

# ruBert-base-russian-emotions-classifier-goEmotions

This model is a fine-tuned version of [ai-forever/ruBert-base](https://huggingface.co/ai-forever/ruBert-base) on [Djacon/ru_goemotions](https://huggingface.co/datasets/Djacon/ru_goemotions).
It achieves the following results on the evaluation set (2nd epoch):
- Loss: 0.2088
- AUC: 0.9240

The quality of the predicted probabilities on the test dataset is the following:

| label    | joy    | interest | surpise | sadness | anger  | disgust | fear   | guilt  | neutral | average |
|----------|--------|----------|---------|---------|--------|---------|--------|--------|---------|---------|
| AUC      | 0.9369 | 0.9213   | 0.9325  | 0.8791  | 0.8374 | 0.9041  | 0.9470 | 0.9758 | 0.8518  | 0.9095  |
| F1-micro | 0.9528 | 0.9157   | 0.9697  | 0.9284  | 0.8690 | 0.9658  | 0.9851 | 0.9875 | 0.7654  | 0.9266  |
| F1-macro | 0.8369 | 0.7922   | 0.7561  | 0.7392  | 0.7351 | 0.7356  | 0.8176 | 0.8247 | 0.7650  | 0.7781  |


## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-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: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | AUC      |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1755        | 1.0   | 1685 | 0.1717          | 0.9220   |
| 0.1391        | 2.0   | 3370 | 0.1757          | 0.9240   |
| 0.0899        | 3.0   | 5055 | 0.2088          | 0.9106   |


### Framework versions

- Transformers 4.24.0
- Pytorch 2.0.1
- Datasets 2.12.0
- Tokenizers 0.11.0
{"language": ["ru"], "license": "apache-2.0", "tags": ["sentiment", "emotion-classification", "multilabel", "multiclass"], "datasets": ["Djacon/ru_goemotions"], "metrics": ["accuracy"], "widget": [{"text": "\u041e\u0447\u0435\u043d\u044c \u0440\u0430\u0434 \u0442\u0435\u0431\u044f \u0432\u0438\u0434\u0435\u0442\u044c!"}, {"text": "\u041a\u0430\u043a \u0434\u0435\u043b\u0430?"}, {"text": "\u041c\u043d\u0435 \u043d\u0435\u043c\u043d\u043e\u0433\u043e \u043e\u0442\u0432\u0440\u0430\u0442\u043d\u043e \u044d\u0442\u043e \u0434\u0435\u043b\u0430\u0442\u044c"}, {"text": "\u042f \u0438\u0441\u043f\u044b\u0442\u0430\u043b \u043c\u0443\u0440\u0430\u0448\u043a\u0438 \u043e\u0442 \u0441\u0442\u0440\u0430\u0445\u0430"}, {"text": "\u041d\u0435\u0442 \u043d\u0438\u0447\u0435\u0433\u043e \u0440\u0430\u0434\u043e\u0441\u0442\u043d\u043e\u0433\u043e \u0432 \u044d\u0442\u0438\u0445 \u0433\u043e\u0440\u044c\u043a\u0438\u0445 \u043d\u043e\u0432\u043e\u0441\u0442\u044f\u0445"}, {"text": "\u041e\u0433\u043e, \u043d\u0435\u043e\u0436\u0438\u0434\u0430\u043b \u0442\u0435\u0431\u044f \u0437\u0434\u0435\u0441\u044c \u0443\u0432\u0438\u0434\u0435\u0442\u044c!"}, {"text": "\u0424\u0443 \u043d\u0443 \u0438 \u043c\u0435\u0440\u0437\u043e\u0441\u0442\u044c"}, {"text": "\u041c\u043d\u0435 \u043d\u0435\u043f\u0440\u0438\u044f\u0442\u043d\u043e \u043e\u0431\u0449\u0435\u043d\u0438\u0435 \u0441 \u0442\u043e\u0431\u043e\u0439"}], "base_model": "ai-forever/ruBert-base", "model-index": [{"name": "ruBert-base-russian-emotions-classifier-goEmotions", "results": [{"task": {"type": "multilabel-text-classification", "name": "Multilabel Text Classification"}, "dataset": {"name": "ru_goemotions", "type": "Djacon/ru_goemotions", "args": "ru"}, "metrics": [{"type": "roc_auc", "value": "92%", "name": "multilabel ROC AUC"}]}]}]}
transformers pytorch safetensors bert text-classification sentiment emotion-classification multilabel multiclass ru dataset:Djacon/ru_goemotions base_model:ai-forever/ruBert-base license:apache-2.0 model-index autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #safetensors #bert #text-classification #sentiment #emotion-classification #multilabel #multiclass #ru #dataset-Djacon/ru_goemotions #base_model-ai-forever/ruBert-base #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us 

ruBert-base-russian-emotions-classifier-goEmotions
==================================================


This model is a fine-tuned version of ai-forever/ruBert-base on Djacon/ru\_goemotions.
It achieves the following results on the evaluation set (2nd epoch):


* Loss: 0.2088
* AUC: 0.9240


The quality of the predicted probabilities on the test dataset is the following:



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


### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-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: 3


### Training results



### Framework versions


* Transformers 4.24.0
* Pytorch 2.0.1
* Datasets 2.12.0
* Tokenizers 0.11.0



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-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: 3 ### Training results ### Framework versions


* Transformers 4.24.0
* Pytorch 2.0.1
* Datasets 2.12.0
* Tokenizers 0.11.0
TAGS
#transformers #pytorch #safetensors #bert #text-classification #sentiment #emotion-classification #multilabel #multiclass #ru #dataset-Djacon/ru_goemotions #base_model-ai-forever/ruBert-base #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-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: 3 ### Training results ### Framework versions


* Transformers 4.24.0
* Pytorch 2.0.1
* Datasets 2.12.0
* Tokenizers 0.11.0