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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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- layoutlm_v3 |
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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: layoutlm_v3 |
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type: layoutlm_v3 |
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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: 0.9297856614929786 |
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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: 0.9356638155448121 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9393039049235993 |
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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 layoutlm_v3 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2976 |
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- Precision: 0.9298 |
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- Recall: 0.9416 |
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- F1: 0.9357 |
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- Accuracy: 0.9393 |
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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 | 1.0222 | 0.7468 | 0.7949 | 0.7701 | 0.8014 | |
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| 1.3962 | 8.33 | 500 | 0.5292 | 0.8414 | 0.8735 | 0.8571 | 0.8778 | |
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| 1.3962 | 12.5 | 750 | 0.3844 | 0.9049 | 0.9192 | 0.9120 | 0.9249 | |
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| 0.335 | 16.67 | 1000 | 0.3302 | 0.9243 | 0.9326 | 0.9285 | 0.9342 | |
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| 0.335 | 20.83 | 1250 | 0.3062 | 0.9204 | 0.9349 | 0.9276 | 0.9406 | |
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| 0.1419 | 25.0 | 1500 | 0.2931 | 0.9268 | 0.9386 | 0.9327 | 0.9414 | |
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| 0.1419 | 29.17 | 1750 | 0.2925 | 0.9248 | 0.9386 | 0.9316 | 0.9359 | |
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| 0.0801 | 33.33 | 2000 | 0.2963 | 0.9276 | 0.9394 | 0.9334 | 0.9359 | |
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| 0.0801 | 37.5 | 2250 | 0.2916 | 0.9283 | 0.9401 | 0.9342 | 0.9363 | |
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| 0.0584 | 41.67 | 2500 | 0.2976 | 0.9298 | 0.9416 | 0.9357 | 0.9393 | |
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### Framework versions |
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- Transformers 4.39.3 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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