|
--- |
|
base_model: Samuael/geez_t5-15k |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- wer |
|
- bleu |
|
model-index: |
|
- name: geez_t5-15k |
|
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. --> |
|
|
|
# geez_t5-15k |
|
|
|
This model is a fine-tuned version of [Samuael/geez_t5-15k](https://huggingface.co/Samuael/geez_t5-15k) on an unknown dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.4822 |
|
- Wer: 0.2284 |
|
- Cer: 0.0780 |
|
- Bleu: 67.0096 |
|
|
|
## 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: 0.0002 |
|
- train_batch_size: 64 |
|
- eval_batch_size: 128 |
|
- 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 | Wer | Cer | Bleu | |
|
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:-------:| |
|
| 0.2496 | 1.0 | 313 | 0.3291 | 0.2456 | 0.1077 | 67.2734 | |
|
| 0.2573 | 2.0 | 626 | 0.3445 | 0.2488 | 0.1114 | 67.1612 | |
|
| 0.2368 | 3.0 | 939 | 0.3483 | 0.2075 | 0.0698 | 69.4248 | |
|
| 0.1459 | 4.0 | 1252 | 0.3815 | 0.2212 | 0.0806 | 68.5281 | |
|
| 0.1552 | 5.0 | 1565 | 0.4081 | 0.2372 | 0.0934 | 66.8604 | |
|
| 0.1031 | 6.0 | 1878 | 0.4143 | 0.2084 | 0.0692 | 69.6668 | |
|
| 0.1067 | 7.0 | 2191 | 0.4325 | 0.2305 | 0.0853 | 67.3479 | |
|
| 0.1111 | 8.0 | 2504 | 0.4380 | 0.2070 | 0.0638 | 68.9579 | |
|
| 0.0965 | 9.0 | 2817 | 0.4541 | 0.2195 | 0.0735 | 67.8519 | |
|
| 0.0843 | 10.0 | 3130 | 0.4822 | 0.2284 | 0.0780 | 67.0096 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.38.2 |
|
- Pytorch 2.2.1+cu121 |
|
- Datasets 2.18.0 |
|
- Tokenizers 0.15.2 |
|
|
