Upload 8 files

config.json

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+{
+  "_name_or_path": "/home.local/jianwei/workspace/archive/SparseOptimizer/output/Layer_7_12_Hid_160_768_Head_10_12_IMRatio_3.5",
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "embedding_size": 160,
+  "finetuning_task": "rte",
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 160,
+  "initializer_range": 0.02,
+  "intermediate_size": 560,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 10,
+  "num_hidden_layers": 7,
+  "output_intermediate": true,
+  "output_past": true,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "problem_type": "single_label_classification",
+  "torch_dtype": "float32",
+  "transformers_version": "4.17.0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

log_bs32_lr3e-05_20221118_060236_793692.txt

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+------------> log file ==runs2/rte/1/log_bs32_lr3e-05_20221118_060236_793692.txt
+Namespace(aug_train=False, data_dir='/home.local/jianwei/datasets/nlp/glue_data/RTE', do_eval=False, early_stop=True, early_stop_metric='accuracy', eval_step=120, gradient_accumulation_steps=1, learning_rate=3e-05, local_rank=0, lr_scheduler_type=<SchedulerType.CONSTANT_WITH_WARMUP: 'constant_with_warmup'>, max_length=128, max_train_steps=None, model_name_or_path='/home.local/jianwei/workspace/archive/SparseOptimizer/output/Layer_7_12_Hid_160_768_Head_10_12_IMRatio_3.5', num_train_epochs=30, num_warmup_steps=0, output_dir='runs2/rte/1', pad_to_max_length=False, per_device_eval_batch_size=32, per_device_train_batch_size=32, print_step=5, save_last=False, seed=None, task_name='rte', train_file=None, use_slow_tokenizer=False, validation_file=None, weight_decay=0.0)
+Distributed environment: NO
+Num processes: 1
+Process index: 0
+Local process index: 0
+Device: cuda
+Mixed precision type: fp16
+
+Sample 595 of the training set: (tensor([  101, 11929,  1010,  5553,  1012,  2570,  1006,  8418, 25311, 13860,
+         3388,  1007,  1011,  1011,  2019, 18410,  2140,  6187, 24887,  2080,
+        11183,  1010,  1037,  2280,  3539,  2704,  1010,  2180,  5978,  1005,
+         1055,  4883,  2602,  2006,  4465,  1012,   102,  2047,  5077,  3539,
+         2704,  2003,  2700,  1012,   102,     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,     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,     0,
+            0,     0,     0,     0,     0,     0,     0,     0,     0,     0,
+            0,     0,     0,     0,     0,     0,     0,     0]), tensor([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, 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, 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, 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, 0, 0, 0, 0, 0]), tensor([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, 0, 0, 0, 0, 0, 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, 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, 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]), tensor(1)).
+Sample 2375 of the training set: (tensor([  101,  1996,  5611,  2390,  2749,  3344,  2041,  1010,  2006,  5095,
+         1010,  1037,  6923,  2510,  3169,  2046,  1996,  2225,  2924,  2237,
+         1997, 15419,  2378,  1998,  2049, 13141,  3409,  1010,  2334,  9302,
+         4216,  2056,  1012,   102,  1996,  5611,  2390,  3344,  2041,  1037,
+         6923,  3169,  1999, 15419,  2378,  1012,   102,     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,     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,     0,     0,     0,     0,     0,     0,     0,     0,     0,
+            0,     0,     0,     0,     0,     0,     0,     0]), tensor([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, 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, 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, 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,
+        0, 0, 0, 0, 0, 0, 0, 0]), tensor([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, 0, 0, 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, 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, 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]), tensor(0)).
+Sample 149 of the training set: (tensor([  101,  2048,  9767,  8461,  2379,  2019,  5499,  2082,  1999,  4501,
+         2730,  2809,  2111,  1998,  5229,  4413,  2500,  7483,  1999,  1996,
+         6745,  8293,  1997,  4808, 13940,  1996,  2670,  3417,  1997, 15381,
+         1012,   102,  2809,  2111,  8461,  2048,  9767,  2379,  2019,  5499,
+         2082,  1999,  4501,  1012,   102,     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,     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,     0,
+            0,     0,     0,     0,     0,     0,     0,     0,     0,     0,
+            0,     0,     0,     0,     0,     0,     0,     0]), tensor([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, 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, 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, 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, 0, 0, 0, 0, 0]), tensor([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, 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, 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,
+        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]), tensor(1)).
