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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: 0.9415247964470762 |
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- name: Recall |
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type: recall |
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value: 0.9520958083832335 |
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- name: F1 |
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type: f1 |
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value: 0.9467807964272422 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9575551782682513 |
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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: 0.2246 |
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- Precision: 0.9415 |
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- Recall: 0.9521 |
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- F1: 0.9468 |
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- Accuracy: 0.9576 |
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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 | 1.56 | 250 | 1.0265 | 0.7630 | 0.8099 | 0.7858 | 0.8086 | |
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| 1.4021 | 3.12 | 500 | 0.5804 | 0.8290 | 0.8638 | 0.8460 | 0.8718 | |
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| 1.4021 | 4.69 | 750 | 0.3937 | 0.8882 | 0.9034 | 0.8957 | 0.9126 | |
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| 0.4062 | 6.25 | 1000 | 0.3171 | 0.9137 | 0.9274 | 0.9205 | 0.9351 | |
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| 0.4062 | 7.81 | 1250 | 0.2798 | 0.9332 | 0.9409 | 0.9370 | 0.9444 | |
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| 0.2212 | 9.38 | 1500 | 0.2558 | 0.9277 | 0.9416 | 0.9346 | 0.9461 | |
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| 0.2212 | 10.94 | 1750 | 0.2479 | 0.9335 | 0.9454 | 0.9394 | 0.9516 | |
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| 0.1525 | 12.5 | 2000 | 0.2356 | 0.9444 | 0.9536 | 0.9490 | 0.9588 | |
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| 0.1525 | 14.06 | 2250 | 0.2286 | 0.9365 | 0.9491 | 0.9428 | 0.9563 | |
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| 0.1134 | 15.62 | 2500 | 0.2246 | 0.9415 | 0.9521 | 0.9468 | 0.9576 | |
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### Framework versions |
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- Transformers 4.25.1 |
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- Pytorch 1.13.0+cu116 |
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- Datasets 2.8.0 |
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- Tokenizers 0.13.2 |
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