metadata
library_name: transformers
base_model: facebook/mbart-large-50-many-to-one-mmt
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: ae3f6f9a8fb3c68f9bee3e66321b3d68
results: []
ae3f6f9a8fb3c68f9bee3e66321b3d68
This model is a fine-tuned version of facebook/mbart-large-50-many-to-one-mmt on the Helsinki-NLP/opus_books [en-fi] dataset. It achieves the following results on the evaluation set:
- Loss: 3.8653
- Data Size: 1.0
- Epoch Runtime: 26.1910
- Bleu: 4.4308
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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- total_train_batch_size: 32
- total_eval_batch_size: 32
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: constant
- num_epochs: 50
Training results
| Training Loss | Epoch | Step | Validation Loss | Data Size | Epoch Runtime | Bleu |
|---|---|---|---|---|---|---|
| No log | 0 | 0 | 7.3951 | 0 | 2.4994 | 0.3194 |
| No log | 1 | 91 | 6.7682 | 0.0078 | 3.4514 | 0.2915 |
| No log | 2 | 182 | 6.2207 | 0.0156 | 4.5152 | 0.3771 |
| No log | 3 | 273 | 5.8200 | 0.0312 | 5.9047 | 0.4447 |
| No log | 4 | 364 | 5.3328 | 0.0625 | 7.6367 | 0.6466 |
| No log | 5 | 455 | 4.8644 | 0.125 | 8.9408 | 1.3717 |
| No log | 6 | 546 | 4.3926 | 0.25 | 11.7857 | 1.9901 |
| 0.469 | 7 | 637 | 3.9825 | 0.5 | 16.7760 | 2.5710 |
| 3.3066 | 8.0 | 728 | 3.5313 | 1.0 | 28.6812 | 3.2830 |
| 2.5231 | 9.0 | 819 | 3.3818 | 1.0 | 26.4448 | 3.4047 |
| 1.8958 | 10.0 | 910 | 3.4478 | 1.0 | 27.1888 | 3.7493 |
| 1.4316 | 11.0 | 1001 | 3.5424 | 1.0 | 27.6848 | 4.6297 |
| 0.987 | 12.0 | 1092 | 3.7127 | 1.0 | 26.6558 | 5.0275 |
| 0.695 | 13.0 | 1183 | 3.8653 | 1.0 | 26.1910 | 4.4308 |
Framework versions
- Transformers 4.57.0
- Pytorch 2.8.0+cu128
- Datasets 4.2.0
- Tokenizers 0.22.1