| --- |
| library_name: transformers |
| license: mit |
| base_model: microsoft/deberta-v3-small |
| tags: |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| - f1 |
| - precision |
| - recall |
| model-index: |
| - name: doc-topic-model_eval-00_train-03 |
| 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. --> |
|
|
| # doc-topic-model_eval-00_train-03 |
|
|
| This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on an unknown dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.0381 |
| - Accuracy: 0.9878 |
| - F1: 0.6228 |
| - Precision: 0.7288 |
| - Recall: 0.5437 |
|
|
| ## 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: 4 |
| - eval_batch_size: 256 |
| - seed: 42 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - num_epochs: 100 |
| - mixed_precision_training: Native AMP |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |
| |:-------------:|:------:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:| |
| | 0.0935 | 0.4931 | 1000 | 0.0895 | 0.9815 | 0.0 | 0.0 | 0.0 | |
| | 0.0764 | 0.9862 | 2000 | 0.0700 | 0.9815 | 0.0 | 0.0 | 0.0 | |
| | 0.0621 | 1.4793 | 3000 | 0.0567 | 0.9821 | 0.0730 | 0.8925 | 0.0381 | |
| | 0.0542 | 1.9724 | 4000 | 0.0497 | 0.9841 | 0.2891 | 0.8391 | 0.1747 | |
| | 0.0468 | 2.4655 | 5000 | 0.0465 | 0.9853 | 0.4216 | 0.7739 | 0.2897 | |
| | 0.0441 | 2.9586 | 6000 | 0.0435 | 0.9861 | 0.4879 | 0.7667 | 0.3578 | |
| | 0.0395 | 3.4517 | 7000 | 0.0417 | 0.9862 | 0.5322 | 0.7197 | 0.4222 | |
| | 0.0384 | 3.9448 | 8000 | 0.0401 | 0.9866 | 0.5600 | 0.7182 | 0.4589 | |
| | 0.0343 | 4.4379 | 9000 | 0.0393 | 0.9870 | 0.5789 | 0.7217 | 0.4833 | |
| | 0.0337 | 4.9310 | 10000 | 0.0378 | 0.9873 | 0.5907 | 0.7358 | 0.4934 | |
| | 0.0305 | 5.4241 | 11000 | 0.0375 | 0.9875 | 0.5960 | 0.7457 | 0.4963 | |
| | 0.0295 | 5.9172 | 12000 | 0.0378 | 0.9874 | 0.6050 | 0.7213 | 0.5210 | |
| | 0.0271 | 6.4103 | 13000 | 0.0376 | 0.9877 | 0.6048 | 0.7457 | 0.5087 | |
| | 0.0257 | 6.9034 | 14000 | 0.0379 | 0.9875 | 0.6068 | 0.7269 | 0.5208 | |
| | 0.0234 | 7.3964 | 15000 | 0.0377 | 0.9876 | 0.6246 | 0.7108 | 0.5571 | |
| | 0.0241 | 7.8895 | 16000 | 0.0381 | 0.9878 | 0.6228 | 0.7288 | 0.5437 | |
|
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|
|
| ### Framework versions |
|
|
| - Transformers 4.44.2 |
| - Pytorch 2.4.1+cu121 |
| - Datasets 2.21.0 |
| - Tokenizers 0.19.1 |
|
|
