MTDNN_FULL_8_ALL.log

01/07/2021 10:37:52 1
01/07/2021 10:37:52 Launching the MT-DNN training
01/07/2021 10:37:52 Loading data/canonical_data/bert_base_uncased_lower/mnli_train.json as task 0
01/07/2021 10:38:00 Loading data/canonical_data/bert_base_uncased_lower/rte_train.json as task 1
01/07/2021 10:38:01 Loading data/canonical_data/bert_base_uncased_lower/qqp_train.json as task 2
01/07/2021 10:38:08 Loading data/canonical_data/bert_base_uncased_lower/qnli_train.json as task 3
01/07/2021 10:38:11 Loading data/canonical_data/bert_base_uncased_lower/mrpc_train.json as task 4
01/07/2021 10:38:11 Loading data/canonical_data/bert_base_uncased_lower/sst_train.json as task 5
01/07/2021 10:38:12 Loading data/canonical_data/bert_base_uncased_lower/cola_train.json as task 6
01/07/2021 10:38:12 Loading data/canonical_data/bert_base_uncased_lower/stsb_train.json as task 7
01/07/2021 10:38:22 ####################
01/07/2021 10:38:22 {'log_file': 'checkpoints/2021-01-07T2237_full8/log.log', 'tensorboard': False, 'tensorboard_logdir': 'tensorboard_logdir', 'init_checkpoint': 'bert-base-uncased', 'data_dir': 'data/canonical_data/bert_base_uncased_lower', 'data_sort_on': False, 'name': 'farmer', 'task_def': 'experiments/glue/glue_task_def.yml', 'train_datasets': ['mnli', 'rte', 'qqp', 'qnli', 'mrpc', 'sst', 'cola', 'stsb'], 'test_datasets': ['mnli_matched', 'mnli_mismatched', 'rte', 'qqp', 'qnli', 'mrpc', 'sst', 'cola', 'stsb'], 'glue_format_on': False, 'mkd_opt': 0, 'do_padding': False, 'update_bert_opt': 0, 'multi_gpu_on': True, 'mem_cum_type': 'simple', 'answer_num_turn': 5, 'answer_mem_drop_p': 0.1, 'answer_att_hidden_size': 128, 'answer_att_type': 'bilinear', 'answer_rnn_type': 'gru', 'answer_sum_att_type': 'bilinear', 'answer_merge_opt': 1, 'answer_mem_type': 1, 'max_answer_len': 10, 'answer_dropout_p': 0.1, 'answer_weight_norm_on': False, 'dump_state_on': False, 'answer_opt': 1, 'mtl_opt': 0, 'ratio': 0, 'mix_opt': 0, 'max_seq_len': 512, 'init_ratio': 1, 'encoder_type': <EncoderModelType.BERT: 1>, 'num_hidden_layers': -1, 'bert_model_type': 'bert-base-uncased', 'do_lower_case': False, 'masked_lm_prob': 0.15, 'short_seq_prob': 0.2, 'max_predictions_per_seq': 128, 'bin_on': False, 'bin_size': 64, 'bin_grow_ratio': 0.5, 'cuda': True, 'log_per_updates': 500, 'save_per_updates': 10000, 'save_per_updates_on': False, 'epochs': 5, 'batch_size': 8, 'batch_size_eval': 8, 'optimizer': 'adamax', 'grad_clipping': 0.0, 'global_grad_clipping': 1.0, 'weight_decay': 0, 'learning_rate': 5e-05, 'momentum': 0, 'warmup': 0.1, 'warmup_schedule': 'warmup_linear', 'adam_eps': 1e-06, 'vb_dropout': True, 'dropout_p': 0.1, 'dropout_w': 0.0, 'bert_dropout_p': 0.1, 'model_ckpt': 'checkpoints/model_0.pt', 'resume': False, 'have_lr_scheduler': True, 'multi_step_lr': '10,20,30', 'lr_gamma': 0.5, 'scheduler_type': 'ms', 'output_dir': 'checkpoints/2021-01-07T2237_full8', 'seed': 2018, 'grad_accumulation_step': 1, 'fp16': False, 'fp16_opt_level': 'O1', 'adv_train': False, 'adv_opt': 0, 'adv_norm_level': 0, 'adv_p_norm': 'inf', 'adv_alpha': 1, 'adv_k': 1, 'adv_step_size': 0.001, 'adv_noise_var': 1e-05, 'adv_epsilon': 1e-06, 'loss_pred': True, 'collect_uncertainty': None, 'collect_topk': 0.1, 'load_ranked_data': None, 'mc_dropout': 0, 'finetune': False, 'encode_mode': False, 'task_def_list': [{'self': '{}', 'label_vocab': '<data_utils.vocab.Vocabulary object at 0x7f2dd4b954c0>', 'n_class': '3', 'data_type': '<DataFormat.PremiseAndOneHypothesis: 2>', 'task_type': '<TaskType.Classification: 1>', 'metric_meta': '(<Metric.ACC: 0>,)', 'split_names': "['train', 'matched_dev', 'mismatched_dev', 'matched_test', 'mismatched_test']", 'enable_san': 'False', 