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--- |
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base_model: Samuael/geez_t5-15k |
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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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- bleu |
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model-index: |
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- name: geez_t5-15k |
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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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# geez_t5-15k |
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This model is a fine-tuned version of [Samuael/geez_t5-15k](https://huggingface.co/Samuael/geez_t5-15k) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.6541 |
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- Wer: 0.2404 |
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- Cer: 0.0668 |
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- Bleu: 54.5297 |
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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: 0.0002 |
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- train_batch_size: 64 |
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- eval_batch_size: 128 |
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- seed: 42 |
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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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- 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 | Cer | Bleu | |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:-------:| |
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| 0.0382 | 1.0 | 313 | 1.4979 | 0.2436 | 0.0667 | 55.2918 | |
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| 0.0411 | 2.0 | 626 | 1.5980 | 0.2419 | 0.0653 | 55.4560 | |
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| 0.0622 | 3.0 | 939 | 1.5221 | 0.2383 | 0.0683 | 55.5429 | |
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| 0.0505 | 4.0 | 1252 | 1.5719 | 0.2388 | 0.0662 | 55.6735 | |
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| 0.0517 | 5.0 | 1565 | 1.6251 | 0.2373 | 0.0666 | 55.7046 | |
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| 0.0396 | 6.0 | 1878 | 1.5978 | 0.2431 | 0.0680 | 54.5671 | |
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| 0.0512 | 7.0 | 2191 | 1.5677 | 0.2675 | 0.1019 | 54.3154 | |
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| 0.0655 | 8.0 | 2504 | 1.5891 | 0.2448 | 0.0676 | 54.6644 | |
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| 0.05 | 9.0 | 2817 | 1.5952 | 0.2470 | 0.0703 | 54.7796 | |
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| 0.0415 | 10.0 | 3130 | 1.6541 | 0.2404 | 0.0668 | 54.5297 | |
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### Framework versions |
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- Transformers 4.38.2 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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