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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: 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: 0.9135893648449039 |
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- name: Recall |
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type: recall |
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value: 0.9258982035928144 |
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- name: F1 |
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type: f1 |
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value: 0.9197026022304833 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9252971137521222 |
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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: 0.3248 |
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- Precision: 0.9136 |
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- Recall: 0.9259 |
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- F1: 0.9197 |
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- Accuracy: 0.9253 |
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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 | 1.0188 | 0.7447 | 0.7949 | 0.7690 | 0.8031 | |
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| 1.4061 | 8.33 | 500 | 0.5545 | 0.8420 | 0.8653 | 0.8535 | 0.8616 | |
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| 1.4061 | 12.5 | 750 | 0.4298 | 0.8884 | 0.9057 | 0.8970 | 0.9045 | |
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| 0.3563 | 16.67 | 1000 | 0.3477 | 0.9094 | 0.9244 | 0.9169 | 0.9295 | |
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| 0.3563 | 20.83 | 1250 | 0.3189 | 0.9137 | 0.9274 | 0.9205 | 0.9312 | |
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| 0.1617 | 25.0 | 1500 | 0.3189 | 0.9210 | 0.9341 | 0.9275 | 0.9393 | |
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| 0.1617 | 29.17 | 1750 | 0.3158 | 0.9096 | 0.9259 | 0.9177 | 0.9300 | |
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| 0.0942 | 33.33 | 2000 | 0.3198 | 0.9117 | 0.9274 | 0.9195 | 0.9283 | |
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| 0.0942 | 37.5 | 2250 | 0.3259 | 0.9112 | 0.9289 | 0.9199 | 0.9300 | |
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| 0.0674 | 41.67 | 2500 | 0.3248 | 0.9136 | 0.9259 | 0.9197 | 0.9253 | |
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### Framework versions |
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- Transformers 4.26.1 |
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- Pytorch 1.13.1+cu116 |
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- Datasets 2.10.0 |
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- Tokenizers 0.13.2 |
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