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--- |
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license: cc-by-nc-sa-4.0 |
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base_model: microsoft/layoutlmv3-base |
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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: 0.9458054936896808 |
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- name: Recall |
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type: recall |
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value: 0.9535928143712575 |
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- name: F1 |
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type: f1 |
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value: 0.9496831904584422 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9588285229202037 |
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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: 0.2033 |
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- Precision: 0.9458 |
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- Recall: 0.9536 |
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- F1: 0.9497 |
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- Accuracy: 0.9588 |
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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 | 1.0015 | 0.7227 | 0.7822 | 0.7513 | 0.7963 | |
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| 1.3862 | 3.12 | 500 | 0.5334 | 0.8591 | 0.8765 | 0.8677 | 0.8837 | |
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| 1.3862 | 4.69 | 750 | 0.3689 | 0.8925 | 0.9072 | 0.8998 | 0.9164 | |
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| 0.3835 | 6.25 | 1000 | 0.2877 | 0.9281 | 0.9371 | 0.9326 | 0.9431 | |
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| 0.3835 | 7.81 | 1250 | 0.2506 | 0.9312 | 0.9424 | 0.9368 | 0.9452 | |
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| 0.2048 | 9.38 | 1500 | 0.2373 | 0.9480 | 0.9543 | 0.9511 | 0.9554 | |
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| 0.2048 | 10.94 | 1750 | 0.2184 | 0.9379 | 0.9491 | 0.9435 | 0.9542 | |
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| 0.1365 | 12.5 | 2000 | 0.2057 | 0.9393 | 0.9506 | 0.9449 | 0.9567 | |
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| 0.1365 | 14.06 | 2250 | 0.2024 | 0.9487 | 0.9543 | 0.9515 | 0.9576 | |
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| 0.1067 | 15.62 | 2500 | 0.2033 | 0.9458 | 0.9536 | 0.9497 | 0.9588 | |
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### Framework versions |
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- Transformers 4.35.0 |
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- Pytorch 2.1.0+cu118 |
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- Datasets 2.14.6 |
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- Tokenizers 0.14.1 |
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