| --- |
| license: apache-2.0 |
| base_model: t5-base |
| tags: |
| - generated_from_trainer |
| metrics: |
| - bleu |
| - wer |
| model-index: |
| - name: randomization_model |
| 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. --> |
|
|
| # randomization_model |
| |
| This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the None dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 1.2766 |
| - Bleu: 0.0001 |
| - Wer: 0.9507 |
| - Rougel: 0.1324 |
| - Gen Len: 18.9988 |
| |
| ## 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: 8 |
| - eval_batch_size: 8 |
| - seed: 42 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - num_epochs: 2 |
| - mixed_precision_training: Native AMP |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Bleu | Wer | Rougel | Gen Len | |
| |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:-------:| |
| | 1.6112 | 0.16 | 1000 | 1.2949 | 0.0001 | 0.9506 | 0.1323 | 18.9988 | |
| | 1.5684 | 0.32 | 2000 | 1.2655 | 0.0001 | 0.9503 | 0.1328 | 18.9988 | |
| | 1.5378 | 0.48 | 3000 | 1.2461 | 0.0001 | 0.95 | 0.1334 | 18.9985 | |
| | 1.5183 | 0.64 | 4000 | 1.2246 | 0.0001 | 0.95 | 0.1334 | 18.9985 | |
| | 1.5065 | 0.8 | 5000 | 1.2108 | 0.0001 | 0.9499 | 0.1336 | 18.9985 | |
| | 1.4787 | 0.96 | 6000 | 1.1975 | 0.0001 | 0.9498 | 0.1338 | 18.9986 | |
| | 1.5081 | 1.12 | 7000 | 1.2173 | 0.0001 | 0.9498 | 0.134 | 18.9986 | |
| | 1.6302 | 1.28 | 8000 | 1.2801 | 0.0001 | 0.9507 | 0.1325 | 18.9988 | |
| | 1.6337 | 1.44 | 9000 | 1.2766 | 0.0001 | 0.9507 | 0.1324 | 18.9988 | |
| | 1.6333 | 1.6 | 10000 | 1.2766 | 0.0001 | 0.9507 | 0.1324 | 18.9988 | |
| | 1.6246 | 1.76 | 11000 | 1.2766 | 0.0001 | 0.9507 | 0.1324 | 18.9988 | |
| | 1.6219 | 1.92 | 12000 | 1.2766 | 0.0001 | 0.9507 | 0.1324 | 18.9988 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.37.1 |
| - Pytorch 2.3.0.dev20240122+cu121 |
| - Datasets 2.16.1 |
| - Tokenizers 0.15.1 |
| |
