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README.md
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---
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license: cc-by-nc-sa-4.0
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base_model: microsoft/layoutlmv3-base
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tags:
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- generated_from_trainer
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metrics:
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- precision
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- recall
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- f1
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- accuracy
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model-index:
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- name: layoutlmv3-test
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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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# layoutlmv3-test
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This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.8036
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- Precision: 0.8973
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- Recall: 0.9200
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- F1: 0.9085
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- Accuracy: 0.8481
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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: 1e-05
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- train_batch_size: 8
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- eval_batch_size: 8
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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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- training_steps: 1000
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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| No log | 5.26 | 100 | 0.5115 | 0.8071 | 0.8624 | 0.8338 | 0.8407 |
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| No log | 10.53 | 200 | 0.4661 | 0.8730 | 0.9086 | 0.8905 | 0.8546 |
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| No log | 15.79 | 300 | 0.5613 | 0.8914 | 0.9091 | 0.9001 | 0.8552 |
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| No log | 21.05 | 400 | 0.6767 | 0.8937 | 0.8982 | 0.8959 | 0.8507 |
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| 0.3022 | 26.32 | 500 | 0.7020 | 0.8935 | 0.9165 | 0.9049 | 0.8626 |
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| 0.3022 | 31.58 | 600 | 0.7108 | 0.9040 | 0.9220 | 0.9129 | 0.8591 |
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| 0.3022 | 36.84 | 700 | 0.7378 | 0.9049 | 0.9175 | 0.9112 | 0.8517 |
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| 0.3022 | 42.11 | 800 | 0.7892 | 0.9026 | 0.9210 | 0.9117 | 0.8537 |
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| 0.3022 | 47.37 | 900 | 0.8133 | 0.8995 | 0.9205 | 0.9099 | 0.8490 |
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| 0.0223 | 52.63 | 1000 | 0.8036 | 0.8973 | 0.9200 | 0.9085 | 0.8481 |
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### Framework versions
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- Transformers 4.35.2
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- Pytorch 2.1.0+cu121
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- Datasets 2.16.1
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- Tokenizers 0.15.1
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