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layoutlmv3-finetuned-cord_800

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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_800
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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: 0.9445266272189349
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+ - name: Recall
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+ type: recall
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+ value: 0.9558383233532934
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+ - name: F1
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+ type: f1
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+ value: 0.9501488095238095
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+ - name: Accuracy
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+ type: accuracy
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+ value: 0.9605263157894737
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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_800
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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: 0.2042
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+ - Precision: 0.9445
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+ - Recall: 0.9558
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+ - F1: 0.9501
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+ - Accuracy: 0.9605
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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 | 1.56 | 250 | 0.9737 | 0.7787 | 0.8166 | 0.7972 | 0.8188 |
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+ | 1.3706 | 3.12 | 500 | 0.5489 | 0.8480 | 0.8645 | 0.8562 | 0.8680 |
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+ | 1.3706 | 4.69 | 750 | 0.3857 | 0.8913 | 0.9087 | 0.8999 | 0.9147 |
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+ | 0.3693 | 6.25 | 1000 | 0.3192 | 0.9117 | 0.9274 | 0.9195 | 0.9317 |
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+ | 0.3693 | 7.81 | 1250 | 0.2816 | 0.9189 | 0.9326 | 0.9257 | 0.9355 |
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+ | 0.1903 | 9.38 | 1500 | 0.2521 | 0.9277 | 0.9409 | 0.9342 | 0.9465 |
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+ | 0.1903 | 10.94 | 1750 | 0.2353 | 0.9357 | 0.9476 | 0.9416 | 0.9550 |
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+ | 0.1231 | 12.5 | 2000 | 0.2361 | 0.9293 | 0.9446 | 0.9369 | 0.9516 |
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+ | 0.1231 | 14.06 | 2250 | 0.2194 | 0.9402 | 0.9528 | 0.9465 | 0.9576 |
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+ | 0.0766 | 15.62 | 2500 | 0.2133 | 0.9416 | 0.9528 | 0.9472 | 0.9580 |
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+ | 0.0766 | 17.19 | 2750 | 0.2117 | 0.9438 | 0.9558 | 0.9498 | 0.9597 |
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+ | 0.0585 | 18.75 | 3000 | 0.2152 | 0.9417 | 0.9551 | 0.9483 | 0.9605 |
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+ | 0.0585 | 20.31 | 3250 | 0.2070 | 0.9431 | 0.9551 | 0.9491 | 0.9588 |
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+ | 0.0454 | 21.88 | 3500 | 0.2093 | 0.9489 | 0.9588 | 0.9538 | 0.9622 |
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+ | 0.0454 | 23.44 | 3750 | 0.2034 | 0.9453 | 0.9566 | 0.9509 | 0.9610 |
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+ | 0.0409 | 25.0 | 4000 | 0.2042 | 0.9445 | 0.9558 | 0.9501 | 0.9605 |
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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