'dropout_p': '0.1', 'loss': '<LossCriterion.CeCriterion: 0>', 'kd_loss': '<LossCriterion.MseCriterion: 1>', 'adv_loss': '<LossCriterion.SymKlCriterion: 7>', '__class__': "<class 'experiments.exp_def.TaskDef'>"}, {'self': '{}', 'label_vocab': '<data_utils.vocab.Vocabulary object at 0x7f2d43df9460>', 'n_class': '2', 'data_type': '<DataFormat.PremiseAndOneHypothesis: 2>', 'task_type': '<TaskType.Classification: 1>', 'metric_meta': '(<Metric.ACC: 0>,)', 'split_names': "['train', 'dev', 'test']", 'enable_san': 'False', 'dropout_p': 'None', 'loss': '<LossCriterion.CeCriterion: 0>', 'kd_loss': '<LossCriterion.MseCriterion: 1>', 'adv_loss': '<LossCriterion.SymKlCriterion: 7>', '__class__': "<class 'experiments.exp_def.TaskDef'>"}, {'self': '{}', 'label_vocab': 'None', 'n_class': '2', 'data_type': '<DataFormat.PremiseAndOneHypothesis: 2>', 'task_type': '<TaskType.Classification: 1>', 'metric_meta': '(<Metric.ACC: 0>, <Metric.F1: 1>)', 'split_names': "['train', 'dev', 'test']", 'enable_san': 'False', 'dropout_p': 'None', 'loss': '<LossCriterion.CeCriterion: 0>', 'kd_loss': '<LossCriterion.MseCriterion: 1>', 'adv_loss': '<LossCriterion.SymKlCriterion: 7>', '__class__': "<class 'experiments.exp_def.TaskDef'>"}, {'self': '{}', 'label_vocab': '<data_utils.vocab.Vocabulary object at 0x7f2d43df93d0>', 'n_class': '2', 'data_type': '<DataFormat.PremiseAndOneHypothesis: 2>', 'task_type': '<TaskType.Classification: 1>', 'metric_meta': '(<Metric.ACC: 0>,)', 'split_names': "['train', 'dev', 'test']", 'enable_san': 'False', 'dropout_p': 'None', 'loss': '<LossCriterion.CeCriterion: 0>', 'kd_loss': '<LossCriterion.MseCriterion: 1>', 'adv_loss': '<LossCriterion.SymKlCriterion: 7>', '__class__': "<class 'experiments.exp_def.TaskDef'>"}, {'self': '{}', 'label_vocab': 'None', 'n_class': '2', 'data_type': '<DataFormat.PremiseAndOneHypothesis: 2>', 'task_type': '<TaskType.Classification: 1>', 'metric_meta': '(<Metric.ACC: 0>, <Metric.F1: 1>)', 'split_names': "['train', 'dev', 'test']", 'enable_san': 'False', 'dropout_p': 'None', 'loss': '<LossCriterion.CeCriterion: 0>', 'kd_loss': '<LossCriterion.MseCriterion: 1>', 'adv_loss': '<LossCriterion.SymKlCriterion: 7>', '__class__': "<class 'experiments.exp_def.TaskDef'>"}, {'self': '{}', 'label_vocab': 'None', 'n_class': '2', 'data_type': '<DataFormat.PremiseOnly: 1>', 'task_type': '<TaskType.Classification: 1>', 'metric_meta': '(<Metric.ACC: 0>,)', 'split_names': "['train', 'dev', 'test']", 'enable_san': 'False', 'dropout_p': 'None', 'loss': '<LossCriterion.CeCriterion: 0>', 'kd_loss': '<LossCriterion.MseCriterion: 1>', 'adv_loss': '<LossCriterion.SymKlCriterion: 7>', '__class__': "<class 'experiments.exp_def.TaskDef'>"}, {'self': '{}', 'label_vocab': 'None', 'n_class': '2', 'data_type': '<DataFormat.PremiseOnly: 1>', 'task_type': '<TaskType.Classification: 1>', 'metric_meta': '(<Metric.ACC: 0>, <Metric.MCC: 2>)', 'split_names': "['train', 'dev', 'test']", 'enable_san': 'False', 'dropout_p': '0.05', 'loss': '<LossCriterion.CeCriterion: 0>', 'kd_loss': '<LossCriterion.MseCriterion: 1>', 'adv_loss': '<LossCriterion.SymKlCriterion: 7>', '__class__': "<class 'experiments.exp_def.TaskDef'>"}, {'self': '{}', 'label_vocab': 'None', 'n_class': '1', 'data_type': '<DataFormat.PremiseAndOneHypothesis: 2>', 'task_type': '<TaskType.Regression: 2>', 'metric_meta': '(<Metric.Pearson: 3>, <Metric.Spearman: 4>)', 'split_names': "['train', 'dev', 'test']", 'enable_san': 'False', 'dropout_p': 'None', 'loss': '<LossCriterion.MseCriterion: 1>', 'kd_loss': '<LossCriterion.MseCriterion: 1>', 'adv_loss': '<LossCriterion.MseCriterion: 1>', '__class__': "<class 'experiments.exp_def.TaskDef'>"}]}
