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
File size: 936 Bytes
aae9944 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 | {
"model_name": "microsoft/unixcoder-base",
"output_dir": "output_checkpoints/unixcoder-base/",
"num_epochs": 0.5,
"max_steps": 50,
"batch_size": 32,
"learning_rate": 1e-06,
"max_length": 512,
"num_labels": 2,
"use_wandb": true,
"freeze_base": true,
"loss_type": "r-drop",
"focal_alpha": 1.0,
"focal_gamma": 2.0,
"r_drop_alpha": 6.0,
"infonce_temperature": 0.07,
"infonce_weight": 0.5,
"seed": 42,
"resume_from_checkpoint": null,
"label_smoothing": 0.3,
"adversarial_epsilon": 0.5,
"use_swa": false,
"swa_start_epoch": 0,
"swa_lr": 1e-05,
"data_augmentation": true,
"aug_rename_prob": 0.6,
"aug_format_prob": 0.6,
"mixup_alpha": 1.0,
"low_pass_keep_ratio": 0.5,
"freq_consistency_weight": 0.2,
"hidden_dropout_prob": 0.3,
"attention_probs_dropout_prob": 0.3,
"classifier_dropout": 0.3,
"device": "cuda"
} |