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--- |
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tags: |
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- generated_from_trainer |
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datasets: |
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- i2b22014 |
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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: electramed-small-deid2014-ner-v5-classweights |
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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: i2b22014 |
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type: i2b22014 |
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config: i2b22014-deid |
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split: train |
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args: i2b22014-deid |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.8832236842105263 |
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- name: Recall |
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type: recall |
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value: 0.6910561632502987 |
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- name: F1 |
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type: f1 |
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value: 0.7754112732711052 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9883040491052534 |
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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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# electramed-small-deid2014-ner-v5-classweights |
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This model is a fine-tuned version of [giacomomiolo/electramed_small_scivocab](https://huggingface.co./giacomomiolo/electramed_small_scivocab) on the i2b22014 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0009 |
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- Precision: 0.8832 |
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- Recall: 0.6911 |
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- F1: 0.7754 |
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- Accuracy: 0.9883 |
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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: 10 |
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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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| 0.0001 | 1.0 | 1838 | 0.0008 | 0.7702 | 0.3780 | 0.5071 | 0.9771 | |
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| 0.0 | 2.0 | 3676 | 0.0007 | 0.8753 | 0.5671 | 0.6883 | 0.9827 | |
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| 0.0 | 3.0 | 5514 | 0.0006 | 0.8074 | 0.4128 | 0.5463 | 0.9775 | |
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| 0.0 | 4.0 | 7352 | 0.0007 | 0.8693 | 0.6102 | 0.7170 | 0.9848 | |
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| 0.0 | 5.0 | 9190 | 0.0006 | 0.8710 | 0.6022 | 0.7121 | 0.9849 | |
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| 0.0 | 6.0 | 11028 | 0.0007 | 0.8835 | 0.6547 | 0.7521 | 0.9867 | |
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| 0.0 | 7.0 | 12866 | 0.0009 | 0.8793 | 0.6661 | 0.7579 | 0.9873 | |
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| 0.0 | 8.0 | 14704 | 0.0008 | 0.8815 | 0.6740 | 0.7639 | 0.9876 | |
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| 0.0 | 9.0 | 16542 | 0.0009 | 0.8812 | 0.6851 | 0.7709 | 0.9880 | |
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| 0.0 | 10.0 | 18380 | 0.0009 | 0.8832 | 0.6911 | 0.7754 | 0.9883 | |
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### Framework versions |
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- Transformers 4.22.1 |
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- Pytorch 1.12.1+cu113 |
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- Datasets 2.5.1 |
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- Tokenizers 0.12.1 |
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