| { |
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| "model_dir": "/mnt/d/work2/graphcodebert-code-clone-detection/models/graphcodebert-clone-detection", |
| "dataset_name": "PoolC/1-fold-clone-detection-600k-5fold", |
| "dataset_split_strategy": { |
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| "note": "The repository provides one of 5 predefined folds as `train` + `val`; those groups are disjoint and are kept as-is. `val` is partitioned further into validation/test along problem-group boundaries.", |
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| "class_weighting": { |
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| "reason": "Measured majority-class share 0.5000 is within the 0.6 threshold, so weighted cross entropy is NOT used." |
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| "per_device_train_batch_size": 16, |
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| "learning_rate": 2e-05, |
| "num_train_epochs": 3.0, |
| "optimizer": "adamw_torch", |
| "scheduler": "linear", |
| "warmup_ratio": 0.1, |
| "warmup_steps": 938, |
| "weight_decay": 0.01, |
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| "mixed_precision": "fp16", |
| "gradient_checkpointing": false, |
| "seed": 42, |
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| "training_time_hours": 1.923, |
| "train_runtime_metrics": { |
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| "torch_version": "2.11.0+cu128", |
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| "python_version": "3.12.3", |
| "platform": "Linux-6.18.33.2-microsoft-standard-WSL2-x86_64-with-glibc2.39", |
| "gpu_name": "NVIDIA GeForce RTX 5060 Ti", |
| "gpu_count": 1, |
| "gpu_total_memory_gb": 15.9, |
| "gpu_capability": "12.0", |
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| "best_checkpoint": "./outputs/checkpoint-9000", |
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| "precision": 0.8032395566922421, |
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| "preprocessing": { |
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| "status_counts": { |
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| "subsampling": { |
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| "test": { |
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| "sanity_check": { |
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| "device": "cuda", |
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| { |
| "check": "1_dataset_loading", |
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| "detail": "train=50000 val=20000 test=20000" |
| }, |
| { |
| "check": "2_column_detection", |
| "passed": true, |
| "detail": "code columns=code1/code2, label=similar; excluded from features: ['code1_group', 'code2_group', 'pair_id', 'question_pair_id']" |
| }, |
| { |
| "check": "3_label_correctness", |
| "passed": true, |
| "detail": "labels in {0,1}; validation positive ratio=0.5" |
| }, |
| { |
| "check": "4_tokenisation", |
| "passed": true, |
| "detail": "<s>...</s> wrapping OK, 93 code tokens, decodes to 'import math a , b , c = map ( int , input ( ) . split ( ) ) '" |
| }, |
| { |
| "check": "5_dataflow_extraction", |
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| "detail": "44687/44950 snippets have a non-empty data-flow graph, 2481388 edges, mean nodes=44.22" |
| }, |
| { |
| "check": "6_attention_mask", |
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| "detail": "shape=(640, 640) (code_length 512 + data_flow_length 128), 93 code tokens, 30 nodes, density=0.0215, padding rows empty" |
| }, |
| { |
| "check": "7_forward_pass", |
| "passed": true, |
| "detail": "logits shape=(16, 2) for batch of 16 pairs; mask tensor=(16, 640, 640)" |
| }, |
| { |
| "check": "8_finite_loss", |
| "passed": true, |
| "detail": "loss=0.6978 (chance level ~0.6931)" |
| }, |
| { |
| "check": "9_training_step", |
| "passed": true, |
| "detail": "loss=0.6798, grad_norm=2.4929, weights updated (amp=on, dtype=torch.float16)" |
| } |
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| "parameters": { |
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| "total_parameters": 125236994 |
| }, |
| "sequence_length": 640 |
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| "test_metrics_provenance": { |
| "note": "Re-scored after fixing a transformers v5 dataloader-caching bug: Trainer.get_eval_dataloader caches under the key 'eval' when a Dataset object is passed and dataloader_persistent_workers=True, so the original in-training test evaluation silently re-scored the validation split. evaluate_split now uses Trainer.predict and verifies the returned labels.", |
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| "corrected_test_f1": 0.8805450993472149, |
| "validation_f1_unchanged": 0.8671882190520018 |
| } |
| } |
