Instructions to use dzungpham/graphcodebert-code-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dzungpham/graphcodebert-code-classification with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("dzungpham/graphcodebert-code-classification", dtype="auto") - Notebooks
- Google Colab
- Kaggle
| 2026-04-23 14:58:06,425 - INFO - ===== Training Configuration ===== | |
| 2026-04-23 14:58:06,427 - INFO - model_name : microsoft/codebert-base | |
| 2026-04-23 14:58:06,428 - INFO - output_dir : output_checkpoints/codebert-base/ | |
| 2026-04-23 14:58:06,429 - INFO - num_epochs : 0.5 | |
| 2026-04-23 14:58:06,431 - INFO - max_steps : 50 | |
| 2026-04-23 14:58:06,432 - INFO - batch_size : 32 | |
| 2026-04-23 14:58:06,433 - INFO - learning_rate : 1e-06 | |
| 2026-04-23 14:58:06,434 - INFO - max_length : 512 | |
| 2026-04-23 14:58:06,436 - INFO - num_labels : 2 | |
| 2026-04-23 14:58:06,437 - INFO - use_wandb : True | |
| 2026-04-23 14:58:06,439 - INFO - freeze_base : True | |
| 2026-04-23 14:58:06,440 - INFO - loss_type : r-drop | |
| 2026-04-23 14:58:06,442 - INFO - focal_alpha : 1.0 | |
| 2026-04-23 14:58:06,443 - INFO - focal_gamma : 2.0 | |
| 2026-04-23 14:58:06,444 - INFO - r_drop_alpha : 6.0 | |
| 2026-04-23 14:58:06,446 - INFO - infonce_temperature : 0.07 | |
| 2026-04-23 14:58:06,447 - INFO - infonce_weight : 0.5 | |
| 2026-04-23 14:58:06,448 - INFO - seed : 42 | |
| 2026-04-23 14:58:06,449 - INFO - resume_from_checkpoint : None | |
| 2026-04-23 14:58:06,451 - INFO - label_smoothing : 0.3 | |
| 2026-04-23 14:58:06,452 - INFO - adversarial_epsilon : 0.5 | |
| 2026-04-23 14:58:06,453 - INFO - use_swa : False | |
| 2026-04-23 14:58:06,454 - INFO - swa_start_epoch : 0 | |
| 2026-04-23 14:58:06,455 - INFO - swa_lr : 1e-05 | |
| 2026-04-23 14:58:06,457 - INFO - data_augmentation : True | |
| 2026-04-23 14:58:06,458 - INFO - aug_rename_prob : 0.6 | |
| 2026-04-23 14:58:06,459 - INFO - aug_format_prob : 0.6 | |
| 2026-04-23 14:58:06,460 - INFO - mixup_alpha : 1.0 | |
| 2026-04-23 14:58:06,462 - INFO - low_pass_keep_ratio : 0.5 | |
| 2026-04-23 14:58:06,463 - INFO - freq_consistency_weight : 0.2 | |
| 2026-04-23 14:58:06,464 - INFO - hidden_dropout_prob : 0.3 | |
| 2026-04-23 14:58:06,466 - INFO - attention_probs_dropout_prob : 0.3 | |
| 2026-04-23 14:58:06,467 - INFO - classifier_dropout : 0.3 | |
| 2026-04-23 14:58:06,469 - INFO - ================================= | |
| 2026-04-23 14:58:13,859 - INFO - Model placed on cuda | |
| 2026-04-23 14:58:13,865 - INFO - ===== Model Architecture ===== | |
| 2026-04-23 14:58:13,867 - INFO - | |
| RobertaForSequenceClassification( | |
| (roberta): RobertaModel( | |
| (embeddings): RobertaEmbeddings( | |
| (word_embeddings): Embedding(50265, 768, padding_idx=1) | |
| (position_embeddings): Embedding(514, 768, padding_idx=1) | |
| (token_type_embeddings): Embedding(1, 768) | |
| (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) | |
| (dropout): Dropout(p=0.3, inplace=False) | |
| ) | |
| (encoder): RobertaEncoder( | |
| (layer): ModuleList( | |
| (0-11): 12 x RobertaLayer( | |
| (attention): RobertaAttention( | |
| (self): RobertaSdpaSelfAttention( | |
| (query): Linear(in_features=768, out_features=768, bias=True) | |
| (key): Linear(in_features=768, out_features=768, bias=True) | |
| (value): Linear(in_features=768, out_features=768, bias=True) | |
| (dropout): Dropout(p=0.3, inplace=False) | |
| ) | |
| (output): RobertaSelfOutput( | |
| (dense): Linear(in_features=768, out_features=768, bias=True) | |
| (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) | |
| (dropout): Dropout(p=0.3, inplace=False) | |
| ) | |
| ) | |
| (intermediate): RobertaIntermediate( | |
| (dense): Linear(in_features=768, out_features=3072, bias=True) | |
| (intermediate_act_fn): GELUActivation() | |
| ) | |
| (output): RobertaOutput( | |
| (dense): Linear(in_features=3072, out_features=768, bias=True) | |
| (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) | |
| (dropout): Dropout(p=0.3, inplace=False) | |
| ) | |
| ) | |
| ) | |
| ) | |
| ) | |
| (classifier): RobertaClassificationHead( | |
| (dense): Linear(in_features=768, out_features=768, bias=True) | |
| (dropout): Dropout(p=0.3, inplace=False) | |
| (out_proj): Linear(in_features=768, out_features=2, bias=True) | |
| ) | |
| ) | |
| 2026-04-23 14:58:13,869 - INFO - ===== Parameter Summary ===== | |
| 2026-04-23 14:58:13,871 - INFO - Total Parameters: 124,647,170 | |
| 2026-04-23 14:58:13,872 - INFO - Trainable Parameters: 592,130 | |
| 2026-04-23 14:58:13,874 - INFO - Non-trainable Parameters: 124,055,040 | |
| 2026-04-23 14:58:13,875 - INFO - ===== Tokenizer Summary ===== | |
| 2026-04-23 14:58:13,888 - INFO - Vocab size: 50265 | Special tokens: | |
| 2026-04-23 14:58:13,889 - INFO - ===== End of Architecture Log ===== | |
| 2026-04-23 14:58:13,890 - INFO - Data augmentation enabled (rename=0.6, format=0.6) | |
| 2026-04-23 15:01:32,389 - INFO - === Starting training with MixCode + FFT low-pass consistency === | |
| 2026-04-23 15:03:22,966 - INFO - Training complete! | |
| 2026-04-23 15:03:23,707 - INFO - Final model saved to output_checkpoints/codebert-base/final_model | |