metadata

tags:
  - generated_from_trainer
datasets:
  - i2b22014
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: electramed-small-deid2014-ner-v5-classweights
    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: 0.8832236842105263
          - name: Recall
            type: recall
            value: 0.6910561632502987
          - name: F1
            type: f1
            value: 0.7754112732711052
          - name: Accuracy
            type: accuracy
            value: 0.9883040491052534

electramed-small-deid2014-ner-v5-classweights

This model is a fine-tuned version of giacomomiolo/electramed_small_scivocab on the i2b22014 dataset. It achieves the following results on the evaluation set:

Loss: 0.0009
Precision: 0.8832
Recall: 0.6911
F1: 0.7754
Accuracy: 0.9883

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
0.0001	1.0	1838	0.0008	0.7702	0.3780	0.5071	0.9771
0.0	2.0	3676	0.0007	0.8753	0.5671	0.6883	0.9827
0.0	3.0	5514	0.0006	0.8074	0.4128	0.5463	0.9775
0.0	4.0	7352	0.0007	0.8693	0.6102	0.7170	0.9848
0.0	5.0	9190	0.0006	0.8710	0.6022	0.7121	0.9849
0.0	6.0	11028	0.0007	0.8835	0.6547	0.7521	0.9867
0.0	7.0	12866	0.0009	0.8793	0.6661	0.7579	0.9873
0.0	8.0	14704	0.0008	0.8815	0.6740	0.7639	0.9876
0.0	9.0	16542	0.0009	0.8812	0.6851	0.7709	0.9880
0.0	10.0	18380	0.0009	0.8832	0.6911	0.7754	0.9883

Framework versions

Transformers 4.22.1
Pytorch 1.12.1+cu113
Datasets 2.5.1
Tokenizers 0.12.1