metadata

license: mit
tags:
  - generated_from_trainer
datasets:
  - wikiann_sk
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: output_dir
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: wikiann_sk
          type: wikiann_sk
          args: sk
        metrics:
          - name: Precision
            type: precision
            value: 0.9327115256495669
          - name: Recall
            type: recall
            value: 0.9470124013528749
          - name: F1
            type: f1
            value: 0.9398075632132469
          - name: Accuracy
            type: accuracy
            value: 0.9785228256835333

output_dir

This model is a fine-tuned version of gerulata/slovakbert on the wikiann_sk dataset. It achieves the following results on the evaluation set:

Loss: 0.1600
Precision: 0.9327
Recall: 0.9470
F1: 0.9398
Accuracy: 0.9785

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: 5e-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: 15.0

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.2342	1.0	625	0.1233	0.8891	0.9076	0.8982	0.9667
0.1114	2.0	1250	0.1079	0.9118	0.9269	0.9193	0.9725
0.0817	3.0	1875	0.1093	0.9173	0.9315	0.9243	0.9747
0.0438	4.0	2500	0.1076	0.9188	0.9353	0.9270	0.9743
0.028	5.0	3125	0.1230	0.9143	0.9387	0.9264	0.9744
0.0256	6.0	3750	0.1204	0.9246	0.9423	0.9334	0.9765
0.018	7.0	4375	0.1332	0.9292	0.9416	0.9353	0.9770
0.0107	8.0	5000	0.1339	0.9280	0.9427	0.9353	0.9769
0.0079	9.0	5625	0.1368	0.9326	0.9442	0.9383	0.9785
0.0065	10.0	6250	0.1490	0.9284	0.9445	0.9364	0.9772
0.0061	11.0	6875	0.1566	0.9328	0.9433	0.9380	0.9778
0.0031	12.0	7500	0.1555	0.9339	0.9473	0.9406	0.9787
0.0024	13.0	8125	0.1548	0.9349	0.9462	0.9405	0.9787
0.0015	14.0	8750	0.1562	0.9330	0.9469	0.9399	0.9788
0.0013	15.0	9375	0.1600	0.9327	0.9470	0.9398	0.9785

Framework versions

Transformers 4.13.0.dev0
Pytorch 1.10.0+cu113
Datasets 1.15.1
Tokenizers 0.10.3