metadata

language:
  - ru
license: apache-2.0
tags:
  - generated_from_trainer
  - named-entity-recognition
  - russian
  - ner
datasets:
  - RCC-MSU/collection3
metrics:
  - precision
  - recall
  - f1
  - accuracy
thumbnail: Sberbank RuBERT-base fintuned on Collection3 dataset
base_model: sberbank-ai/ruBert-base
model-index:
  - name: sberbank-rubert-base-collection3
    results:
      - task:
          type: token-classification
          name: Token Classification
        dataset:
          name: RCC-MSU/collection3
          type: named-entity-recognition
          config: default
          split: validation
          args: default
        metrics:
          - type: precision
            value: 0.938019472809309
            name: Precision
          - type: recall
            value: 0.9594364828758805
            name: Recall
          - type: f1
            value: 0.9486071085494716
            name: F1
          - type: accuracy
            value: 0.9860420020488805
            name: Accuracy
      - task:
          type: token-classification
          name: Token Classification
        dataset:
          name: RCC-MSU/collection3
          type: named-entity-recognition
          config: default
          split: test
          args: default
        metrics:
          - type: precision
            value: 0.9419896321895829
            name: Precision
          - type: recall
            value: 0.9537615596100975
            name: Recall
          - type: f1
            value: 0.947839046199702
            name: F1
          - type: accuracy
            value: 0.9847255179564897
            name: Accuracy

sberbank-rubert-base-collection3

This model is a fine-tuned version of sberbank-ai/ruBert-base on the collection3 dataset. It achieves the following results on the validation set:

Loss: 0.0772
Precision: 0.9380
Recall: 0.9594
F1: 0.9486
Accuracy: 0.9860

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: 5e-05
train_batch_size: 4
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 0.1
num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.0899	1.0	2326	0.0760	0.9040	0.9330	0.9182	0.9787
0.0522	2.0	4652	0.0680	0.9330	0.9339	0.9335	0.9821
0.0259	3.0	6978	0.0745	0.9308	0.9512	0.9409	0.9838
0.0114	4.0	9304	0.0731	0.9372	0.9573	0.9471	0.9857
0.0027	5.0	11630	0.0772	0.9380	0.9594	0.9486	0.9860

Framework versions

Transformers 4.26.1
Pytorch 1.7.0
Datasets 2.10.1
Tokenizers 0.13.2