metadata

license: cc-by-sa-4.0
datasets:
  - Kostya165/ru_emotion_dvach
language:
  - ru
metrics:
  - accuracy
base_model:
  - cointegrated/rubert-tiny2
pipeline_tag: text-classification
tags:
  - russian
  - emotion
  - sentiment
  - sentiment-analisys
  - emotion-analisys
  - emotion-classification
  - emotion-detection
  - rubert
  - rubert-tiny

rubert_tiny2_russian_emotion_sentiment

Описание

Модель rubert_tiny2_russian_emotion_sentiment — это дообученная версия легковесной модели cointegrated/rubert-tiny2 для классификации пяти эмоций в русскоязычных сообщениях:

0: aggression (агрессия)
1: anxiety (тревожность)
2: neutral (нейтральное состояние)
3: positive (позитив)
4: sarcasm (сарказм)

Результаты на валидации

Метрика	Значение
Accuracy	0.8911
F1 macro	0.8910
F1 micro	0.8911

Точность по классам:

агрессия (0): 0.9120
тревожность (1): 0.9462
нейтральное (2): 0.8663
позитив (3): 0.8884
сарказм (4): 0.8426

Использование

pip install transformers torch

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

# Загружаем модель и токенизатор
MODEL_ID = "Kostya165/rubert_tiny2_russian_emotion_sentiment"
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
model     = AutoModelForSequenceClassification.from_pretrained(MODEL_ID)
model.eval()

texts = [
    "Сегодня отличный день!",
    "Меня это всё бесит и раздражает."
]

# Токенизация
enc = tokenizer(texts, padding=True, truncation=True, max_length=128, return_tensors="pt")
with torch.no_grad():
    logits = model(**enc).logits
    preds = logits.argmax(dim=-1).tolist()

# Преобразуем ID обратно в метки
id2label = model.config.id2label
labels = [id2label[p] for p in preds]
print(labels)  # например: ['positive', 'aggression']

Как было обучено

База: cointegrated/rubert-tiny2
Датасет: Kostya165/ru_emotion_dvach
Эпохи: 2
Batch size: 32
LR: 1e-5
Mixed precision: FP16
Регуляризация: Dropout 0.1, weight_decay 0.01, warmup_ratio 0.1

Зависимости

transformers>=4.30.0
torch>=1.10.0
datasets
evaluate

Лицензия

CC-BY-SA 4.0.

Цитирование

@article{rubert_tiny2_russian_emotion_sentiment,
  title   = {Russian Emotion Sentiment Classification with RuBERT-tiny2},
  author  = {Kostya165},
  year    = {2024},
  howpublished = {\url{https://huggingface.co/Kostya165/rubert_tiny2_russian_emotion_sentiment}}
}

English

rubert_tiny2_russian_emotion_sentiment

Description

The rubert_tiny2_russian_emotion_sentiment model is a fine‑tuned version of the lightweight cointegrated/rubert-tiny2 for classifying five emotions in Russian text:

0: aggression
1: anxiety
2: neutral
3: positive
4: sarcasm

Validation Results

Metric	Value
Accuracy	0.8911
F1 macro	0.8910
F1 micro	0.8911

Per‑class accuracy:

aggression: 0.9120
anxiety: 0.9462
neutral: 0.8663
positive: 0.8884
sarcasm: 0.8426

Usage

pip install transformers torch

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

MODEL_ID = "Kostya165/rubert_tiny2_russian_emotion_sentiment"
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
model     = AutoModelForSequenceClassification.from_pretrained(MODEL_ID)
model.eval()

texts = ["Сегодня отличный день!", "Меня это всё бесит и раздражает."]
enc = tokenizer(texts, padding=True, truncation=True, max_length=128, return_tensors="pt")
with torch.no_grad():
    logits = model(**enc).logits
    preds = logits.argmax(dim=-1).tolist()

labels = [model.config.id2label[p] for p in preds]
print(labels)  # e.g. ['positive', 'aggression']

Training Details

Base: cointegrated/rubert-tiny2
Dataset: Kostya165/ru_emotion_dvach (train/validation)
Epochs: 2
Batch size: 32
Learning rate: 1e‑5
Mixed precision: FP16
Regularization: Dropout 0.1, weight_decay 0.01, warmup_ratio 0.1

Requirements

transformers>=4.30.0
torch>=1.10.0
datasets
evaluate

License

CC-BY-SA 4.0.

Citation

@article{rubert_tiny2_russian_emotion_sentiment,
  title   = {Russian Emotion Sentiment Classification with RuBERT-tiny2},
  author  = {Kostya165},
  year    = {2024},
  howpublished = {\url{https://huggingface.co/Kostya165/rubert_tiny2_russian_emotion_sentiment}}
}