README.md

---
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
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# 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