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--- |
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language: |
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- ru |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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- named-entity-recognition |
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- russian |
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- ner |
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datasets: |
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- RCC-MSU/collection3 |
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metrics: |
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- precision |
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- recall |
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- f1 |
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- accuracy |
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thumbnail: Sberbank RuBERT-base fintuned on Collection3 dataset |
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base_model: sberbank-ai/ruBert-base |
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model-index: |
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- name: sberbank-rubert-base-collection3 |
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results: |
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- task: |
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type: token-classification |
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name: Token Classification |
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dataset: |
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name: RCC-MSU/collection3 |
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type: named-entity-recognition |
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config: default |
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split: validation |
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args: default |
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metrics: |
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- type: precision |
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value: 0.938019472809309 |
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name: Precision |
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- type: recall |
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value: 0.9594364828758805 |
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name: Recall |
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- type: f1 |
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value: 0.9486071085494716 |
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name: F1 |
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- type: accuracy |
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value: 0.9860420020488805 |
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name: Accuracy |
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- task: |
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type: token-classification |
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name: Token Classification |
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dataset: |
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name: RCC-MSU/collection3 |
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type: named-entity-recognition |
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config: default |
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split: test |
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args: default |
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metrics: |
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- type: precision |
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value: 0.9419896321895829 |
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name: Precision |
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- type: recall |
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value: 0.9537615596100975 |
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name: Recall |
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- type: f1 |
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value: 0.947839046199702 |
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name: F1 |
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- type: accuracy |
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value: 0.9847255179564897 |
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name: Accuracy |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# sberbank-rubert-base-collection3 |
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This model is a fine-tuned version of [sberbank-ai/ruBert-base](https://huggingface.co/sberbank-ai/ruBert-base) on the collection3 dataset. |
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It achieves the following results on the validation set: |
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- Loss: 0.0772 |
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- Precision: 0.9380 |
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- Recall: 0.9594 |
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- F1: 0.9486 |
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- Accuracy: 0.9860 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 4 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_steps: 0.1 |
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- num_epochs: 5 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| 0.0899 | 1.0 | 2326 | 0.0760 | 0.9040 | 0.9330 | 0.9182 | 0.9787 | |
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| 0.0522 | 2.0 | 4652 | 0.0680 | 0.9330 | 0.9339 | 0.9335 | 0.9821 | |
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| 0.0259 | 3.0 | 6978 | 0.0745 | 0.9308 | 0.9512 | 0.9409 | 0.9838 | |
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| 0.0114 | 4.0 | 9304 | 0.0731 | 0.9372 | 0.9573 | 0.9471 | 0.9857 | |
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| 0.0027 | 5.0 | 11630 | 0.0772 | 0.9380 | 0.9594 | 0.9486 | 0.9860 | |
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### Framework versions |
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- Transformers 4.26.1 |
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- Pytorch 1.7.0 |
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- Datasets 2.10.1 |
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- Tokenizers 0.13.2 |
