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# rubert-tiny2-russe-toxicity

This model is a fine-tuned version of [cointegrated/rubert-tiny2](https://huggingface.co/cointegrated/rubert-tiny2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2087
- Precision Macro: 0.9395
- Recall Macro: 0.9394
- F1 Macro: 0.9394
- F1 Neutral: 0.9398
- F1 Toxic: 0.9390

## 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: 2e-05
- train_batch_size: 32
- eval_batch_size: 64
- 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 Macro | Recall Macro | F1 Macro | F1 Neutral | F1 Toxic |
|:-------------:|:-----:|:----:|:---------------:|:---------------:|:------------:|:--------:|:----------:|:--------:|
| 0.6127        | 0.23  | 100  | 0.4477          | 0.8252          | 0.825        | 0.8250   | 0.8274     | 0.8226   |
| 0.3728        | 0.46  | 200  | 0.2852          | 0.8877          | 0.8875       | 0.8875   | 0.8886     | 0.8864   |
| 0.2888        | 0.69  | 300  | 0.2405          | 0.9039          | 0.9038       | 0.9037   | 0.9047     | 0.9028   |
| 0.2724        | 0.92  | 400  | 0.2288          | 0.9110          | 0.9094       | 0.9093   | 0.9122     | 0.9064   |
| 0.2387        | 1.15  | 500  | 0.2161          | 0.9132          | 0.9125       | 0.9125   | 0.9143     | 0.9106   |
| 0.2189        | 1.38  | 600  | 0.2098          | 0.9206          | 0.9206       | 0.9206   | 0.9208     | 0.9205   |
| 0.1783        | 1.61  | 700  | 0.2045          | 0.9233          | 0.9231       | 0.9231   | 0.9238     | 0.9224   |
| 0.1911        | 1.84  | 800  | 0.2098          | 0.9184          | 0.9175       | 0.9175   | 0.9155     | 0.9194   |
| 0.1749        | 2.07  | 900  | 0.1947          | 0.9271          | 0.9269       | 0.9269   | 0.9277     | 0.9260   |
| 0.1728        | 2.3   | 1000 | 0.1893          | 0.9263          | 0.9263       | 0.9262   | 0.9265     | 0.9260   |
| 0.1628        | 2.53  | 1100 | 0.1926          | 0.9276          | 0.9275       | 0.9275   | 0.9270     | 0.9280   |
| 0.1545        | 2.76  | 1200 | 0.1889          | 0.9299          | 0.9294       | 0.9294   | 0.9306     | 0.9281   |
| 0.1659        | 2.99  | 1300 | 0.1893          | 0.9263          | 0.9263       | 0.9263   | 0.9263     | 0.9263   |
| 0.1433        | 3.22  | 1400 | 0.1952          | 0.9264          | 0.9263       | 0.9262   | 0.9256     | 0.9269   |
| 0.1243        | 3.45  | 1500 | 0.1838          | 0.9300          | 0.93         | 0.9300   | 0.9303     | 0.9296   |
| 0.1357        | 3.68  | 1600 | 0.1846          | 0.9370          | 0.9356       | 0.9356   | 0.9374     | 0.9338   |
| 0.1419        | 3.91  | 1700 | 0.1793          | 0.9339          | 0.9331       | 0.9331   | 0.9345     | 0.9317   |
| 0.1205        | 4.14  | 1800 | 0.1896          | 0.9306          | 0.9306       | 0.9306   | 0.9306     | 0.9307   |
| 0.1159        | 4.37  | 1900 | 0.1905          | 0.9354          | 0.935        | 0.9350   | 0.9360     | 0.9340   |
| 0.1185        | 4.6   | 2000 | 0.1893          | 0.9400          | 0.9387       | 0.9387   | 0.9403     | 0.9371   |
| 0.102         | 4.83  | 2100 | 0.1944          | 0.9415          | 0.9406       | 0.9406   | 0.9419     | 0.9393   |
| 0.1222        | 5.06  | 2200 | 0.1880          | 0.9390          | 0.9387       | 0.9387   | 0.9394     | 0.9381   |
| 0.1006        | 5.29  | 2300 | 0.1964          | 0.9331          | 0.9331       | 0.9331   | 0.9330     | 0.9333   |
| 0.108         | 5.52  | 2400 | 0.1901          | 0.9382          | 0.9381       | 0.9381   | 0.9384     | 0.9379   |