+***** Running training *****
+  Num examples = 2490
+  Num Epochs = 30
+  Instantaneous batch size per device = 32
+  Total train batch size (w. parallel, distributed & accumulation) = 32
+  Gradient Accumulation steps = 1
+  Total optimization steps = 2340
+000005/002340, loss: 0.694824, avg_loss: 0.691177
+000010/002340, loss: 0.707565, avg_loss: 0.693715
+000015/002340, loss: 0.699615, avg_loss: 0.693022
+000020/002340, loss: 0.699615, avg_loss: 0.693939
+000025/002340, loss: 0.699310, avg_loss: 0.694436
+000030/002340, loss: 0.698532, avg_loss: 0.694941
+000035/002340, loss: 0.686935, avg_loss: 0.694372
+000040/002340, loss: 0.696411, avg_loss: 0.694273
+000045/002340, loss: 0.692871, avg_loss: 0.693708
+000050/002340, loss: 0.687256, avg_loss: 0.693756
+000055/002340, loss: 0.701004, avg_loss: 0.693827
+000060/002340, loss: 0.691040, avg_loss: 0.693579
+000065/002340, loss: 0.689056, avg_loss: 0.693324
+000070/002340, loss: 0.696518, avg_loss: 0.693440
+000075/002340, loss: 0.696930, avg_loss: 0.693460
+000080/002340, loss: 0.693802, avg_loss: 0.693340
+000085/002340, loss: 0.688171, avg_loss: 0.693318
+000090/002340, loss: 0.698029, avg_loss: 0.693154
+000095/002340, loss: 0.689453, avg_loss: 0.692949
+000100/002340, loss: 0.690857, avg_loss: 0.692921
+000105/002340, loss: 0.689819, avg_loss: 0.692827
+000110/002340, loss: 0.682220, avg_loss: 0.692768
+000115/002340, loss: 0.700806, avg_loss: 0.692803
+000120/002340, loss: 0.701385, avg_loss: 0.692652
+***** Running dev evaluation *****
+  Num examples = 277
+  Instantaneous batch size per device = 32
+epoch 1, step 120/2340: {'accuracy': 0.5523465703971119}
+000125/002340, loss: 0.693527, avg_loss: 0.692706
+000130/002340, loss: 0.689957, avg_loss: 0.692658
+000135/002340, loss: 0.685425, avg_loss: 0.692536
+000140/002340, loss: 0.690201, avg_loss: 0.692434
+000145/002340, loss: 0.686600, avg_loss: 0.692396
+000150/002340, loss: 0.678986, avg_loss: 0.692177
+000155/002340, loss: 0.679138, avg_loss: 0.691975
+000160/002340, loss: 0.694275, avg_loss: 0.691769
+000165/002340, loss: 0.692368, avg_loss: 0.691443
+000170/002340, loss: 0.680664, avg_loss: 0.691252
+000175/002340, loss: 0.666016, avg_loss: 0.690698
+000180/002340, loss: 0.671844, avg_loss: 0.690296
+000185/002340, loss: 0.651184, avg_loss: 0.689748
+000190/002340, loss: 0.659752, avg_loss: 0.688919
+000195/002340, loss: 0.662926, avg_loss: 0.688697
+000200/002340, loss: 0.643776, avg_loss: 0.688136
+000205/002340, loss: 0.693794, avg_loss: 0.687406
+000210/002340, loss: 0.716675, avg_loss: 0.686937
+000215/002340, loss: 0.665474, avg_loss: 0.686136
+000220/002340, loss: 0.625298, avg_loss: 0.685308
+000225/002340, loss: 0.656639, avg_loss: 0.685019
+000230/002340, loss: 0.673508, avg_loss: 0.684550
+000235/002340, loss: 0.575394, avg_loss: 0.682954
+000240/002340, loss: 0.615173, avg_loss: 0.681390
+***** Running dev evaluation *****
+  Num examples = 277
+  Instantaneous batch size per device = 32
+epoch 3, step 240/2340: {'accuracy': 0.5884476534296029}
+000245/002340, loss: 0.566116, avg_loss: 0.679216
+000250/002340, loss: 0.662231, avg_loss: 0.677990
+000255/002340, loss: 0.742844, avg_loss: 0.677457
+000260/002340, loss: 0.744896, avg_loss: 0.677289
+000265/002340, loss: 0.524788, avg_loss: 0.675974
+000270/002340, loss: 0.573128, avg_loss: 0.674871
+000275/002340, loss: 0.698616, avg_loss: 0.674028
+000280/002340, loss: 0.661125, avg_loss: 0.672997
+000285/002340, loss: 0.577705, avg_loss: 0.671527
+000290/002340, loss: 0.529144, avg_loss: 0.669498
+000295/002340, loss: 0.548820, avg_loss: 0.668429
+000300/002340, loss: 0.533775, avg_loss: 0.667589
+000305/002340, loss: 0.724682, avg_loss: 0.666549
+000310/002340, loss: 0.618702, avg_loss: 0.667052
