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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: 0.9485842026825634 |
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- name: Recall |
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type: recall |
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value: 0.9528443113772455 |
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- name: F1 |
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type: f1 |
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value: 0.9507094846900671 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9592529711375212 |
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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: 0.1978 |
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- Precision: 0.9486 |
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- Recall: 0.9528 |
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- F1: 0.9507 |
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- Accuracy: 0.9593 |
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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 | 0.9543 | 0.7832 | 0.8166 | 0.7996 | 0.8226 | |
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| 1.3644 | 3.12 | 500 | 0.5338 | 0.8369 | 0.8683 | 0.8523 | 0.8824 | |
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| 1.3644 | 4.69 | 750 | 0.3658 | 0.8840 | 0.9072 | 0.8955 | 0.9232 | |
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| 0.3802 | 6.25 | 1000 | 0.3019 | 0.9156 | 0.9251 | 0.9203 | 0.9334 | |
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| 0.3802 | 7.81 | 1250 | 0.2833 | 0.9094 | 0.9237 | 0.9165 | 0.9346 | |
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| 0.2061 | 9.38 | 1500 | 0.2241 | 0.9377 | 0.9469 | 0.9423 | 0.9525 | |
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| 0.2061 | 10.94 | 1750 | 0.2282 | 0.9304 | 0.9409 | 0.9356 | 0.9474 | |
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| 0.1416 | 12.5 | 2000 | 0.2017 | 0.9509 | 0.9566 | 0.9537 | 0.9610 | |
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| 0.1416 | 14.06 | 2250 | 0.2006 | 0.9472 | 0.9536 | 0.9504 | 0.9614 | |
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| 0.1056 | 15.62 | 2500 | 0.1978 | 0.9486 | 0.9528 | 0.9507 | 0.9593 | |
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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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