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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: 0.596078431372549 |
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- name: Recall |
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type: recall |
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value: 0.6826347305389222 |
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- name: F1 |
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type: f1 |
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value: 0.636427076064201 |
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- name: Accuracy |
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type: accuracy |
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value: 0.684634974533107 |
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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: 1.9357 |
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- Precision: 0.5961 |
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- Recall: 0.6826 |
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- F1: 0.6364 |
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- Accuracy: 0.6846 |
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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 | 250.0 | 250 | 1.5298 | 0.5778 | 0.6781 | 0.6240 | 0.6825 | |
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| 0.6654 | 500.0 | 500 | 1.6175 | 0.5942 | 0.6849 | 0.6363 | 0.6880 | |
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| 0.6654 | 750.0 | 750 | 1.7087 | 0.5947 | 0.6841 | 0.6363 | 0.6876 | |
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| 0.0208 | 1000.0 | 1000 | 1.7729 | 0.5948 | 0.6834 | 0.6360 | 0.6859 | |
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| 0.0208 | 1250.0 | 1250 | 1.8273 | 0.5949 | 0.6826 | 0.6358 | 0.6851 | |
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| 0.0099 | 1500.0 | 1500 | 1.8693 | 0.5957 | 0.6826 | 0.6362 | 0.6846 | |
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| 0.0099 | 1750.0 | 1750 | 1.8969 | 0.5950 | 0.6819 | 0.6355 | 0.6842 | |
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| 0.0066 | 2000.0 | 2000 | 1.9196 | 0.5972 | 0.6826 | 0.6371 | 0.6842 | |
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| 0.0066 | 2250.0 | 2250 | 1.9312 | 0.5946 | 0.6819 | 0.6353 | 0.6838 | |
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| 0.0054 | 2500.0 | 2500 | 1.9357 | 0.5961 | 0.6826 | 0.6364 | 0.6846 | |
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### Framework versions |
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- Transformers 4.34.0 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.5 |
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- Tokenizers 0.14.1 |
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