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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: 0.9266666666666666 |
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- name: Recall |
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type: recall |
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value: 0.936377245508982 |
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- name: F1 |
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type: f1 |
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value: 0.9314966492926285 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9354838709677419 |
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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: 0.3194 |
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- Precision: 0.9267 |
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- Recall: 0.9364 |
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- F1: 0.9315 |
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- Accuracy: 0.9355 |
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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 | 4.17 | 250 | 1.0054 | 0.7555 | 0.8024 | 0.7782 | 0.8081 | |
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| 1.4019 | 8.33 | 500 | 0.5287 | 0.8320 | 0.8638 | 0.8476 | 0.8739 | |
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| 1.4019 | 12.5 | 750 | 0.3790 | 0.9043 | 0.9192 | 0.9117 | 0.9236 | |
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| 0.3185 | 16.67 | 1000 | 0.3253 | 0.9178 | 0.9281 | 0.9230 | 0.9355 | |
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| 0.3185 | 20.83 | 1250 | 0.3231 | 0.9223 | 0.9334 | 0.9278 | 0.9304 | |
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| 0.1319 | 25.0 | 1500 | 0.3039 | 0.9317 | 0.9394 | 0.9355 | 0.9419 | |
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| 0.1319 | 29.17 | 1750 | 0.3142 | 0.9287 | 0.9364 | 0.9325 | 0.9334 | |
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| 0.0725 | 33.33 | 2000 | 0.2982 | 0.9296 | 0.9386 | 0.9341 | 0.9419 | |
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| 0.0725 | 37.5 | 2250 | 0.3189 | 0.9288 | 0.9371 | 0.9329 | 0.9346 | |
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| 0.0549 | 41.67 | 2500 | 0.3194 | 0.9267 | 0.9364 | 0.9315 | 0.9355 | |
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### Framework versions |
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- Transformers 4.36.2 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.16.1 |
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- Tokenizers 0.15.0 |
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