|
--- |
|
license: cc-by-nc-4.0 |
|
base_model: KevinKibe/nllb-200-distilled-1.3B-finetuned |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- rouge |
|
model-index: |
|
- name: nllb-200-distilled-1.3B-finetuned-finetuned |
|
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. --> |
|
|
|
# nllb-200-distilled-1.3B-finetuned-finetuned |
|
|
|
This model is a fine-tuned version of [KevinKibe/nllb-200-distilled-1.3B-finetuned](https://huggingface.co/KevinKibe/nllb-200-distilled-1.3B-finetuned) on an unknown dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.7134 |
|
- Rouge: 0.467 |
|
- Gen Len: 49.0 |
|
|
|
## 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 |
|
- training_steps: 1000 |
|
- mixed_precision_training: Native AMP |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Rouge | Gen Len | |
|
|:-------------:|:------:|:----:|:---------------:|:------:|:-------:| |
|
| 6.4789 | 100.0 | 100 | 5.8836 | 0.082 | 32.5 | |
|
| 4.8961 | 200.0 | 200 | 4.8026 | 0.1552 | 29.0 | |
|
| 3.636 | 300.0 | 300 | 3.5860 | 0.0292 | 63.0 | |
|
| 2.4528 | 400.0 | 400 | 2.4531 | 0.2018 | 28.0 | |
|
| 1.4112 | 500.0 | 500 | 1.6682 | 0.1899 | 70.0 | |
|
| 0.6738 | 600.0 | 600 | 1.1858 | 0.1907 | 68.0 | |
|
| 0.2921 | 700.0 | 700 | 0.8546 | 0.2776 | 28.0 | |
|
| 0.1361 | 800.0 | 800 | 0.7109 | 0.3649 | 59.5 | |
|
| 0.0764 | 900.0 | 900 | 0.7293 | 0.4568 | 54.0 | |
|
| 0.0559 | 1000.0 | 1000 | 0.7134 | 0.467 | 49.0 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.39.2 |
|
- Pytorch 2.2.2+cu121 |
|
- Datasets 2.21.0 |
|
- Tokenizers 0.15.2 |
|
|
