| --- |
| library_name: transformers |
| license: mit |
| base_model: Mardiyyah/cellate2.0-tapt_base-LR_5e-05 |
| tags: |
| - generated_from_trainer |
| metrics: |
| - precision |
| - recall |
| - f1 |
| - accuracy |
| model-index: |
| - name: no_vague_no_downsample |
| 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. --> |
|
|
| # no_vague_no_downsample |
| |
| This model is a fine-tuned version of [Mardiyyah/cellate2.0-tapt_base-LR_5e-05](https://huggingface.co/Mardiyyah/cellate2.0-tapt_base-LR_5e-05) on an unknown dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.0743 |
| - Precision: 0.7128 |
| - Recall: 0.7825 |
| - F1: 0.7460 |
| - Accuracy: 0.9815 |
| |
| ## 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: 16 |
| - seed: 3407 |
| - 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 |
| - lr_scheduler_warmup_ratio: 0.01 |
| - num_epochs: 20 |
| - mixed_precision_training: Native AMP |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
| |:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
| | 0.7697 | 0.4950 | 100 | 0.1502 | 0.3079 | 0.2172 | 0.2547 | 0.9608 | |
| | 0.1727 | 0.9901 | 200 | 0.1198 | 0.4065 | 0.6694 | 0.5058 | 0.9620 | |
| | 0.1057 | 1.4851 | 300 | 0.0818 | 0.7075 | 0.6856 | 0.6964 | 0.9804 | |
| | 0.0753 | 1.9802 | 400 | 0.0765 | 0.7167 | 0.7244 | 0.7205 | 0.9807 | |
| | 0.0555 | 2.4752 | 500 | 0.1019 | 0.3659 | 0.8505 | 0.5117 | 0.9471 | |
| | 0.0511 | 2.9703 | 600 | 0.0741 | 0.7128 | 0.7825 | 0.7460 | 0.9815 | |
| | 0.0381 | 3.4653 | 700 | 0.0898 | 0.7111 | 0.7458 | 0.7280 | 0.9811 | |
| | 0.0369 | 3.9604 | 800 | 0.0846 | 0.7078 | 0.7804 | 0.7423 | 0.9818 | |
| | 0.0295 | 4.4554 | 900 | 0.0919 | 0.6923 | 0.7723 | 0.7301 | 0.9809 | |
|
|
|
|
| ### Framework versions |
|
|
| - Transformers 4.48.2 |
| - Pytorch 2.4.1+cu121 |
| - Datasets 3.0.2 |
| - Tokenizers 0.21.0 |
|
|
