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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-v4 |
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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: 0.7571112095702259 |
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- name: Recall |
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type: recall |
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value: 0.7853663020498207 |
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- name: F1 |
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type: f1 |
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value: 0.770979967514889 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9906153616114308 |
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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-v4 |
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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: 0.0362 |
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- Precision: 0.7571 |
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- Recall: 0.7854 |
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- F1: 0.7710 |
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- Accuracy: 0.9906 |
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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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| 0.0143 | 1.0 | 1838 | 0.1451 | 0.3136 | 0.3463 | 0.3291 | 0.9700 | |
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| 0.0033 | 2.0 | 3676 | 0.0940 | 0.4293 | 0.4861 | 0.4559 | 0.9758 | |
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| 0.0014 | 3.0 | 5514 | 0.0725 | 0.4906 | 0.5766 | 0.5301 | 0.9799 | |
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| 0.0007 | 4.0 | 7352 | 0.0568 | 0.6824 | 0.7022 | 0.6921 | 0.9860 | |
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| 0.0112 | 5.0 | 9190 | 0.0497 | 0.6966 | 0.7400 | 0.7177 | 0.9870 | |
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| 0.0002 | 6.0 | 11028 | 0.0442 | 0.7126 | 0.7549 | 0.7332 | 0.9878 | |
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| 0.0002 | 7.0 | 12866 | 0.0404 | 0.7581 | 0.7591 | 0.7586 | 0.9896 | |
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| 0.0002 | 8.0 | 14704 | 0.0376 | 0.7540 | 0.7804 | 0.7670 | 0.9904 | |
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| 0.0002 | 9.0 | 16542 | 0.0367 | 0.7548 | 0.7825 | 0.7684 | 0.9905 | |
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| 0.0001 | 10.0 | 18380 | 0.0362 | 0.7571 | 0.7854 | 0.7710 | 0.9906 | |
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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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