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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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+ datasets:
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+ - cord-layoutlmv3
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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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+ - 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: cord-layoutlmv3
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+ type: cord-layoutlmv3
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+ config: cord
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+ split: train
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+ args: cord
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+ metrics:
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+ - name: Precision
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+ type: precision
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+ value: 0.917960088691796
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+ - name: Recall
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+ type: recall
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+ value: 0.9296407185628742
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+ - name: F1
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+ type: f1
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+ value: 0.9237634808478989
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+ - name: Accuracy
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+ type: accuracy
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+ value: 0.9303904923599321
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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-finetuned-cord_100
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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 the cord-layoutlmv3 dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.2854
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+ - Precision: 0.9180
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+ - Recall: 0.9296
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+ - F1: 0.9238
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+ - Accuracy: 0.9304
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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: 2500
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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 | 0.62 | 250 | 1.2967 | 0.6175 | 0.7021 | 0.6571 | 0.7296 |
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+ | 1.6872 | 1.25 | 500 | 0.7576 | 0.8140 | 0.8383 | 0.8260 | 0.8383 |
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+ | 1.6872 | 1.88 | 750 | 0.5695 | 0.8301 | 0.8518 | 0.8408 | 0.8544 |
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+ | 0.6109 | 2.5 | 1000 | 0.4778 | 0.8564 | 0.875 | 0.8656 | 0.8812 |
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+ | 0.6109 | 3.12 | 1250 | 0.3825 | 0.8694 | 0.8922 | 0.8807 | 0.8986 |
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+ | 0.3905 | 3.75 | 1500 | 0.3546 | 0.8831 | 0.9049 | 0.8939 | 0.9143 |
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+ | 0.3905 | 4.38 | 1750 | 0.3153 | 0.8998 | 0.9207 | 0.9101 | 0.9223 |
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+ | 0.275 | 5.0 | 2000 | 0.3065 | 0.8926 | 0.9147 | 0.9035 | 0.9202 |
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+ | 0.275 | 5.62 | 2250 | 0.2872 | 0.9131 | 0.9281 | 0.9206 | 0.9291 |
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+ | 0.2275 | 6.25 | 2500 | 0.2854 | 0.9180 | 0.9296 | 0.9238 | 0.9304 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.25.1
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+ - Pytorch 1.13.1+cpu
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+ - Datasets 2.8.0
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+ - Tokenizers 0.13.2