01/07/2021 10:38:22 ####################
01/07/2021 10:38:22 ############# Gradient Accumulation Info #############
01/07/2021 10:38:22 number of step: 595515
01/07/2021 10:38:22 number of grad grad_accumulation step: 1
01/07/2021 10:38:22 adjusted number of step: 595515
01/07/2021 10:38:22 ############# Gradient Accumulation Info #############
01/07/2021 10:38:33 
############# Model Arch of MT-DNN #############
SANBertNetwork(
  (dropout_list): ModuleList(
    (0): DropoutWrapper()
    (1): DropoutWrapper()
    (2): DropoutWrapper()
    (3): DropoutWrapper()
    (4): DropoutWrapper()
    (5): DropoutWrapper()
    (6): DropoutWrapper()
    (7): DropoutWrapper()
  )
  (bert): BertModel(
    (embeddings): BertEmbeddings(
      (word_embeddings): Embedding(30522, 768, padding_idx=0)
      (position_embeddings): Embedding(512, 768)
      (token_type_embeddings): Embedding(2, 768)
      (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
      (dropout): Dropout(p=0.1, inplace=False)
    )
    (encoder): BertEncoder(
      (layer): ModuleList(
        (0): BertLayer(
          (attention): BertAttention(
            (self): BertSelfAttention(
              (query): Linear(in_features=768, out_features=768, bias=True)
              (key): Linear(in_features=768, out_features=768, bias=True)
              (value): Linear(in_features=768, out_features=768, bias=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (output): BertSelfOutput(
              (dense): Linear(in_features=768, out_features=768, bias=True)
              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
          )
          (intermediate): BertIntermediate(
            (dense): Linear(in_features=768, out_features=3072, bias=True)
          )
          (output): BertOutput(
            (dense): Linear(in_features=3072, out_features=768, bias=True)
            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
            (dropout): Dropout(p=0.1, inplace=False)
          )
        )
        (1): BertLayer(
          (attention): BertAttention(
            (self): BertSelfAttention(
              (query): Linear(in_features=768, out_features=768, bias=True)
              (key): Linear(in_features=768, out_features=768, bias=True)
              (value): Linear(in_features=768, out_features=768, bias=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (output): BertSelfOutput(
              (dense): Linear(in_features=768, out_features=768, bias=True)
              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
          )
          (intermediate): BertIntermediate(
            (dense): Linear(in_features=768, out_features=3072, bias=True)
          )
          (output): BertOutput(
            (dense): Linear(in_features=3072, out_features=768, bias=True)
            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
            (dropout): Dropout(p=0.1, inplace=False)
          )
        )
        (2): BertLayer(
          (attention): BertAttention(
            (self): BertSelfAttention(
              (query): Linear(in_features=768, out_features=768, bias=True)
              (key): Linear(in_features=768, out_features=768, bias=True)
              (value): Linear(in_features=768, out_features=768, bias=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (output): BertSelfOutput(
              (dense): Linear(in_features=768, out_features=768, bias=True)
              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
          )
          (intermediate): BertIntermediate(
            (dense): Linear(in_features=768, out_features=3072, bias=True)
          )
          (output): BertOutput(