+000315/002340, loss: 0.600662, avg_loss: 0.666212
+000320/002340, loss: 0.560127, avg_loss: 0.665015
+000325/002340, loss: 0.667423, avg_loss: 0.663344
+000330/002340, loss: 0.520096, avg_loss: 0.661692
+000335/002340, loss: 0.589901, avg_loss: 0.659812
+000340/002340, loss: 0.718616, avg_loss: 0.658405
+000345/002340, loss: 0.523731, avg_loss: 0.657693
+000350/002340, loss: 0.597912, avg_loss: 0.656364
+000355/002340, loss: 0.510841, avg_loss: 0.654704
+000360/002340, loss: 0.598392, avg_loss: 0.652629
+***** Running dev evaluation *****
+  Num examples = 277
+  Instantaneous batch size per device = 32
+epoch 4, step 360/2340: {'accuracy': 0.6137184115523465}
+000365/002340, loss: 0.509396, avg_loss: 0.650652
+000370/002340, loss: 0.625957, avg_loss: 0.649372
+000375/002340, loss: 0.632420, avg_loss: 0.648425
+000380/002340, loss: 0.562641, avg_loss: 0.647222
+000385/002340, loss: 0.649609, avg_loss: 0.645501
+000390/002340, loss: 0.361694, avg_loss: 0.643182
+000395/002340, loss: 0.425430, avg_loss: 0.642246
+000400/002340, loss: 0.577938, avg_loss: 0.640067
+000405/002340, loss: 0.554668, avg_loss: 0.638333
+000410/002340, loss: 0.505466, avg_loss: 0.636457
+000415/002340, loss: 0.531124, avg_loss: 0.634969
+000420/002340, loss: 0.425911, avg_loss: 0.633147
+000425/002340, loss: 0.532368, avg_loss: 0.632082
+000430/002340, loss: 0.569756, avg_loss: 0.630961
+000435/002340, loss: 0.451645, avg_loss: 0.629107
+000440/002340, loss: 0.459530, avg_loss: 0.627486
+000445/002340, loss: 0.380501, avg_loss: 0.625123
+000450/002340, loss: 0.565880, avg_loss: 0.624122
+000455/002340, loss: 0.422201, avg_loss: 0.621911
+000460/002340, loss: 0.671333, avg_loss: 0.620993
+000465/002340, loss: 0.427799, avg_loss: 0.618575
+000470/002340, loss: 0.301590, avg_loss: 0.616753
+000475/002340, loss: 0.517204, avg_loss: 0.614735
+000480/002340, loss: 0.473822, avg_loss: 0.612666
+***** Running dev evaluation *****
+  Num examples = 277
+  Instantaneous batch size per device = 32
+epoch 6, step 480/2340: {'accuracy': 0.6209386281588448}
+000485/002340, loss: 0.235840, avg_loss: 0.610187
+000490/002340, loss: 0.535803, avg_loss: 0.608769
+000495/002340, loss: 0.447842, avg_loss: 0.606833
+000500/002340, loss: 0.359915, avg_loss: 0.604468
+000505/002340, loss: 0.473944, avg_loss: 0.601928
+000510/002340, loss: 0.487707, avg_loss: 0.600405
+000515/002340, loss: 0.280029, avg_loss: 0.599008
+000520/002340, loss: 0.509848, avg_loss: 0.597484
+000525/002340, loss: 0.646320, avg_loss: 0.596454
+000530/002340, loss: 0.350674, avg_loss: 0.594710
+000535/002340, loss: 0.480106, avg_loss: 0.593436
+000540/002340, loss: 0.560251, avg_loss: 0.593214
+000545/002340, loss: 0.387239, avg_loss: 0.591432
+000550/002340, loss: 0.277430, avg_loss: 0.589320
+000555/002340, loss: 0.280695, avg_loss: 0.587417
+000560/002340, loss: 0.330351, avg_loss: 0.585310
+000565/002340, loss: 0.391579, avg_loss: 0.583662
+000570/002340, loss: 0.280355, avg_loss: 0.582107
+000575/002340, loss: 0.359081, avg_loss: 0.580171
+000580/002340, loss: 0.367201, avg_loss: 0.578450
+000585/002340, loss: 0.430851, avg_loss: 0.577231
+000590/002340, loss: 0.331879, avg_loss: 0.575557
+000595/002340, loss: 0.333700, avg_loss: 0.573829
+000600/002340, loss: 0.309275, avg_loss: 0.571686
+***** Running dev evaluation *****
+  Num examples = 277
+  Instantaneous batch size per device = 32
+epoch 7, step 600/2340: {'accuracy': 0.6425992779783394}
+000605/002340, loss: 0.461454, avg_loss: 0.570168
+000610/002340, loss: 0.434152, avg_loss: 0.568408
+000615/002340, loss: 0.565701, avg_loss: 0.567013
+000620/002340, loss: 0.281487, avg_loss: 0.564378
+000625/002340, loss: 0.183996, avg_loss: 0.562576
+000630/002340, loss: 0.308249, avg_loss: 0.560548
