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--- |
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library_name: transformers |
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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: 0.8836524300441826 |
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- name: Recall |
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type: recall |
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value: 0.8982035928143712 |
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- name: F1 |
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type: f1 |
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value: 0.8908685968819599 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9057724957555179 |
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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: 0.3809 |
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- Precision: 0.8837 |
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- Recall: 0.8982 |
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- F1: 0.8909 |
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- Accuracy: 0.9058 |
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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: 1 |
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- eval_batch_size: 1 |
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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 | 0.3125 | 250 | 1.4179 | 0.5786 | 0.6751 | 0.6231 | 0.7037 | |
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| 1.8601 | 0.625 | 500 | 0.9021 | 0.7458 | 0.8016 | 0.7727 | 0.7988 | |
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| 1.8601 | 0.9375 | 750 | 0.6900 | 0.8096 | 0.8338 | 0.8215 | 0.8294 | |
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| 0.7675 | 1.25 | 1000 | 0.5915 | 0.8128 | 0.8481 | 0.8300 | 0.8544 | |
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| 0.7675 | 1.5625 | 1250 | 0.5041 | 0.8381 | 0.8638 | 0.8507 | 0.8722 | |
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| 0.4979 | 1.875 | 1500 | 0.4669 | 0.8413 | 0.8728 | 0.8567 | 0.8850 | |
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| 0.4979 | 2.1875 | 1750 | 0.4080 | 0.8628 | 0.8847 | 0.8736 | 0.8990 | |
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| 0.384 | 2.5 | 2000 | 0.3878 | 0.8731 | 0.8907 | 0.8818 | 0.9003 | |
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| 0.384 | 2.8125 | 2250 | 0.3880 | 0.8794 | 0.8952 | 0.8872 | 0.9032 | |
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| 0.3439 | 3.125 | 2500 | 0.3809 | 0.8837 | 0.8982 | 0.8909 | 0.9058 | |
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### Framework versions |
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- Transformers 4.45.1 |
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- Pytorch 2.4.1+cu121 |
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- Datasets 3.0.1 |
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- Tokenizers 0.20.0 |
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