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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: 0.9328908554572272 |
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- name: Recall |
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type: recall |
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value: 0.9468562874251497 |
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- name: F1 |
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type: f1 |
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value: 0.9398216939078752 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9516129032258065 |
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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: 0.2213 |
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- Precision: 0.9329 |
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- Recall: 0.9469 |
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- F1: 0.9398 |
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- Accuracy: 0.9516 |
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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 | 1.0664 | 0.6765 | 0.7530 | 0.7127 | 0.7818 | |
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| 1.4379 | 3.12 | 500 | 0.6115 | 0.8199 | 0.8518 | 0.8355 | 0.8646 | |
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| 1.4379 | 4.69 | 750 | 0.4192 | 0.8794 | 0.9004 | 0.8898 | 0.9028 | |
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| 0.4232 | 6.25 | 1000 | 0.3239 | 0.9180 | 0.9296 | 0.9238 | 0.9304 | |
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| 0.4232 | 7.81 | 1250 | 0.2840 | 0.9197 | 0.9341 | 0.9268 | 0.9389 | |
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| 0.2273 | 9.38 | 1500 | 0.2562 | 0.9217 | 0.9341 | 0.9279 | 0.9376 | |
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| 0.2273 | 10.94 | 1750 | 0.2574 | 0.9304 | 0.9401 | 0.9352 | 0.9410 | |
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| 0.157 | 12.5 | 2000 | 0.2327 | 0.9293 | 0.9439 | 0.9365 | 0.9482 | |
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| 0.157 | 14.06 | 2250 | 0.2217 | 0.9351 | 0.9491 | 0.9421 | 0.9520 | |
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| 0.1208 | 15.62 | 2500 | 0.2213 | 0.9329 | 0.9469 | 0.9398 | 0.9516 | |
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### Framework versions |
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- Transformers 4.23.1 |
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- Pytorch 1.12.1+cu113 |
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- Datasets 2.6.1 |
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- Tokenizers 0.13.1 |
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