trocr-wf-pilot-ellinia-byp
This model is a fine-tuned version of cyttic/exp2-frozen-benyehuda-cont on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.3663
- Cer Raw: 0.0184
- Wer Raw: 0.0542
- Cer Norm: 0.0184
- Wer Norm: 0.0542
- Cer Let: 0.0188
- Wer Let: 0.0557
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 Raw | Wer Raw | Cer Norm | Wer Norm | Cer Let | Wer Let |
|---|---|---|---|---|---|---|---|---|---|
| 2.7661 | 0.1290 | 2000 | 1.2211 | 0.1204 | 0.2853 | 0.1204 | 0.2853 | 0.1184 | 0.2780 |
| 2.2384 | 0.2581 | 4000 | 0.9848 | 0.0835 | 0.2083 | 0.0835 | 0.2083 | 0.0853 | 0.2147 |
| 1.9904 | 0.3871 | 6000 | 0.8762 | 0.0642 | 0.1715 | 0.0642 | 0.1715 | 0.0659 | 0.1781 |
| 1.7989 | 0.5161 | 8000 | 0.8141 | 0.0611 | 0.1581 | 0.0611 | 0.1581 | 0.0624 | 0.1637 |
| 1.6579 | 0.6452 | 10000 | 0.7075 | 0.0444 | 0.1226 | 0.0444 | 0.1226 | 0.0458 | 0.1281 |
| 1.5060 | 0.7742 | 12000 | 0.6551 | 0.0414 | 0.1144 | 0.0414 | 0.1144 | 0.0427 | 0.1195 |
| 1.3796 | 0.9032 | 14000 | 0.6056 | 0.0355 | 0.1008 | 0.0355 | 0.1008 | 0.0364 | 0.1052 |
| 1.0286 | 1.0323 | 16000 | 0.5895 | 0.0376 | 0.1004 | 0.0376 | 0.1004 | 0.0387 | 0.1047 |
| 0.9954 | 1.1613 | 18000 | 0.5418 | 0.0320 | 0.0885 | 0.0320 | 0.0885 | 0.0330 | 0.0922 |
| 0.9543 | 1.2903 | 20000 | 0.5124 | 0.0305 | 0.0857 | 0.0305 | 0.0857 | 0.0314 | 0.0895 |
| 1.0751 | 1.4194 | 22000 | 0.4943 | 0.0277 | 0.0778 | 0.0277 | 0.0778 | 0.0284 | 0.0804 |
| 0.9025 | 1.5484 | 24000 | 0.4708 | 0.0246 | 0.0697 | 0.0246 | 0.0697 | 0.0253 | 0.0722 |
| 0.8890 | 1.6774 | 26000 | 0.4488 | 0.0255 | 0.0730 | 0.0255 | 0.0730 | 0.0261 | 0.0754 |
| 0.8902 | 1.8065 | 28000 | 0.4371 | 0.0230 | 0.0669 | 0.0230 | 0.0669 | 0.0235 | 0.0691 |
| 0.8134 | 1.9355 | 30000 | 0.4213 | 0.0232 | 0.0669 | 0.0232 | 0.0669 | 0.0237 | 0.0686 |
| 0.6284 | 2.0645 | 32000 | 0.4115 | 0.0221 | 0.0651 | 0.0221 | 0.0651 | 0.0226 | 0.0670 |
| 0.6568 | 2.1935 | 34000 | 0.4001 | 0.0208 | 0.0603 | 0.0208 | 0.0603 | 0.0211 | 0.0618 |
| 0.6354 | 2.3226 | 36000 | 0.3942 | 0.0206 | 0.0594 | 0.0206 | 0.0594 | 0.0209 | 0.0608 |
| 0.6238 | 2.4516 | 38000 | 0.3873 | 0.0211 | 0.0607 | 0.0211 | 0.0607 | 0.0216 | 0.0623 |
| 0.5749 | 2.5806 | 40000 | 0.3788 | 0.0200 | 0.0574 | 0.0200 | 0.0574 | 0.0205 | 0.0590 |
| 0.6141 | 2.7097 | 42000 | 0.3730 | 0.0187 | 0.0550 | 0.0187 | 0.0550 | 0.0191 | 0.0564 |
| 0.6050 | 2.8387 | 44000 | 0.3682 | 0.0195 | 0.0557 | 0.0195 | 0.0557 | 0.0200 | 0.0573 |
| 0.5947 | 2.9677 | 46000 | 0.3666 | 0.0184 | 0.0541 | 0.0184 | 0.0541 | 0.0187 | 0.0555 |
| 0.6032 | 3.0 | 46500 | 0.3663 | 0.0184 | 0.0542 | 0.0184 | 0.0542 | 0.0188 | 0.0557 |
Framework versions
- Transformers 5.15.0
- Pytorch 2.11.0+cu128
- Datasets 5.0.1
- Tokenizers 0.22.2