            (dense): Linear(in_features=3072, out_features=768, bias=True)
            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
            (dropout): Dropout(p=0.1, inplace=False)
          )
        )
        (3): BertLayer(
          (attention): BertAttention(
            (self): BertSelfAttention(
              (query): Linear(in_features=768, out_features=768, bias=True)
              (key): Linear(in_features=768, out_features=768, bias=True)
              (value): Linear(in_features=768, out_features=768, bias=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (output): BertSelfOutput(
              (dense): Linear(in_features=768, out_features=768, bias=True)
              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
          )
          (intermediate): BertIntermediate(
            (dense): Linear(in_features=768, out_features=3072, bias=True)
          )
          (output): BertOutput(
            (dense): Linear(in_features=3072, out_features=768, bias=True)
            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
            (dropout): Dropout(p=0.1, inplace=False)
          )
        )
        (4): BertLayer(
          (attention): BertAttention(
            (self): BertSelfAttention(
              (query): Linear(in_features=768, out_features=768, bias=True)
              (key): Linear(in_features=768, out_features=768, bias=True)
              (value): Linear(in_features=768, out_features=768, bias=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (output): BertSelfOutput(
              (dense): Linear(in_features=768, out_features=768, bias=True)
              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
          )
          (intermediate): BertIntermediate(
            (dense): Linear(in_features=768, out_features=3072, bias=True)
          )
          (output): BertOutput(
            (dense): Linear(in_features=3072, out_features=768, bias=True)
            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
            (dropout): Dropout(p=0.1, inplace=False)
          )
        )
        (5): BertLayer(
          (attention): BertAttention(
            (self): BertSelfAttention(
              (query): Linear(in_features=768, out_features=768, bias=True)
              (key): Linear(in_features=768, out_features=768, bias=True)
              (value): Linear(in_features=768, out_features=768, bias=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (output): BertSelfOutput(
              (dense): Linear(in_features=768, out_features=768, bias=True)
              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
          )
          (intermediate): BertIntermediate(
            (dense): Linear(in_features=768, out_features=3072, bias=True)
          )
          (output): BertOutput(
            (dense): Linear(in_features=3072, out_features=768, bias=True)
            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
            (dropout): Dropout(p=0.1, inplace=False)
          )
        )
        (6): BertLayer(
          (attention): BertAttention(
            (self): BertSelfAttention(
              (query): Linear(in_features=768, out_features=768, bias=True)
              (key): Linear(in_features=768, out_features=768, bias=True)
              (value): Linear(in_features=768, out_features=768, bias=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (output): BertSelfOutput(
              (dense): Linear(in_features=768, out_features=768, bias=True)
              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
          )
          (intermediate): BertIntermediate(
            (dense): Linear(in_features=768, out_features=3072, bias=True)
          )
