---
library_name: transformers
base_model: cyttic/exp2-frozen-benyehuda-cont
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: trocr-bigram5-BY
  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. -->

# trocr-bigram5-BY

This model is a fine-tuned version of [cyttic/exp2-frozen-benyehuda-cont](https://huggingface.co/cyttic/exp2-frozen-benyehuda-cont) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5955
- Cer: 0.0292
- Wer: 0.0838

## 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: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED 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: 4650
- num_epochs: 3

### Training results

| Training Loss | Epoch  | Step  | Validation Loss | Cer    | Wer    |
|:-------------:|:------:|:-----:|:---------------:|:------:|:------:|
| 4.0314        | 0.1290 | 2000  | 1.7102          | 0.1973 | 0.3994 |
| 2.9555        | 0.2581 | 4000  | 1.4864          | 0.1415 | 0.3105 |
| 2.8902        | 0.3871 | 6000  | 1.2645          | 0.1040 | 0.2529 |
| 2.5335        | 0.5161 | 8000  | 1.1481          | 0.0841 | 0.2173 |
| 2.2147        | 0.6452 | 10000 | 1.0777          | 0.0772 | 0.2002 |
| 2.1987        | 0.7742 | 12000 | 0.9946          | 0.0666 | 0.1747 |
| 2.1294        | 0.9032 | 14000 | 0.9288          | 0.0605 | 0.1616 |
| 1.7200        | 1.0323 | 16000 | 0.8747          | 0.0541 | 0.1501 |
| 1.5009        | 1.1613 | 18000 | 0.8430          | 0.0500 | 0.1391 |
| 1.4939        | 1.2903 | 20000 | 0.7990          | 0.0451 | 0.1266 |
| 1.3924        | 1.4194 | 22000 | 0.7779          | 0.0426 | 0.1194 |
| 1.3807        | 1.5484 | 24000 | 0.7468          | 0.0431 | 0.1200 |
| 1.5015        | 1.6774 | 26000 | 0.7113          | 0.0355 | 0.1034 |
| 1.3585        | 1.8065 | 28000 | 0.6908          | 0.0350 | 0.1011 |
| 1.3697        | 1.9355 | 30000 | 0.6745          | 0.0359 | 0.1016 |
| 1.0135        | 2.0645 | 32000 | 0.6620          | 0.0346 | 0.0994 |
| 0.9966        | 2.1935 | 34000 | 0.6456          | 0.0349 | 0.0985 |
| 0.9714        | 2.3226 | 36000 | 0.6364          | 0.0324 | 0.0934 |
| 0.9632        | 2.4516 | 38000 | 0.6226          | 0.0307 | 0.0886 |
| 0.9263        | 2.5806 | 40000 | 0.6129          | 0.0303 | 0.0887 |
| 1.0649        | 2.7097 | 42000 | 0.6051          | 0.0298 | 0.0863 |
| 1.0234        | 2.8387 | 44000 | 0.5983          | 0.0285 | 0.0823 |
| 1.0024        | 2.9677 | 46000 | 0.5955          | 0.0293 | 0.0839 |
| 0.8827        | 3.0    | 46500 | 0.5955          | 0.0292 | 0.0838 |


### Framework versions

- Transformers 5.15.0
- Pytorch 2.11.0+cu128
- Datasets 5.0.1
- Tokenizers 0.22.2