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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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- cne-layoutlmv3-data |
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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-cne_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: cne-layoutlmv3-data |
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type: cne-layoutlmv3-data |
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config: cne-dataset |
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split: test |
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args: cne-dataset |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.9950738916256158 |
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- name: Recall |
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type: recall |
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value: 0.9950738916256158 |
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- name: F1 |
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type: f1 |
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value: 0.9950738916256159 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9992716678805535 |
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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-cne_100 |
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This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the cne-layoutlmv3-data dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0008 |
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- Precision: 0.9951 |
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- Recall: 0.9951 |
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- F1: 0.9951 |
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- Accuracy: 0.9993 |
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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: 3 |
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- eval_batch_size: 3 |
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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 | 7.81 | 250 | 0.0028 | 0.9951 | 0.9951 | 0.9951 | 0.9993 | |
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| 0.0229 | 15.62 | 500 | 0.0015 | 0.9951 | 0.9951 | 0.9951 | 0.9993 | |
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| 0.0229 | 23.44 | 750 | 0.0011 | 0.9951 | 0.9951 | 0.9951 | 0.9993 | |
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| 0.0031 | 31.25 | 1000 | 0.0009 | 0.9951 | 0.9951 | 0.9951 | 0.9993 | |
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| 0.0031 | 39.06 | 1250 | 0.0009 | 0.9951 | 0.9951 | 0.9951 | 0.9993 | |
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| 0.0019 | 46.88 | 1500 | 0.0008 | 0.9951 | 0.9951 | 0.9951 | 0.9993 | |
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| 0.0019 | 54.69 | 1750 | 0.0008 | 0.9951 | 0.9951 | 0.9951 | 0.9993 | |
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| 0.0014 | 62.5 | 2000 | 0.0008 | 0.9951 | 0.9951 | 0.9951 | 0.9993 | |
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| 0.0014 | 70.31 | 2250 | 0.0008 | 0.9951 | 0.9951 | 0.9951 | 0.9993 | |
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| 0.001 | 78.12 | 2500 | 0.0008 | 0.9951 | 0.9951 | 0.9951 | 0.9993 | |
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### Framework versions |
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- Transformers 4.30.2 |
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- Pytorch 2.0.1 |
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- Datasets 2.13.1 |
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- Tokenizers 0.13.3 |
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