metadata

license: cc-by-nc-sa-4.0
base_model: ufal/robeczech-base
tags:
  - generated_from_trainer
datasets:
  - cnec
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: CNEC_2_0_ext_robeczech-base
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: cnec
          type: cnec
          config: default
          split: validation
          args: default
        metrics:
          - name: Precision
            type: precision
            value: 0.8633093525179856
          - name: Recall
            type: recall
            value: 0.8933002481389578
          - name: F1
            type: f1
            value: 0.8780487804878048
          - name: Accuracy
            type: accuracy
            value: 0.9703429462197973

CNEC_2_0_ext_robeczech-base

This model is a fine-tuned version of ufal/robeczech-base on the cnec dataset. It achieves the following results on the evaluation set:

Loss: 0.1663
Precision: 0.8633
Recall: 0.8933
F1: 0.8780
Accuracy: 0.9703

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: 32
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.2593	4.46	1000	0.1653	0.8195	0.8223	0.8209	0.9593
0.1209	8.93	2000	0.1355	0.8441	0.8789	0.8612	0.9679
0.0763	13.39	3000	0.1310	0.8591	0.8893	0.8739	0.9709
0.0539	17.86	4000	0.1383	0.8656	0.8953	0.8802	0.9719
0.0403	22.32	5000	0.1392	0.8626	0.8943	0.8782	0.9710
0.0316	26.79	6000	0.1539	0.8606	0.8948	0.8774	0.9712
0.0254	31.25	7000	0.1552	0.8660	0.8913	0.8785	0.9706
0.0211	35.71	8000	0.1621	0.8658	0.8968	0.8810	0.9701
0.0183	40.18	9000	0.1593	0.8688	0.8973	0.8828	0.9718
0.0161	44.64	10000	0.1638	0.8653	0.8993	0.8820	0.9714
0.015	49.11	11000	0.1663	0.8633	0.8933	0.8780	0.9703

Framework versions

Transformers 4.36.2
Pytorch 2.1.2+cu121
Datasets 2.16.1
Tokenizers 0.15.0