          (output): BertOutput(
            (dense): Linear(in_features=3072, out_features=768, bias=True)
            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
            (dropout): Dropout(p=0.1, inplace=False)
          )
        )
        (7): BertLayer(
          (attention): BertAttention(
            (self): BertSelfAttention(
              (query): Linear(in_features=768, out_features=768, bias=True)
              (key): Linear(in_features=768, out_features=768, bias=True)
              (value): Linear(in_features=768, out_features=768, bias=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (output): BertSelfOutput(
              (dense): Linear(in_features=768, out_features=768, bias=True)
              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
          )
          (intermediate): BertIntermediate(
            (dense): Linear(in_features=768, out_features=3072, bias=True)
          )
          (output): BertOutput(
            (dense): Linear(in_features=3072, out_features=768, bias=True)
            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
            (dropout): Dropout(p=0.1, inplace=False)
          )
        )
        (8): BertLayer(
          (attention): BertAttention(
            (self): BertSelfAttention(
              (query): Linear(in_features=768, out_features=768, bias=True)
              (key): Linear(in_features=768, out_features=768, bias=True)
              (value): Linear(in_features=768, out_features=768, bias=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (output): BertSelfOutput(
              (dense): Linear(in_features=768, out_features=768, bias=True)
              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
          )
          (intermediate): BertIntermediate(
            (dense): Linear(in_features=768, out_features=3072, bias=True)
          )
          (output): BertOutput(
            (dense): Linear(in_features=3072, out_features=768, bias=True)
            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
            (dropout): Dropout(p=0.1, inplace=False)
          )
        )
        (9): BertLayer(
          (attention): BertAttention(
            (self): BertSelfAttention(
              (query): Linear(in_features=768, out_features=768, bias=True)
              (key): Linear(in_features=768, out_features=768, bias=True)
              (value): Linear(in_features=768, out_features=768, bias=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (output): BertSelfOutput(
              (dense): Linear(in_features=768, out_features=768, bias=True)
              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
          )
          (intermediate): BertIntermediate(
            (dense): Linear(in_features=768, out_features=3072, bias=True)
          )
          (output): BertOutput(
            (dense): Linear(in_features=3072, out_features=768, bias=True)
            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
            (dropout): Dropout(p=0.1, inplace=False)
          )
        )
        (10): BertLayer(
          (attention): BertAttention(
            (self): BertSelfAttention(
              (query): Linear(in_features=768, out_features=768, bias=True)
              (key): Linear(in_features=768, out_features=768, bias=True)
              (value): Linear(in_features=768, out_features=768, bias=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (output): BertSelfOutput(
              (dense): Linear(in_features=768, out_features=768, bias=True)
              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
          )
          (intermediate): BertIntermediate(
