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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_vimal |
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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: 0.717948717948718 |
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- name: Recall |
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type: recall |
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value: 0.7368421052631579 |
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- name: F1 |
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type: f1 |
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value: 0.7272727272727273 |
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- name: Accuracy |
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type: accuracy |
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value: 0.7333333333333333 |
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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_vimal |
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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: 1.8321 |
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- Precision: 0.7179 |
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- Recall: 0.7368 |
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- F1: 0.7273 |
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- Accuracy: 0.7333 |
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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 | 125.0 | 250 | 1.2027 | 0.7564 | 0.7763 | 0.7662 | 0.7481 | |
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| 0.8449 | 250.0 | 500 | 1.3990 | 0.7089 | 0.7368 | 0.7226 | 0.7333 | |
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| 0.8449 | 375.0 | 750 | 1.5343 | 0.7179 | 0.7368 | 0.7273 | 0.7333 | |
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| 0.0296 | 500.0 | 1000 | 1.6144 | 0.75 | 0.75 | 0.75 | 0.7407 | |
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| 0.0296 | 625.0 | 1250 | 1.6898 | 0.7179 | 0.7368 | 0.7273 | 0.7333 | |
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| 0.0134 | 750.0 | 1500 | 1.7402 | 0.7179 | 0.7368 | 0.7273 | 0.7333 | |
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| 0.0134 | 875.0 | 1750 | 1.7888 | 0.7179 | 0.7368 | 0.7273 | 0.7333 | |
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| 0.0089 | 1000.0 | 2000 | 1.8041 | 0.7179 | 0.7368 | 0.7273 | 0.7333 | |
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| 0.0089 | 1125.0 | 2250 | 1.8209 | 0.7179 | 0.7368 | 0.7273 | 0.7333 | |
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| 0.0073 | 1250.0 | 2500 | 1.8321 | 0.7179 | 0.7368 | 0.7273 | 0.7333 | |
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### Framework versions |
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- Transformers 4.26.0 |
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- Pytorch 1.13.1+cu116 |
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- Datasets 2.9.0 |
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- Tokenizers 0.13.2 |
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