--- tags: - generated_from_trainer datasets: - i2b22014 metrics: - precision - recall - f1 - accuracy model-index: - name: electramed-small-deid2014-ner-v4 results: - task: name: Token Classification type: token-classification dataset: name: i2b22014 type: i2b22014 config: i2b22014-deid split: train args: i2b22014-deid metrics: - name: Precision type: precision value: 0.7571112095702259 - name: Recall type: recall value: 0.7853663020498207 - name: F1 type: f1 value: 0.770979967514889 - name: Accuracy type: accuracy value: 0.9906153616114308 --- # electramed-small-deid2014-ner-v4 This model is a fine-tuned version of [giacomomiolo/electramed_small_scivocab](https://huggingface.co/giacomomiolo/electramed_small_scivocab) on the i2b22014 dataset. It achieves the following results on the evaluation set: - Loss: 0.0362 - Precision: 0.7571 - Recall: 0.7854 - F1: 0.7710 - Accuracy: 0.9906 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0143 | 1.0 | 1838 | 0.1451 | 0.3136 | 0.3463 | 0.3291 | 0.9700 | | 0.0033 | 2.0 | 3676 | 0.0940 | 0.4293 | 0.4861 | 0.4559 | 0.9758 | | 0.0014 | 3.0 | 5514 | 0.0725 | 0.4906 | 0.5766 | 0.5301 | 0.9799 | | 0.0007 | 4.0 | 7352 | 0.0568 | 0.6824 | 0.7022 | 0.6921 | 0.9860 | | 0.0112 | 5.0 | 9190 | 0.0497 | 0.6966 | 0.7400 | 0.7177 | 0.9870 | | 0.0002 | 6.0 | 11028 | 0.0442 | 0.7126 | 0.7549 | 0.7332 | 0.9878 | | 0.0002 | 7.0 | 12866 | 0.0404 | 0.7581 | 0.7591 | 0.7586 | 0.9896 | | 0.0002 | 8.0 | 14704 | 0.0376 | 0.7540 | 0.7804 | 0.7670 | 0.9904 | | 0.0002 | 9.0 | 16542 | 0.0367 | 0.7548 | 0.7825 | 0.7684 | 0.9905 | | 0.0001 | 10.0 | 18380 | 0.0362 | 0.7571 | 0.7854 | 0.7710 | 0.9906 | ### Framework versions - Transformers 4.22.1 - Pytorch 1.12.1+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1