            (dense): Linear(in_features=768, out_features=3072, bias=True)
          )
          (output): BertOutput(
            (dense): Linear(in_features=3072, out_features=768, bias=True)
            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
            (dropout): Dropout(p=0.1, inplace=False)
          )
        )
        (11): BertLayer(
          (attention): BertAttention(
            (self): BertSelfAttention(
              (query): Linear(in_features=768, out_features=768, bias=True)
              (key): Linear(in_features=768, out_features=768, bias=True)
              (value): Linear(in_features=768, out_features=768, bias=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (output): BertSelfOutput(
              (dense): Linear(in_features=768, out_features=768, bias=True)
              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
          )
          (intermediate): BertIntermediate(
            (dense): Linear(in_features=768, out_features=3072, bias=True)
          )
          (output): BertOutput(
            (dense): Linear(in_features=3072, out_features=768, bias=True)
            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
            (dropout): Dropout(p=0.1, inplace=False)
          )
        )
      )
    )
    (pooler): BertPooler(
      (dense): Linear(in_features=768, out_features=768, bias=True)
      (activation): Tanh()
    )
  )
  (loss_pred_fc): Linear(in_features=768, out_features=1, bias=True)
  (scoring_list): ModuleList(
    (0): Linear(in_features=768, out_features=3, bias=True)
    (1): Linear(in_features=768, out_features=2, bias=True)
    (2): Linear(in_features=768, out_features=2, bias=True)
    (3): Linear(in_features=768, out_features=2, bias=True)
    (4): Linear(in_features=768, out_features=2, bias=True)
    (5): Linear(in_features=768, out_features=2, bias=True)
    (6): Linear(in_features=768, out_features=2, bias=True)
    (7): Linear(in_features=768, out_features=1, bias=True)
  )
)

01/07/2021 10:38:33 Total number of params: 109495313
01/07/2021 10:38:33 At epoch 0
01/07/2021 10:38:34 Task [ 3] updates[     1] train loss[0.78224] remaining[8:58:02]
01/07/2021 10:39:44 Task [ 0] updates[   500] train loss[0.94179] remaining[4:37:04]
01/07/2021 10:40:57 Task [ 0] updates[  1000] train loss[0.93442] remaining[4:42:03]
01/07/2021 10:42:06 Task [ 2] updates[  1500] train loss[0.96032] remaining[4:37:35]
01/07/2021 10:43:13 Task [ 0] updates[  2000] train loss[0.97163] remaining[4:32:40]
01/07/2021 10:44:21 Task [ 0] updates[  2500] train loss[0.96360] remaining[4:30:00]
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01/08/2021 03:02:44 Task mnli_matched -- epoch 0 -- Dev ACC: 82.119
01/08/2021 03:03:12 [new test scores saved.]
01/08/2021 03:03:41 Task mnli_mismatched -- epoch 0 -- Dev ACC: 82.384
01/08/2021 03:04:10 [new test scores saved.]
01/08/2021 03:04:12 Task rte -- epoch 0 -- Dev ACC: 71.480
01/08/2021 03:04:23 [new test scores saved.]
01/08/2021 03:06:02 Task qqp -- epoch 0 -- Dev ACC: 88.546
01/08/2021 03:06:02 Task qqp -- epoch 0 -- Dev F1: 84.138
01/08/2021 03:22:06 [new test scores saved.]
01/08/2021 03:22:24 Task qnli -- epoch 0 -- Dev ACC: 85.101
01/08/2021 03:22:45 [new test scores saved.]
01/08/2021 03:22:47 Task mrpc -- epoch 0 -- Dev ACC: 78.676
01/08/2021 03:22:47 Task mrpc -- epoch 0 -- Dev F1: 85.572
01/08/2021 03:22:52 [new test scores saved.]
01/08/2021 03:22:54 Task sst -- epoch 0 -- Dev ACC: 92.431
01/08/2021 03:22:57 [new test scores saved.]
01/08/2021 03:22:59 Task cola -- epoch 0 -- Dev ACC: 76.702
01/08/2021 03:22:59 Task cola -- epoch 0 -- Dev MCC: 40.644
01/08/2021 03:23:01 [new test scores saved.]
