| --- |
| library_name: transformers |
| license: mit |
| base_model: camembert-base |
| tags: |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| - precision |
| - recall |
| model-index: |
| - name: camembert-pcg-annotation |
| 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. --> |
|
|
| # camembert-pcg-annotation |
|
|
| This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-base) on the None dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 6.4139 |
| - Accuracy: 0.1360 |
| - Top3 Accuracy: 0.3559 |
| - Top5 Accuracy: 0.4498 |
| - Precision: 0.0977 |
| - Recall: 0.1360 |
| - F1 Weighted: 0.0718 |
| - F1 Macro: 0.0015 |
|
|
| ## 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: 2e-05 |
| - train_batch_size: 32 |
| - eval_batch_size: 64 |
| - seed: 42 |
| - gradient_accumulation_steps: 2 |
| - total_train_batch_size: 64 |
| - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
| - lr_scheduler_type: cosine |
| - lr_scheduler_warmup_steps: 0.1 |
| - num_epochs: 10 |
| - mixed_precision_training: Native AMP |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | Top3 Accuracy | Top5 Accuracy | Precision | Recall | F1 Weighted | F1 Macro | |
| |:-------------:|:------:|:-----:|:---------------:|:--------:|:-------------:|:-------------:|:---------:|:------:|:-----------:|:--------:| |
| | 14.3746 | 0.0460 | 500 | 7.1874 | 0.0001 | 0.0004 | 0.0011 | 0.0000 | 0.0001 | 0.0000 | 0.0000 | |
| | 14.3475 | 0.0919 | 1000 | 7.1713 | 0.0002 | 0.0025 | 0.0065 | 0.0005 | 0.0002 | 0.0001 | 0.0000 | |
| | 14.2752 | 0.1379 | 1500 | 7.1294 | 0.0203 | 0.0284 | 0.0316 | 0.0005 | 0.0203 | 0.0008 | 0.0000 | |
| | 14.1118 | 0.1838 | 2000 | 7.0500 | 0.0203 | 0.0422 | 0.0553 | 0.0004 | 0.0203 | 0.0008 | 0.0000 | |
| | 13.9231 | 0.2298 | 2500 | 6.9662 | 0.0203 | 0.0424 | 0.0553 | 0.0004 | 0.0203 | 0.0008 | 0.0000 | |
| | 13.7866 | 0.2757 | 3000 | 6.9052 | 0.0207 | 0.0425 | 0.1148 | 0.0007 | 0.0207 | 0.0012 | 0.0001 | |
| | 13.5777 | 0.3217 | 3500 | 6.8596 | 0.0203 | 0.0948 | 0.2596 | 0.0037 | 0.0203 | 0.0008 | 0.0000 | |
| | 13.5243 | 0.3677 | 4000 | 6.8313 | 0.0318 | 0.1927 | 0.3050 | 0.0110 | 0.0318 | 0.0148 | 0.0002 | |
| | 13.4701 | 0.4136 | 4500 | 6.8140 | 0.0414 | 0.1453 | 0.3289 | 0.0017 | 0.0414 | 0.0033 | 0.0001 | |
| | 13.3691 | 0.4596 | 5000 | 6.8055 | 0.0469 | 0.0912 | 0.2763 | 0.0033 | 0.0469 | 0.0058 | 0.0002 | |
| | 13.4375 | 0.5055 | 5500 | 6.7851 | 0.0548 | 0.2899 | 0.4550 | 0.0301 | 0.0548 | 0.0224 | 0.0003 | |
| | 13.2271 | 0.5515 | 6000 | 6.7735 | 0.0422 | 0.2361 | 0.3570 | 0.0081 | 0.0422 | 0.0132 | 0.0003 | |
| | 13.1913 | 0.5975 | 6500 | 6.7579 | 0.1073 | 0.2300 | 0.3041 | 0.0181 | 0.1073 | 0.0307 | 0.0007 | |
| | 13.2021 | 0.6434 | 7000 | 6.7508 | 0.1209 | 0.2456 | 0.3223 | 0.0426 | 0.1209 | 0.0606 | 0.0008 | |
| | 13.0196 | 0.6894 | 7500 | 6.6944 | 0.1899 | 0.4371 | 0.5526 | 0.0781 | 0.1899 | 0.1057 | 0.0014 | |
| | 13.0377 | 0.7353 | 8000 | 6.6679 | 0.2436 | 0.4310 | 0.5533 | 0.1251 | 0.2436 | 0.1564 | 0.0024 | |
| | 12.7614 | 0.7813 | 8500 | 6.6127 | 0.1679 | 0.3864 | 0.5012 | 0.0542 | 0.1679 | 0.0799 | 0.0013 | |
| | 12.6990 | 0.8272 | 9000 | 6.5721 | 0.2370 | 0.4391 | 0.5454 | 0.1489 | 0.2370 | 0.1534 | 0.0023 | |
| | 12.5293 | 0.8732 | 9500 | 6.5496 | 0.1423 | 0.3454 | 0.4803 | 0.1231 | 0.1423 | 0.0857 | 0.0016 | |
| | 12.4603 | 0.9192 | 10000 | 6.5019 | 0.1150 | 0.2962 | 0.4191 | 0.1250 | 0.1150 | 0.0548 | 0.0013 | |
| | 12.5206 | 0.9651 | 10500 | 6.4139 | 0.1360 | 0.3559 | 0.4498 | 0.0977 | 0.1360 | 0.0718 | 0.0015 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 5.2.0 |
| - Pytorch 2.10.0+cu128 |
| - Datasets 4.5.0 |
| - Tokenizers 0.22.2 |
| |
