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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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datasets:
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- violations
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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-violations-test
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results:
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- task:
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name: Token Classification
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type: token-classification
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dataset:
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name: violations
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type: violations
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config: ViolationsExtraction
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split: test
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args: ViolationsExtraction
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metrics:
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- name: Precision
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type: precision
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value: 0.9482758620689655
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- name: Recall
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type: recall
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value: 0.9116022099447514
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- name: F1
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type: f1
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value: 0.9295774647887324
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- name: Accuracy
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type: accuracy
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value: 0.9502762430939227
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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-violations-test
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This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the violations dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.3685
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- Precision: 0.9483
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- Recall: 0.9116
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- F1: 0.9296
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- Accuracy: 0.9503
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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 | 9.0909 | 100 | 0.2997 | 0.9543 | 0.9227 | 0.9382 | 0.9558 |
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| No log | 18.1818 | 200 | 0.3729 | 0.9425 | 0.9061 | 0.9239 | 0.9448 |
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| No log | 27.2727 | 300 | 0.3408 | 0.9543 | 0.9227 | 0.9382 | 0.9558 |
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| No log | 36.3636 | 400 | 0.3566 | 0.9483 | 0.9116 | 0.9296 | 0.9503 |
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| 0.0997 | 45.4545 | 500 | 0.3685 | 0.9483 | 0.9116 | 0.9296 | 0.9503 |
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| 0.0997 | 54.5455 | 600 | 0.3736 | 0.9483 | 0.9116 | 0.9296 | 0.9503 |
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| 0.0997 | 63.6364 | 700 | 0.3866 | 0.9483 | 0.9116 | 0.9296 | 0.9503 |
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| 0.0997 | 72.7273 | 800 | 0.3990 | 0.9483 | 0.9116 | 0.9296 | 0.9503 |
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| 0.0997 | 81.8182 | 900 | 0.4018 | 0.9483 | 0.9116 | 0.9296 | 0.9503 |
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| 0.001 | 90.9091 | 1000 | 0.3979 | 0.9483 | 0.9116 | 0.9296 | 0.9503 |
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### Framework versions
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- Transformers 4.42.1
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- Pytorch 2.3.1+cu118
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- Datasets 2.20.0
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- Tokenizers 0.19.1
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