+000635/002340, loss: 0.492087, avg_loss: 0.558905
+000640/002340, loss: 0.276144, avg_loss: 0.556907
+000645/002340, loss: 0.379016, avg_loss: 0.555011
+000650/002340, loss: 0.257240, avg_loss: 0.553119
+000655/002340, loss: 0.260510, avg_loss: 0.550735
+000660/002340, loss: 0.482807, avg_loss: 0.549067
+000665/002340, loss: 0.313425, avg_loss: 0.547653
+000670/002340, loss: 0.244961, avg_loss: 0.545744
+000675/002340, loss: 0.386663, avg_loss: 0.544380
+000680/002340, loss: 0.137331, avg_loss: 0.541812
+000685/002340, loss: 0.301256, avg_loss: 0.539778
+000690/002340, loss: 0.284186, avg_loss: 0.537928
+000695/002340, loss: 0.521972, avg_loss: 0.536261
+000700/002340, loss: 0.718600, avg_loss: 0.535717
+000705/002340, loss: 0.237306, avg_loss: 0.534266
+000710/002340, loss: 0.164028, avg_loss: 0.532027
+000715/002340, loss: 0.235560, avg_loss: 0.530920
+000720/002340, loss: 0.224425, avg_loss: 0.529428
+***** Running dev evaluation *****
+  Num examples = 277
+  Instantaneous batch size per device = 32
+epoch 9, step 720/2340: {'accuracy': 0.6462093862815884}
+000725/002340, loss: 0.250054, avg_loss: 0.527996
+000730/002340, loss: 0.213790, avg_loss: 0.526521
+000735/002340, loss: 0.339844, avg_loss: 0.525346
+000740/002340, loss: 0.192316, avg_loss: 0.523399
+000745/002340, loss: 0.322181, avg_loss: 0.521820
+000750/002340, loss: 0.114270, avg_loss: 0.519722
+000755/002340, loss: 0.242498, avg_loss: 0.517846
+000760/002340, loss: 0.234197, avg_loss: 0.515497
+000765/002340, loss: 0.332447, avg_loss: 0.513969
+000770/002340, loss: 0.163693, avg_loss: 0.512496
+000775/002340, loss: 0.260910, avg_loss: 0.511088
+000780/002340, loss: 0.236919, avg_loss: 0.509495
+000785/002340, loss: 0.151022, avg_loss: 0.507580
+000790/002340, loss: 0.489914, avg_loss: 0.506298
+000795/002340, loss: 0.175525, avg_loss: 0.504419
+000800/002340, loss: 0.274471, avg_loss: 0.502310
+000805/002340, loss: 0.308759, avg_loss: 0.500468
+000810/002340, loss: 0.227170, avg_loss: 0.498888
+000815/002340, loss: 0.112951, avg_loss: 0.496910
+000820/002340, loss: 0.168542, avg_loss: 0.495333
+000825/002340, loss: 0.163078, avg_loss: 0.493526
+000830/002340, loss: 0.208418, avg_loss: 0.492144
+000835/002340, loss: 0.204179, avg_loss: 0.490463
+000840/002340, loss: 0.262290, avg_loss: 0.488488
+***** Running dev evaluation *****
+  Num examples = 277
+  Instantaneous batch size per device = 32
+epoch 10, step 840/2340: {'accuracy': 0.6245487364620939}
+000845/002340, loss: 0.166388, avg_loss: 0.486870
+000850/002340, loss: 0.221429, avg_loss: 0.485510
+000855/002340, loss: 0.376082, avg_loss: 0.484030
+000860/002340, loss: 0.083231, avg_loss: 0.482307
+000865/002340, loss: 0.161541, avg_loss: 0.480355
+000870/002340, loss: 0.180701, avg_loss: 0.478405
+000875/002340, loss: 0.175531, avg_loss: 0.476498
+000880/002340, loss: 0.148172, avg_loss: 0.475174
+000885/002340, loss: 0.110148, avg_loss: 0.473676
+000890/002340, loss: 0.177225, avg_loss: 0.472175
+000895/002340, loss: 0.051785, avg_loss: 0.470479
+000900/002340, loss: 0.239419, avg_loss: 0.469122
+000905/002340, loss: 0.294643, avg_loss: 0.467460
+000910/002340, loss: 0.372546, avg_loss: 0.466119
+000915/002340, loss: 0.160401, avg_loss: 0.464562
+000920/002340, loss: 0.389829, avg_loss: 0.463444
+000925/002340, loss: 0.461596, avg_loss: 0.462050
+000930/002340, loss: 0.169349, avg_loss: 0.460443
+000935/002340, loss: 0.274192, avg_loss: 0.459206
+000940/002340, loss: 0.245536, avg_loss: 0.457409
+000945/002340, loss: 0.124900, avg_loss: 0.455669
+000950/002340, loss: 0.258810, avg_loss: 0.453951
+000955/002340, loss: 0.328007, avg_loss: 0.452289
+000960/002340, loss: 0.243825, avg_loss: 0.450600
+***** Running dev evaluation *****
