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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: 0.9393042190969653 |
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- name: Recall |
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type: recall |
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value: 0.9498502994011976 |
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- name: F1 |
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type: f1 |
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value: 0.9445478228507629 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9494906621392191 |
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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: 0.2454 |
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- Precision: 0.9393 |
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- Recall: 0.9499 |
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- F1: 0.9445 |
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- Accuracy: 0.9495 |
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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 | 2.5 | 250 | 1.0544 | 0.7297 | 0.7822 | 0.7551 | 0.7852 | |
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| 1.4348 | 5.0 | 500 | 0.5651 | 0.8477 | 0.8705 | 0.8589 | 0.8693 | |
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| 1.4348 | 7.5 | 750 | 0.4012 | 0.8833 | 0.9012 | 0.8922 | 0.9083 | |
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| 0.4052 | 10.0 | 1000 | 0.3168 | 0.9208 | 0.9311 | 0.9259 | 0.9338 | |
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| 0.4052 | 12.5 | 1250 | 0.2823 | 0.9304 | 0.9401 | 0.9352 | 0.9410 | |
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| 0.2039 | 15.0 | 1500 | 0.2626 | 0.9242 | 0.9394 | 0.9317 | 0.9397 | |
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| 0.2039 | 17.5 | 1750 | 0.2504 | 0.9305 | 0.9424 | 0.9364 | 0.9448 | |
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| 0.1333 | 20.0 | 2000 | 0.2425 | 0.9324 | 0.9491 | 0.9407 | 0.9503 | |
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| 0.1333 | 22.5 | 2250 | 0.2442 | 0.9371 | 0.9484 | 0.9427 | 0.9486 | |
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| 0.1042 | 25.0 | 2500 | 0.2454 | 0.9393 | 0.9499 | 0.9445 | 0.9495 | |
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### Framework versions |
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- Transformers 4.31.0 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.4 |
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- Tokenizers 0.13.3 |
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