metadata

tags:
  - generated_from_trainer
datasets:
  - universal_dependencies
metrics:
  - precision
  - recall
  - f1
  - accuracy
inference: false
model-index:
  - name: distil-slovakbert-upos
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: universal_dependencies sk_snk
          type: universal_dependencies
          args: sk_snk
        metrics:
          - name: Precision
            type: precision
            value: 0.9771104035797263
          - name: Recall
            type: recall
            value: 0.9785418821096173
          - name: F1
            type: f1
            value: 0.9778256189451022
          - name: Accuracy
            type: accuracy
            value: 0.9800851200513933

distil-slovakbert-upos

This model is a fine-tuned version of crabz/distil-slovakbert on the universal_dependencies sk_snk dataset. It achieves the following results on the evaluation set:

Loss: 0.1207
Precision: 0.9771
Recall: 0.9785
F1: 0.9778
Accuracy: 0.9801

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: 32
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 10.0

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	266	0.2168	0.9570	0.9554	0.9562	0.9610
0.3935	2.0	532	0.1416	0.9723	0.9736	0.9730	0.9740
0.3935	3.0	798	0.1236	0.9722	0.9735	0.9728	0.9747
0.0664	4.0	1064	0.1195	0.9722	0.9741	0.9732	0.9766
0.0664	5.0	1330	0.1160	0.9764	0.9772	0.9768	0.9789
0.0377	6.0	1596	0.1194	0.9763	0.9776	0.9770	0.9790
0.0377	7.0	1862	0.1188	0.9740	0.9755	0.9748	0.9777
0.024	8.0	2128	0.1188	0.9762	0.9777	0.9769	0.9793
0.024	9.0	2394	0.1207	0.9774	0.9789	0.9781	0.9802
0.0184	10.0	2660	0.1207	0.9771	0.9785	0.9778	0.9801

Framework versions

Transformers 4.17.0.dev0
Pytorch 1.10.0
Datasets 1.16.1
Tokenizers 0.11.0