metadata

tags:
  - generated_from_trainer
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: 0.9645112274185379

bert-srb-ner-setimes

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

Loss: 0.1955
Precision: 0.8229
Recall: 0.8465
F1: 0.8345
Accuracy: 0.9645

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	0.2281	0.6589	0.7001	0.6789	0.9350
No log	2.0	208	0.1833	0.7105	0.7694	0.7388	0.9470
No log	3.0	312	0.1573	0.7461	0.7778	0.7616	0.9525
No log	4.0	416	0.1489	0.7665	0.8091	0.7872	0.9557
0.1898	5.0	520	0.1445	0.7881	0.8327	0.8098	0.9587
0.1898	6.0	624	0.1473	0.7913	0.8316	0.8109	0.9601
0.1898	7.0	728	0.1558	0.8101	0.8347	0.8222	0.9620
0.1898	8.0	832	0.1616	0.8026	0.8302	0.8162	0.9612
0.1898	9.0	936	0.1716	0.8127	0.8409	0.8266	0.9631
0.0393	10.0	1040	0.1751	0.8140	0.8369	0.8253	0.9628
0.0393	11.0	1144	0.1775	0.8096	0.8420	0.8255	0.9626
0.0393	12.0	1248	0.1763	0.8161	0.8386	0.8272	0.9636
0.0393	13.0	1352	0.1949	0.8259	0.8400	0.8329	0.9634
0.0393	14.0	1456	0.1842	0.8205	0.8420	0.8311	0.9642
0.0111	15.0	1560	0.1862	0.8160	0.8493	0.8323	0.9646
0.0111	16.0	1664	0.1989	0.8176	0.8367	0.8270	0.9627
0.0111	17.0	1768	0.1945	0.8246	0.8409	0.8327	0.9638
0.0111	18.0	1872	0.1997	0.8270	0.8426	0.8347	0.9634
0.0111	19.0	1976	0.1917	0.8258	0.8491	0.8373	0.9651
0.0051	20.0	2080	0.1955	0.8229	0.8465	0.8345	0.9645

Framework versions

Transformers 4.9.2
Pytorch 1.9.0
Datasets 1.11.0
Tokenizers 0.10.1