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End of training

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README.md ADDED
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+ ---
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+ license: cc-by-nc-sa-4.0
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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_97_2
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+ results: []
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+ ---
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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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+
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+ # LayoutLMv3_97_2
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+
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+ This model is a fine-tuned version of [microsoft/layoutlmv3-large](https://huggingface.co/microsoft/layoutlmv3-large) on the None dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.5892
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+ - Precision: 0.8315
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+ - Recall: 0.7721
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+ - F1: 0.8007
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+ - Accuracy: 0.9122
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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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: 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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+ - training_steps: 2000
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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+ |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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+ | No log | 2.56 | 100 | 0.4807 | 0.6058 | 0.4966 | 0.5458 | 0.8297 |
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+ | No log | 5.13 | 200 | 0.3940 | 0.7553 | 0.6088 | 0.6742 | 0.8771 |
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+ | No log | 7.69 | 300 | 0.3804 | 0.7438 | 0.7109 | 0.7270 | 0.9008 |
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+ | No log | 10.26 | 400 | 0.3900 | 0.8185 | 0.8129 | 0.8157 | 0.9096 |
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+ | 0.2035 | 12.82 | 500 | 0.4102 | 0.8255 | 0.7721 | 0.7979 | 0.9087 |
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+ | 0.2035 | 15.38 | 600 | 0.4077 | 0.8095 | 0.8095 | 0.8095 | 0.9148 |
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+ | 0.2035 | 17.95 | 700 | 0.4915 | 0.7867 | 0.7653 | 0.7759 | 0.8982 |
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+ | 0.2035 | 20.51 | 800 | 0.4861 | 0.8269 | 0.7959 | 0.8111 | 0.9131 |
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+ | 0.2035 | 23.08 | 900 | 0.5051 | 0.7818 | 0.7313 | 0.7557 | 0.9052 |
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+ | 0.0117 | 25.64 | 1000 | 0.5404 | 0.8303 | 0.7653 | 0.7965 | 0.9069 |
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+ | 0.0117 | 28.21 | 1100 | 0.6110 | 0.8492 | 0.7279 | 0.7839 | 0.9061 |
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+ | 0.0117 | 30.77 | 1200 | 0.5379 | 0.8014 | 0.7823 | 0.7917 | 0.9096 |
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+ | 0.0117 | 33.33 | 1300 | 0.5343 | 0.8057 | 0.7755 | 0.7903 | 0.9131 |
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+ | 0.0117 | 35.9 | 1400 | 0.5590 | 0.8333 | 0.7653 | 0.7979 | 0.9140 |
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+ | 0.0013 | 38.46 | 1500 | 0.6296 | 0.8488 | 0.7449 | 0.7935 | 0.9122 |
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+ | 0.0013 | 41.03 | 1600 | 0.6089 | 0.8421 | 0.7619 | 0.8 | 0.9122 |
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+ | 0.0013 | 43.59 | 1700 | 0.5869 | 0.8291 | 0.7755 | 0.8014 | 0.9140 |
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+ | 0.0013 | 46.15 | 1800 | 0.5847 | 0.8291 | 0.7755 | 0.8014 | 0.9140 |
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+ | 0.0013 | 48.72 | 1900 | 0.5881 | 0.8285 | 0.7721 | 0.7993 | 0.9131 |
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+ | 0.0004 | 51.28 | 2000 | 0.5892 | 0.8315 | 0.7721 | 0.8007 | 0.9122 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.29.2
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+ - Pytorch 2.0.1+cu118
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+ - Datasets 2.14.4
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+ - Tokenizers 0.13.3
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