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# 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
{"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"}]}]}]}
transformers pytorch tensorboard safetensors bert token-classification generated_from_trainer named-entity-recognition russian ner ru dataset:RCC-MSU/collection3 base_model:sberbank-ai/ruBert-base doi:10.57967/hf/0473 license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #safetensors #bert #token-classification #generated_from_trainer #named-entity-recognition #russian #ner #ru #dataset-RCC-MSU/collection3 #base_model-sberbank-ai/ruBert-base #doi-10.57967/hf/0473 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

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



### Framework versions


* Transformers 4.26.1
* Pytorch 1.7.0
* Datasets 2.10.1
* Tokenizers 0.13.2



### 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 ### Framework versions


* Transformers 4.26.1
* Pytorch 1.7.0
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #safetensors #bert #token-classification #generated_from_trainer #named-entity-recognition #russian #ner #ru #dataset-RCC-MSU/collection3 #base_model-sberbank-ai/ruBert-base #doi-10.57967/hf/0473 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
 ### 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 ### Framework versions


* Transformers 4.26.1
* Pytorch 1.7.0
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #safetensors #bert #token-classification #generated_from_trainer #named-entity-recognition #russian #ner #ru #dataset-RCC-MSU/collection3 #base_model-sberbank-ai/ruBert-base #doi-10.57967/hf/0473 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
### 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### Framework versions


* Transformers 4.26.1
* Pytorch 1.7.0
* Datasets 2.10.1
* Tokenizers 0.13.2