metadata

tags:
  - generated_from_trainer
datasets:
  - species_800
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: electramed-small-SPECIES800-ner
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: species_800
          type: species_800
          config: species_800
          split: train
          args: species_800
        metrics:
          - name: Precision
            type: precision
            value: 0.6221498371335505
          - name: Recall
            type: recall
            value: 0.7470664928292047
          - name: F1
            type: f1
            value: 0.6789099526066352
          - name: Accuracy
            type: accuracy
            value: 0.9831434110359828

electramed-small-SPECIES800-ner

This model is a fine-tuned version of giacomomiolo/electramed_small_scivocab on the species_800 dataset. It achieves the following results on the evaluation set:

Loss: 0.0513
Precision: 0.6221
Recall: 0.7471
F1: 0.6789
Accuracy: 0.9831

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
0.0536	1.0	359	0.0971	0.6138	0.5554	0.5832	0.9795
0.0309	2.0	718	0.0692	0.6175	0.6063	0.6118	0.9808
0.0563	3.0	1077	0.0582	0.6424	0.6910	0.6658	0.9819
0.0442	4.0	1436	0.0553	0.5900	0.7523	0.6613	0.9814
0.0069	5.0	1795	0.0511	0.6291	0.7497	0.6841	0.9827
0.0141	6.0	2154	0.0505	0.6579	0.7471	0.6996	0.9837
0.0052	7.0	2513	0.0513	0.5965	0.7458	0.6628	0.9826
0.0573	8.0	2872	0.0509	0.6140	0.7445	0.6730	0.9828
0.0203	9.0	3231	0.0516	0.6118	0.7458	0.6722	0.9830
0.0101	10.0	3590	0.0513	0.6221	0.7471	0.6789	0.9831

Framework versions

Transformers 4.21.1
Pytorch 1.12.1+cu113
Datasets 2.4.0
Tokenizers 0.12.1