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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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- doc_lay_net-small |
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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-DocLayNet-test |
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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: doc_lay_net-small |
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type: doc_lay_net-small |
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config: DocLayNet_2022.08_processed_on_2023.01 |
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split: test |
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args: DocLayNet_2022.08_processed_on_2023.01 |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.6647646219686163 |
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- name: Recall |
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type: recall |
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value: 0.6763425253991292 |
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- name: F1 |
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type: f1 |
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value: 0.6705035971223021 |
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- name: Accuracy |
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type: accuracy |
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value: 0.8582839474362278 |
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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-DocLayNet-test |
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This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the doc_lay_net-small dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.8293 |
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- Precision: 0.6648 |
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- Recall: 0.6763 |
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- F1: 0.6705 |
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- Accuracy: 0.8583 |
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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: 2 |
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- eval_batch_size: 2 |
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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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- lr_scheduler_warmup_ratio: 0.1 |
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- training_steps: 1000 |
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- mixed_precision_training: Native AMP |
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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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| 1.5039 | 0.3660 | 250 | 1.1856 | 0.1597 | 0.2785 | 0.2030 | 0.5852 | |
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| 0.8176 | 0.7321 | 500 | 0.6027 | 0.4143 | 0.5506 | 0.4728 | 0.8651 | |
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| 0.5533 | 1.0981 | 750 | 0.6755 | 0.5946 | 0.6266 | 0.6102 | 0.8649 | |
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| 0.4021 | 1.4641 | 1000 | 0.6233 | 0.6017 | 0.6646 | 0.6316 | 0.8804 | |
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### Framework versions |
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- Transformers 4.42.4 |
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- Pytorch 2.3.1+cu121 |
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- Datasets 2.20.0 |
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- Tokenizers 0.19.1 |
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