| --- |
| 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 |
