metadata

base_model: DeepPavlov/bert-base-bg-cs-pl-ru-cased
tags:
  - generated_from_trainer
datasets:
  - cnec
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: CNEC_1_1_ext_slavicbert
    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: 0.8606811145510835
          - name: Recall
            type: recall
            value: 0.8915018706574025
          - name: F1
            type: f1
            value: 0.8758204253084799
          - name: Accuracy
            type: accuracy
            value: 0.9626885008032336

CNEC_1_1_ext_slavicbert

This model is a fine-tuned version of DeepPavlov/bert-base-bg-cs-pl-ru-cased on the cnec dataset. It achieves the following results on the evaluation set:

Loss: 0.2572
Precision: 0.8607
Recall: 0.8915
F1: 0.8758
Accuracy: 0.9627

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: 16
eval_batch_size: 16
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
0.3946	1.72	500	0.1925	0.7835	0.8471	0.8141	0.9467
0.1653	3.44	1000	0.1627	0.8340	0.8675	0.8504	0.9572
0.1183	5.15	1500	0.1700	0.8378	0.8808	0.8588	0.9595
0.0869	6.87	2000	0.1901	0.8554	0.8728	0.8640	0.9589
0.0661	8.59	2500	0.2037	0.8482	0.8867	0.8670	0.9595
0.053	10.31	3000	0.2011	0.8460	0.8867	0.8659	0.9609
0.043	12.03	3500	0.2216	0.8555	0.8888	0.8718	0.9593
0.0358	13.75	4000	0.2245	0.8492	0.8878	0.8680	0.9603
0.0296	15.46	4500	0.2401	0.8513	0.8872	0.8689	0.9603
0.0264	17.18	5000	0.2415	0.8564	0.8862	0.8710	0.9610
0.0212	18.9	5500	0.2570	0.8557	0.8872	0.8712	0.9622
0.0205	20.62	6000	0.2540	0.8567	0.8883	0.8722	0.9616
0.0167	22.34	6500	0.2573	0.8568	0.8894	0.8728	0.9614
0.0161	24.05	7000	0.2572	0.8607	0.8915	0.8758	0.9627

Framework versions

Transformers 4.36.2
Pytorch 2.1.2+cu121
Datasets 2.16.1
Tokenizers 0.15.0