End of training

README.md

@@ -4,70 +4,9 @@ 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
+model-index:
+- name: oo-method-test-model-bylibrary
+  results: []
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -75,11 +14,11 @@ 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.
+This model is a fine-tuned version of [huggingface/CodeBERTa-small-v1](https://huggingface.co/huggingface/CodeBERTa-small-v1) on the None dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.3303
-- Accuracy: 0.9161
-- Best Accuracy: 0.9161
+- Loss: 0.3131
+- Accuracy: 0.9207
+- Best Accuracy: 0.9207
 
 ## Model description
 
@@ -98,23 +37,23 @@ More information needed
 ### Training hyperparameters
 
 The following hyperparameters were used during training:
-- learning_rate: 2.386135927313411e-05
+- learning_rate: 2.34314e-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_steps: 813
 
 ### 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        |
+| 0.4448        | 0.17  | 163  | 0.2735          | 0.9066   | 0.9066        |
+| 0.2779        | 0.33  | 326  | 0.2817          | 0.9185   | 0.9185        |
+| 0.1733        | 0.5   | 489  | 0.3446          | 0.9027   | 0.9185        |
+| 0.1861        | 0.66  | 652  | 0.3131          | 0.9207   | 0.9207        |
 
 
 ### Framework versions

pytorch_model.bin

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 size 333845425

training_args.bin

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+oid sha256:7bf67dcb71ffa11c78c64dc3957dd33ed75fac61ffd16a42836d64dc8b9cca85
+size 4091