|
--- |
|
license: apache-2.0 |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- bleu |
|
model-index: |
|
- name: toki-en-mt |
|
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. --> |
|
|
|
# toki-en-mt |
|
|
|
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-ROMANCE-en](https://huggingface.co/Helsinki-NLP/opus-mt-ROMANCE-en) on an unknown dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 1.2840 |
|
- Bleu: 26.7612 |
|
- Gen Len: 9.0631 |
|
|
|
## 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: 16 |
|
- eval_batch_size: 16 |
|
- 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 | |
|
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:| |
|
| 1.7228 | 1.0 | 1260 | 1.4572 | 19.9464 | 9.2177 | |
|
| 1.3182 | 2.0 | 2520 | 1.3356 | 22.4628 | 9.1263 | |
|
| 1.1241 | 3.0 | 3780 | 1.3028 | 23.5152 | 9.0462 | |
|
| 0.9995 | 4.0 | 5040 | 1.2784 | 23.9526 | 9.1697 | |
|
| 0.8945 | 5.0 | 6300 | 1.2739 | 24.7707 | 9.0914 | |
|
| 0.8331 | 6.0 | 7560 | 1.2725 | 25.3477 | 9.0518 | |
|
| 0.7641 | 7.0 | 8820 | 1.2770 | 26.165 | 9.0245 | |
|
| 0.7163 | 8.0 | 10080 | 1.2809 | 25.8053 | 9.0933 | |
|
| 0.6886 | 9.0 | 11340 | 1.2799 | 26.5752 | 9.0669 | |
|
| 0.6627 | 10.0 | 12600 | 1.2840 | 26.7612 | 9.0631 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.20.1 |
|
- Pytorch 1.11.0 |
|
- Datasets 2.3.2 |
|
- Tokenizers 0.12.1 |
|
|
