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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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- dataset |
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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: sougemi_model |
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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: dataset |
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type: dataset |
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config: discharge |
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split: test |
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args: discharge |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.845360824742268 |
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- name: Recall |
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type: recall |
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value: 0.8913043478260869 |
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- name: F1 |
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type: f1 |
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value: 0.8677248677248677 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9533678756476683 |
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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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# sougemi_model |
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This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the dataset created. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1812 |
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- Precision: 0.8454 |
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- Recall: 0.8913 |
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- F1: 0.8677 |
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- Accuracy: 0.9534 |
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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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- training_steps: 1000 |
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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 | 33.33 | 100 | 0.7803 | 0.8966 | 0.8478 | 0.8715 | 0.9663 | |
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| No log | 66.67 | 200 | 0.3016 | 0.8696 | 0.8696 | 0.8696 | 0.9767 | |
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| No log | 100.0 | 300 | 0.1623 | 0.9130 | 0.9130 | 0.9130 | 0.9819 | |
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| No log | 133.33 | 400 | 0.1680 | 0.8454 | 0.8913 | 0.8677 | 0.9637 | |
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| 0.5801 | 166.67 | 500 | 0.1812 | 0.8454 | 0.8913 | 0.8677 | 0.9534 | |
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| 0.5801 | 200.0 | 600 | 0.1231 | 0.8947 | 0.9239 | 0.9091 | 0.9715 | |
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| 0.5801 | 233.33 | 700 | 0.1363 | 0.8617 | 0.8804 | 0.8710 | 0.9663 | |
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| 0.5801 | 266.67 | 800 | 0.1949 | 0.8333 | 0.8696 | 0.8511 | 0.9508 | |
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| 0.5801 | 300.0 | 900 | 0.1749 | 0.8163 | 0.8696 | 0.8421 | 0.9534 | |
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| 0.0607 | 333.33 | 1000 | 0.1817 | 0.8163 | 0.8696 | 0.8421 | 0.9534 | |
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### Framework versions |
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- Transformers 4.26.0.dev0 |
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- Pytorch 1.13.0+cu116 |
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- Datasets 2.8.0 |
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- Tokenizers 0.13.2 |
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