metadata

license: cc-by-nc-sa-4.0
base_model: ufal/robeczech-base
tags:
  - generated_from_trainer
datasets:
  - stulcrad/CNEC2_0_flat
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: CNEC_2_0_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: 0.853103448275862
          - name: Recall
            type: recall
            value: 0.8848354792560801
          - name: F1
            type: f1
            value: 0.8686797752808989
          - name: Accuracy
            type: accuracy
            value: 0.954457738324971
language:
  - cs

CNEC_2_0_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: 0.3306
Precision: 0.8531
Recall: 0.8848
F1: 0.8687
Accuracy: 0.9545

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: 8
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 30

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.4499	2.22	2000	0.3871	0.7163	0.7099	0.7131	0.9222
0.2342	4.44	4000	0.2576	0.8149	0.8251	0.8200	0.9451
0.1449	6.67	6000	0.2407	0.8231	0.8523	0.8375	0.9492
0.1027	8.89	8000	0.2267	0.8362	0.8748	0.8551	0.9527
0.0751	11.11	10000	0.2429	0.8394	0.8712	0.8550	0.9522
0.0473	13.33	12000	0.2633	0.8439	0.8720	0.8577	0.9535
0.0369	15.56	14000	0.2821	0.8468	0.8755	0.8609	0.9541
0.0286	17.78	16000	0.2797	0.8534	0.8827	0.8678	0.9558
0.0234	20.0	18000	0.2860	0.8550	0.8834	0.8690	0.9558
0.0168	22.22	20000	0.3146	0.8471	0.8795	0.8630	0.9531
0.0142	24.44	22000	0.3165	0.8488	0.8816	0.8649	0.9530
0.011	26.67	24000	0.3291	0.8518	0.8816	0.8664	0.9537
0.0092	28.89	26000	0.3306	0.8531	0.8848	0.8687	0.9545

Framework versions

Transformers 4.36.2
Pytorch 2.1.2+cu121
Datasets 2.16.1
Tokenizers 0.15.0