RuBERT Russian Financial Sentiment
A fine-tuned RuBERT model for 3-class sentiment classification of Russian-language financial and economic text (negative / neutral / positive).
This model fine-tunes blanchefort/rubert-base-cased-sentiment-rusentiment on the SentiRuEval2016 dataset, adapting a general-purpose Russian sentiment model to the financial domain.
Model Details
- Base model:
blanchefort/rubert-base-cased-sentiment-rusentiment(RuBERT) - Architecture: BERT for Sequence Classification
- Language: Russian
- Task: 3-class sentiment classification
- Training dataset: mteb/SentiRuEval2016
- Max sequence length: 128 tokens
- License: Apache 2.0
Labels
| Label ID | Sentiment |
|---|---|
| 0 | Negative |
| 1 | Neutral |
| 2 | Positive |
Usage
from transformers import AutoModelForSequenceClassification, AutoTokenizer
import torch
model_name = "ilyali034/rubert-russian-financial-sentiment"
model = AutoModelForSequenceClassification.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
text = "Акции компании выросли на 15% после публикации отчетности"
inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=128)
with torch.no_grad():
outputs = model(**inputs)
predicted_class_id = torch.argmax(outputs.logits, dim=-1).item()
id2label = {0: "negative", 1: "neutral", 2: "positive"}
print(f"Predicted sentiment: {id2label[predicted_class_id]}")
Or with the pipeline API:
from transformers import pipeline
classifier = pipeline("text-classification", model="ilyali034/rubert-russian-financial-sentiment")
result = classifier("Банк объявил о рекордной прибыли за квартал")
print(result)
Training Details
The model was fine-tuned using the Hugging Face Trainer API.
- Epochs: 3
- Batch size: 16 (train and eval)
- Warmup steps: 500
- Weight decay: 0.01
- Best checkpoint selected by: macro F1 on the validation set
- Train/validation split: 85% / 15% (stratified by label), plus the original held-out test split
Labels in the source dataset (-1, 0, 1) were remapped to (0, 1, 2) for negative, neutral, and positive respectively.
Evaluation Results
Evaluated on a held-out test set of 3,000 examples (1,000 per class).
| Metric | Score |
|---|---|
| Accuracy | 0.8123 |
| Macro F1 | 0.8108 |
| Macro Precision | 0.8211 |
| Macro Recall | 0.8123 |
| Matthews Correlation Coefficient (MCC) | 0.7232 |
| Cohen's Kappa | 0.7185 |
Per-Class Metrics
| Class | Precision | Recall | F1-Score | Support |
|---|---|---|---|---|
| Negative | 0.8505 | 0.8820 | 0.8660 | 1000 |
| Neutral | 0.8277 | 0.7780 | 0.8021 | 1000 |
| Positive | 0.7908 | 0.8090 | 0.7998 | 1000 |
| Macro Avg | 0.8230 | 0.8230 | 0.8226 | 3000 |
| Weighted Avg | 0.8230 | 0.8230 | 0.8226 | 3000 |
Confusion Matrix
| Predicted Negative | Predicted Neutral | Predicted Positive | |
|---|---|---|---|
| Actual Negative | 882 | 49 | 69 |
| Actual Neutral | 77 | 778 | 145 |
| Actual Positive | 78 | 113 | 809 |
The model performs best on negative sentiment (F1 = 0.866). The most common confusion is between neutral and positive text, which is typical for financial language where neutral factual statements and mildly positive statements can overlap.
Intended Use
This model is intended for sentiment analysis of Russian-language financial and economic content such as:
- News headlines and articles about companies, markets, and the economy
- Social media posts and comments discussing financial topics
- Analyst commentary and investor discussions
Limitations
- Trained on general Russian sentiment data (SentiRuEval2016), not a purpose-built financial corpus, so domain-specific nuance (e.g. sarcasm, technical jargon, mixed-sentiment reports) may not be fully captured.
- Maximum input length is 128 tokens; longer documents should be split or truncated.
- As with any sentiment classifier, predictions should not be used as the sole basis for financial decision-making.
Citation
If you use this model, please cite the base model and dataset:
@misc{rubert-financial-sentiment,
author = {ilyali034},
title = {RuBERT Russian Financial Sentiment},
year = {2025},
publisher = {Hugging Face},
url = {https://huggingface.co/ilyali034/rubert-russian-financial-sentiment}
}
Base model: blanchefort/rubert-base-cased-sentiment-rusentiment
Dataset: mteb/SentiRuEval2016