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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_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: 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: 0.917960088691796 |
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- name: Recall |
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type: recall |
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value: 0.9296407185628742 |
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- name: F1 |
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type: f1 |
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value: 0.9237634808478989 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9303904923599321 |
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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: 0.2854 |
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- Precision: 0.9180 |
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- Recall: 0.9296 |
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- F1: 0.9238 |
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- Accuracy: 0.9304 |
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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: 2 |
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- eval_batch_size: 2 |
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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 | 0.62 | 250 | 1.2967 | 0.6175 | 0.7021 | 0.6571 | 0.7296 | |
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| 1.6872 | 1.25 | 500 | 0.7576 | 0.8140 | 0.8383 | 0.8260 | 0.8383 | |
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| 1.6872 | 1.88 | 750 | 0.5695 | 0.8301 | 0.8518 | 0.8408 | 0.8544 | |
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| 0.6109 | 2.5 | 1000 | 0.4778 | 0.8564 | 0.875 | 0.8656 | 0.8812 | |
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| 0.6109 | 3.12 | 1250 | 0.3825 | 0.8694 | 0.8922 | 0.8807 | 0.8986 | |
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| 0.3905 | 3.75 | 1500 | 0.3546 | 0.8831 | 0.9049 | 0.8939 | 0.9143 | |
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| 0.3905 | 4.38 | 1750 | 0.3153 | 0.8998 | 0.9207 | 0.9101 | 0.9223 | |
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| 0.275 | 5.0 | 2000 | 0.3065 | 0.8926 | 0.9147 | 0.9035 | 0.9202 | |
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| 0.275 | 5.62 | 2250 | 0.2872 | 0.9131 | 0.9281 | 0.9206 | 0.9291 | |
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| 0.2275 | 6.25 | 2500 | 0.2854 | 0.9180 | 0.9296 | 0.9238 | 0.9304 | |
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### Framework versions |
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- Transformers 4.25.1 |
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- Pytorch 1.13.1+cpu |
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- Datasets 2.8.0 |
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- Tokenizers 0.13.2 |
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