dzungpham/graphcodebert-code-classification

12026-04-24 20:25:52,892 - INFO - Loading model and tokenizer from: output_checkpoints/graphcodebert-base-lowLR-highBatchSize/checkpoint-100022026-04-24 20:25:53,077 - INFO - ===== Model Architecture =====32026-04-24 20:25:53,078 - 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)482026-04-24 20:25:53,080 - INFO - ===== Parameter Summary =====492026-04-24 20:25:53,081 - INFO - Total Parameters:         124,647,170502026-04-24 20:25:53,082 - INFO - Trainable Parameters:     124,647,170512026-04-24 20:25:53,082 - INFO - Non-trainable Parameters: 0522026-04-24 20:25:53,083 - INFO - ===== Tokenizer Summary =====532026-04-24 20:25:53,095 - INFO - Vocab size: 50265 | Special tokens: ['<s>', '</s>', '<unk>', '<pad>', '<mask>']542026-04-24 20:25:53,096 - INFO - ===== End of Architecture Log =====552026-04-24 20:25:53,251 - INFO - Loading dataset: DaniilOr/SemEval-2026-Task13 (A)562026-04-24 20:25:53,847 - INFO - Tokenizing dataset...572026-04-24 20:25:55,058 - INFO - Running inference on 1000 examples...582026-04-24 20:26:29,083 - INFO - Calculating classification metrics...592026-04-24 20:26:29,105 - INFO - ------------------------------602026-04-24 20:26:29,106 - INFO - METRICS FOR SPLIT: test612026-04-24 20:26:29,106 - INFO - Accuracy:  0.6970622026-04-24 20:26:29,107 - INFO - Precision: 0.6825632026-04-24 20:26:29,108 - INFO - Recall:    0.6970642026-04-24 20:26:29,108 - INFO - F1-Score:  0.6892652026-04-24 20:26:29,109 - INFO - ------------------------------662026-04-24 20:26:29,110 - INFO - Confusion Matrix:67[[640 137]68 [166  57]]692026-04-24 20:26:29,112 - INFO - ✅ Metrics saved to test/inference/graphcodebert-base-lowLR-highBatchSize/checkpoint-1000/metrics.json702026-04-24 20:26:29,114 - INFO - ✅ Predictions saved to test/inference/graphcodebert-base-lowLR-highBatchSize/checkpoint-1000/submission.csv71