+  Num examples = 277
+  Instantaneous batch size per device = 32
+epoch 12, step 960/2340: {'accuracy': 0.6389891696750902}
+000965/002340, loss: 0.201036, avg_loss: 0.449321
+000970/002340, loss: 0.091728, avg_loss: 0.447797
+000975/002340, loss: 0.182425, avg_loss: 0.446324
+000980/002340, loss: 0.159452, avg_loss: 0.444909
+000985/002340, loss: 0.142912, avg_loss: 0.443522
+000990/002340, loss: 0.304327, avg_loss: 0.442004
+000995/002340, loss: 0.117483, avg_loss: 0.440452
+001000/002340, loss: 0.156437, avg_loss: 0.438837
+001005/002340, loss: 0.032182, avg_loss: 0.437682
+001010/002340, loss: 0.063084, avg_loss: 0.436744
+001015/002340, loss: 0.258552, avg_loss: 0.435504
+001020/002340, loss: 0.091414, avg_loss: 0.434340
+001025/002340, loss: 0.100409, avg_loss: 0.432843
+001030/002340, loss: 0.064708, avg_loss: 0.431516
+001035/002340, loss: 0.459350, avg_loss: 0.430340
+001040/002340, loss: 0.195770, avg_loss: 0.428896
+001045/002340, loss: 0.101108, avg_loss: 0.427430
+001050/002340, loss: 0.162723, avg_loss: 0.425868
+001055/002340, loss: 0.170199, avg_loss: 0.424800
+001060/002340, loss: 0.066082, avg_loss: 0.423415
+001065/002340, loss: 0.139599, avg_loss: 0.422219
+001070/002340, loss: 0.089475, avg_loss: 0.420665
+001075/002340, loss: 0.115157, avg_loss: 0.419250
+001080/002340, loss: 0.085939, avg_loss: 0.417821
+***** Running dev evaluation *****
+  Num examples = 277
+  Instantaneous batch size per device = 32
+epoch 13, step 1080/2340: {'accuracy': 0.6173285198555957}
+001085/002340, loss: 0.138964, avg_loss: 0.416740
+001090/002340, loss: 0.385725, avg_loss: 0.415552
+001095/002340, loss: 0.173466, avg_loss: 0.414612
+001100/002340, loss: 0.101382, avg_loss: 0.413397
+001105/002340, loss: 0.098917, avg_loss: 0.412091
+001110/002340, loss: 0.088198, avg_loss: 0.410518
+001115/002340, loss: 0.039977, avg_loss: 0.409207
+001120/002340, loss: 0.126413, avg_loss: 0.407805
+001125/002340, loss: 0.154641, avg_loss: 0.406540
+001130/002340, loss: 0.221717, avg_loss: 0.405238
+001135/002340, loss: 0.155590, avg_loss: 0.403870
+001140/002340, loss: 0.072533, avg_loss: 0.402521
+001145/002340, loss: 0.148947, avg_loss: 0.401401
+001150/002340, loss: 0.202878, avg_loss: 0.400165
+001155/002340, loss: 0.054971, avg_loss: 0.399305
+001160/002340, loss: 0.058926, avg_loss: 0.398088
+001165/002340, loss: 0.187665, avg_loss: 0.396901
+001170/002340, loss: 0.091442, avg_loss: 0.395624
+001175/002340, loss: 0.339817, avg_loss: 0.394529
+001180/002340, loss: 0.029183, avg_loss: 0.393430
+001185/002340, loss: 0.052091, avg_loss: 0.392348
+001190/002340, loss: 0.175309, avg_loss: 0.391464
+001195/002340, loss: 0.269615, avg_loss: 0.390438
+001200/002340, loss: 0.042982, avg_loss: 0.389416
+***** Running dev evaluation *****
+  Num examples = 277
+  Instantaneous batch size per device = 32
+epoch 15, step 1200/2340: {'accuracy': 0.6353790613718412}
+001205/002340, loss: 0.029362, avg_loss: 0.388045
+001210/002340, loss: 0.106356, avg_loss: 0.386842
+001215/002340, loss: 0.055282, avg_loss: 0.385720
+001220/002340, loss: 0.025587, avg_loss: 0.384474
+001225/002340, loss: 0.017830, avg_loss: 0.383314
+001230/002340, loss: 0.156192, avg_loss: 0.382166
+001235/002340, loss: 0.017268, avg_loss: 0.381167
+001240/002340, loss: 0.015908, avg_loss: 0.379919
+001245/002340, loss: 0.024442, avg_loss: 0.378661
+001250/002340, loss: 0.016508, avg_loss: 0.377585
+001255/002340, loss: 0.021355, avg_loss: 0.376479
+001260/002340, loss: 0.024076, avg_loss: 0.375165
+001265/002340, loss: 0.202033, avg_loss: 0.374116
+001270/002340, loss: 0.027793, avg_loss: 0.372882
+001275/002340, loss: 0.027369, avg_loss: 0.372247
+001280/002340, loss: 0.021813, avg_loss: 0.371052
