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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: 0.9458456973293768 |
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- name: Recall |
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type: recall |
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value: 0.9543413173652695 |
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- name: F1 |
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type: f1 |
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value: 0.9500745156482863 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9596774193548387 |
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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: 0.2123 |
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- Precision: 0.9458 |
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- Recall: 0.9543 |
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- F1: 0.9501 |
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- Accuracy: 0.9597 |
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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 | 1.0095 | 0.7120 | 0.7754 | 0.7424 | 0.7946 | |
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| 1.3738 | 3.12 | 500 | 0.5732 | 0.8473 | 0.8683 | 0.8577 | 0.8714 | |
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| 1.3738 | 4.69 | 750 | 0.3840 | 0.8893 | 0.9079 | 0.8985 | 0.9181 | |
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| 0.4085 | 6.25 | 1000 | 0.2933 | 0.9181 | 0.9319 | 0.9250 | 0.9376 | |
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| 0.4085 | 7.81 | 1250 | 0.2704 | 0.9197 | 0.9349 | 0.9272 | 0.9444 | |
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| 0.2239 | 9.38 | 1500 | 0.2504 | 0.9369 | 0.9454 | 0.9411 | 0.9508 | |
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| 0.2239 | 10.94 | 1750 | 0.2375 | 0.9288 | 0.9379 | 0.9333 | 0.9465 | |
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| 0.1544 | 12.5 | 2000 | 0.2326 | 0.9423 | 0.9528 | 0.9475 | 0.9576 | |
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| 0.1544 | 14.06 | 2250 | 0.2147 | 0.9530 | 0.9566 | 0.9548 | 0.9610 | |
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| 0.1231 | 15.62 | 2500 | 0.2123 | 0.9458 | 0.9543 | 0.9501 | 0.9597 | |
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### Framework versions |
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- Transformers 4.34.0 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.5 |
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- Tokenizers 0.14.1 |
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