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---
license: mit
base_model: cportoca/CS224S_Quechua_Project
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: CS224S_Quechua_Project
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. -->
# CS224S_Quechua_Project
This model is a fine-tuned version of [cportoca/CS224S_Quechua_Project](https://huggingface.co/cportoca/CS224S_Quechua_Project) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0264
- Wer: 0.6160
## 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: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 70
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.4218 | 0.625 | 45 | 1.2929 | 0.8437 |
| 0.4233 | 1.25 | 90 | 1.3785 | 0.8580 |
| 0.4098 | 1.875 | 135 | 1.2656 | 0.8277 |
| 0.4212 | 2.5 | 180 | 1.1368 | 0.7781 |
| 0.3174 | 3.125 | 225 | 1.1210 | 0.8134 |
| 0.2819 | 3.75 | 270 | 1.0151 | 0.7221 |
| 0.2226 | 4.375 | 315 | 1.0450 | 0.7723 |
| 0.2152 | 5.0 | 360 | 1.0446 | 0.7100 |
| 0.2023 | 5.625 | 405 | 1.0544 | 0.7339 |
| 0.1547 | 6.25 | 450 | 1.0352 | 0.6932 |
| 0.1358 | 6.875 | 495 | 1.0490 | 0.6562 |
| 0.1229 | 7.5 | 540 | 1.0429 | 0.6500 |
| 0.079 | 8.125 | 585 | 0.9882 | 0.6532 |
| 0.0896 | 8.75 | 630 | 1.0109 | 0.6322 |
| 0.052 | 9.375 | 675 | 1.0006 | 0.6275 |
| 0.0515 | 10.0 | 720 | 1.0264 | 0.6160 |
### Framework versions
- Transformers 4.40.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
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