+001285/002340, loss: 0.021163, avg_loss: 0.370046
+001290/002340, loss: 0.046603, avg_loss: 0.369336
+001295/002340, loss: 0.076338, avg_loss: 0.368328
+001300/002340, loss: 0.183380, avg_loss: 0.367225
+001305/002340, loss: 0.169317, avg_loss: 0.366140
+001310/002340, loss: 0.020987, avg_loss: 0.365018
+001315/002340, loss: 0.169484, avg_loss: 0.364127
+001320/002340, loss: 0.044023, avg_loss: 0.363106
+***** Running dev evaluation *****
+  Num examples = 277
+  Instantaneous batch size per device = 32
+epoch 16, step 1320/2340: {'accuracy': 0.6462093862815884}
+001325/002340, loss: 0.146640, avg_loss: 0.361943
+001330/002340, loss: 0.053370, avg_loss: 0.360778
+001335/002340, loss: 0.024849, avg_loss: 0.359785
+001340/002340, loss: 0.040356, avg_loss: 0.358545
+001345/002340, loss: 0.216520, avg_loss: 0.357564
+001350/002340, loss: 0.020188, avg_loss: 0.356442
+001355/002340, loss: 0.050854, avg_loss: 0.355434
+001360/002340, loss: 0.013922, avg_loss: 0.354336
+001365/002340, loss: 0.034302, avg_loss: 0.353537
+001370/002340, loss: 0.083984, avg_loss: 0.352530
+001375/002340, loss: 0.044313, avg_loss: 0.351671
+001380/002340, loss: 0.197178, avg_loss: 0.350656
+001385/002340, loss: 0.087372, avg_loss: 0.349721
+001390/002340, loss: 0.122292, avg_loss: 0.348657
+001395/002340, loss: 0.161705, avg_loss: 0.347780
+001400/002340, loss: 0.014310, avg_loss: 0.346943
+001405/002340, loss: 0.096345, avg_loss: 0.345930
+001410/002340, loss: 0.142292, avg_loss: 0.345120
+001415/002340, loss: 0.016984, avg_loss: 0.344193
+001420/002340, loss: 0.014843, avg_loss: 0.343171
+001425/002340, loss: 0.054250, avg_loss: 0.342329
+001430/002340, loss: 0.049341, avg_loss: 0.341417
+001435/002340, loss: 0.033567, avg_loss: 0.340340
+001440/002340, loss: 0.108241, avg_loss: 0.339508
+***** Running dev evaluation *****
+  Num examples = 277
+  Instantaneous batch size per device = 32
+epoch 18, step 1440/2340: {'accuracy': 0.6137184115523465}
+001445/002340, loss: 0.148780, avg_loss: 0.338643
+001450/002340, loss: 0.121979, avg_loss: 0.337871
+001455/002340, loss: 0.015762, avg_loss: 0.337010
+001460/002340, loss: 0.197943, avg_loss: 0.336178
+001465/002340, loss: 0.019593, avg_loss: 0.335371
+001470/002340, loss: 0.129545, avg_loss: 0.334404
+001475/002340, loss: 0.015238, avg_loss: 0.333483
+001480/002340, loss: 0.016869, avg_loss: 0.332625
+001485/002340, loss: 0.011418, avg_loss: 0.331565
+001490/002340, loss: 0.338315, avg_loss: 0.330893
+001495/002340, loss: 0.288740, avg_loss: 0.330484
+001500/002340, loss: 0.148870, avg_loss: 0.329575
+001505/002340, loss: 0.013757, avg_loss: 0.328768
+001510/002340, loss: 0.016786, avg_loss: 0.327894
+001515/002340, loss: 0.013239, avg_loss: 0.326989
+001520/002340, loss: 0.024581, avg_loss: 0.326006
+001525/002340, loss: 0.017539, avg_loss: 0.325226
+001530/002340, loss: 0.067678, avg_loss: 0.324287
+001535/002340, loss: 0.024253, avg_loss: 0.323389
+001540/002340, loss: 0.077925, avg_loss: 0.322495
+001545/002340, loss: 0.024680, avg_loss: 0.321567
+001550/002340, loss: 0.012920, avg_loss: 0.320824
+001555/002340, loss: 0.023837, avg_loss: 0.320000
+001560/002340, loss: 0.221982, avg_loss: 0.319304
+***** Running dev evaluation *****
+  Num examples = 277
+  Instantaneous batch size per device = 32
+epoch 19, step 1560/2340: {'accuracy': 0.6137184115523465}
+001565/002340, loss: 0.013699, avg_loss: 0.318449
+001570/002340, loss: 0.011844, avg_loss: 0.317610
+001575/002340, loss: 0.012580, avg_loss: 0.316855
+001580/002340, loss: 0.037540, avg_loss: 0.316005
+001585/002340, loss: 0.019229, avg_loss: 0.315232
+001590/002340, loss: 0.048232, avg_loss: 0.314477
+001595/002340, loss: 0.141452, avg_loss: 0.313963
+001600/002340, loss: 0.015298, avg_loss: 0.313133
