| --- |
| tags: |
| - generated_from_trainer |
| metrics: |
| - bleu |
| model-index: |
| - name: mBART_translator_json_2 |
| 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. --> |
|
|
| # mBART_translator_json_2 |
| |
| This model is a fine-tuned version of [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25) on the None dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.1203 |
| - Bleu: 77.8658 |
| - Gen Len: 36.1527 |
| |
| ## 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: 10 |
| - mixed_precision_training: Native AMP |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |
| |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:| |
| | 1.7858 | 1.0 | 1912 | 0.6568 | 55.2937 | 75.6389 | |
| | 0.994 | 2.0 | 3824 | 0.4015 | 71.3655 | 35.744 | |
| | 0.7267 | 3.0 | 5736 | 0.2971 | 66.7522 | 34.5473 | |
| | 0.5916 | 4.0 | 7648 | 0.2437 | 80.0233 | 37.4331 | |
| | 0.502 | 5.0 | 9560 | 0.2072 | 80.9632 | 36.9833 | |
| | 0.433 | 6.0 | 11472 | 0.1767 | 69.9384 | 36.6381 | |
| | 0.3581 | 7.0 | 13384 | 0.1566 | 64.615 | 34.8954 | |
| | 0.3244 | 8.0 | 15296 | 0.1382 | 77.5563 | 36.1736 | |
| | 0.2815 | 9.0 | 17208 | 0.1259 | 76.1662 | 36.1548 | |
| | 0.2555 | 10.0 | 19120 | 0.1203 | 77.8658 | 36.1527 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.23.1 |
| - Pytorch 1.12.1+cu113 |
| - Datasets 2.5.2 |
| - Tokenizers 0.13.1 |
| |
