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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 |
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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 |
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type: cord |
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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: 0.9174649963154016 |
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- name: Recall |
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type: recall |
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value: 0.9318862275449101 |
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- name: F1 |
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type: f1 |
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value: 0.9246193835870776 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9405772495755518 |
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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 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2834 |
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- Precision: 0.9175 |
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- Recall: 0.9319 |
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- F1: 0.9246 |
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- Accuracy: 0.9406 |
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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 | 4.17 | 250 | 1.0175 | 0.7358 | 0.7882 | 0.7611 | 0.8014 | |
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| 1.406 | 8.33 | 500 | 0.5646 | 0.8444 | 0.8735 | 0.8587 | 0.8671 | |
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| 1.406 | 12.5 | 750 | 0.3943 | 0.8950 | 0.9184 | 0.9065 | 0.9189 | |
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| 0.3467 | 16.67 | 1000 | 0.3379 | 0.9138 | 0.9289 | 0.9213 | 0.9291 | |
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| 0.3467 | 20.83 | 1250 | 0.2842 | 0.9189 | 0.9334 | 0.9261 | 0.9419 | |
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| 0.1484 | 25.0 | 1500 | 0.2822 | 0.9233 | 0.9371 | 0.9302 | 0.9427 | |
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| 0.1484 | 29.17 | 1750 | 0.2906 | 0.9168 | 0.9319 | 0.9243 | 0.9372 | |
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| 0.0825 | 33.33 | 2000 | 0.2922 | 0.9183 | 0.9334 | 0.9258 | 0.9410 | |
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| 0.0825 | 37.5 | 2250 | 0.2842 | 0.9154 | 0.9319 | 0.9236 | 0.9397 | |
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| 0.0596 | 41.67 | 2500 | 0.2834 | 0.9175 | 0.9319 | 0.9246 | 0.9406 | |
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### Framework versions |
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- Transformers 4.20.1 |
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- Pytorch 1.11.0 |
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- Datasets 2.1.0 |
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- Tokenizers 0.12.1 |
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