| 0.1059        | 5.75  | 2500 | 0.1907          | 0.9401          | 0.94         | 0.9400   | 0.9404     | 0.9396   |
| 0.1033        | 5.98  | 2600 | 0.1905          | 0.9409          | 0.94         | 0.9400   | 0.9413     | 0.9386   |
| 0.0969        | 6.21  | 2700 | 0.1969          | 0.9345          | 0.9344       | 0.9344   | 0.9338     | 0.9349   |
| 0.0826        | 6.44  | 2800 | 0.1952          | 0.9407          | 0.9406       | 0.9406   | 0.9410     | 0.9402   |
| 0.1055        | 6.67  | 2900 | 0.1967          | 0.9375          | 0.9375       | 0.9375   | 0.9373     | 0.9377   |
| 0.0982        | 6.9   | 3000 | 0.2063          | 0.9394          | 0.9381       | 0.9381   | 0.9397     | 0.9364   |
| 0.0862        | 7.13  | 3100 | 0.2056          | 0.9414          | 0.9412       | 0.9412   | 0.9418     | 0.9407   |
| 0.0811        | 7.36  | 3200 | 0.2054          | 0.9414          | 0.9412       | 0.9412   | 0.9418     | 0.9407   |
| 0.0908        | 7.59  | 3300 | 0.2078          | 0.9394          | 0.9394       | 0.9394   | 0.9396     | 0.9392   |
| 0.092         | 7.82  | 3400 | 0.2003          | 0.9400          | 0.94         | 0.9400   | 0.9401     | 0.9399   |
| 0.0834        | 8.05  | 3500 | 0.2039          | 0.9394          | 0.9394       | 0.9394   | 0.9392     | 0.9396   |
| 0.0747        | 8.28  | 3600 | 0.2018          | 0.9413          | 0.9413       | 0.9412   | 0.9415     | 0.9410   |
| 0.0898        | 8.51  | 3700 | 0.2009          | 0.94            | 0.94         | 0.94     | 0.94       | 0.94     |
| 0.077         | 8.74  | 3800 | 0.2022          | 0.9410          | 0.9406       | 0.9406   | 0.9415     | 0.9398   |
| 0.087         | 8.97  | 3900 | 0.2055          | 0.9382          | 0.9381       | 0.9381   | 0.9385     | 0.9377   |
| 0.0647        | 9.2   | 4000 | 0.2062          | 0.9401          | 0.94         | 0.9400   | 0.9405     | 0.9395   |
| 0.0736        | 9.43  | 4100 | 0.2081          | 0.9389          | 0.9387       | 0.9387   | 0.9393     | 0.9382   |
| 0.08          | 9.66  | 4200 | 0.2087          | 0.9395          | 0.9394       | 0.9394   | 0.9399     | 0.9388   |
| 0.083         | 9.89  | 4300 | 0.2087          | 0.9395          | 0.9394       | 0.9394   | 0.9398     | 0.9390   |


### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3

{"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "rubert-tiny2-russe-toxicity", "results": []}]}
transformers pytorch tensorboard bert text-classification generated_from_trainer license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 

rubert-tiny2-russe-toxicity
===========================


This model is a fine-tuned version of cointegrated/rubert-tiny2 on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.2087
* Precision Macro: 0.9395
* Recall Macro: 0.9394
* F1 Macro: 0.9394
* F1 Neutral: 0.9398
* F1 Toxic: 0.9390


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: 2e-05
* train\_batch\_size: 32
* eval\_batch\_size: 64
* 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 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 32
* eval\_batch\_size: 64
* 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 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 32
* eval\_batch\_size: 64
* 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 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 32
* eval\_batch\_size: 64
* 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 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3