| --- |
| library_name: transformers |
| license: apache-2.0 |
| base_model: yigagilbert/t5_efficient_small_language_ID |
| tags: |
| - generated_from_trainer |
| datasets: |
| - generator |
| metrics: |
| - accuracy |
| - precision |
| - recall |
| - f1 |
| model-index: |
| - name: t5_small_language_Classification |
| results: |
| - task: |
| type: text-classification |
| name: Text Classification |
| dataset: |
| name: generator |
| type: generator |
| config: default |
| split: train |
| args: default |
| metrics: |
| - type: accuracy |
| value: 0.658879605381663 |
| name: Accuracy |
| - type: precision |
| value: 0.6928469419086497 |
| name: Precision |
| - type: recall |
| value: 0.658879605381663 |
| name: Recall |
| - type: f1 |
| value: 0.6286369104782076 |
| name: F1 |
| --- |
| |
| <!-- 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. --> |
|
|
| # t5_small_language_Classification |
| |
| This model is a fine-tuned version of [yigagilbert/t5_efficient_small_language_ID](https://huggingface.co/yigagilbert/t5_efficient_small_language_ID) on the generator dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 1.6482 |
| - Accuracy: 0.6589 |
| - Precision: 0.6928 |
| - Recall: 0.6589 |
| - F1: 0.6286 |
| |
| ## 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: 0.0005 |
| - train_batch_size: 64 |
| - eval_batch_size: 64 |
| - seed: 42 |
| - gradient_accumulation_steps: 2 |
| - total_train_batch_size: 128 |
| - optimizer: Use adamw_torch_fused with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
| - lr_scheduler_type: cosine_with_restarts |
| - lr_scheduler_warmup_steps: 1000 |
| - training_steps: 60000 |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |
| |:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| |
| | 0.6453 | 0.0083 | 500 | 1.7792 | 0.5575 | 0.6272 | 0.5575 | 0.5283 | |
| | 0.3701 | 0.0167 | 1000 | 2.8566 | 0.4925 | 0.6309 | 0.4925 | 0.4427 | |
| | 0.3602 | 0.025 | 1500 | 3.4108 | 0.4331 | 0.6188 | 0.4331 | 0.3903 | |
| | 0.3573 | 0.0333 | 2000 | 1.9821 | 0.5855 | 0.6303 | 0.5855 | 0.5419 | |
| | 0.4229 | 0.0417 | 2500 | 1.9248 | 0.6071 | 0.6712 | 0.6071 | 0.5731 | |
| | 0.2156 | 0.05 | 3000 | 2.6673 | 0.5217 | 0.6906 | 0.5217 | 0.4851 | |
| | 0.3752 | 0.0583 | 3500 | 1.9381 | 0.5984 | 0.6682 | 0.5984 | 0.5619 | |
| | 0.4996 | 0.0667 | 4000 | 1.5622 | 0.6266 | 0.6757 | 0.6266 | 0.6022 | |
| | 0.2773 | 0.075 | 4500 | 1.8355 | 0.6299 | 0.6892 | 0.6299 | 0.5872 | |
| | 0.2815 | 0.0833 | 5000 | 1.7752 | 0.6423 | 0.6905 | 0.6423 | 0.6034 | |
| | 0.2525 | 0.0917 | 5500 | 1.6552 | 0.6450 | 0.6879 | 0.6450 | 0.6082 | |
| | 0.2271 | 0.1 | 6000 | 1.6523 | 0.6575 | 0.6916 | 0.6575 | 0.6278 | |
| | 0.3591 | 0.1083 | 6500 | 1.7169 | 0.6542 | 0.6985 | 0.6542 | 0.6238 | |
| | 0.2659 | 0.1167 | 7000 | 1.7209 | 0.6439 | 0.7090 | 0.6439 | 0.6180 | |
| | 0.2337 | 0.125 | 7500 | 1.7631 | 0.6531 | 0.7019 | 0.6531 | 0.6158 | |
|
|
|
|
| ### Framework versions |
|
|
| - Transformers 4.57.1 |
| - Pytorch 2.9.0+cu128 |
| - Datasets 4.3.0 |
| - Tokenizers 0.22.1 |
|
|
