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buihungtpd3
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layoutlmv3-finetuned-vinv2

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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+ - drug_bill_layoutv3
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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-vinv2
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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: drug_bill_layoutv3
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+ type: drug_bill_layoutv3
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+ config: Vin_Drug_Bill
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+ split: train
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+ args: Vin_Drug_Bill
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+ metrics:
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+ - name: Precision
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+ type: precision
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+ value: 1.0
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+ - name: Recall
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+ type: recall
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+ value: 1.0
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+ - name: F1
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+ type: f1
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+ value: 1.0
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+ - name: Accuracy
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+ type: accuracy
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+ value: 1.0
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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-vinv2
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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 drug_bill_layoutv3 dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.0001
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+ - Precision: 1.0
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+ - Recall: 1.0
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+ - F1: 1.0
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+ - Accuracy: 1.0
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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 | 1.33 | 250 | 0.0025 | 0.9994 | 0.9994 | 0.9994 | 0.9998 |
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+ | 0.0662 | 2.66 | 500 | 0.0004 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0662 | 3.99 | 750 | 0.0003 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0111 | 5.32 | 1000 | 0.0002 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0111 | 6.65 | 1250 | 0.0001 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0126 | 7.98 | 1500 | 0.0001 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0126 | 9.31 | 1750 | 0.0001 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0032 | 10.64 | 2000 | 0.0001 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0032 | 11.97 | 2250 | 0.0001 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0011 | 13.3 | 2500 | 0.0001 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0011 | 14.63 | 2750 | 0.0001 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0002 | 15.96 | 3000 | 0.0001 | 1.0 | 1.0 | 1.0 | 1.0 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.24.0
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+ - Pytorch 1.12.1+cu113
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+ - Datasets 2.6.1
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+ - Tokenizers 0.13.2