| --- |
| library_name: transformers |
| license: apache-2.0 |
| base_model: google-t5/t5-base |
| tags: |
| - generated_from_trainer |
| datasets: |
| - generator |
| metrics: |
| - accuracy |
| model-index: |
| - name: google_t5_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: 0.6179074697593216 |
| 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. --> |
|
|
| # google_t5_language_ID |
| |
| This model is a fine-tuned version of [google-t5/t5-base](https://huggingface.co/google-t5/t5-base) on the generator dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.5429 |
| - Accuracy: 0.6179 |
| - F1 Macro: 0.3389 |
| - F1 Weighted: 0.5774 |
| - Precision Macro: 0.3873 |
| - Recall Macro: 0.3627 |
| |
| ## 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 | |
| |:-------------:|:------:|:----:|:---------------:|:--------:|:--------:|:-----------:|:---------------:|:------------:| |
| | 0.1943 | 0.0083 | 500 | 0.6981 | 0.4018 | 0.3139 | 0.3488 | 0.4624 | 0.3616 | |
| | 0.0812 | 0.0167 | 1000 | 0.7371 | 0.4086 | 0.3323 | 0.3446 | 0.5179 | 0.3940 | |
| | 0.049 | 0.025 | 1500 | 0.7806 | 0.4534 | 0.3793 | 0.3793 | 0.5316 | 0.4534 | |
| | 0.0518 | 0.0333 | 2000 | 0.5042 | 0.5845 | 0.5071 | 0.5258 | 0.5576 | 0.5637 | |
| | 0.0452 | 0.0417 | 2500 | 0.5120 | 0.6204 | 0.5554 | 0.5554 | 0.6496 | 0.6204 | |
| | 0.0288 | 0.05 | 3000 | 0.4798 | 0.6018 | 0.5230 | 0.5618 | 0.6077 | 0.5603 | |
| | 0.0341 | 0.0583 | 3500 | 0.4764 | 0.6098 | 0.5456 | 0.5658 | 0.6528 | 0.5881 | |
| | 0.0762 | 0.0667 | 4000 | 0.4389 | 0.6251 | 0.5296 | 0.5688 | 0.6091 | 0.5820 | |
| | 0.0189 | 0.075 | 4500 | 0.4167 | 0.6681 | 0.6068 | 0.6068 | 0.7167 | 0.6681 | |
| | 0.0235 | 0.0833 | 5000 | 0.4673 | 0.6599 | 0.6018 | 0.6018 | 0.7393 | 0.6599 | |
| | 0.0274 | 0.0917 | 5500 | 0.3304 | 0.6958 | 0.6102 | 0.6555 | 0.6868 | 0.6478 | |
| | 0.0198 | 0.1 | 6000 | 0.4752 | 0.6569 | 0.5877 | 0.6095 | 0.7165 | 0.6335 | |
| | 0.0246 | 0.1083 | 6500 | 0.4657 | 0.6540 | 0.5800 | 0.6015 | 0.6400 | 0.6306 | |
| | 0.0241 | 0.1167 | 7000 | 0.5429 | 0.6179 | 0.3389 | 0.5774 | 0.3873 | 0.3627 | |
|
|
|
|
| ### Framework versions |
|
|
| - Transformers 4.57.1 |
| - Pytorch 2.9.0+cu128 |
| - Datasets 4.3.0 |
| - Tokenizers 0.22.1 |
|
|
