--- base_model: huggingface/CodeBERTa-small-v1 tags: - generated_from_trainer metrics: - accuracy datasets: - ejschwartz/oo-method-test-split widget: - text: ' L1: 55 ?? push ebp 8b ec ?? mov ebp, esp 5d ?? pop ebp c3 ?? ret ' example_title: Function 1 - text: >2 L1: 55 ?? push ebp 8b ec ?? mov ebp, esp 51 ?? push ecx a1 b0 5d 43 00 ?? mov eax, dword ds:[0x00435db0] 83 f8 fe ?? cmp eax, 0xfe<254,-2> 75 0a ?? jne basic block L4 L2: e8 4e 17 00 00 ?? call function 0x00415d25 L3: a1 b0 5d 43 00 ?? mov eax, dword ds:[0x00435db0] L4: 83 f8 ff ?? cmp eax, 0xff<255,-1> 75 07 ?? jne basic block L6 L5: b8 ff ff 00 00 ?? mov eax, 0x0000ffff eb 1b ?? jmp basic block L9 L6: 6a 00 ?? push 0 8d 4d fc ?? lea ecx, ss:[ebp + 0xfc<252,-4>] 51 ?? push ecx 6a 01 ?? push 1 8d 4d 08 ?? lea ecx, ss:[ebp + 8] 51 ?? push ecx 50 ?? push eax ff 15 28 91 43 00 ?? call dword ds:[0x00439128] L7: 85 c0 ?? test eax, eax 74 e2 ?? je basic block L5 L8: 66 8b 45 08 ?? mov ax, word ss:[ebp + 8] L9: 8b e5 ?? mov esp, ebp 5d ?? pop ebp c3 ?? ret example_title: Function 2 - text: >2 L1: 0f b7 41 32 ?? movzx eax, word ds:[ecx + 0x32<50>] 83 e8 20 ?? sub eax, 0x20<32> 74 2d ?? je basic block L10 L2: 83 e8 03 ?? sub eax, 3 74 22 ?? je basic block L9 L3: 83 e8 08 ?? sub eax, 8 74 17 ?? je basic block L8 L4: 48 ?? dec eax 83 e8 01 ?? sub eax, 1 74 0b ?? je basic block L7 L5: 83 e8 03 ?? sub eax, 3 75 1c ?? jne basic block L11 L6: 83 49 20 08 ?? or dword ds:[ecx + 0x20<32>], 8 eb 16 ?? jmp basic block L11 L7: 83 49 20 04 ?? or dword ds:[ecx + 0x20<32>], 4 eb 10 ?? jmp basic block L11 L8: 83 49 20 01 ?? or dword ds:[ecx + 0x20<32>], 1 eb 0a ?? jmp basic block L11 L9: 83 49 20 20 ?? or dword ds:[ecx + 0x20<32>], 0x20<32> eb 04 ?? jmp basic block L11 L10: 83 49 20 02 ?? or dword ds:[ecx + 0x20<32>], 2 L11: b0 01 ?? mov al, 1 c3 ?? ret example_title: Method 1 - text: >2 L1: 8b ff ?? mov edi, edi 55 ?? push ebp 8b ec ?? mov ebp, esp 83 ec 08 ?? sub esp, 8 89 4d f8 ?? mov dword ss:[ebp + 0xf8<248,-8>], ecx 8b 4d f8 ?? mov ecx, dword ss:[ebp + 0xf8<248,-8>] e8 e6 ac f9 ff ?? call function 0x00401569 L2: 23 45 08 ?? and eax, dword ss:[ebp + 8] 3b 45 08 ?? cmp eax, dword ss:[ebp + 8] 75 09 ?? jne basic block L4 L3: c7 45 fc 01 00 00 00 ?? mov dword ss:[ebp + 0xfc<252,-4>], 1 eb 07 ?? jmp basic block L5 L4: c7 45 fc 00 00 00 00 ?? mov dword ss:[ebp + 0xfc<252,-4>], 0 L5: 8a 45 fc ?? mov al, byte ss:[ebp + 0xfc<252,-4>] 8b e5 ?? mov esp, ebp 5d ?? pop ebp c2 04 00 ?? ret 4 example_title: Method 2 --- # oo-method-test-model-bylibrary This model is a fine-tuned version of [huggingface/CodeBERTa-small-v1](https://huggingface.co/huggingface/CodeBERTa-small-v1) on the ejschwartz/oo-method-test-split dataset. It achieves the following results on the evaluation set: - Loss: 0.3303 - Accuracy: 0.9161 - Best Accuracy: 0.9161 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2.386135927313411e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.05 - training_steps: 887 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Best Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:| | 0.3788 | 0.02 | 178 | 0.6188 | 0.8470 | 0.8470 | | 0.1456 | 0.04 | 356 | 0.6572 | 0.8519 | 0.8519 | | 0.17 | 0.05 | 534 | 0.4926 | 0.8798 | 0.8798 | | 0.1162 | 0.07 | 712 | 0.3303 | 0.9161 | 0.9161 | ### Framework versions - Transformers 4.33.1 - Pytorch 2.0.1+cu117 - Datasets 2.14.5 - Tokenizers 0.13.3