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---
library_name: transformers
language:
- sw
base_model: eolang/whisper-small-sw-WER-13-zindi
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: Whisper Small re-tuned_1
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. -->
# Whisper Small re-tuned_1
This model is a fine-tuned version of [eolang/whisper-small-sw-WER-13-zindi](https://huggingface.co/eolang/whisper-small-sw-WER-13-zindi) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8944
- Wer Ortho: 40.9465
- Wer: 39.9160
## 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: 16
- eval_batch_size: 16
- seed: 42
- 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_with_warmup
- lr_scheduler_warmup_steps: 50
- training_steps: 5000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer |
|:-------------:|:--------:|:----:|:---------------:|:---------:|:-------:|
| 0.0018 | 41.6667 | 500 | 1.6457 | 45.0617 | 43.6975 |
| 0.0011 | 83.3333 | 1000 | 1.7081 | 46.5021 | 45.3782 |
| 0.0009 | 125.0 | 1500 | 1.7469 | 67.9012 | 67.2269 |
| 0.0009 | 166.6667 | 2000 | 1.7766 | 68.9300 | 68.2773 |
| 0.0009 | 208.3333 | 2500 | 1.8003 | 70.3704 | 69.7479 |
| 0.0008 | 250.0 | 3000 | 1.8225 | 70.3704 | 69.7479 |
| 0.0008 | 291.6667 | 3500 | 1.8417 | 40.7407 | 39.9160 |
| 0.0008 | 333.3333 | 4000 | 1.8590 | 40.3292 | 39.4958 |
| 0.0008 | 375.0 | 4500 | 1.8773 | 40.9465 | 40.1261 |
| 0.0008 | 416.6667 | 5000 | 1.8944 | 40.9465 | 39.9160 |
### Framework versions
- Transformers 4.46.1
- Pytorch 2.5.1+cu121
- Datasets 3.3.2
- Tokenizers 0.20.3
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