dzungpham/graphcodebert-code-classification

12026-04-23 15:05:31,479 - INFO - Loading model and tokenizer from: output_checkpoints/codebert-base/final_model22026-04-23 15:05:31,661 - INFO - ===== Model Architecture =====32026-04-23 15:05:31,663 - 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)482026-04-23 15:05:31,666 - INFO - ===== Parameter Summary =====492026-04-23 15:05:31,667 - INFO - Total Parameters:         124,647,170502026-04-23 15:05:31,668 - INFO - Trainable Parameters:     124,647,170512026-04-23 15:05:31,670 - INFO - Non-trainable Parameters: 0522026-04-23 15:05:31,671 - INFO - ===== Tokenizer Summary =====532026-04-23 15:05:31,689 - INFO - Vocab size: 50265 | Special tokens: ['<s>', '</s>', '<unk>', '<pad>', '<mask>']542026-04-23 15:05:31,691 - INFO - ===== End of Architecture Log =====552026-04-23 15:05:31,855 - INFO - Loading dataset: DaniilOr/SemEval-2026-Task13 (A)562026-04-23 15:05:32,531 - INFO - Tokenizing dataset...572026-04-23 15:05:33,814 - INFO - Running inference on 1000 examples...582026-04-23 15:06:01,067 - INFO - Calculating classification metrics...592026-04-23 15:06:01,086 - INFO - ------------------------------602026-04-23 15:06:01,088 - INFO - METRICS FOR SPLIT: test612026-04-23 15:06:01,089 - INFO - Accuracy:  0.7770622026-04-23 15:06:01,091 - INFO - Precision: 0.6037632026-04-23 15:06:01,092 - INFO - Recall:    0.7770642026-04-23 15:06:01,094 - INFO - F1-Score:  0.6795652026-04-23 15:06:01,095 - INFO - ------------------------------662026-04-23 15:06:01,098 - INFO - Confusion Matrix:67[[777   0]68 [223   0]]692026-04-23 15:06:01,101 - INFO - ✅ Predictions saved to test/inference/codebert-base/submission.csv70