layoutlmv3-finetuned-cord_100
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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-finetuned-cord_100
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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-finetuned-cord_100
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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: 0.3036
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- Precision: 0.9149
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- Recall: 0.9309
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- F1: 0.9228
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- Accuracy: 0.9419
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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: 5
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- eval_batch_size: 5
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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: 2500
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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 | 4.17 | 250 | 0.6391 | 0.8080 | 0.8093 | 0.8087 | 0.8312 |
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| 0.9327 | 8.33 | 500 | 0.3636 | 0.8790 | 0.8891 | 0.8840 | 0.9088 |
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| 0.9327 | 12.5 | 750 | 0.3144 | 0.9001 | 0.9103 | 0.9052 | 0.9288 |
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| 0.1743 | 16.67 | 1000 | 0.2957 | 0.9102 | 0.9240 | 0.9170 | 0.9360 |
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| 0.1743 | 20.83 | 1250 | 0.2963 | 0.9109 | 0.9248 | 0.9178 | 0.9334 |
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| 0.0551 | 25.0 | 1500 | 0.2943 | 0.9207 | 0.9263 | 0.9235 | 0.9411 |
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| 0.0551 | 29.17 | 1750 | 0.3034 | 0.9145 | 0.9263 | 0.9203 | 0.9360 |
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| 0.0249 | 33.33 | 2000 | 0.3059 | 0.9162 | 0.9301 | 0.9231 | 0.9394 |
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| 0.0249 | 37.5 | 2250 | 0.3019 | 0.9147 | 0.9293 | 0.9220 | 0.9385 |
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| 0.0153 | 41.67 | 2500 | 0.3036 | 0.9149 | 0.9309 | 0.9228 | 0.9419 |
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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 0.15.0
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