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--- |
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library_name: transformers |
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license: cc-by-nc-sa-4.0 |
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base_model: microsoft/layoutlmv3-large |
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tags: |
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- generated_from_trainer |
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datasets: |
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- mp-02/cord |
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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-large-cord |
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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: mp-02/cord |
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type: mp-02/cord |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.970467596390484 |
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- name: Recall |
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type: recall |
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value: 0.980115990057995 |
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- name: F1 |
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type: f1 |
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value: 0.975267930750206 |
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- name: Accuracy |
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type: accuracy |
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value: 0.973924977127173 |
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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-large-cord |
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This model is a fine-tuned version of [microsoft/layoutlmv3-large](https://huggingface.co/microsoft/layoutlmv3-large) on the mp-02/cord dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1373 |
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- Precision: 0.9705 |
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- Recall: 0.9801 |
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- F1: 0.9753 |
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- Accuracy: 0.9739 |
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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: 10 |
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- eval_batch_size: 10 |
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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: 1000 |
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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 | 1.25 | 100 | 0.5589 | 0.8209 | 0.8583 | 0.8392 | 0.8394 | |
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| No log | 2.5 | 200 | 0.1936 | 0.9433 | 0.9644 | 0.9537 | 0.9579 | |
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| No log | 3.75 | 300 | 0.1456 | 0.9569 | 0.9760 | 0.9664 | 0.9698 | |
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| No log | 5.0 | 400 | 0.1368 | 0.9584 | 0.9743 | 0.9663 | 0.9726 | |
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| 0.4619 | 6.25 | 500 | 0.1448 | 0.9689 | 0.9809 | 0.9749 | 0.9744 | |
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| 0.4619 | 7.5 | 600 | 0.1286 | 0.9689 | 0.9818 | 0.9753 | 0.9753 | |
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| 0.4619 | 8.75 | 700 | 0.1311 | 0.9697 | 0.9809 | 0.9753 | 0.9748 | |
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| 0.4619 | 10.0 | 800 | 0.1335 | 0.9721 | 0.9809 | 0.9765 | 0.9758 | |
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| 0.4619 | 11.25 | 900 | 0.1355 | 0.9689 | 0.9793 | 0.9740 | 0.9753 | |
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| 0.0424 | 12.5 | 1000 | 0.1373 | 0.9705 | 0.9801 | 0.9753 | 0.9739 | |
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### Framework versions |
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- Transformers 4.44.2 |
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- Pytorch 2.4.0+cu118 |
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- Datasets 2.21.0 |
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- Tokenizers 0.19.1 |
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