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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-v3 |
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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: 0.7776378519384726 |
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- name: Recall |
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type: recall |
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value: 0.7946502435885652 |
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- name: F1 |
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type: f1 |
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value: 0.7860520094562647 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9908687950002661 |
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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-v3 |
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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: 0.0354 |
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- Precision: 0.7776 |
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- Recall: 0.7947 |
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- F1: 0.7861 |
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- Accuracy: 0.9909 |
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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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| 0.0125 | 1.0 | 1838 | 0.1338 | 0.3514 | 0.3812 | 0.3657 | 0.9715 | |
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| 0.0032 | 2.0 | 3676 | 0.0856 | 0.4444 | 0.5156 | 0.4774 | 0.9778 | |
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| 0.0012 | 3.0 | 5514 | 0.0678 | 0.5222 | 0.5994 | 0.5581 | 0.9819 | |
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| 0.0006 | 4.0 | 7352 | 0.0547 | 0.6900 | 0.7025 | 0.6962 | 0.9865 | |
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| 0.018 | 5.0 | 9190 | 0.0466 | 0.7227 | 0.7468 | 0.7345 | 0.9881 | |
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| 0.0002 | 6.0 | 11028 | 0.0419 | 0.7396 | 0.7664 | 0.7528 | 0.9891 | |
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| 0.0002 | 7.0 | 12866 | 0.0390 | 0.7730 | 0.7693 | 0.7712 | 0.9901 | |
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| 0.0002 | 8.0 | 14704 | 0.0368 | 0.7778 | 0.7822 | 0.7800 | 0.9906 | |
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| 0.0001 | 9.0 | 16542 | 0.0359 | 0.7765 | 0.7898 | 0.7831 | 0.9907 | |
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| 0.0001 | 10.0 | 18380 | 0.0354 | 0.7776 | 0.7947 | 0.7861 | 0.9909 | |
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### Framework versions |
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- Transformers 4.21.3 |
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- Pytorch 1.12.1+cu113 |
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- Datasets 2.4.0 |
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- Tokenizers 0.12.1 |
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