metadata

tags:
  - generated_from_trainer
metrics:
  - precision
  - recall
  - f1
  - accuracy
model_index:
  - name: distilbert-srb-ner-setimes
    results:
      - task:
          name: Token Classification
          type: token-classification
        metric:
          name: Accuracy
          type: accuracy
          value: 0.9665376552169005

distilbert-srb-ner-setimes

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

Loss: 0.1838
Precision: 0.8370
Recall: 0.8617
F1: 0.8492
Accuracy: 0.9665

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: 32
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	104	0.2319	0.6668	0.7029	0.6844	0.9358
No log	2.0	208	0.1850	0.7265	0.7508	0.7385	0.9469
No log	3.0	312	0.1584	0.7555	0.7937	0.7741	0.9538
No log	4.0	416	0.1484	0.7644	0.8128	0.7879	0.9571
0.1939	5.0	520	0.1383	0.7850	0.8131	0.7988	0.9604
0.1939	6.0	624	0.1409	0.7914	0.8359	0.8130	0.9632
0.1939	7.0	728	0.1526	0.8176	0.8392	0.8283	0.9637
0.1939	8.0	832	0.1536	0.8195	0.8409	0.8301	0.9641
0.1939	9.0	936	0.1538	0.8242	0.8523	0.8380	0.9661
0.0364	10.0	1040	0.1612	0.8228	0.8413	0.8319	0.9652
0.0364	11.0	1144	0.1721	0.8289	0.8503	0.8395	0.9656
0.0364	12.0	1248	0.1645	0.8301	0.8590	0.8443	0.9663
0.0364	13.0	1352	0.1747	0.8352	0.8540	0.8445	0.9665
0.0364	14.0	1456	0.1703	0.8277	0.8573	0.8422	0.9663
0.011	15.0	1560	0.1770	0.8314	0.8624	0.8466	0.9665
0.011	16.0	1664	0.1903	0.8399	0.8537	0.8467	0.9661
0.011	17.0	1768	0.1837	0.8363	0.8590	0.8475	0.9665
0.011	18.0	1872	0.1820	0.8338	0.8570	0.8453	0.9667
0.011	19.0	1976	0.1855	0.8382	0.8620	0.8499	0.9666
0.0053	20.0	2080	0.1838	0.8370	0.8617	0.8492	0.9665

Framework versions

Transformers 4.9.2
Pytorch 1.9.0
Datasets 1.11.0
Tokenizers 0.10.1