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layoutlmv3-finetuned-cord_200

Token Classification
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layoutlmv3
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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_200
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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: 0.9033923303834809
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+ - name: Recall
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+ type: recall
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+ value: 0.9169161676646707
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+ - name: F1
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+ type: f1
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+ value: 0.9101040118870729
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+ - name: Accuracy
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+ type: accuracy
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+ value: 0.9121392190152802
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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_200
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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: 0.4529
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+ - Precision: 0.9034
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+ - Recall: 0.9169
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+ - F1: 0.9101
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+ - Accuracy: 0.9121
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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: 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: 3000
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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 | 6.25 | 250 | 1.0785 | 0.6815 | 0.7575 | 0.7175 | 0.7780 |
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+ | 1.3902 | 12.5 | 500 | 0.5871 | 0.8542 | 0.8683 | 0.8612 | 0.8604 |
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+ | 1.3902 | 18.75 | 750 | 0.4572 | 0.8728 | 0.8937 | 0.8831 | 0.8905 |
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+ | 0.298 | 25.0 | 1000 | 0.3947 | 0.8936 | 0.9117 | 0.9026 | 0.9092 |
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+ | 0.298 | 31.25 | 1250 | 0.3925 | 0.8982 | 0.9177 | 0.9078 | 0.9117 |
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+ | 0.1023 | 37.5 | 1500 | 0.4290 | 0.8908 | 0.9102 | 0.9004 | 0.9041 |
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+ | 0.1023 | 43.75 | 1750 | 0.4220 | 0.8980 | 0.9162 | 0.9070 | 0.9117 |
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+ | 0.0475 | 50.0 | 2000 | 0.4755 | 0.8944 | 0.9064 | 0.9004 | 0.8990 |
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+ | 0.0475 | 56.25 | 2250 | 0.4635 | 0.8992 | 0.9147 | 0.9069 | 0.9070 |
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+ | 0.0296 | 62.5 | 2500 | 0.4475 | 0.9019 | 0.9154 | 0.9086 | 0.9117 |
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+ | 0.0296 | 68.75 | 2750 | 0.4484 | 0.9004 | 0.9139 | 0.9071 | 0.9079 |
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+ | 0.0242 | 75.0 | 3000 | 0.4529 | 0.9034 | 0.9169 | 0.9101 | 0.9121 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.21.2
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+ - Pytorch 1.12.1+cu113
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+ - Datasets 2.4.0
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+ - Tokenizers 0.12.1