Ruroberta Large Ru Go Emotions

🚀 ruRoberta-large for Russian Emotion Classification

This project presents a fine - tuned [ruRoberta - large](https://huggingface.co/ai - forever/ruRoberta - large) model on the ru_go_emotions dataset for multilabel emotion classification. It can extract all emotions from text or detect certain emotions.

✨ Features

• Multilabel Classification: Capable of detecting 27 types of emotions from text.
• High - Performance Variants: Available in ONNX and INT8 quantized versions for faster inference.

📦 Installation

No specific installation steps were provided in the original README.

💻 Usage Examples

Basic Usage

import torch
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))

Advanced Usage

Extract emotions contained in text

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)  
  class_binary_labels = (probas > torch.tensor(best_thresholds)).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']

Get all emotions and their scores

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,
 'remorse': 0.001,
 'sadness': 0.002,
 'embarrassment': 0.001,
 'nervousness': 0.001}
'''

📚 Documentation

Model Performance Comparison

This is the best Russian opensource model for detecting all 27 types of emotions. The following table shows the performance comparison with other models:

Eval results on test split of ru - go - emotions

ONNX and quantized versions of model

• Full precision ONNX model (onnx/model.onnx): 1.5x faster than Transformer model, with the same quality.
• INT8 quantized model (onnx/model_quantized.onnx): 2.5x faster than Transformer model, with almost the same quality.

The following table shows the test results of inference of 5427 samples of the test_set on an Intel Xeon CPU with 2 vCPUs (Google Colab) with batch_size 1:

How to use ONNX versions

Loading full precision model

from optimum.onnxruntime import ORTModelForSequenceClassification
model_id = "fyaronskiy/ruRoberta-large-ru-go-emotions"
file_name = "onnx/model.onnx"
model = ORTModelForSequenceClassification.from_pretrained(model_id, file_name=file_name)
tokenizer = AutoTokenizer.from_pretrained(model_id)

INT8 quantized model

model_id = "fyaronskiy/ruRoberta-large-ru-go-emotions"
file_name = "onnx/model_quantized.onnx"

model = ORTModelForSequenceClassification.from_pretrained(model_id, file_name=file_name)
tokenizer = AutoTokenizer.from_pretrained(model_id)

After loading, using ONNX models for inference is the same as for the usual Transformer model:

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))

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)  
  class_binary_labels = (probas > torch.tensor(best_thresholds)).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']

🔧 Technical Details

The thresholds are selected on the validation set by maximizing f1 macro over all labels. The model's performance varies across different emotion classes, which may be related to the number of examples in the training data.

📄 License

This project is licensed under the MIT license.