CIRCL/cwe-parent-vulnerability-classification-microsoft-codebert-base

README.md

@@ -0,0 +1,100 @@
+---
+library_name: transformers
+base_model: microsoft/codebert-base
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+model-index:
+- name: cwe-parent-vulnerability-classification-microsoft-codebert-base
+  results: []
+---
+
+<!-- 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. -->
+
+# cwe-parent-vulnerability-classification-microsoft-codebert-base
+
+This model is a fine-tuned version of [microsoft/codebert-base](https://huggingface.co/microsoft/codebert-base) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 2.6351
+- Accuracy: 0.6517
+- F1 Macro: 0.3129
+
+## 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: 1e-05
+- train_batch_size: 32
+- eval_batch_size: 32
+- seed: 42
+- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: linear
+- num_epochs: 40
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|
+| 3.2599        | 1.0   | 25   | 3.2873          | 0.0225   | 0.0037   |
+| 3.1436        | 2.0   | 50   | 3.3011          | 0.0562   | 0.0216   |
+| 3.1168        | 3.0   | 75   | 3.3439          | 0.0449   | 0.0113   |
+| 3.0315        | 4.0   | 100  | 3.3314          | 0.1461   | 0.0645   |
+| 3.0604        | 5.0   | 125  | 3.3334          | 0.0899   | 0.0581   |
+| 2.9746        | 6.0   | 150  | 3.3430          | 0.1124   | 0.0546   |
+| 2.9773        | 7.0   | 175  | 3.3535          | 0.4157   | 0.0990   |
+| 2.8666        | 8.0   | 200  | 3.2720          | 0.4831   | 0.2052   |
+| 2.8196        | 9.0   | 225  | 3.2289          | 0.4270   | 0.1442   |
+| 2.6704        | 10.0  | 250  | 3.1301          | 0.2584   | 0.1440   |
+| 2.6964        | 11.0  | 275  | 3.0508          | 0.2809   | 0.1197   |
+| 2.5442        | 12.0  | 300  | 2.9618          | 0.3596   | 0.1644   |
+| 2.4519        | 13.0  | 325  | 2.9271          | 0.3596   | 0.1637   |
+| 2.4064        | 14.0  | 350  | 2.8342          | 0.3933   | 0.2154   |
+| 2.2469        | 15.0  | 375  | 2.7950          | 0.3596   | 0.2097   |
+| 2.1662        | 16.0  | 400  | 2.7928          | 0.3596   | 0.1926   |
+| 2.126         | 17.0  | 425  | 2.6786          | 0.4157   | 0.2223   |
+| 2.0579        | 18.0  | 450  | 2.7615          | 0.3820   | 0.1987   |
+| 1.8908        | 19.0  | 475  | 2.6469          | 0.4157   | 0.2015   |
+| 1.8119        | 20.0  | 500  | 2.7396          | 0.4157   | 0.2097   |
+| 1.8234        | 21.0  | 525  | 2.7319          | 0.3933   | 0.2101   |
+| 1.6483        | 22.0  | 550  | 2.7024          | 0.4607   | 0.2504   |
+| 1.7195        | 23.0  | 575  | 2.6693          | 0.4944   | 0.2345   |
+| 1.5326        | 24.0  | 600  | 2.6387          | 0.5169   | 0.2341   |
+| 1.5649        | 25.0  | 625  | 2.6509          | 0.6180   | 0.2934   |
+| 1.4294        | 26.0  | 650  | 2.7232          | 0.6292   | 0.3175   |
+| 1.4872        | 27.0  | 675  | 2.6745          | 0.6404   | 0.3005   |
+| 1.3451        | 28.0  | 700  | 2.6499          | 0.6517   | 0.3100   |
+| 1.296         | 29.0  | 725  | 2.6788          | 0.6517   | 0.3290   |
+| 1.2962        | 30.0  | 750  | 2.6351          | 0.6517   | 0.3129   |
+| 1.2969        | 31.0  | 775  | 2.6432          | 0.6742   | 0.3226   |
+| 1.1886        | 32.0  | 800  | 2.6496          | 0.6742   | 0.3226   |
+| 1.1426        | 33.0  | 825  | 2.6603          | 0.6742   | 0.3230   |
+| 1.1833        | 34.0  | 850  | 2.6660          | 0.6742   | 0.3253   |
+| 1.14          | 35.0  | 875  | 2.6588          | 0.6854   | 0.3477   |
+| 1.0947        | 36.0  | 900  | 2.6501          | 0.6854   | 0.3477   |
+| 1.0714        | 37.0  | 925  | 2.6654          | 0.6854   | 0.3477   |
+| 1.0678        | 38.0  | 950  | 2.6454          | 0.6854   | 0.3477   |
+| 1.0535        | 39.0  | 975  | 2.6375          | 0.6854   | 0.3481   |
+| 1.0273        | 40.0  | 1000 | 2.6422          | 0.6854   | 0.3481   |
+
+
+### Framework versions
+
+- Transformers 4.55.4
+- Pytorch 2.7.1+cu126
+- Datasets 4.0.0
+- Tokenizers 0.21.2

config.json

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   "max_position_embeddings": 514,

emissions.csv

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+2025-09-03T13:30:34,codecarbon,9d880529-ac6f-4dee-a770-4b5fe9c7c287,5b0fa12a-3dd7-45bb-9766-cc326314d9f1,390.7287475480698,0.006325022847261015,1.618775912177507e-05,42.5,178.6612275683419,94.34468507766725,0.004608292719416204,0.04525022397793066,0.010229290483343567,0.060087807180690414,Luxembourg,LUX,luxembourg,,,Linux-6.8.0-71-generic-x86_64-with-glibc2.39,3.12.3,2.8.4,64,AMD EPYC 9124 16-Core Processor,2,2 x NVIDIA L40S,6.1294,49.6113,251.5858268737793,machine,N,1.0

metrics.json

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+{
+    "eval_loss": 2.6350972652435303,
+    "eval_accuracy": 0.651685393258427,
+    "eval_f1_macro": 0.3128829740025724,
+    "eval_runtime": 0.3092,
+    "eval_samples_per_second": 287.838,
+    "eval_steps_per_second": 9.702,
+    "epoch": 40.0
+}

model.safetensors

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