| --- |
| library_name: transformers |
| base_model: facebook/mbart-large-50-many-to-many-mmt |
| tags: |
| - generated_from_trainer |
| metrics: |
| - sacrebleu |
| - bleu |
| - rouge |
| model-index: |
| - name: results |
| 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. --> |
|
|
| # results |
|
|
| This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on an unknown dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.1030 |
| - Sacrebleu: 36.1071 |
| - Bleu: 0.3611 |
| - Rouge1: 0.6827 |
| - Rouge2: 0.4557 |
| - Rougel: 0.6584 |
| - Rougelsum: 0.6574 |
| - Ter: 44.4372 |
|
|
| ## 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: 1e-05 |
| - train_batch_size: 4 |
| - eval_batch_size: 4 |
| - seed: 42 |
| - gradient_accumulation_steps: 4 |
| - total_train_batch_size: 16 |
| - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
| - lr_scheduler_type: linear |
| - lr_scheduler_warmup_steps: 100 |
| - num_epochs: 6 |
| - mixed_precision_training: Native AMP |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Sacrebleu | Bleu | Rouge1 | Rouge2 | Rougel | Rougelsum | Ter | |
| |:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:------:|:------:|:---------:|:-------:| |
| | 9.9972 | 0.3534 | 50 | 9.4988 | 19.6720 | 0.1967 | 0.5861 | 0.3417 | 0.5513 | 0.5522 | 58.0351 | |
| | 7.8177 | 0.7067 | 100 | 6.7801 | 26.8668 | 0.2687 | 0.6116 | 0.3719 | 0.5788 | 0.5795 | 53.6109 | |
| | 4.1506 | 1.0565 | 150 | 3.1994 | 28.3941 | 0.2839 | 0.6266 | 0.3892 | 0.5946 | 0.5955 | 52.1145 | |
| | 1.2625 | 1.4099 | 200 | 0.7019 | 32.1449 | 0.3214 | 0.6506 | 0.4209 | 0.6206 | 0.6210 | 49.5771 | |
| | 0.1995 | 1.7633 | 250 | 0.1521 | 32.0543 | 0.3205 | 0.6496 | 0.4085 | 0.6208 | 0.6198 | 48.5361 | |
| | 0.0878 | 2.1131 | 300 | 0.1119 | 32.5852 | 0.3259 | 0.6653 | 0.4304 | 0.6378 | 0.6376 | 48.2759 | |
| | 0.0726 | 2.4664 | 350 | 0.1039 | 35.1243 | 0.3512 | 0.6755 | 0.4455 | 0.6478 | 0.6474 | 45.2180 | |
| | 0.0672 | 2.8198 | 400 | 0.1007 | 36.5523 | 0.3655 | 0.6749 | 0.4555 | 0.6538 | 0.6541 | 44.6975 | |
| | 0.0475 | 3.1696 | 450 | 0.1030 | 36.1071 | 0.3611 | 0.6827 | 0.4557 | 0.6584 | 0.6574 | 44.4372 | |
|
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|
|
| ### Framework versions |
|
|
| - Transformers 4.47.0 |
| - Pytorch 2.1.2 |
| - Datasets 3.1.0 |
| - Tokenizers 0.21.0 |
|
|
