| --- |
| license: apache-2.0 |
| base_model: t5-base |
| tags: |
| - generated_from_trainer |
| metrics: |
| - bleu |
| - wer |
| model-index: |
| - name: 10_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. --> |
|
|
| # 10_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: 0.1399 |
| - Bleu: 0.0001 |
| - Wer: 0.9311 |
| - Rougel: 0.1663 |
| - Gen Len: 18.9987 |
|
|
| ## 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 | |
| |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:-------:| |
| | 0.716 | 0.16 | 1000 | 0.2572 | 0.0001 | 0.932 | 0.1648 | 18.9987 | |
| | 0.2981 | 0.32 | 2000 | 0.2055 | 0.0001 | 0.9317 | 0.1655 | 18.9987 | |
| | 0.2596 | 0.48 | 3000 | 0.1836 | 0.0001 | 0.9315 | 0.1658 | 18.9987 | |
| | 0.2371 | 0.64 | 4000 | 0.1685 | 0.0001 | 0.9314 | 0.1659 | 18.9987 | |
| | 0.2266 | 0.8 | 5000 | 0.1616 | 0.0001 | 0.9313 | 0.1661 | 18.9987 | |
| | 0.2134 | 0.96 | 6000 | 0.1531 | 0.0001 | 0.9313 | 0.1662 | 18.9987 | |
| | 0.2035 | 1.12 | 7000 | 0.1505 | 0.0001 | 0.9312 | 0.1662 | 18.9987 | |
| | 0.1973 | 1.28 | 8000 | 0.1466 | 0.0001 | 0.9312 | 0.1663 | 18.9987 | |
| | 0.1942 | 1.44 | 9000 | 0.1430 | 0.0001 | 0.9312 | 0.1663 | 18.9987 | |
| | 0.1905 | 1.6 | 10000 | 0.1416 | 0.0001 | 0.9312 | 0.1663 | 18.9987 | |
| | 0.1892 | 1.76 | 11000 | 0.1402 | 0.0001 | 0.9312 | 0.1663 | 18.9987 | |
| | 0.1867 | 1.92 | 12000 | 0.1399 | 0.0001 | 0.9311 | 0.1663 | 18.9987 | |
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|
| ### Framework versions |
|
|
| - Transformers 4.37.1 |
| - Pytorch 2.3.0.dev20240122+cu121 |
| - Datasets 2.16.1 |
| - Tokenizers 0.15.1 |
|
|
