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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: 0.9478778853313478 |
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- name: Recall |
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type: recall |
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value: 0.9528443113772455 |
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- name: F1 |
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type: f1 |
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value: 0.950354609929078 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9541595925297114 |
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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: 0.2176 |
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- Precision: 0.9479 |
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- Recall: 0.9528 |
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- F1: 0.9504 |
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- Accuracy: 0.9542 |
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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 | 1.0378 | 0.7404 | 0.7964 | 0.7674 | 0.8035 | |
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| 1.4104 | 3.12 | 500 | 0.5605 | 0.8291 | 0.8645 | 0.8465 | 0.8790 | |
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| 1.4104 | 4.69 | 750 | 0.3959 | 0.8728 | 0.8990 | 0.8857 | 0.9155 | |
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| 0.4054 | 6.25 | 1000 | 0.3111 | 0.9231 | 0.9349 | 0.9290 | 0.9393 | |
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| 0.4054 | 7.81 | 1250 | 0.2847 | 0.9135 | 0.9251 | 0.9193 | 0.9317 | |
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| 0.2124 | 9.38 | 1500 | 0.2457 | 0.9281 | 0.9379 | 0.9330 | 0.9410 | |
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| 0.2124 | 10.94 | 1750 | 0.2390 | 0.9371 | 0.9484 | 0.9427 | 0.9520 | |
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| 0.1438 | 12.5 | 2000 | 0.2196 | 0.9443 | 0.9513 | 0.9478 | 0.9546 | |
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| 0.1438 | 14.06 | 2250 | 0.2182 | 0.9478 | 0.9521 | 0.9500 | 0.9533 | |
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| 0.1093 | 15.62 | 2500 | 0.2176 | 0.9479 | 0.9528 | 0.9504 | 0.9542 | |
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### Framework versions |
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- Transformers 4.22.1 |
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- Pytorch 1.12.1+cu113 |
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- Datasets 2.5.1 |
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- Tokenizers 0.12.1 |
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