metadata

base_model: UWB-AIR/Czert-B-base-cased
tags:
  - generated_from_trainer
datasets:
  - cnec
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: CNEC_2_0_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: 0.8034274193548387
          - name: Recall
            type: recall
            value: 0.8551502145922747
          - name: F1
            type: f1
            value: 0.8284823284823285
          - name: Accuracy
            type: accuracy
            value: 0.9442021732913927

CNEC_2_0_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: 0.3023
Precision: 0.8034
Recall: 0.8552
F1: 0.8285
Accuracy: 0.9442

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
0.6206	2.22	500	0.3126	0.7032	0.7489	0.7253	0.9228
0.2721	4.44	1000	0.2733	0.7487	0.8065	0.7765	0.9338
0.1976	6.67	1500	0.2585	0.7652	0.8230	0.7930	0.9372
0.1538	8.89	2000	0.2489	0.7823	0.8391	0.8097	0.9419
0.1265	11.11	2500	0.2603	0.7937	0.8448	0.8184	0.9424
0.1044	13.33	3000	0.2814	0.7933	0.8494	0.8204	0.9430
0.0915	15.56	3500	0.2855	0.7987	0.8512	0.8241	0.9432
0.0803	17.78	4000	0.2921	0.8035	0.8569	0.8294	0.9440
0.0738	20.0	4500	0.2936	0.8020	0.8530	0.8267	0.9433
0.0668	22.22	5000	0.3020	0.8042	0.8552	0.8289	0.9445
0.0643	24.44	5500	0.3023	0.8034	0.8552	0.8285	0.9442

Framework versions

Transformers 4.36.2
Pytorch 2.1.2+cu121
Datasets 2.16.1
Tokenizers 0.15.0