| --- |
| library_name: transformers |
| license: apache-2.0 |
| base_model: google/t5-efficient-small |
| tags: |
| - generated_from_trainer |
| datasets: |
| - generator |
| metrics: |
| - accuracy |
| model-index: |
| - name: t5_efficient_small_language_ID |
| results: |
| - task: |
| type: text2text-generation |
| name: Sequence-to-sequence Language Modeling |
| dataset: |
| name: generator |
| type: generator |
| config: default |
| split: train |
| args: default |
| metrics: |
| - type: accuracy |
| value: 0.6577572709259952 |
| name: Accuracy |
| --- |
| |
| <!-- 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_efficient_small_language_ID |
|
|
| This model is a fine-tuned version of [google/t5-efficient-small](https://huggingface.co/google/t5-efficient-small) on the generator dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.4285 |
| - Accuracy: 0.6578 |
| - F1 Macro: 0.5633 |
| - F1 Weighted: 0.6050 |
| - Precision Macro: 0.6452 |
| - Recall Macro: 0.6124 |
|
|
| ## 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 | F1 Macro | F1 Weighted | Precision Macro | Recall Macro | |
| |:-------------:|:------:|:----:|:---------------:|:--------:|:--------:|:-----------:|:---------------:|:------------:| |
| | 0.3649 | 0.0083 | 500 | 0.8746 | 0.2458 | 0.1941 | 0.2013 | 0.2757 | 0.2370 | |
| | 0.1204 | 0.0167 | 1000 | 0.8914 | 0.3442 | 0.2543 | 0.2637 | 0.4155 | 0.3319 | |
| | 0.0788 | 0.025 | 1500 | 1.0181 | 0.3853 | 0.3001 | 0.3001 | 0.4832 | 0.3853 | |
| | 0.0771 | 0.0333 | 2000 | 0.5361 | 0.5775 | 0.4982 | 0.5166 | 0.5265 | 0.5569 | |
| | 0.0737 | 0.0417 | 2500 | 0.6765 | 0.5442 | 0.4678 | 0.4851 | 0.5409 | 0.5248 | |
| | 0.0399 | 0.05 | 3000 | 0.6103 | 0.5444 | 0.4692 | 0.4866 | 0.5858 | 0.5250 | |
| | 0.0557 | 0.0583 | 3500 | 0.4436 | 0.6128 | 0.5453 | 0.5655 | 0.6635 | 0.5909 | |
| | 0.0963 | 0.0667 | 4000 | 0.4755 | 0.6027 | 0.5328 | 0.5526 | 0.6001 | 0.5812 | |
| | 0.0282 | 0.075 | 4500 | 0.4607 | 0.6347 | 0.5728 | 0.5728 | 0.6121 | 0.6347 | |
| | 0.0386 | 0.0833 | 5000 | 0.5344 | 0.6501 | 0.5574 | 0.5781 | 0.6186 | 0.6269 | |
| | 0.0355 | 0.0917 | 5500 | 0.4191 | 0.6575 | 0.5793 | 0.6008 | 0.6199 | 0.6340 | |
| | 0.0244 | 0.1 | 6000 | 0.4040 | 0.6802 | 0.5880 | 0.6316 | 0.6406 | 0.6333 | |
| | 0.0331 | 0.1083 | 6500 | 0.4438 | 0.6517 | 0.6053 | 0.6053 | 0.7090 | 0.6517 | |
| | 0.0224 | 0.1167 | 7000 | 0.4869 | 0.6649 | 0.5878 | 0.6096 | 0.6689 | 0.6412 | |
| | 0.0263 | 0.125 | 7500 | 0.4285 | 0.6578 | 0.5633 | 0.6050 | 0.6452 | 0.6124 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.57.1 |
| - Pytorch 2.9.0+cu128 |
| - Datasets 4.3.0 |
| - Tokenizers 0.22.1 |
| |
