metadata

license: mit
language:
  - ru
tags:
  - russian
  - classification
  - emotion
  - emotion-detection
  - emotion-recognition
  - multiclass
widget:
  - text: Как дела?
  - text: Дурак твой дед
  - text: Только попробуй!!!
  - text: Не хочу в школу(
  - text: Сейчас ровно час дня
  - text: >-
      А ты уверен, что эти полоски снизу не врут? Точно уверен? Вот прям 100
      процентов?
datasets:
  - Aniemore/cedr-m7
model-index:
  - name: RuBERT tiny2 For Russian Text Emotion Detection by Ilya Lubenets
    results:
      - task:
          name: Multilabel Text Classification
          type: multilabel-text-classification
        dataset:
          name: CEDR M7
          type: Aniemore/cedr-m7
          args: ru
        metrics:
          - name: multilabel accuracy
            type: accuracy
            value: 85%
      - task:
          name: Text Classification
          type: text-classification
        dataset:
          name: CEDR M7
          type: Aniemore/cedr-m7
          args: ru
        metrics:
          - name: accuracy
            type: accuracy
            value: 76%

First - you should prepare few functions to talk to model

import torch
from transformers import BertForSequenceClassification, AutoTokenizer

LABELS = ['neutral', 'happiness', 'sadness', 'enthusiasm', 'fear', 'anger', 'disgust']
tokenizer = AutoTokenizer.from_pretrained('Aniemore/rubert-tiny2-russian-emotion-detection')
model = BertForSequenceClassification.from_pretrained('Aniemore/rubert-tiny2-russian-emotion-detection')

@torch.no_grad()
def predict_emotion(text: str) -> str:
    """
        We take the input text, tokenize it, pass it through the model, and then return the predicted label
        :param text: The text to be classified
        :type text: str
        :return: The predicted emotion
    """
    inputs = tokenizer(text, max_length=512, padding=True, truncation=True, return_tensors='pt')
    outputs = model(**inputs)
    predicted = torch.nn.functional.softmax(outputs.logits, dim=1)
    predicted = torch.argmax(predicted, dim=1).numpy()
        
    return LABELS[predicted[0]]

@torch.no_grad()    
def predict_emotions(text: str) -> list:
    """
        It takes a string of text, tokenizes it, feeds it to the model, and returns a dictionary of emotions and their
        probabilities
        :param text: The text you want to classify
        :type text: str
        :return: A dictionary of emotions and their probabilities.
    """
    inputs = tokenizer(text, max_length=512, padding=True, truncation=True, return_tensors='pt')
    outputs = model(**inputs)
    predicted = torch.nn.functional.softmax(outputs.logits, dim=1)
    emotions_list = {}
    for i in range(len(predicted.numpy()[0].tolist())):
        emotions_list[LABELS[i]] = predicted.numpy()[0].tolist()[i]
    return emotions_list

And then - just gently ask a model to predict your emotion

simple_prediction = predict_emotion("Какой же сегодня прекрасный день, братья")
not_simple_prediction = predict_emotions("Какой же сегодня прекрасный день, братья")

print(simple_prediction)
print(not_simple_prediction)
# happiness
# {'neutral': 0.0004941817605867982, 'happiness': 0.9979524612426758, 'sadness': 0.0002536600804887712, 'enthusiasm': 0.0005498139653354883, 'fear': 0.00025326196919195354, 'anger': 0.0003583927755244076, 'disgust': 0.00013807788491249084}

Or, just simply use our package (GitHub), that can do whatever you want (or maybe not)

🤗

Citations

@misc{Aniemore,
  author = {Артем Аментес, Илья Лубенец, Никита Давидчук},
  title = {Открытая библиотека искусственного интеллекта для анализа и выявления эмоциональных оттенков речи человека},
  year = {2022},
  publisher = {Hugging Face},
  journal = {Hugging Face Hub},
  howpublished = {\url{https://huggingface.com/aniemore/Aniemore}},
  email = {hello@socialcode.ru}
}