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--- |
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license: mit |
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base_model: cportoca/CS224S_Quechua_Project |
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tags: |
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- generated_from_trainer |
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metrics: |
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- wer |
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model-index: |
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- name: CS224S_Quechua_Project |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# CS224S_Quechua_Project |
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This model is a fine-tuned version of [cportoca/CS224S_Quechua_Project](https://huggingface.co/cportoca/CS224S_Quechua_Project) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.0264 |
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- Wer: 0.6160 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 4 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 8 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 70 |
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- num_epochs: 10 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Wer | |
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|:-------------:|:-----:|:----:|:---------------:|:------:| |
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| 0.4218 | 0.625 | 45 | 1.2929 | 0.8437 | |
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| 0.4233 | 1.25 | 90 | 1.3785 | 0.8580 | |
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| 0.4098 | 1.875 | 135 | 1.2656 | 0.8277 | |
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| 0.4212 | 2.5 | 180 | 1.1368 | 0.7781 | |
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| 0.3174 | 3.125 | 225 | 1.1210 | 0.8134 | |
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| 0.2819 | 3.75 | 270 | 1.0151 | 0.7221 | |
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| 0.2226 | 4.375 | 315 | 1.0450 | 0.7723 | |
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| 0.2152 | 5.0 | 360 | 1.0446 | 0.7100 | |
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| 0.2023 | 5.625 | 405 | 1.0544 | 0.7339 | |
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| 0.1547 | 6.25 | 450 | 1.0352 | 0.6932 | |
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| 0.1358 | 6.875 | 495 | 1.0490 | 0.6562 | |
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| 0.1229 | 7.5 | 540 | 1.0429 | 0.6500 | |
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| 0.079 | 8.125 | 585 | 0.9882 | 0.6532 | |
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| 0.0896 | 8.75 | 630 | 1.0109 | 0.6322 | |
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| 0.052 | 9.375 | 675 | 1.0006 | 0.6275 | |
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| 0.0515 | 10.0 | 720 | 1.0264 | 0.6160 | |
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### Framework versions |
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- Transformers 4.40.2 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
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