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--- |
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base_model: microsoft/trocr-small-printed |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: seq2seq_model_printed |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# seq2seq_model_printed |
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This model is a fine-tuned version of [microsoft/trocr-small-printed](https://huggingface.co/microsoft/trocr-small-printed) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.5578 |
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- Cer: 0.2138 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 2 |
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- eval_batch_size: 2 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 35 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Cer | |
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|:-------------:|:-----:|:----:|:---------------:|:------:| |
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| 4.6751 | 1.0 | 244 | 3.5585 | 1.0652 | |
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| 3.3624 | 2.0 | 488 | 2.7206 | 0.7373 | |
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| 2.9125 | 3.0 | 732 | 1.9523 | 0.5418 | |
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| 2.5734 | 4.0 | 976 | 2.0470 | 0.7149 | |
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| 2.4143 | 5.0 | 1220 | 1.4577 | 0.3585 | |
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| 2.2459 | 6.0 | 1464 | 1.5741 | 0.4501 | |
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| 2.1514 | 7.0 | 1708 | 1.2625 | 0.3218 | |
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| 2.0268 | 8.0 | 1952 | 1.4816 | 0.3625 | |
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| 1.9513 | 9.0 | 2196 | 1.6085 | 0.3259 | |
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| 1.7768 | 10.0 | 2440 | 1.4458 | 0.4033 | |
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| 1.7156 | 11.0 | 2684 | 1.4845 | 0.3055 | |
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| 1.5976 | 12.0 | 2928 | 1.6491 | 0.3503 | |
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| 1.4664 | 13.0 | 3172 | 1.3220 | 0.3381 | |
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| 1.3276 | 14.0 | 3416 | 1.4486 | 0.3503 | |
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| 1.2354 | 15.0 | 3660 | 1.6394 | 0.3177 | |
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| 1.1072 | 16.0 | 3904 | 1.5189 | 0.3035 | |
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| 0.9209 | 17.0 | 4148 | 1.3820 | 0.2485 | |
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| 0.7356 | 18.0 | 4392 | 1.4799 | 0.2607 | |
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| 0.6336 | 19.0 | 4636 | 1.5075 | 0.2220 | |
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| 0.5035 | 20.0 | 4880 | 1.5413 | 0.2179 | |
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| 0.406 | 21.0 | 5124 | 1.5602 | 0.2464 | |
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| 0.3294 | 22.0 | 5368 | 1.4495 | 0.2159 | |
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| 0.2515 | 23.0 | 5612 | 1.5809 | 0.2240 | |
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| 0.2207 | 24.0 | 5856 | 1.5188 | 0.2281 | |
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| 0.1689 | 25.0 | 6100 | 1.5153 | 0.2118 | |
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| 0.1426 | 26.0 | 6344 | 1.5616 | 0.2118 | |
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| 0.1142 | 27.0 | 6588 | 1.7044 | 0.2179 | |
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| 0.0785 | 28.0 | 6832 | 1.6267 | 0.2281 | |
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| 0.0751 | 29.0 | 7076 | 1.6769 | 0.2159 | |
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| 0.0507 | 30.0 | 7320 | 1.7316 | 0.2342 | |
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| 0.0388 | 31.0 | 7564 | 1.5750 | 0.2220 | |
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| 0.0264 | 32.0 | 7808 | 1.7028 | 0.2159 | |
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| 0.021 | 33.0 | 8052 | 1.6861 | 0.2322 | |
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| 0.0195 | 34.0 | 8296 | 1.7154 | 0.2077 | |
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| 0.0167 | 35.0 | 8540 | 1.5578 | 0.2138 | |
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### Framework versions |
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- Transformers 4.42.4 |
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- Pytorch 2.1.0a0+b5021ba |
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- Datasets 2.20.0 |
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- Tokenizers 0.19.1 |
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