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cwe-parent-vulnerability-classification-roberta-base

Text Classification
Transformers
PyTorch
Safetensors
roberta
Generated from Trainer
classification
nlp
vulnerability
CWE
text-embeddings-inference
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CIRCL/cwe-parent-vulnerability-classification-roberta-base

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Files changed (5) hide show
  1. README.md +48 -48
  2. config.json +52 -52
  3. emissions.csv +2 -2
  4. metrics.json +6 -6
  5. model.safetensors +1 -1
README.md CHANGED
@@ -18,9 +18,9 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 1.6858
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- - Accuracy: 0.6126
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- - F1 Macro: 0.3737
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  ## Model description
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@@ -40,8 +40,8 @@ More information needed
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  The following hyperparameters were used during training:
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  - learning_rate: 1e-05
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- - train_batch_size: 32
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- - eval_batch_size: 32
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  - seed: 42
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  - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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  - lr_scheduler_type: linear
@@ -51,51 +51,51 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|
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- | 3.078 | 1.0 | 237 | 3.0510 | 0.1776 | 0.0529 |
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- | 2.4726 | 2.0 | 474 | 2.2886 | 0.4398 | 0.2407 |
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- | 2.2031 | 3.0 | 711 | 1.9511 | 0.5185 | 0.3141 |
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- | 1.7872 | 4.0 | 948 | 1.7893 | 0.5638 | 0.3511 |
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- | 1.4324 | 5.0 | 1185 | 1.7492 | 0.6305 | 0.3805 |
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- | 1.2675 | 6.0 | 1422 | 1.6858 | 0.6126 | 0.3737 |
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- | 1.0437 | 7.0 | 1659 | 1.7359 | 0.6675 | 0.4296 |
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- | 0.8699 | 8.0 | 1896 | 1.7641 | 0.6746 | 0.4246 |
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- | 0.8832 | 9.0 | 2133 | 1.8097 | 0.6746 | 0.4444 |
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- | 0.8027 | 10.0 | 2370 | 1.8753 | 0.6698 | 0.4380 |
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- | 0.4583 | 11.0 | 2607 | 1.8919 | 0.6830 | 0.4473 |
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- | 0.5493 | 12.0 | 2844 | 1.8456 | 0.7080 | 0.4915 |
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- | 0.4808 | 13.0 | 3081 | 1.9593 | 0.6841 | 0.4555 |
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- | 0.4466 | 14.0 | 3318 | 2.0736 | 0.6865 | 0.4454 |
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- | 0.2989 | 15.0 | 3555 | 2.1972 | 0.6961 | 0.4474 |
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- | 0.255 | 16.0 | 3792 | 2.2513 | 0.7008 | 0.4638 |
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- | 0.2474 | 17.0 | 4029 | 2.2991 | 0.7223 | 0.4609 |
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- | 0.1648 | 18.0 | 4266 | 2.4582 | 0.7128 | 0.4614 |
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- | 0.2112 | 19.0 | 4503 | 2.5944 | 0.7247 | 0.4714 |
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- | 0.1185 | 20.0 | 4740 | 2.5292 | 0.7128 | 0.4557 |
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- | 0.1453 | 21.0 | 4977 | 2.6173 | 0.7104 | 0.4466 |
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- | 0.1126 | 22.0 | 5214 | 2.7072 | 0.7104 | 0.4461 |
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- | 0.0872 | 23.0 | 5451 | 2.8997 | 0.7235 | 0.4577 |
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- | 0.0768 | 24.0 | 5688 | 2.8199 | 0.7294 | 0.4623 |
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- | 0.0643 | 25.0 | 5925 | 2.9228 | 0.7211 | 0.4587 |
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- | 0.0828 | 26.0 | 6162 | 3.0185 | 0.7330 | 0.4774 |
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- | 0.0407 | 27.0 | 6399 | 3.1037 | 0.7211 | 0.4586 |
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- | 0.0386 | 28.0 | 6636 | 3.1938 | 0.7235 | 0.4622 |
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- | 0.0321 | 29.0 | 6873 | 3.2786 | 0.7318 | 0.4612 |
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- | 0.0189 | 30.0 | 7110 | 3.4453 | 0.7330 | 0.4559 |
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- | 0.0223 | 31.0 | 7347 | 3.3558 | 0.7366 | 0.4583 |
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- | 0.0255 | 32.0 | 7584 | 3.3787 | 0.7354 | 0.4682 |
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- | 0.0123 | 33.0 | 7821 | 3.4288 | 0.7306 | 0.4633 |
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- | 0.0128 | 34.0 | 8058 | 3.4361 | 0.7366 | 0.4645 |
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- | 0.0201 | 35.0 | 8295 | 3.6213 | 0.7235 | 0.4559 |
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- | 0.014 | 36.0 | 8532 | 3.7080 | 0.7247 | 0.4554 |
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- | 0.0159 | 37.0 | 8769 | 3.6249 | 0.7330 | 0.4622 |
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- | 0.027 | 38.0 | 9006 | 3.6598 | 0.7294 | 0.4604 |
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- | 0.0086 | 39.0 | 9243 | 3.7176 | 0.7342 | 0.4637 |
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- | 0.0096 | 40.0 | 9480 | 3.7223 | 0.7306 | 0.4614 |
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  ### Framework versions
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- - Transformers 4.57.1
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  - Pytorch 2.9.1+cu128
100
- - Datasets 4.4.1
101
- - Tokenizers 0.22.1
 