+001605/002340, loss: 0.013662, avg_loss: 0.312229
+001610/002340, loss: 0.160849, avg_loss: 0.311404
+001615/002340, loss: 0.012301, avg_loss: 0.310524
+001620/002340, loss: 0.063877, avg_loss: 0.309759
+001625/002340, loss: 0.032892, avg_loss: 0.309026
+001630/002340, loss: 0.177563, avg_loss: 0.308279
+001635/002340, loss: 0.157313, avg_loss: 0.307644
+001640/002340, loss: 0.130090, avg_loss: 0.306819
+001645/002340, loss: 0.021889, avg_loss: 0.306081
+001650/002340, loss: 0.152882, avg_loss: 0.305300
+001655/002340, loss: 0.009122, avg_loss: 0.304627
+001660/002340, loss: 0.015140, avg_loss: 0.303849
+001665/002340, loss: 0.164985, avg_loss: 0.303089
+001670/002340, loss: 0.008990, avg_loss: 0.302396
+001675/002340, loss: 0.010757, avg_loss: 0.301671
+001680/002340, loss: 0.009137, avg_loss: 0.300904
+***** Running dev evaluation *****
+  Num examples = 277
+  Instantaneous batch size per device = 32
+epoch 21, step 1680/2340: {'accuracy': 0.6173285198555957}
+001685/002340, loss: 0.053387, avg_loss: 0.300194
+001690/002340, loss: 0.022511, avg_loss: 0.299502
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+001700/002340, loss: 0.013549, avg_loss: 0.297988
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+001710/002340, loss: 0.014491, avg_loss: 0.296600
+001715/002340, loss: 0.154422, avg_loss: 0.295955
+001720/002340, loss: 0.163267, avg_loss: 0.295310
+001725/002340, loss: 0.136114, avg_loss: 0.294759
+001730/002340, loss: 0.015310, avg_loss: 0.294064
+001735/002340, loss: 0.087005, avg_loss: 0.293422
+001740/002340, loss: 0.020296, avg_loss: 0.292756
+001745/002340, loss: 0.018787, avg_loss: 0.292135
+001750/002340, loss: 0.034191, avg_loss: 0.291526
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+001780/002340, loss: 0.038289, avg_loss: 0.287933
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+001790/002340, loss: 0.033570, avg_loss: 0.286735
+001795/002340, loss: 0.012522, avg_loss: 0.286079
+001800/002340, loss: 0.053891, avg_loss: 0.285344
+***** Running dev evaluation *****
+  Num examples = 277
+  Instantaneous batch size per device = 32
+epoch 23, step 1800/2340: {'accuracy': 0.6245487364620939}
+001805/002340, loss: 0.126177, avg_loss: 0.284716
+001810/002340, loss: 0.011923, avg_loss: 0.284070
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+001820/002340, loss: 0.010828, avg_loss: 0.282998
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+001830/002340, loss: 0.273915, avg_loss: 0.281916
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+001850/002340, loss: 0.020906, avg_loss: 0.279696
+001855/002340, loss: 0.165239, avg_loss: 0.279069
+001860/002340, loss: 0.053642, avg_loss: 0.278450
+001865/002340, loss: 0.133574, avg_loss: 0.277862
+001870/002340, loss: 0.097644, avg_loss: 0.277226
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+001880/002340, loss: 0.016699, avg_loss: 0.275948
+001885/002340, loss: 0.146401, avg_loss: 0.275488
+001890/002340, loss: 0.011636, avg_loss: 0.274799
+001895/002340, loss: 0.018686, avg_loss: 0.274214
+001900/002340, loss: 0.026965, avg_loss: 0.273611
+001905/002340, loss: 0.013933, avg_loss: 0.272935
+001910/002340, loss: 0.125580, avg_loss: 0.272318
+001915/002340, loss: 0.129783, avg_loss: 0.271802
+001920/002340, loss: 0.116678, avg_loss: 0.271278
+***** Running dev evaluation *****
+  Num examples = 277
+  Instantaneous batch size per device = 32
+epoch 24, step 1920/2340: {'accuracy': 0.6173285198555957}
+001925/002340, loss: 0.254784, avg_loss: 0.270806
+001930/002340, loss: 0.157526, avg_loss: 0.270238
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+001965/002340, loss: 0.046325, avg_loss: 0.266676
+001970/002340, loss: 0.041966, avg_loss: 0.266192
