dzungpham/graphcodebert-code-classification

12026-04-23 14:58:06,425 - INFO - ===== Training Configuration =====22026-04-23 14:58:06,427 - INFO - model_name           : microsoft/codebert-base32026-04-23 14:58:06,428 - INFO - output_dir           : output_checkpoints/codebert-base/42026-04-23 14:58:06,429 - INFO - num_epochs           : 0.552026-04-23 14:58:06,431 - INFO - max_steps            : 5062026-04-23 14:58:06,432 - INFO - batch_size           : 3272026-04-23 14:58:06,433 - INFO - learning_rate        : 1e-0682026-04-23 14:58:06,434 - INFO - max_length           : 51292026-04-23 14:58:06,436 - INFO - num_labels           : 2102026-04-23 14:58:06,437 - INFO - use_wandb            : True112026-04-23 14:58:06,439 - INFO - freeze_base          : True122026-04-23 14:58:06,440 - INFO - loss_type            : r-drop132026-04-23 14:58:06,442 - INFO - focal_alpha          : 1.0142026-04-23 14:58:06,443 - INFO - focal_gamma          : 2.0152026-04-23 14:58:06,444 - INFO - r_drop_alpha         : 6.0162026-04-23 14:58:06,446 - INFO - infonce_temperature  : 0.07172026-04-23 14:58:06,447 - INFO - infonce_weight       : 0.5182026-04-23 14:58:06,448 - INFO - seed                 : 42192026-04-23 14:58:06,449 - INFO - resume_from_checkpoint : None202026-04-23 14:58:06,451 - INFO - label_smoothing      : 0.3212026-04-23 14:58:06,452 - INFO - adversarial_epsilon  : 0.5222026-04-23 14:58:06,453 - INFO - use_swa              : False232026-04-23 14:58:06,454 - INFO - swa_start_epoch      : 0242026-04-23 14:58:06,455 - INFO - swa_lr               : 1e-05252026-04-23 14:58:06,457 - INFO - data_augmentation    : True262026-04-23 14:58:06,458 - INFO - aug_rename_prob      : 0.6272026-04-23 14:58:06,459 - INFO - aug_format_prob      : 0.6282026-04-23 14:58:06,460 - INFO - mixup_alpha          : 1.0292026-04-23 14:58:06,462 - INFO - low_pass_keep_ratio  : 0.5302026-04-23 14:58:06,463 - INFO - freq_consistency_weight : 0.2312026-04-23 14:58:06,464 - INFO - hidden_dropout_prob  : 0.3322026-04-23 14:58:06,466 - INFO - attention_probs_dropout_prob : 0.3332026-04-23 14:58:06,467 - INFO - classifier_dropout   : 0.3342026-04-23 14:58:06,469 - INFO - =================================352026-04-23 14:58:13,859 - INFO - Model placed on cuda362026-04-23 14:58:13,865 - INFO - ===== Model Architecture =====372026-04-23 14:58:13,867 - INFO - 38RobertaForSequenceClassification(39  (roberta): RobertaModel(40    (embeddings): RobertaEmbeddings(41      (word_embeddings): Embedding(50265, 768, padding_idx=1)42      (position_embeddings): Embedding(514, 768, padding_idx=1)43      (token_type_embeddings): Embedding(1, 768)44      (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)45      (dropout): Dropout(p=0.3, inplace=False)46    )47    (encoder): RobertaEncoder(48      (layer): ModuleList(49        (0-11): 12 x RobertaLayer(50          (attention): RobertaAttention(51            (self): RobertaSdpaSelfAttention(52              (query): Linear(in_features=768, out_features=768, bias=True)53              (key): Linear(in_features=768, out_features=768, bias=True)54              (value): Linear(in_features=768, out_features=768, bias=True)55              (dropout): Dropout(p=0.3, inplace=False)56            )57            (output): RobertaSelfOutput(58              (dense): Linear(in_features=768, out_features=768, bias=True)59              (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)60              (dropout): Dropout(p=0.3, inplace=False)61            )62          )63          (intermediate): RobertaIntermediate(64            (dense): Linear(in_features=768, out_features=3072, bias=True)65            (intermediate_act_fn): GELUActivation()66          )67          (output): RobertaOutput(68            (dense): Linear(in_features=3072, out_features=768, bias=True)69            (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)70            (dropout): Dropout(p=0.3, inplace=False)71          )72        )73      )74    )75  )76  (classifier): RobertaClassificationHead(77    (dense): Linear(in_features=768, out_features=768, bias=True)78    (dropout): Dropout(p=0.3, inplace=False)79    (out_proj): Linear(in_features=768, out_features=2, bias=True)80  )81)822026-04-23 14:58:13,869 - INFO - ===== Parameter Summary =====832026-04-23 14:58:13,871 - INFO - Total Parameters:         124,647,170842026-04-23 14:58:13,872 - INFO - Trainable Parameters:     592,130852026-04-23 14:58:13,874 - INFO - Non-trainable Parameters: 124,055,040862026-04-23 14:58:13,875 - INFO - ===== Tokenizer Summary =====872026-04-23 14:58:13,888 - INFO - Vocab size: 50265 | Special tokens: ['<s>', '</s>', '<unk>', '<pad>', '<mask>']882026-04-23 14:58:13,889 - INFO - ===== End of Architecture Log =====892026-04-23 14:58:13,890 - INFO - Data augmentation enabled (rename=0.6, format=0.6)902026-04-23 15:01:32,389 - INFO - === Starting training with MixCode + FFT low-pass consistency ===912026-04-23 15:03:22,966 - INFO - Training complete!922026-04-23 15:03:23,707 - INFO - Final model saved to output_checkpoints/codebert-base/final_model93