pytorch_pretrained_bert/bert-base-uncased.tar.gz
03/13/2019 17:33:15 - INFO - pytorch_pretrained_bert.modeling -   extracting archive file /home/xgg/.pytorch_pretrained_bert/bert-base-uncased.tar.gz to temp dir /tmp/tmpmfk_dqz7
03/13/2019 17:33:18 - 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 17:33:20 - 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: 4.6030231380462645
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