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---
library_name: transformers
license: apache-2.0
base_model: google/byt5-small
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: byt5-small-finetuned-yiddish-experiment-8
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. -->
# byt5-small-finetuned-yiddish-experiment-8
This model is a fine-tuned version of [google/byt5-small](https://huggingface.co/google/byt5-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3482
- Cer: 0.1504
- Wer: 0.4654
## 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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 600
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Cer | Wer |
|:-------------:|:-------:|:----:|:---------------:|:------:|:------:|
| 10.7996 | 0.4728 | 100 | 10.9325 | 0.2905 | 0.7232 |
| 7.586 | 0.9456 | 200 | 10.5771 | 0.2698 | 0.6850 |
| 8.641 | 1.4161 | 300 | 10.0041 | 0.2570 | 0.6571 |
| 8.2901 | 1.8889 | 400 | 9.1435 | 0.2478 | 0.6396 |
| 8.076 | 2.3593 | 500 | 8.1677 | 0.2394 | 0.6277 |
| 7.8061 | 2.8322 | 600 | 7.0784 | 0.2317 | 0.6142 |
| 5.6823 | 3.3026 | 700 | 6.0599 | 0.2232 | 0.6094 |
| 5.3586 | 3.7754 | 800 | 5.1075 | 0.2181 | 0.6038 |
| 4.9348 | 4.2459 | 900 | 4.2898 | 0.2155 | 0.6038 |
| 3.9539 | 4.7187 | 1000 | 3.6152 | 0.2119 | 0.5967 |
| 3.5873 | 5.1891 | 1100 | 2.9509 | 0.2096 | 0.5935 |
| 2.9099 | 5.6619 | 1200 | 2.4046 | 0.2062 | 0.5903 |
| 2.3472 | 6.1324 | 1300 | 1.9122 | 0.2044 | 0.5911 |
| 1.9884 | 6.6052 | 1400 | 1.4625 | 0.2007 | 0.5792 |
| 1.7857 | 7.0757 | 1500 | 1.2051 | 0.1973 | 0.5744 |
| 1.4299 | 7.5485 | 1600 | 1.1644 | 0.1950 | 0.5712 |
| 1.2853 | 8.0189 | 1700 | 1.1406 | 0.1928 | 0.5696 |
| 1.1917 | 8.4917 | 1800 | 1.0735 | 0.1910 | 0.5680 |
| 1.0714 | 8.9645 | 1900 | 0.9061 | 0.1910 | 0.5680 |
| 0.8871 | 9.4350 | 2000 | 0.7903 | 0.1684 | 0.4996 |
| 0.8589 | 9.9078 | 2100 | 0.7640 | 0.1667 | 0.4964 |
| 0.8172 | 10.3783 | 2200 | 0.7431 | 0.1646 | 0.4940 |
| 0.7284 | 10.8511 | 2300 | 0.7017 | 0.1622 | 0.4893 |
| 0.7358 | 11.3215 | 2400 | 0.6680 | 0.1613 | 0.4869 |
| 0.6926 | 11.7943 | 2500 | 0.6318 | 0.1595 | 0.4813 |
| 0.6425 | 12.2648 | 2600 | 0.5897 | 0.1601 | 0.4837 |
| 0.6201 | 12.7376 | 2700 | 0.5611 | 0.1585 | 0.4797 |
| 0.5984 | 13.2080 | 2800 | 0.5155 | 0.1585 | 0.4837 |
| 0.5619 | 13.6809 | 2900 | 0.4781 | 0.1575 | 0.4797 |
| 0.5316 | 14.1513 | 3000 | 0.4500 | 0.1562 | 0.4773 |
| 0.5086 | 14.6241 | 3100 | 0.4255 | 0.1558 | 0.4757 |
| 0.4776 | 15.0946 | 3200 | 0.4101 | 0.1551 | 0.4757 |
| 0.4841 | 15.5674 | 3300 | 0.4005 | 0.1558 | 0.4765 |
| 0.4533 | 16.0378 | 3400 | 0.3891 | 0.1544 | 0.4741 |
| 0.4599 | 16.5106 | 3500 | 0.3794 | 0.1542 | 0.4749 |
| 0.435 | 16.9835 | 3600 | 0.3801 | 0.1538 | 0.4718 |
| 0.4272 | 17.4539 | 3700 | 0.3748 | 0.1541 | 0.4718 |
| 0.4327 | 17.9267 | 3800 | 0.3685 | 0.1536 | 0.4718 |
| 0.418 | 18.3972 | 3900 | 0.3682 | 0.1542 | 0.4741 |
| 0.4082 | 18.8700 | 4000 | 0.3671 | 0.1541 | 0.4718 |
| 0.406 | 19.3404 | 4100 | 0.3625 | 0.1530 | 0.4694 |
| 0.4079 | 19.8132 | 4200 | 0.3605 | 0.1522 | 0.4686 |
| 0.3961 | 20.2837 | 4300 | 0.3592 | 0.1517 | 0.4678 |
| 0.3913 | 20.7565 | 4400 | 0.3575 | 0.1516 | 0.4678 |
| 0.391 | 21.2270 | 4500 | 0.3566 | 0.1514 | 0.4686 |
| 0.3865 | 21.6998 | 4600 | 0.3564 | 0.1507 | 0.4662 |
| 0.3884 | 22.1702 | 4700 | 0.3541 | 0.1510 | 0.4654 |
| 0.3855 | 22.6430 | 4800 | 0.3533 | 0.1508 | 0.4654 |
| 0.3794 | 23.1135 | 4900 | 0.3511 | 0.1508 | 0.4662 |
| 0.3926 | 23.5863 | 5000 | 0.3497 | 0.1507 | 0.4662 |
| 0.3802 | 24.0567 | 5100 | 0.3497 | 0.1508 | 0.4654 |
| 0.3798 | 24.5296 | 5200 | 0.3490 | 0.1508 | 0.4662 |
| 0.3722 | 25.0 | 5300 | 0.3489 | 0.1510 | 0.4654 |
| 0.3824 | 25.4728 | 5400 | 0.3484 | 0.1505 | 0.4654 |
| 0.3729 | 25.9456 | 5500 | 0.3482 | 0.1504 | 0.4654 |
| 0.3635 | 26.4161 | 5600 | 0.3486 | 0.1505 | 0.4654 |
| 0.3834 | 26.8889 | 5700 | 0.3475 | 0.1505 | 0.4654 |
| 0.3692 | 27.3593 | 5800 | 0.3470 | 0.1505 | 0.4654 |
| 0.3722 | 27.8322 | 5900 | 0.3466 | 0.1504 | 0.4654 |
| 0.3657 | 28.3026 | 6000 | 0.3461 | 0.1505 | 0.4654 |
| 0.3729 | 28.7754 | 6100 | 0.3466 | 0.1505 | 0.4646 |
| 0.3632 | 29.2459 | 6200 | 0.3464 | 0.1505 | 0.4646 |
| 0.372 | 29.7187 | 6300 | 0.3464 | 0.1504 | 0.4646 |
### Framework versions
- Transformers 4.47.0
- Pytorch 2.5.1+cu121
- Datasets 2.14.4
- Tokenizers 0.21.0
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