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