---
library_name: transformers
license: mit
datasets:
- seara/ru_go_emotions
language:
- ru
metrics:
- f1
---


This is [ruRoberta-large](https://huggingface.co/ai-forever/ruRoberta-large) model finetuned on [ru_go_emotions](https://huggingface.co/datasets/seara/ru_go_emotions) 
dataset for multilabel classification. Model can be used to extract all emotions from text or detect certain emotions. Thresholds are selected on validation set by maximizing f1 macro over all labels.

# Usage
Using model with Huggingface Transformers:
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("fyaronskiy/ruRoberta-large-ru-go-emotions")
model = AutoModelForSequenceClassification.from_pretrained("fyaronskiy/ruRoberta-large-ru-go-emotions")

best_thresholds = [0.36734693877551017, 0.2857142857142857, 0.2857142857142857, 0.16326530612244897, 0.14285714285714285, 0.14285714285714285, 0.18367346938775508, 0.3469387755102041, 0.32653061224489793, 0.22448979591836732, 0.2040816326530612, 0.2857142857142857, 0.18367346938775508, 0.2857142857142857, 0.24489795918367346, 0.7142857142857142, 0.02040816326530612, 0.3061224489795918, 0.44897959183673464, 0.061224489795918366, 0.18367346938775508, 0.04081632653061224, 0.08163265306122448, 0.1020408163265306, 0.22448979591836732, 0.3877551020408163, 0.3469387755102041, 0.24489795918367346]
LABELS = ['admiration', 'amusement', 'anger', 'annoyance', 'approval', 'caring', 'confusion', 'curiosity', 'desire', 'disappointment', 'disapproval', 'disgust', 'embarrassment', 'excitement', 'fear', 'gratitude', 'grief', 'joy', 'love', 'nervousness', 'optimism', 'pride', 'realization', 'relief', 'remorse', 'sadness', 'surprise', 'neutral']
ID2LABEL = dict(enumerate(LABELS))
```

Here is how you can extract emotions contained in text: 

```python
def predict_emotions(text):
  inputs = tokenizer(text, truncation=True, add_special_tokens=True, max_length=128, return_tensors='pt')
  with torch.no_grad():
      logits = model(**inputs).logits
  probas = torch.sigmoid(logits).squeeze(dim=0)
  probas = probas.cpu().numpy()
  
  class_binary_labels = (probas > np.array(best_thresholds)).astype(int)
  return [ID2LABEL[label_id] for label_id, value in enumerate(class_binary_labels) if value == 1]

print(predict_emotions('У вас отличный сервис и лучший кофе в городе, обожаю вашу кофейню!'))
#['admiration', 'love']
```

This is the way to get all emotions and their scores:

```python
def predict(text):
    inputs = tokenizer(text, truncation=True, add_special_tokens=True, max_length=128, return_tensors='pt')
    with torch.no_grad():
        logits = model(**inputs).logits
    probas = torch.sigmoid(logits).squeeze(dim=0).tolist()
    probas = [round(proba, 3) for proba in probas]    
    
    labels2probas = dict(zip(LABELS, probas))
    probas_dict_sorted = dict(sorted(labels2probas.items(), key=lambda x: x[1], reverse=True))
    return probas_dict_sorted

print(predict('У вас отличный сервис и лучший кофе в городе, обожаю вашу кофейню!'))
'''{'admiration': 0.81,
 'love': 0.538,
 'joy': 0.041,
 'gratitude': 0.031,
 'approval': 0.026,
 'excitement': 0.023,
 'neutral': 0.009,
 'curiosity': 0.006,
 'amusement': 0.005,
 'desire': 0.005,
 'realization': 0.005,
 'caring': 0.004,
 'confusion': 0.004,
 'surprise': 0.004,
 'disappointment': 0.003,
 'disapproval': 0.003,
 'anger': 0.002,
 'annoyance': 0.002,
 'disgust': 0.002,
 'fear': 0.002,
 'grief': 0.002,
 'optimism': 0.002,
 'pride': 0.002,
 'relief': 0.002,
 'sadness': 0.002,
 'embarrassment': 0.001,
 'nervousness': 0.001,
 'remorse': 0.001}
'''
```

# Eval results on test split of ru-go-emotions                                             


|              |precision|recall|f1-score|support|threshold|
|--------------|---------|------|--------|-------|---------|
|admiration    |0.63     |0.75  |0.69    |504    |0.37     |
|amusement     |0.76     |0.91  |0.83    |264    |0.29     |
|anger         |0.47     |0.32  |0.38    |198    |0.29     |
|annoyance     |0.33     |0.39  |0.36    |320    |0.16     |
|approval      |0.27     |0.58  |0.37    |351    |0.14     |
|caring        |0.32     |0.59  |0.41    |135    |0.14     |
|confusion     |0.41     |0.52  |0.46    |153    |0.18     |
|curiosity     |0.45     |0.73  |0.55    |284    |0.35     |
|desire        |0.54     |0.31  |0.40    |83     |0.33     |
|disappointment|0.31     |0.34  |0.33    |151    |0.22     |
|disapproval   |0.31     |0.57  |0.40    |267    |0.20     |
|disgust       |0.44     |0.40  |0.42    |123    |0.29     |
|embarrassment |0.48     |0.38  |0.42    |37     |0.18     |
|excitement    |0.29     |0.43  |0.34    |103    |0.29     |
|fear          |0.56     |0.78  |0.65    |78     |0.24     |
|gratitude     |0.95     |0.85  |0.89    |352    |0.71     |
|grief         |0.03     |0.33  |0.05    |6      |0.02     |
|joy           |0.48     |0.58  |0.53    |161    |0.31     |
|love          |0.73     |0.84  |0.78    |238    |0.45     |
|nervousness   |0.24     |0.48  |0.32    |23     |0.06     |
|optimism      |0.57     |0.54  |0.56    |186    |0.18     |
|pride         |0.67     |0.38  |0.48    |16     |0.04     |
|realization   |0.18     |0.31  |0.23    |145    |0.08     |
|relief        |0.30     |0.27  |0.29    |11     |0.10     |
|remorse       |0.53     |0.84  |0.65    |56     |0.22     |
|sadness       |0.56     |0.53  |0.55    |156    |0.39     |
|surprise      |0.55     |0.57  |0.56    |141    |0.35     |
|neutral       |0.59     |0.79  |0.68    |1787   |0.24     |
|micro avg     |0.50     |0.66  |0.57    |6329   |         |
|macro avg     |0.46     |0.55  |0.48    |6329   |         |
|weighted avg  |0.53     |0.66  |0.58    |6329   |         |