18
 
19
  This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
20
  It achieves the following results on the evaluation set:
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+ - Loss: 1.7510
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+ - Accuracy: 0.5455
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+ - F1 Macro: 0.3776
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  ## Model description
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40
 
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  The following hyperparameters were used during training:
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  - learning_rate: 1e-05
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+ - train_batch_size: 64
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+ - eval_batch_size: 64
45
  - seed: 42
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  - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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  - lr_scheduler_type: linear
 
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|
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+ | 3.226 | 1.0 | 125 | 3.1362 | 0.0382 | 0.0035 |
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+ | 3.0244 | 2.0 | 250 | 2.9390 | 0.2155 | 0.1215 |
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+ | 2.589 | 3.0 | 375 | 2.3469 | 0.4141 | 0.2521 |
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+ | 2.1614 | 4.0 | 500 | 2.0701 | 0.4355 | 0.2551 |
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+ | 1.8396 | 5.0 | 625 | 1.9336 | 0.4467 | 0.2748 |
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+ | 1.5698 | 6.0 | 750 | 1.9086 | 0.4905 | 0.2938 |
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+ | 1.4142 | 7.0 | 875 | 1.7933 | 0.5174 | 0.3416 |
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+ | 1.2292 | 8.0 | 1000 | 1.7510 | 0.5455 | 0.3776 |
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+ | 1.1182 | 9.0 | 1125 | 1.7681 | 0.5713 | 0.3803 |
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+ | 0.9924 | 10.0 | 1250 | 1.8151 | 0.6083 | 0.4059 |
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+ | 0.9307 | 11.0 | 1375 | 1.8391 | 0.6218 | 0.4379 |
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+ | 0.7875 | 12.0 | 1500 | 1.8065 | 0.6038 | 0.4048 |
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+ | 0.6308 | 13.0 | 1625 | 1.9221 | 0.6409 | 0.4210 |
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+ | 0.7327 | 14.0 | 1750 | 1.9986 | 0.6465 | 0.4775 |
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+ | 0.5175 | 15.0 | 1875 | 2.0520 | 0.6644 | 0.4316 |
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+ | 0.5302 | 16.0 | 2000 | 2.0989 | 0.6712 | 0.4528 |
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+ | 0.38 | 17.0 | 2125 | 2.0826 | 0.6734 | 0.4669 |
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+ | 0.3768 | 18.0 | 2250 | 2.1953 | 0.6611 | 0.4544 |
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+ | 0.3653 | 19.0 | 2375 | 2.2217 | 0.6880 | 0.5000 |
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+ | 0.3349 | 20.0 | 2500 | 2.1911 | 0.6880 | 0.4951 |
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+ | 0.2563 | 21.0 | 2625 | 2.2999 | 0.6813 | 0.4771 |
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+ | 0.2513 | 22.0 | 2750 | 2.4158 | 0.7037 | 0.4640 |
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+ | 0.2154 | 23.0 | 2875 | 2.4323 | 0.7138 | 0.4689 |
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+ | 0.1889 | 24.0 | 3000 | 2.4296 | 0.7037 | 0.4733 |
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+ | 0.2042 | 25.0 | 3125 | 2.5223 | 0.7071 | 0.4411 |
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+ | 0.1774 | 26.0 | 3250 | 2.5476 | 0.7037 | 0.5083 |
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+ | 0.156 | 27.0 | 3375 | 2.5737 | 0.7205 | 0.5236 |
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+ | 0.1406 | 28.0 | 3500 | 2.6518 | 0.7048 | 0.5220 |
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+ | 0.144 | 29.0 | 3625 | 2.6388 | 0.7015 | 0.4789 |
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+ | 0.1119 | 30.0 | 3750 | 2.7159 | 0.7228 | 0.5003 |
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+ | 0.1187 | 31.0 | 3875 | 2.7170 | 0.7071 | 0.4973 |
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+ | 0.1095 | 32.0 | 4000 | 2.7796 | 0.7160 | 0.4707 |
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+ | 0.1082 | 33.0 | 4125 | 2.7926 | 0.7239 | 0.5038 |
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+ | 0.0976 | 34.0 | 4250 | 2.8240 | 0.7149 | 0.4515 |
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+ | 0.0885 | 35.0 | 4375 | 2.8532 | 0.7149 | 0.4466 |
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+ | 0.0872 | 36.0 | 4500 | 2.8697 | 0.7183 | 0.4700 |
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+ | 0.0795 | 37.0 | 4625 | 2.8467 | 0.7138 | 0.4994 |
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+ | 0.0878 | 38.0 | 4750 | 2.8566 | 0.7104 | 0.4673 |
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+ | 0.0886 | 39.0 | 4875 | 2.8951 | 0.7127 | 0.4667 |
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+ | 0.086 | 40.0 | 5000 | 2.8841 | 0.7127 | 0.4683 |
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  ### Framework versions
97
 
98
+ - Transformers 4.57.3
99
  - Pytorch 2.9.1+cu128
100
+ - Datasets 4.4.2
101
+ - Tokenizers 0.22.2
config.json CHANGED
@@ -11,62 +11,62 @@
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  "hidden_size": 768,
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  "layer_norm_eps": 1e-05,
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emissions.csv CHANGED
@@ -1,2 +1,2 @@
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metrics.json CHANGED
@@ -1,9 +1,9 @@
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  {
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- "eval_samples_per_second": 426.01,
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- "eval_steps_per_second": 13.71,
8
  "epoch": 40.0
9
  }
 
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  "epoch": 40.0
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