---
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
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# sberbank-rubert-base-collection3

This model is a fine-tuned version of [sberbank-ai/ruBert-base](https://huggingface.co/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