dzungpham/graphcodebert-code-classification

12026-04-28 04:22:23,122 - INFO - Loading model and tokenizer from: checkpoints/graphcodebert-base-lowLR-highBatchSize/checkpoint-102222026-04-28 04:22:23,386 - INFO - ===== Model Architecture =====32026-04-28 04:22:23,387 - INFO - 4RobertaForSequenceClassification(5  (roberta): RobertaModel(6    (embeddings): RobertaEmbeddings(7      (word_embeddings): Embedding(50265, 768, padding_idx=1)8      (position_embeddings): Embedding(514, 768, padding_idx=1)9      (token_type_embeddings): Embedding(1, 768)10      (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)11      (dropout): Dropout(p=0.3, inplace=False)12    )13    (encoder): RobertaEncoder(14      (layer): ModuleList(15        (0-11): 12 x RobertaLayer(16          (attention): RobertaAttention(17            (self): RobertaSdpaSelfAttention(18              (query): Linear(in_features=768, out_features=768, bias=True)19              (key): Linear(in_features=768, out_features=768, bias=True)20              (value): Linear(in_features=768, out_features=768, bias=True)21              (dropout): Dropout(p=0.3, inplace=False)22            )23            (output): RobertaSelfOutput(24              (dense): Linear(in_features=768, out_features=768, bias=True)25              (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)26              (dropout): Dropout(p=0.3, inplace=False)27            )28          )29          (intermediate): RobertaIntermediate(30            (dense): Linear(in_features=768, out_features=3072, bias=True)31            (intermediate_act_fn): GELUActivation()32          )33          (output): RobertaOutput(34            (dense): Linear(in_features=3072, out_features=768, bias=True)35            (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)36            (dropout): Dropout(p=0.3, inplace=False)37          )38        )39      )40    )41  )42  (classifier): RobertaClassificationHead(43    (dense): Linear(in_features=768, out_features=768, bias=True)44    (dropout): Dropout(p=0.3, inplace=False)45    (out_proj): Linear(in_features=768, out_features=2, bias=True)46  )47)482026-04-28 04:22:23,389 - INFO - ===== Parameter Summary =====492026-04-28 04:22:23,390 - INFO - Total Parameters:         124,647,170502026-04-28 04:22:23,391 - INFO - Trainable Parameters:     124,647,170512026-04-28 04:22:23,392 - INFO - Non-trainable Parameters: 0522026-04-28 04:22:23,393 - INFO - ===== Tokenizer Summary =====532026-04-28 04:22:23,408 - INFO - Vocab size: 50265 | Special tokens: ['<s>', '</s>', '<unk>', '<pad>', '<mask>']542026-04-28 04:22:23,409 - INFO - ===== End of Architecture Log =====552026-04-28 04:22:23,831 - INFO - Loading dataset from: /kaggle/input/datasets/dzung271828/semeval/Task_A/test.parquet562026-04-28 04:22:23,832 - INFO - Detected .parquet file – loading directly with datasets (memory-mapped)572026-04-28 04:22:30,067 - INFO - Loaded Parquet file with 500000 examples (memory-mapped)582026-04-28 04:22:30,068 - INFO - Columns found: ['ID', 'code', '__index_level_0__']592026-04-28 04:22:30,072 - INFO - Tokenizing dataset...602026-04-28 04:27:31,809 - INFO - Running inference on 500000 examples...612026-04-28 08:29:06,190 - WARNING - No 'label' column found. Skipping metric calculation.622026-04-28 08:29:11,935 - INFO - ✅ Predictions saved to test/inference/graphcodebert-base-lowLR-highBatchSize/checkpoint-1022/checkpoint-1022-submission.csv63