---
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