library_name: transformers license: mit datasets:

• seara/ru_go_emotions base_model: ai-forever/ruRoberta-large base_model_relation: finetune language:
• ru tags:
• Text Classification
• emotion-classification
• emotion-recognition
• emotion-detection
• emotion metrics:
• f1
• precision
• recall pipeline_tag: text-classification

This is the best russian opensource model for detecting all 27 types of emotions:

Summary

This is ruRoberta-large model finetuned on 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.

The quality of the model varies greatly across all classes (look at the table with metrics below). There are classes like amusement, gratitude, where the model shows high recognition quality, and classes that pose difficulties for the model - grief, relief, that do have much fewer examples in the training data.

Also ONNX version of model and INT8 quantized model is available. information about them is posted in the last sections.

Usage

Using model with Huggingface Transformers:

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))

Here is how you can 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']

This is the way to 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,
 'sadness': 0.002,
 'embarrassment': 0.001,
 'nervousness': 0.001,
 'remorse': 0.001}
'''

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, quality is the same.

INT8 quantized model (onnx/model_quantized.onnx) - 2.5x faster than Transformer model, quality is almost the same.

In table below results of tests of inference of 5427 samples of test_set. I tested inference with batch_size 1 on Intel Xeon CPU with 2 vCPUs (Google Colab).

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 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']