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
| 2026-04-16 09:16:18,911 - INFO - Loading model and tokenizer from: ./checkpoints/graphcodebert-robust/checkpoint-200 | |
| 2026-04-16 09:16:20,848 - INFO - ===== Model Architecture ===== | |
| 2026-04-16 09:16:20,851 - 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=0.2, 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=0.2, 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=0.2, 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=0.2, inplace=False) | |
| ) | |
| ) | |
| ) | |
| ) | |
| ) | |
| (classifier): RobertaClassificationHead( | |
| (dense): Linear(in_features=768, out_features=768, bias=True) | |
| (dropout): Dropout(p=0.2, inplace=False) | |
| (out_proj): Linear(in_features=768, out_features=2, bias=True) | |
| ) | |
| ) | |
| 2026-04-16 09:16:20,853 - INFO - ===== Parameter Summary ===== | |
| 2026-04-16 09:16:20,855 - INFO - Total Parameters: 124,647,170 | |
| 2026-04-16 09:16:20,857 - INFO - Trainable Parameters: 124,647,170 | |
| 2026-04-16 09:16:20,858 - INFO - Non-trainable Parameters: 0 | |
| 2026-04-16 09:16:20,859 - INFO - ===== Tokenizer Summary ===== | |
| 2026-04-16 09:16:20,874 - INFO - Vocab size: 50265 | Special tokens: ['<s>', '</s>', '<unk>', '<pad>', '<mask>'] | |
| 2026-04-16 09:16:20,876 - INFO - ===== End of Architecture Log ===== | |
| 2026-04-16 09:16:21,287 - INFO - Loading dataset: DaniilOr/SemEval-2026-Task13 (A) | |
| 2026-04-16 09:16:28,538 - INFO - Tokenizing dataset... | |
| 2026-04-16 09:16:29,324 - INFO - Running inference on 1000 examples... | |
| 2026-04-16 09:16:55,877 - INFO - Calculating classification metrics... | |
| 2026-04-16 09:16:55,902 - INFO - ------------------------------ | |
| 2026-04-16 09:16:55,904 - INFO - METRICS FOR SPLIT: test | |
| 2026-04-16 09:16:55,905 - INFO - Accuracy: 0.5030 | |
| 2026-04-16 09:16:55,907 - INFO - Precision: 0.6228 | |
| 2026-04-16 09:16:55,909 - INFO - Recall: 0.5030 | |
| 2026-04-16 09:16:55,910 - INFO - F1-Score: 0.5438 | |
| 2026-04-16 09:16:55,912 - INFO - ------------------------------ | |
| 2026-04-16 09:16:55,918 - INFO - Confusion Matrix: | |
| [[422 355] | |
| [142 81]] | |
| 2026-04-16 09:16:55,921 - INFO - ✅ Predictions saved to test/inference/graphcodebert-robust/submission.csv | |