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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: 0.953125 |
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- name: Recall |
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type: recall |
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value: 0.9588323353293413 |
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- name: F1 |
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type: f1 |
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value: 0.9559701492537314 |
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- name: Accuracy |
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type: accuracy |
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value: 0.965195246179966 |
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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: 0.1913 |
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- Precision: 0.9531 |
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- Recall: 0.9588 |
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- F1: 0.9560 |
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- Accuracy: 0.9652 |
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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 | 1.56 | 250 | 1.0033 | 0.7434 | 0.7957 | 0.7686 | 0.8060 | |
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| 1.3714 | 3.12 | 500 | 0.5413 | 0.8534 | 0.8757 | 0.8644 | 0.8769 | |
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| 1.3714 | 4.69 | 750 | 0.3792 | 0.9013 | 0.9162 | 0.9087 | 0.9219 | |
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| 0.3763 | 6.25 | 1000 | 0.2743 | 0.9333 | 0.9431 | 0.9382 | 0.9457 | |
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| 0.3763 | 7.81 | 1250 | 0.2404 | 0.9313 | 0.9439 | 0.9375 | 0.9495 | |
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| 0.2026 | 9.38 | 1500 | 0.2479 | 0.9325 | 0.9409 | 0.9367 | 0.9431 | |
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| 0.2026 | 10.94 | 1750 | 0.2001 | 0.9338 | 0.9499 | 0.9417 | 0.9559 | |
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| 0.1349 | 12.5 | 2000 | 0.2102 | 0.9407 | 0.9499 | 0.9453 | 0.9571 | |
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| 0.1349 | 14.06 | 2250 | 0.1961 | 0.9560 | 0.9603 | 0.9582 | 0.9648 | |
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| 0.104 | 15.62 | 2500 | 0.1913 | 0.9531 | 0.9588 | 0.9560 | 0.9652 | |
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### Framework versions |
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- Transformers 4.31.0 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.0 |
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- Tokenizers 0.13.3 |
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