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--- |
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license: cc-by-nc-sa-4.0 |
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base_model: microsoft/layoutlmv3-base |
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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: test |
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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: 0.9561011904761905 |
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- name: Recall |
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type: recall |
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value: 0.9618263473053892 |
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- name: F1 |
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type: f1 |
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value: 0.958955223880597 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9702886247877759 |
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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: 0.1726 |
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- Precision: 0.9561 |
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- Recall: 0.9618 |
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- F1: 0.9590 |
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- Accuracy: 0.9703 |
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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: 3000 |
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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 | 1.56 | 250 | 1.0075 | 0.7597 | 0.8046 | 0.7815 | 0.8145 | |
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| 1.3907 | 3.12 | 500 | 0.5155 | 0.8388 | 0.8683 | 0.8533 | 0.8841 | |
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| 1.3907 | 4.69 | 750 | 0.3486 | 0.8917 | 0.9117 | 0.9016 | 0.9283 | |
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| 0.3755 | 6.25 | 1000 | 0.2722 | 0.9211 | 0.9356 | 0.9283 | 0.9435 | |
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| 0.3755 | 7.81 | 1250 | 0.2399 | 0.9356 | 0.9461 | 0.9408 | 0.9533 | |
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| 0.1857 | 9.38 | 1500 | 0.2170 | 0.9376 | 0.9454 | 0.9415 | 0.9542 | |
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| 0.1857 | 10.94 | 1750 | 0.1917 | 0.9510 | 0.9588 | 0.9549 | 0.9660 | |
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| 0.1236 | 12.5 | 2000 | 0.1821 | 0.9502 | 0.9573 | 0.9538 | 0.9652 | |
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| 0.1236 | 14.06 | 2250 | 0.1870 | 0.9538 | 0.9588 | 0.9563 | 0.9669 | |
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| 0.0858 | 15.62 | 2500 | 0.1741 | 0.9583 | 0.9633 | 0.9608 | 0.9711 | |
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| 0.0858 | 17.19 | 2750 | 0.1726 | 0.9561 | 0.9611 | 0.9586 | 0.9690 | |
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| 0.0708 | 18.75 | 3000 | 0.1726 | 0.9561 | 0.9618 | 0.9590 | 0.9703 | |
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### Framework versions |
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- Transformers 4.35.2 |
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- Pytorch 2.1.1+cu121 |
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- Datasets 2.15.0 |
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- Tokenizers 0.15.0 |
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