+001975/002340, loss: 0.020202, avg_loss: 0.265597
+001980/002340, loss: 0.125195, avg_loss: 0.265071
+001985/002340, loss: 0.019307, avg_loss: 0.264558
+001990/002340, loss: 0.011511, avg_loss: 0.263954
+001995/002340, loss: 0.092994, avg_loss: 0.263384
+002000/002340, loss: 0.098703, avg_loss: 0.262809
+002005/002340, loss: 0.017836, avg_loss: 0.262371
+002010/002340, loss: 0.047947, avg_loss: 0.261831
+002015/002340, loss: 0.157151, avg_loss: 0.261291
+002020/002340, loss: 0.063095, avg_loss: 0.260695
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+002035/002340, loss: 0.008303, avg_loss: 0.259056
+002040/002340, loss: 0.133496, avg_loss: 0.258505
+***** Running dev evaluation *****
+  Num examples = 277
+  Instantaneous batch size per device = 32
+epoch 26, step 2040/2340: {'accuracy': 0.6173285198555957}
+002045/002340, loss: 0.070495, avg_loss: 0.258069
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+002155/002340, loss: 0.006945, avg_loss: 0.247294
+002160/002340, loss: 0.149141, avg_loss: 0.246973
+***** Running dev evaluation *****
+  Num examples = 277
+  Instantaneous batch size per device = 32
+epoch 27, step 2160/2340: {'accuracy': 0.6173285198555957}
+002165/002340, loss: 0.070260, avg_loss: 0.246627
+002170/002340, loss: 0.018735, avg_loss: 0.246110
+002175/002340, loss: 0.011750, avg_loss: 0.245641
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+002195/002340, loss: 0.147473, avg_loss: 0.243797
+002200/002340, loss: 0.008439, avg_loss: 0.243311
+002205/002340, loss: 0.009392, avg_loss: 0.242842
+002210/002340, loss: 0.007260, avg_loss: 0.242363
+002215/002340, loss: 0.006505, avg_loss: 0.241869
+002220/002340, loss: 0.036663, avg_loss: 0.241415
+002225/002340, loss: 0.010591, avg_loss: 0.240936
+002230/002340, loss: 0.008057, avg_loss: 0.240418
+002235/002340, loss: 0.005135, avg_loss: 0.240005
+002240/002340, loss: 0.009763, avg_loss: 0.239661
+002245/002340, loss: 0.009173, avg_loss: 0.239206
+002250/002340, loss: 0.015700, avg_loss: 0.238819
+002255/002340, loss: 0.021340, avg_loss: 0.238346
+002260/002340, loss: 0.060185, avg_loss: 0.237882
+002265/002340, loss: 0.038913, avg_loss: 0.237484
+002270/002340, loss: 0.016376, avg_loss: 0.237112
+002275/002340, loss: 0.010828, avg_loss: 0.236714
+002280/002340, loss: 0.129731, avg_loss: 0.236370
+***** Running dev evaluation *****
+  Num examples = 277
+  Instantaneous batch size per device = 32
+epoch 29, step 2280/2340: {'accuracy': 0.6064981949458483}
+002285/002340, loss: 0.044581, avg_loss: 0.235897
+002290/002340, loss: 0.008923, avg_loss: 0.235524
+002295/002340, loss: 0.011697, avg_loss: 0.235179
+002300/002340, loss: 0.020234, avg_loss: 0.234708
+002305/002340, loss: 0.024606, avg_loss: 0.234225
+002310/002340, loss: 0.007431, avg_loss: 0.233798
+002315/002340, loss: 0.006717, avg_loss: 0.233382
+002320/002340, loss: 0.017990, avg_loss: 0.232940
+002325/002340, loss: 0.145197, avg_loss: 0.232597
+002330/002340, loss: 0.013951, avg_loss: 0.232139
+002335/002340, loss: 0.014238, avg_loss: 0.231719
+002340/002340, loss: 0.019154, avg_loss: 0.231268
+***** Running train evaluation *****
+  Num examples = 2490
+  Instantaneous batch size per device = 32
+Train Dataset Result: {'accuracy': 0.9955823293172691}
+***** Running dev evaluation *****
+  Num examples = 277
+  Instantaneous batch size per device = 32
+Dev Dataset Result: {'accuracy': 0.6101083032490975}
+DEV Best Result: accuracy, 0.6462093862815884
+Training time 0:02:36

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result.txt

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special_tokens_map.json

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tokenizer_config.json

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