First - you should prepare few functions to talk to model

import torch
from transformers import BertForSequenceClassification, AutoTokenizer

LABELS = ['радость', 'интерес', 'удивление', 'печаль', 'гнев', 'отвращение', 'страх', 'вина', 'нейтрально']
tokenizer = AutoTokenizer.from_pretrained('Djacon/rubert-tiny2-russian-emotion-detection')
model = BertForSequenceClassification.from_pretrained('Djacon/rubert-tiny2-russian-emotion-detection')

# Predicting emotion in text
@torch.no_grad()
def predict_emotion(text: str) -> str:
    inputs = tokenizer(text, truncation=True, return_tensors='pt')
    inputs = inputs.to(model.device)

    outputs = model(**inputs)

    pred = torch.nn.functional.softmax(outputs.logits, dim=1)
    pred = pred.argmax(dim=1)

    return LABELS[pred[0]]

# Probabilistic prediction of emotion in a text
@torch.no_grad()
def predict_emotions(text: str) -> list:
    inputs = tokenizer(text, truncation=True, return_tensors='pt')
    inputs = inputs.to(model.device)

    outputs = model(**inputs)

    pred = torch.nn.functional.softmax(outputs.logits, dim=1)

    emotions_list = {}
    for i in range(len(pred[0].tolist())):
        emotions_list[LABELS[i]] = round(pred[0].tolist()[i], 4)
    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}

Citations

@misc{Djacon,
  author = {Djacon},
  year = {2023},
  publisher = {Hugging Face},
  journal = {Hugging Face Hub},
}