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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: 0.9022777369581191 |
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- name: Recall |
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type: recall |
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value: 0.9191616766467066 |
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- name: F1 |
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type: f1 |
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value: 0.9106414534668149 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9202037351443124 |
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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: 0.3848 |
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- Precision: 0.9023 |
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- Recall: 0.9192 |
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- F1: 0.9106 |
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- Accuracy: 0.9202 |
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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 | 6.25 | 250 | 0.9576 | 0.7878 | 0.8196 | 0.8034 | 0.8166 | |
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| 1.3167 | 12.5 | 500 | 0.5210 | 0.8536 | 0.8772 | 0.8653 | 0.8846 | |
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| 1.3167 | 18.75 | 750 | 0.4077 | 0.8798 | 0.9042 | 0.8918 | 0.9113 | |
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| 0.2603 | 25.0 | 1000 | 0.3943 | 0.8902 | 0.9102 | 0.9001 | 0.9147 | |
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| 0.2603 | 31.25 | 1250 | 0.3691 | 0.8980 | 0.9162 | 0.9070 | 0.9194 | |
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| 0.1009 | 37.5 | 1500 | 0.3496 | 0.9130 | 0.9274 | 0.9202 | 0.9266 | |
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| 0.1009 | 43.75 | 1750 | 0.3700 | 0.9078 | 0.9214 | 0.9146 | 0.9266 | |
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| 0.056 | 50.0 | 2000 | 0.3724 | 0.9065 | 0.9214 | 0.9139 | 0.9215 | |
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| 0.056 | 56.25 | 2250 | 0.3773 | 0.9051 | 0.9207 | 0.9128 | 0.9202 | |
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| 0.0413 | 62.5 | 2500 | 0.3848 | 0.9023 | 0.9192 | 0.9106 | 0.9202 | |
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### Framework versions |
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- Transformers 4.22.1 |
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- Pytorch 1.12.1+cu113 |
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- Datasets 2.5.1 |
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- Tokenizers 0.12.1 |
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