01/08/2021 03:23:04 Task stsb -- epoch 0 -- Dev Pearson: 85.041
01/08/2021 03:23:04 Task stsb -- epoch 0 -- Dev Spearman: 85.877
01/08/2021 03:23:07 [new test scores saved.]
01/08/2021 03:23:11 At epoch 1
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01/08/2021 07:46:17 Task mnli_matched -- epoch 1 -- Dev ACC: 83.698
01/08/2021 07:46:45 [new test scores saved.]
01/08/2021 07:47:15 Task mnli_mismatched -- epoch 1 -- Dev ACC: 83.666
01/08/2021 07:47:44 [new test scores saved.]
01/08/2021 07:47:45 Task rte -- epoch 1 -- Dev ACC: 77.978
01/08/2021 07:47:56 [new test scores saved.]
01/08/2021 07:49:35 Task qqp -- epoch 1 -- Dev ACC: 89.797
01/08/2021 07:49:35 Task qqp -- epoch 1 -- Dev F1: 86.064
01/08/2021 08:05:42 [new test scores saved.]
01/08/2021 08:06:01 Task qnli -- epoch 1 -- Dev ACC: 86.811
01/08/2021 08:06:22 [new test scores saved.]
01/08/2021 08:06:23 Task mrpc -- epoch 1 -- Dev ACC: 81.127
01/08/2021 08:06:23 Task mrpc -- epoch 1 -- Dev F1: 87.188
01/08/2021 08:06:28 [new test scores saved.]
01/08/2021 08:06:30 Task sst -- epoch 1 -- Dev ACC: 91.972
01/08/2021 08:06:34 [new test scores saved.]
01/08/2021 08:06:35 Task cola -- epoch 1 -- Dev ACC: 79.578
01/08/2021 08:06:35 Task cola -- epoch 1 -- Dev MCC: 48.997
01/08/2021 08:06:37 [new test scores saved.]
01/08/2021 08:06:40 Task stsb -- epoch 1 -- Dev Pearson: 86.617
01/08/2021 08:06:40 Task stsb -- epoch 1 -- Dev Spearman: 86.820
01/08/2021 08:06:43 [new test scores saved.]
01/08/2021 08:06:48 At epoch 2
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01/08/2021 12:22:01 Task mnli_matched -- epoch 2 -- Dev ACC: 84.004
01/08/2021 12:22:30 [new test scores saved.]
01/08/2021 12:23:00 Task mnli_mismatched -- epoch 2 -- Dev ACC: 84.062
01/08/2021 12:23:29 [new test scores saved.]
01/08/2021 12:23:30 Task rte -- epoch 2 -- Dev ACC: 77.256
01/08/2021 12:23:41 [new test scores saved.]
01/08/2021 12:25:20 Task qqp -- epoch 2 -- Dev ACC: 90.408
01/08/2021 12:25:20 Task qqp -- epoch 2 -- Dev F1: 86.973
01/08/2021 12:41:44 [new test scores saved.]
01/08/2021 12:42:03 Task qnli -- epoch 2 -- Dev ACC: 87.526
01/08/2021 12:42:24 [new test scores saved.]
01/08/2021 12:42:25 Task mrpc -- epoch 2 -- Dev ACC: 80.882
01/08/2021 12:42:25 Task mrpc -- epoch 2 -- Dev F1: 86.411
01/08/2021 12:42:31 [new test scores saved.]
01/08/2021 12:42:33 Task sst -- epoch 2 -- Dev ACC: 92.661
01/08/2021 12:42:36 [new test scores saved.]
01/08/2021 12:42:38 Task cola -- epoch 2 -- Dev ACC: 79.386
01/08/2021 12:42:38 Task cola -- epoch 2 -- Dev MCC: 48.480
01/08/2021 12:42:40 [new test scores saved.]
01/08/2021 12:42:43 Task stsb -- epoch 2 -- Dev Pearson: 86.365
01/08/2021 12:42:43 Task stsb -- epoch 2 -- Dev Spearman: 86.568
01/08/2021 12:42:46 [new test scores saved.]
