metadata

base_model: UWB-AIR/Czert-B-base-cased
tags:
  - generated_from_trainer
datasets:
  - cnec
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: CNEC_1_1_ext_Czert-B-base-cased
    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: 0.8383838383838383
          - name: Recall
            type: recall
            value: 0.8872260823089257
          - name: F1
            type: f1
            value: 0.8621137366917683
          - name: Accuracy
            type: accuracy
            value: 0.9569787813899163

CNEC_1_1_ext_Czert-B-base-cased

This model is a fine-tuned version of UWB-AIR/Czert-B-base-cased on the cnec dataset. It achieves the following results on the evaluation set:

Loss: 0.2513
Precision: 0.8384
Recall: 0.8872
F1: 0.8621
Accuracy: 0.9570

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: 25

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.3012	3.42	500	0.1677	0.8115	0.8626	0.8363	0.9518
0.1081	6.85	1000	0.1869	0.8218	0.8749	0.8475	0.9548
0.0654	10.27	1500	0.2132	0.8311	0.8813	0.8555	0.9559
0.0449	13.7	2000	0.2284	0.8296	0.8797	0.8540	0.9559
0.0341	17.12	2500	0.2353	0.8348	0.8856	0.8594	0.9575
0.0267	20.55	3000	0.2413	0.8397	0.8872	0.8628	0.9581
0.0227	23.97	3500	0.2513	0.8384	0.8872	0.8621	0.9570

Framework versions

Transformers 4.36.2
Pytorch 2.1.2+cu121
Datasets 2.16.1
Tokenizers 0.15.0