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: 0.8093464273620048
          - name: Recall
            type: recall
            value: 0.8547925608011445
          - name: F1
            type: f1
            value: 0.8314489476430683
          - name: Accuracy
            type: accuracy
            value: 0.9446311123820418

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: 0.3352
Precision: 0.8093
Recall: 0.8548
F1: 0.8314
Accuracy: 0.9446

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
0.5496	2.22	500	0.2782	0.7301	0.7750	0.7519	0.9275
0.2133	4.44	1000	0.2487	0.7811	0.8219	0.8010	0.9399
0.144	6.67	1500	0.2580	0.7737	0.8290	0.8004	0.9396
0.1029	8.89	2000	0.2576	0.7997	0.8480	0.8231	0.9446
0.0776	11.11	2500	0.2849	0.7990	0.8516	0.8244	0.9444
0.0601	13.33	3000	0.2971	0.8021	0.8523	0.8264	0.9450
0.0494	15.56	3500	0.3077	0.8014	0.8473	0.8237	0.9440
0.0408	17.78	4000	0.3145	0.8131	0.8555	0.8337	0.9448
0.0353	20.0	4500	0.3260	0.8097	0.8569	0.8327	0.9445
0.0311	22.22	5000	0.3356	0.8076	0.8541	0.8302	0.9441
0.0281	24.44	5500	0.3352	0.8093	0.8548	0.8314	0.9446

Framework versions

Transformers 4.36.2
Pytorch 2.1.2+cu121
Datasets 2.16.1
Tokenizers 0.15.0