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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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- my_csv_dataset3 |
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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: pharma_label_v3.1 |
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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: my_csv_dataset3 |
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type: my_csv_dataset3 |
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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: 0.9623287671232876 |
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- name: Recall |
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type: recall |
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value: 0.9740034662045061 |
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- name: F1 |
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type: f1 |
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value: 0.9681309216192937 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9890616004605642 |
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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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# pharma_label_v3.1 |
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This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the my_csv_dataset3 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0671 |
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- Precision: 0.9623 |
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- Recall: 0.9740 |
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- F1: 0.9681 |
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- Accuracy: 0.9891 |
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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: 1500 |
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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 | 1.2987 | 100 | 0.5492 | 0.7759 | 0.7140 | 0.7437 | 0.9102 | |
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| No log | 2.5974 | 200 | 0.1522 | 0.9281 | 0.9393 | 0.9337 | 0.9747 | |
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| No log | 3.8961 | 300 | 0.1063 | 0.9332 | 0.9445 | 0.9388 | 0.9793 | |
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| No log | 5.1948 | 400 | 0.0891 | 0.9448 | 0.9497 | 0.9473 | 0.9810 | |
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| 0.375 | 6.4935 | 500 | 0.0879 | 0.9435 | 0.9549 | 0.9492 | 0.9839 | |
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| 0.375 | 7.7922 | 600 | 0.0908 | 0.9485 | 0.9584 | 0.9534 | 0.9822 | |
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| 0.375 | 9.0909 | 700 | 0.0764 | 0.9636 | 0.9636 | 0.9636 | 0.9862 | |
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| 0.375 | 10.3896 | 800 | 0.0819 | 0.9671 | 0.9671 | 0.9671 | 0.9873 | |
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| 0.375 | 11.6883 | 900 | 0.0802 | 0.9686 | 0.9636 | 0.9661 | 0.9873 | |
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| 0.0225 | 12.9870 | 1000 | 0.0602 | 0.9722 | 0.9705 | 0.9714 | 0.9902 | |
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| 0.0225 | 14.2857 | 1100 | 0.0989 | 0.9438 | 0.9601 | 0.9519 | 0.9816 | |
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| 0.0225 | 15.5844 | 1200 | 0.0859 | 0.9538 | 0.9671 | 0.9604 | 0.9839 | |
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| 0.0225 | 16.8831 | 1300 | 0.0781 | 0.9554 | 0.9653 | 0.9603 | 0.9856 | |
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| 0.0225 | 18.1818 | 1400 | 0.0653 | 0.9605 | 0.9705 | 0.9655 | 0.9891 | |
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| 0.0105 | 19.4805 | 1500 | 0.0671 | 0.9623 | 0.9740 | 0.9681 | 0.9891 | |
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### Framework versions |
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- Transformers 4.40.1 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.19.0 |
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- Tokenizers 0.19.1 |
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