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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_300 |
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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: 0.9325426241660489 |
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- name: Recall |
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type: recall |
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value: 0.9416167664670658 |
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- name: F1 |
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type: f1 |
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value: 0.9370577281191806 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9363327674023769 |
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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_300 |
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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: 0.3434 |
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- Precision: 0.9325 |
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- Recall: 0.9416 |
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- F1: 0.9371 |
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- Accuracy: 0.9363 |
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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: 4000 |
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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 | 1.0379 | 0.7204 | 0.7829 | 0.7504 | 0.7941 | |
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| 1.4162 | 8.33 | 500 | 0.5642 | 0.8462 | 0.8772 | 0.8614 | 0.8820 | |
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| 1.4162 | 12.5 | 750 | 0.3836 | 0.9055 | 0.9184 | 0.9119 | 0.9206 | |
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| 0.3211 | 16.67 | 1000 | 0.3209 | 0.9139 | 0.9296 | 0.9217 | 0.9334 | |
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| 0.3211 | 20.83 | 1250 | 0.2962 | 0.9275 | 0.9386 | 0.9330 | 0.9435 | |
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| 0.1191 | 25.0 | 1500 | 0.2979 | 0.9254 | 0.9379 | 0.9316 | 0.9402 | |
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| 0.1191 | 29.17 | 1750 | 0.3079 | 0.9282 | 0.9386 | 0.9334 | 0.9355 | |
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| 0.059 | 33.33 | 2000 | 0.3039 | 0.9232 | 0.9364 | 0.9298 | 0.9325 | |
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| 0.059 | 37.5 | 2250 | 0.3254 | 0.9248 | 0.9386 | 0.9316 | 0.9355 | |
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| 0.0342 | 41.67 | 2500 | 0.3404 | 0.9246 | 0.9364 | 0.9305 | 0.9334 | |
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| 0.0342 | 45.83 | 2750 | 0.3386 | 0.9354 | 0.9431 | 0.9392 | 0.9355 | |
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| 0.0226 | 50.0 | 3000 | 0.3274 | 0.9354 | 0.9431 | 0.9392 | 0.9359 | |
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| 0.0226 | 54.17 | 3250 | 0.3282 | 0.9341 | 0.9446 | 0.9393 | 0.9393 | |
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| 0.017 | 58.33 | 3500 | 0.3475 | 0.9319 | 0.9424 | 0.9371 | 0.9363 | |
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| 0.017 | 62.5 | 3750 | 0.3367 | 0.9340 | 0.9431 | 0.9385 | 0.9372 | |
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| 0.0145 | 66.67 | 4000 | 0.3434 | 0.9325 | 0.9416 | 0.9371 | 0.9363 | |
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### Framework versions |
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- Transformers 4.21.2 |
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- Pytorch 1.12.1+cu113 |
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- Datasets 2.4.0 |
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- Tokenizers 0.12.1 |
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