---
language:
- ru
library_name: fasttext
pipeline_tag: text-classification
tags:
- news
- media
- russian
- multilingual
---

# FastText Text Classifier

This is a FastText model for text classification, trained on my news dataset data-silence/rus_news_classifier, consisting of news from the last 5 years, hosted on Hugging Face Hub.

## Model Description

This model uses FastText to classify text into 11 categories. It has been trained on ~70_000 examples and achieves an accuracy of 0.8691016964865116 on a test dataset.

### Task

The model is designed to classify any languages news articles into 11 categories, but was originally trained to categorize Russian-language news.


### Categories


The news category is assigned by the classifier to one of 11 categories:
- climate (климат)
- conflicts (конфликты)
- culture (культура)
- economy (экономика)
- gloss (глянец)
- health (здоровье)
- politics (политика)
- science (наука)
- society (общество)
- sports (спорт)
- travel (путешествия)
}

## Usage

To use this model, you will need the `fasttext` and `transformers` libraries. Install them using pip:

`pip install fasttext transformers`

Example of how to use the model:

```python
from transformers import pipeline

classifier = pipeline("text-classification", model="data-silence/fasttext-rus-news-classifier")

text = "Your text to classify here"
result = classifier(text)
print(result)
```

## Contacts

If you have any questions or suggestions for improving the model, please create an issue in this repository or contact me at enjoy@data-silence.com.