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layoutlmv3-finetuned-cord_500

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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_500
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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: 0.9509293680297398
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+ - name: Recall
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+ type: recall
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+ value: 0.9573353293413174
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+ - name: F1
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+ type: f1
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+ value: 0.9541215964192465
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+ - name: Accuracy
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+ type: accuracy
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+ value: 0.9609507640067911
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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_500
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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: 0.2339
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+ - Precision: 0.9509
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+ - Recall: 0.9573
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+ - F1: 0.9541
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+ - Accuracy: 0.9610
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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: 4000
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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 | 2.5 | 250 | 0.9950 | 0.7114 | 0.7784 | 0.7434 | 0.7903 |
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+ | 1.3831 | 5.0 | 500 | 0.5152 | 0.8483 | 0.8787 | 0.8632 | 0.8816 |
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+ | 1.3831 | 7.5 | 750 | 0.3683 | 0.9013 | 0.9154 | 0.9083 | 0.9240 |
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+ | 0.3551 | 10.0 | 1000 | 0.3051 | 0.9201 | 0.9304 | 0.9252 | 0.9363 |
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+ | 0.3551 | 12.5 | 1250 | 0.2636 | 0.9375 | 0.9424 | 0.9399 | 0.9457 |
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+ | 0.1562 | 15.0 | 1500 | 0.2498 | 0.9385 | 0.9476 | 0.9430 | 0.9508 |
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+ | 0.1562 | 17.5 | 1750 | 0.2380 | 0.9414 | 0.9499 | 0.9456 | 0.9559 |
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+ | 0.0863 | 20.0 | 2000 | 0.2355 | 0.9400 | 0.9491 | 0.9445 | 0.9542 |
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+ | 0.0863 | 22.5 | 2250 | 0.2268 | 0.9451 | 0.9536 | 0.9493 | 0.9601 |
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+ | 0.0512 | 25.0 | 2500 | 0.2277 | 0.9429 | 0.9513 | 0.9471 | 0.9588 |
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+ | 0.0512 | 27.5 | 2750 | 0.2315 | 0.9473 | 0.9551 | 0.9512 | 0.9593 |
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+ | 0.0358 | 30.0 | 3000 | 0.2294 | 0.9509 | 0.9573 | 0.9541 | 0.9605 |
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+ | 0.0358 | 32.5 | 3250 | 0.2330 | 0.9458 | 0.9543 | 0.9501 | 0.9593 |
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+ | 0.028 | 35.0 | 3500 | 0.2374 | 0.9487 | 0.9558 | 0.9523 | 0.9597 |
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+ | 0.028 | 37.5 | 3750 | 0.2374 | 0.9501 | 0.9558 | 0.9530 | 0.9593 |
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+ | 0.0244 | 40.0 | 4000 | 0.2339 | 0.9509 | 0.9573 | 0.9541 | 0.9610 |
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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