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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: 0.8719646799116998 |
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- name: Recall |
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type: recall |
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value: 0.8869760479041916 |
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- name: F1 |
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type: f1 |
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value: 0.8794063079777364 |
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- name: Accuracy |
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type: accuracy |
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value: 0.8790322580645161 |
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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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# layoutlmv3-finetuned-cord_100 |
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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: 0.7215 |
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- Precision: 0.8720 |
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- Recall: 0.8870 |
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- F1: 0.8794 |
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- Accuracy: 0.8790 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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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: 2500 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| No log | 12.5 | 250 | 1.0892 | 0.7345 | 0.7867 | 0.7597 | 0.7806 | |
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| 1.3039 | 25.0 | 500 | 0.7150 | 0.8054 | 0.8428 | 0.8237 | 0.8281 | |
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| 1.3039 | 37.5 | 750 | 0.6320 | 0.8335 | 0.8615 | 0.8473 | 0.8540 | |
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| 0.2171 | 50.0 | 1000 | 0.6427 | 0.8651 | 0.8832 | 0.8741 | 0.8722 | |
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| 0.2171 | 62.5 | 1250 | 0.6640 | 0.8672 | 0.8847 | 0.8759 | 0.8765 | |
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| 0.0654 | 75.0 | 1500 | 0.6758 | 0.8650 | 0.8825 | 0.8737 | 0.8731 | |
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| 0.0654 | 87.5 | 1750 | 0.7028 | 0.8684 | 0.8840 | 0.8761 | 0.8765 | |
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| 0.0338 | 100.0 | 2000 | 0.7252 | 0.8710 | 0.8847 | 0.8778 | 0.8769 | |
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| 0.0338 | 112.5 | 2250 | 0.7227 | 0.8710 | 0.8847 | 0.8778 | 0.8778 | |
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| 0.0257 | 125.0 | 2500 | 0.7215 | 0.8720 | 0.8870 | 0.8794 | 0.8790 | |
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### Framework versions |
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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 |
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