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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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+ 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-funsd
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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-funsd
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+
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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: 0.8190
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+ - Precision: 0.8973
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+ - Recall: 0.9111
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+ - F1: 0.9041
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+ - Accuracy: 0.8473
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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: 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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+ - mixed_precision_training: Native AMP
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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 | 5.26 | 100 | 0.5958 | 0.8295 | 0.8723 | 0.8504 | 0.8178 |
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+ | No log | 10.53 | 200 | 0.6181 | 0.8505 | 0.8932 | 0.8713 | 0.8241 |
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+ | No log | 15.79 | 300 | 0.6992 | 0.8841 | 0.9021 | 0.8930 | 0.8470 |
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+ | No log | 21.05 | 400 | 0.8182 | 0.8931 | 0.9006 | 0.8969 | 0.8328 |
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+ | 0.3071 | 26.32 | 500 | 0.8190 | 0.8973 | 0.9111 | 0.9041 | 0.8473 |
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+ | 0.3071 | 31.58 | 600 | 0.9001 | 0.8812 | 0.9061 | 0.8935 | 0.8326 |
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+ | 0.3071 | 36.84 | 700 | 0.9112 | 0.8958 | 0.9101 | 0.9029 | 0.8435 |
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+ | 0.3071 | 42.11 | 800 | 0.9196 | 0.8951 | 0.9026 | 0.8988 | 0.8408 |
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+ | 0.3071 | 47.37 | 900 | 0.9256 | 0.8990 | 0.9061 | 0.9025 | 0.8454 |
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+ | 0.0245 | 52.63 | 1000 | 0.9584 | 0.8962 | 0.9046 | 0.9004 | 0.8431 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.36.0
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+ - Pytorch 2.0.0
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+ - Datasets 2.16.1
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+ - Tokenizers 0.15.0
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