| --- |
| library_name: transformers |
| license: mit |
| base_model: somukandula/maskara-tiny |
| tags: |
| - generated_from_trainer |
| metrics: |
| - precision |
| - recall |
| - f1 |
| model-index: |
| - name: maskara-tiny-v2 |
| 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. --> |
|
|
| # maskara-tiny-v2 |
|
|
| This model is a fine-tuned version of [somukandula/maskara-tiny](https://huggingface.co/somukandula/maskara-tiny) on an unknown dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 1.1264 |
| - Precision: 0.0627 |
| - Recall: 0.2523 |
| - F1: 0.1004 |
|
|
| ## 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: 8e-06 |
| - train_batch_size: 32 |
| - eval_batch_size: 64 |
| - seed: 42 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - lr_scheduler_warmup_ratio: 0.06 |
| - num_epochs: 2 |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | |
| |:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:| |
| | 1.2386 | 0.0936 | 1000 | 1.4658 | 0.0466 | 0.1926 | 0.0751 | |
| | 1.1062 | 0.1873 | 2000 | 1.2917 | 0.0576 | 0.2296 | 0.0921 | |
| | 1.0024 | 0.2809 | 3000 | 1.2243 | 0.0605 | 0.2386 | 0.0966 | |
| | 1.0004 | 0.3745 | 4000 | 1.1891 | 0.0600 | 0.2425 | 0.0961 | |
| | 0.962 | 0.4682 | 5000 | 1.1587 | 0.0641 | 0.2515 | 0.1022 | |
| | 0.9539 | 0.5618 | 6000 | 1.1307 | 0.0627 | 0.2444 | 0.0998 | |
| | 0.9417 | 0.6554 | 7000 | 1.1264 | 0.0627 | 0.2523 | 0.1004 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.44.2 |
| - Pytorch 2.4.0+cu121 |
| - Datasets 2.21.0 |
| - Tokenizers 0.19.1 |
| |
