metadata

tags:
  - generated_from_trainer
datasets:
  - jnlpba
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: electramed-small-JNLPBA-ner
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: jnlpba
          type: jnlpba
          config: jnlpba
          split: train
          args: jnlpba
        metrics:
          - name: Precision
            type: precision
            value: 0.8224512128396863
          - name: Recall
            type: recall
            value: 0.878188899707887
          - name: F1
            type: f1
            value: 0.8494066679223958
          - name: Accuracy
            type: accuracy
            value: 0.9620705451213926

electramed-small-JNLPBA-ner

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

Loss: 0.1167
Precision: 0.8225
Recall: 0.8782
F1: 0.8494
Accuracy: 0.9621

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
0.398	1.0	2087	0.1941	0.7289	0.7936	0.7599	0.9441
0.0771	2.0	4174	0.1542	0.7734	0.8348	0.8029	0.9514
0.1321	3.0	6261	0.1413	0.7890	0.8492	0.8180	0.9546
0.2302	4.0	8348	0.1326	0.8006	0.8589	0.8287	0.9562
0.0723	5.0	10435	0.1290	0.7997	0.8715	0.8340	0.9574
0.171	6.0	12522	0.1246	0.8115	0.8722	0.8408	0.9593
0.1058	7.0	14609	0.1204	0.8148	0.8757	0.8441	0.9604
0.1974	8.0	16696	0.1178	0.8181	0.8779	0.8470	0.9614
0.0663	9.0	18783	0.1168	0.8239	0.8781	0.8501	0.9620
0.1022	10.0	20870	0.1167	0.8225	0.8782	0.8494	0.9621

Framework versions

Transformers 4.21.1
Pytorch 1.12.1+cu113
Datasets 2.4.0
Tokenizers 0.12.1