01/08/2021 12:42:50 At epoch 3
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01/08/2021 04:58:48 Task mnli_matched -- epoch 3 -- Dev ACC: 83.800
01/08/2021 04:59:18 [new test scores saved.]
01/08/2021 04:59:49 Task mnli_mismatched -- epoch 3 -- Dev ACC: 84.103
01/08/2021 05:00:19 [new test scores saved.]
01/08/2021 05:00:20 Task rte -- epoch 3 -- Dev ACC: 78.700
01/08/2021 05:00:32 [new test scores saved.]
01/08/2021 05:02:18 Task qqp -- epoch 3 -- Dev ACC: 90.764
01/08/2021 05:02:18 Task qqp -- epoch 3 -- Dev F1: 87.569
01/08/2021 05:19:08 [new test scores saved.]
01/08/2021 05:19:27 Task qnli -- epoch 3 -- Dev ACC: 87.927
01/08/2021 05:19:49 [new test scores saved.]
01/08/2021 05:19:50 Task mrpc -- epoch 3 -- Dev ACC: 81.127
01/08/2021 05:19:50 Task mrpc -- epoch 3 -- Dev F1: 86.655
01/08/2021 05:19:55 [new test scores saved.]
01/08/2021 05:19:57 Task sst -- epoch 3 -- Dev ACC: 91.399
01/08/2021 05:20:01 [new test scores saved.]
01/08/2021 05:20:02 Task cola -- epoch 3 -- Dev ACC: 79.482
01/08/2021 05:20:02 Task cola -- epoch 3 -- Dev MCC: 48.824
01/08/2021 05:20:04 [new test scores saved.]
01/08/2021 05:20:07 Task stsb -- epoch 3 -- Dev Pearson: 86.628
01/08/2021 05:20:07 Task stsb -- epoch 3 -- Dev Spearman: 86.699
01/08/2021 05:20:10 [new test scores saved.]
01/08/2021 05:20:15 At epoch 4
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01/08/2021 09:35:40 Task mnli_matched -- epoch 4 -- Dev ACC: 84.137
01/08/2021 09:36:10 [new test scores saved.]
01/08/2021 09:36:40 Task mnli_mismatched -- epoch 4 -- Dev ACC: 84.774
01/08/2021 09:37:10 [new test scores saved.]
01/08/2021 09:37:11 Task rte -- epoch 4 -- Dev ACC: 78.339
01/08/2021 09:37:23 [new test scores saved.]
01/08/2021 09:39:02 Task qqp -- epoch 4 -- Dev ACC: 90.811
01/08/2021 09:39:02 Task qqp -- epoch 4 -- Dev F1: 87.648
01/08/2021 09:55:32 [new test scores saved.]
01/08/2021 09:55:51 Task qnli -- epoch 4 -- Dev ACC: 87.666
01/08/2021 09:56:12 [new test scores saved.]
01/08/2021 09:56:13 Task mrpc -- epoch 4 -- Dev ACC: 79.412
01/08/2021 09:56:13 Task mrpc -- epoch 4 -- Dev F1: 84.892
01/08/2021 09:56:19 [new test scores saved.]
01/08/2021 09:56:20 Task sst -- epoch 4 -- Dev ACC: 91.514
01/08/2021 09:56:24 [new test scores saved.]
01/08/2021 09:56:26 Task cola -- epoch 4 -- Dev ACC: 79.962
01/08/2021 09:56:26 Task cola -- epoch 4 -- Dev MCC: 49.924
01/08/2021 09:56:27 [new test scores saved.]
01/08/2021 09:56:31 Task stsb -- epoch 4 -- Dev Pearson: 86.689
01/08/2021 09:56:31 Task stsb -- epoch 4 -- Dev Spearman: 86.588
01/08/2021 09:56:33 [new test scores saved.]