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--- |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: ner_model_ep3 |
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results: [] |
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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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# ner_model_ep3 |
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This model was trained from scratch on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3874 |
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- allergy Name F1: 0.7968 |
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- allergy Name Pres: 0.7706 |
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- allergy Name Rec: 0.8249 |
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- cancer F1: 0.7556 |
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- cancer Pres: 0.7589 |
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- cancer Rec: 0.7524 |
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- chronic Disease F1: 0.7776 |
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- chronic Disease Pres: 0.7562 |
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- chronic Disease Rec: 0.8002 |
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- treatment F1: 0.7804 |
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- treatmen Prest: 0.7620 |
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- treatment Rec: 0.7996 |
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- Over All Precision: 0.7596 |
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- Over All Recall: 0.7936 |
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- Over All F1: 0.7762 |
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- Over All Accuracy: 0.8806 |
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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: 2e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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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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- num_epochs: 5 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | allergy Name F1 | allergy Name Pres | allergy Name Rec | cancer F1 | cancer Pres | cancer Rec | chronic Disease F1 | chronic Disease Pres | chronic Disease Rec | treatment F1 | treatmen Prest | treatment Rec | Over All Precision | Over All Recall | Over All F1 | Over All Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:----------------:|:------------------:|:-----------------:|:----------:|:------------:|:-----------:|:-------------------:|:---------------------:|:--------------------:|:-------------:|:---------------:|:--------------:|:------------------:|:---------------:|:-----------:|:-----------------:| |
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| 0.3761 | 1.0 | 324 | 0.3480 | 0.7346 | 0.6720 | 0.8099 | 0.7108 | 0.7584 | 0.6688 | 0.7657 | 0.7619 | 0.7695 | 0.7700 | 0.7437 | 0.7983 | 0.7499 | 0.7687 | 0.7592 | 0.8738 | |
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| 0.29 | 2.0 | 648 | 0.3548 | 0.7593 | 0.7023 | 0.8263 | 0.7406 | 0.7683 | 0.7149 | 0.7710 | 0.7608 | 0.7816 | 0.7738 | 0.7435 | 0.8067 | 0.7519 | 0.7842 | 0.7677 | 0.8775 | |
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| 0.232 | 3.0 | 972 | 0.3579 | 0.8046 | 0.7787 | 0.8323 | 0.7446 | 0.7472 | 0.7421 | 0.7763 | 0.7568 | 0.7968 | 0.7798 | 0.7658 | 0.7944 | 0.7601 | 0.7887 | 0.7741 | 0.8809 | |
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| 0.1945 | 4.0 | 1296 | 0.3829 | 0.7942 | 0.7645 | 0.8263 | 0.7463 | 0.7678 | 0.7260 | 0.7749 | 0.7584 | 0.7920 | 0.7808 | 0.7683 | 0.7938 | 0.7643 | 0.7840 | 0.7741 | 0.8792 | |
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| 0.1734 | 5.0 | 1620 | 0.3874 | 0.7968 | 0.7706 | 0.8249 | 0.7556 | 0.7589 | 0.7524 | 0.7776 | 0.7562 | 0.8002 | 0.7804 | 0.7620 | 0.7996 | 0.7596 | 0.7936 | 0.7762 | 0.8806 | |
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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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