| --- |
| library_name: transformers |
| base_model: microsoft/trocr-base-str |
| tags: |
| - generated_from_trainer |
| metrics: |
| - wer |
| model-index: |
| - name: microsoft/trocr-base-str |
| 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. --> |
|
|
| # microsoft/trocr-base-str |
|
|
| This model is a fine-tuned version of [microsoft/trocr-base-str](https://huggingface.co/microsoft/trocr-base-str) on an unknown dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.1131 |
| - Cer: 0.0102 |
| - Wer: 0.0601 |
|
|
| ## 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: 16 |
| - eval_batch_size: 16 |
| - seed: 42 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - num_epochs: 25 |
| - mixed_precision_training: Native AMP |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Cer | Wer | |
| |:-------------:|:-----:|:----:|:---------------:|:------:|:------:| |
| | 1.8737 | 1.0 | 173 | 0.5194 | 0.0668 | 0.2809 | |
| | 0.3674 | 2.0 | 346 | 0.1408 | 0.0183 | 0.1015 | |
| | 0.0907 | 3.0 | 519 | 0.1151 | 0.0150 | 0.0821 | |
| | 0.0584 | 4.0 | 692 | 0.0987 | 0.0140 | 0.0778 | |
| | 0.0374 | 5.0 | 865 | 0.1041 | 0.0113 | 0.0711 | |
| | 0.0237 | 6.0 | 1038 | 0.0988 | 0.0102 | 0.0626 | |
| | 0.0172 | 7.0 | 1211 | 0.0981 | 0.0109 | 0.0643 | |
| | 0.0177 | 8.0 | 1384 | 0.1019 | 0.0109 | 0.0643 | |
| | 0.0134 | 9.0 | 1557 | 0.1080 | 0.0106 | 0.0643 | |
| | 0.009 | 10.0 | 1730 | 0.1023 | 0.0094 | 0.0567 | |
| | 0.0092 | 11.0 | 1903 | 0.1021 | 0.0090 | 0.0558 | |
| | 0.0085 | 12.0 | 2076 | 0.1057 | 0.0105 | 0.0635 | |
| | 0.006 | 13.0 | 2249 | 0.1055 | 0.0107 | 0.0618 | |
| | 0.006 | 14.0 | 2422 | 0.1066 | 0.0105 | 0.0618 | |
| | 0.0046 | 15.0 | 2595 | 0.1110 | 0.0103 | 0.0618 | |
| | 0.0038 | 16.0 | 2768 | 0.1093 | 0.0101 | 0.0601 | |
| | 0.0028 | 17.0 | 2941 | 0.1095 | 0.0099 | 0.0575 | |
| | 0.0026 | 18.0 | 3114 | 0.1095 | 0.0099 | 0.0575 | |
| | 0.0021 | 19.0 | 3287 | 0.1088 | 0.0097 | 0.0584 | |
| | 0.0017 | 20.0 | 3460 | 0.1127 | 0.0105 | 0.0601 | |
| | 0.0014 | 21.0 | 3633 | 0.1127 | 0.0099 | 0.0575 | |
| | 0.0015 | 22.0 | 3806 | 0.1129 | 0.0101 | 0.0592 | |
| | 0.0008 | 23.0 | 3979 | 0.1132 | 0.0101 | 0.0592 | |
| | 0.0008 | 24.0 | 4152 | 0.1132 | 0.0102 | 0.0601 | |
| | 0.0006 | 25.0 | 4325 | 0.1131 | 0.0102 | 0.0601 | |
|
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|
|
| ### Framework versions |
|
|
| - Transformers 4.44.2 |
| - Pytorch 2.4.1+cu121 |
| - Datasets 2.17.0 |
| - Tokenizers 0.19.1 |
|
|
