metadata

tags:
  - generated_from_trainer
datasets:
  - null
metrics:
  - precision
  - recall
  - f1
  - accuracy
model_index:
  - name: bert-srb-ner-setimes
    results:
      - task:
          name: Token Classification
          type: token-classification
        metric:
          name: Accuracy
          type: accuracy
          value: 0.9632618605436434

bert-srb-ner-setimes

This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.1682
Precision: 0.8153
Recall: 0.8353
F1: 0.8251
Accuracy: 0.9633

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: 8
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
No log	1.0	207	0.2003	0.6991	0.7440	0.7209	0.9415
No log	2.0	414	0.1597	0.7202	0.7770	0.7475	0.9509
0.2342	3.0	621	0.1505	0.7619	0.8012	0.7811	0.9558
0.2342	4.0	828	0.1710	0.7822	0.8020	0.7920	0.9564
0.082	5.0	1035	0.1434	0.7969	0.8277	0.8120	0.9614
0.082	6.0	1242	0.1526	0.8001	0.8350	0.8171	0.9625
0.082	7.0	1449	0.1590	0.8061	0.8384	0.8219	0.9633
0.0353	8.0	1656	0.1616	0.8143	0.8296	0.8219	0.9634
0.0353	9.0	1863	0.1684	0.8167	0.8367	0.8265	0.9631
0.017	10.0	2070	0.1682	0.8153	0.8353	0.8251	0.9633

Framework versions

Transformers 4.9.2
Pytorch 1.9.0
Datasets 1.11.0
Tokenizers 0.10.1