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# rubert-base-cased-token

This model is a fine-tuned version of [DeepPavlov/rubert-base-cased](https://huggingface.co/DeepPavlov/rubert-base-cased) on the OpenCorpora dataset [opencorpora.org](http://opencorpora.org/).
It achieves the following results on the evaluation set:
- Loss: 0.2595
- Precision: 0.9304
- Recall: 0.9334
- F1: 0.9319
- Accuracy: 0.9424

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

Tokens classification from OpenCorpora: [opencorpora.org](http://opencorpora.org/dict.php?act=gram)

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 69   | 0.6926          | 0.7731    | 0.7674 | 0.7702 | 0.8200   |
| No log        | 2.0   | 138  | 0.3744          | 0.8665    | 0.8807 | 0.8735 | 0.9003   |
| No log        | 3.0   | 207  | 0.2891          | 0.9004    | 0.9071 | 0.9037 | 0.9231   |
| No log        | 4.0   | 276  | 0.2566          | 0.9123    | 0.9217 | 0.9170 | 0.9327   |
| No log        | 5.0   | 345  | 0.2587          | 0.9211    | 0.9255 | 0.9233 | 0.9366   |
| No log        | 6.0   | 414  | 0.2472          | 0.9264    | 0.9289 | 0.9276 | 0.9401   |
| No log        | 7.0   | 483  | 0.2589          | 0.9267    | 0.9313 | 0.9290 | 0.9406   |
| 0.3825        | 8.0   | 552  | 0.2559          | 0.9286    | 0.9334 | 0.9310 | 0.9416   |
| 0.3825        | 9.0   | 621  | 0.2578          | 0.9304    | 0.9339 | 0.9321 | 0.9425   |
| 0.3825        | 10.0  | 690  | 0.2595          | 0.9304    | 0.9334 | 0.9319 | 0.9424   |


### Framework versions

- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
{"language": ["ru"], "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "widget": [{"text": "\u041e\u0434\u043d\u0430\u0436\u0434\u044b \u044f \u043f\u043e\u0441\u0435\u0442\u0438\u043b \u043f\u0440\u0435\u043a\u0440\u0430\u0441\u043d\u044b\u0439 \u0433\u043e\u0440\u043e\u0434\u043e\u043a \u0432 \u0433\u043e\u0440\u0430\u0445. \u041d\u0430 \u0435\u0433\u043e \u0443\u043b\u0438\u0446\u0430\u0445 \u0440\u043e\u0441\u043b\u0438 \u0443\u0434\u0438\u0432\u0438\u0442\u0435\u043b\u044c\u043d\u044b\u0435 \u0446\u0432\u0435\u0442\u044b.", "example_title": "Example_1"}], "pipeline_tag": "token-classification", "base_model": "DeepPavlov/rubert-base-cased", "model-index": [{"name": "rubert-base-cased-token", "results": []}]}
transformers pytorch tensorboard safetensors bert token-classification generated_from_trainer ru base_model:DeepPavlov/rubert-base-cased autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #safetensors #bert #token-classification #generated_from_trainer #ru #base_model-DeepPavlov/rubert-base-cased #autotrain_compatible #endpoints_compatible #region-us 

rubert-base-cased-token
=======================


This model is a fine-tuned version of DeepPavlov/rubert-base-cased on the OpenCorpora dataset URL.
It achieves the following results on the evaluation set:


* Loss: 0.2595
* Precision: 0.9304
* Recall: 0.9334
* F1: 0.9319
* Accuracy: 0.9424


Model description
-----------------


More information needed


Intended uses & limitations
---------------------------


More information needed


Training and evaluation data
----------------------------


Tokens classification from OpenCorpora: URL


Training procedure
------------------


### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10


### Training results



### Framework versions


* Transformers 4.25.1
* Pytorch 1.13.0+cu116
* Datasets 2.8.0
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10 ### Training results ### Framework versions


* Transformers 4.25.1
* Pytorch 1.13.0+cu116
* Datasets 2.8.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #safetensors #bert #token-classification #generated_from_trainer #ru #base_model-DeepPavlov/rubert-base-cased #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10 ### Training results ### Framework versions


* Transformers 4.25.1
* Pytorch 1.13.0+cu116
* Datasets 2.8.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #safetensors #bert #token-classification #generated_from_trainer #ru #base_model-DeepPavlov/rubert-base-cased #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10### Training results### Framework versions


* Transformers 4.25.1
* Pytorch 1.13.0+cu116
* Datasets 2.8.0
* Tokenizers 0.13.2