| --- |
| license: mit |
| base_model: microsoft/deberta-v3-large |
| tags: |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| model-index: |
| - name: Classifier_with_external_sets_02 |
| 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. --> |
|
|
| # Classifier_with_external_sets_02 |
|
|
| This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the None dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.6968 |
| - Accuracy: 0.5034 |
|
|
| ## 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 |
| - gradient_accumulation_steps: 4 |
| - total_train_batch_size: 32 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - num_epochs: 10 |
| - mixed_precision_training: Native AMP |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | |
| |:-------------:|:------:|:----:|:---------------:|:--------:| |
| | No log | 0.9983 | 289 | 0.6937 | 0.4966 | |
| | 0.6958 | 2.0 | 579 | 0.6958 | 0.4966 | |
| | 0.6958 | 2.9983 | 868 | 0.6972 | 0.4966 | |
| | 0.6845 | 4.0 | 1158 | 0.6931 | 0.5034 | |
| | 0.6845 | 4.9983 | 1447 | 0.7009 | 0.5034 | |
| | 0.6548 | 6.0 | 1737 | 0.7251 | 0.5034 | |
| | 0.6484 | 6.9983 | 2026 | 0.7186 | 0.5034 | |
| | 0.6484 | 8.0 | 2316 | 0.7049 | 0.5034 | |
| | 0.6453 | 8.9983 | 2605 | 0.6997 | 0.5034 | |
| | 0.6453 | 9.9827 | 2890 | 0.6968 | 0.5034 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.40.0 |
| - Pytorch 2.2.2+cu121 |
| - Datasets 2.19.0 |
| - Tokenizers 0.19.1 |
| |
