metadata

tags:
  - generated_from_trainer
datasets:
  - ade_drug_effect_ner
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: electramed-small-ADE-DRUG-EFFECT-ner
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: ade_drug_effect_ner
          type: ade_drug_effect_ner
          config: ade
          split: train
          args: ade
        metrics:
          - name: Precision
            type: precision
            value: 0.7745054945054946
          - name: Recall
            type: recall
            value: 0.6555059523809523
          - name: F1
            type: f1
            value: 0.7100544025790851
          - name: Accuracy
            type: accuracy
            value: 0.9310355073540336

electramed-small-ADE-DRUG-EFFECT-ner

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

Loss: 0.1630
Precision: 0.7745
Recall: 0.6555
F1: 0.7101
Accuracy: 0.9310

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
0.4498	1.0	336	0.3042	0.5423	0.6295	0.5826	0.9114
0.2572	2.0	672	0.2146	0.7596	0.6194	0.6824	0.9276
0.1542	3.0	1008	0.1894	0.7806	0.6168	0.6891	0.9299
0.1525	4.0	1344	0.1771	0.7832	0.625	0.6952	0.9309
0.1871	5.0	1680	0.1723	0.7271	0.6920	0.7091	0.9304
0.1425	6.0	2016	0.1683	0.7300	0.6979	0.7136	0.9297
0.1638	7.0	2352	0.1654	0.7432	0.6771	0.7086	0.9306
0.1592	8.0	2688	0.1635	0.7613	0.6585	0.7062	0.9305
0.1882	9.0	3024	0.1625	0.7858	0.6373	0.7038	0.9309
0.1339	10.0	3360	0.1630	0.7745	0.6555	0.7101	0.9310

Framework versions

Transformers 4.22.1
Pytorch 1.12.1+cu113
Datasets 2.4.0
Tokenizers 0.12.1