| --- |
| library_name: transformers |
| license: mit |
| base_model: microsoft/deberta-base |
| tags: |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| model-index: |
| - name: deberta_textclassification |
| 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. --> |
|
|
| # deberta_textclassification |
| |
| This model is a fine-tuned version of [microsoft/deberta-base](https://huggingface.co/microsoft/deberta-base) on the None dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.3436 |
| - Accuracy: 0.9520 |
| |
| ## 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: 8 |
| - eval_batch_size: 8 |
| - seed: 42 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - num_epochs: 5 |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | |
| |:-------------:|:------:|:----:|:---------------:|:--------:| |
| | 0.3919 | 0.2924 | 500 | 0.2821 | 0.9184 | |
| | 0.274 | 0.5848 | 1000 | 0.2795 | 0.9362 | |
| | 0.2552 | 0.8772 | 1500 | 0.2469 | 0.9355 | |
| | 0.2148 | 1.1696 | 2000 | 0.3214 | 0.9421 | |
| | 0.1876 | 1.4620 | 2500 | 0.2636 | 0.9382 | |
| | 0.1505 | 1.7544 | 3000 | 0.2323 | 0.9467 | |
| | 0.1411 | 2.0468 | 3500 | 0.3445 | 0.9395 | |
| | 0.0676 | 2.3392 | 4000 | 0.3280 | 0.9414 | |
| | 0.1089 | 2.6316 | 4500 | 0.4225 | 0.9270 | |
| | 0.0888 | 2.9240 | 5000 | 0.2458 | 0.9520 | |
| | 0.0544 | 3.2164 | 5500 | 0.2877 | 0.9539 | |
| | 0.0366 | 3.5088 | 6000 | 0.3010 | 0.9553 | |
| | 0.0322 | 3.8012 | 6500 | 0.3508 | 0.9474 | |
| | 0.0313 | 4.0936 | 7000 | 0.3302 | 0.9520 | |
| | 0.0191 | 4.3860 | 7500 | 0.3527 | 0.9493 | |
| | 0.0118 | 4.6784 | 8000 | 0.3378 | 0.9513 | |
| | 0.0189 | 4.9708 | 8500 | 0.3436 | 0.9520 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.44.2 |
| - Pytorch 2.4.1+cu121 |
| - Datasets 2.4.0 |
| - Tokenizers 0.19.1 |
| |
