---
license: apache-2.0
base_model: sentence-transformers/LaBSE
tags:
- generated_from_trainer
- news
- russian
- media
- text-classification
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: news_classifier_ft
  results: []
datasets:
- data-silence/rus_news_classifier
pipeline_tag: text-classification
language:
- ru
widgets:
- text: Введите новостной текст для классификации
  example_title: Классификация новостей
  button_text: Классифицировать
  api_name: classify
library_name: transformers
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# any-news-classifier

This model is a fine-tuned version of [sentence-transformers/LaBSE](https://huggingface.co/sentence-transformers/LaBSE) on my [news dataset](https://huggingface.co/datasets/data-silence/rus_news_classifier).
The learning news dataset is a well-balanced sample of recent news from the last five years.

It achieves the following results on the evaluation set:
- Loss: 0.3820
- Accuracy: 0.9029
- F1: 0.9025
- Precision: 0.9030
- Recall: 0.9029

## Model description

This is a multi-class classifier of Russian news, made with the LaBSE model finetune for [AntiSMI Project](https://github.com/data-silence/antiSMI-Project). 
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)

## Testing this model on `Spaces`

You can try the model and evaluate its quality [here](https://huggingface.co/spaces/data-silence/rus-news-classifier)


## How to use

```python

from transformers import pipeline

category_mapper = {
'LABEL_0': 'climate',
'LABEL_1': 'conflicts',
'LABEL_2': 'culture',
'LABEL_3': 'economy',
'LABEL_4': 'gloss',
'LABEL_5': 'health',
'LABEL_6': 'politics',
'LABEL_7': 'science',
'LABEL_8': 'society',
'LABEL_9': 'sports',
'LABEL_10': 'travel'
}

# Используйте предобученную модель из Hugging Face Hub
classifier = pipeline("text-classification", model="data-silence/rus-news-classifier")

def predict_category(text):
    result = classifier(text)
    category = category_mapper[result[0]['label']]
    score = result[0]['score']
    return category, score

predict_category("В Париже завершилась церемония закрытия Олимпийских игр")
# ('sports', 0.9959506988525391)
```


## Intended uses & limitations

The "gloss" category is used to select yellow press, trashy and dubious news. The model can get confused in the classification of news categories politics, society and conflicts.

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.3544        | 1.0   | 3596  | 0.3517          | 0.8861   | 0.8860 | 0.8915    | 0.8861 |
| 0.2738        | 2.0   | 7192  | 0.3190          | 0.8995   | 0.8987 | 0.9025    | 0.8995 |
| 0.19          | 3.0   | 10788 | 0.3524          | 0.9016   | 0.9015 | 0.9019    | 0.9016 |
| 0.1402        | 4.0   | 14384 | 0.3820          | 0.9029   | 0.9025 | 0.9030    | 0.9029 |
| 0.1055        | 5.0   | 17980 | 0.4399          | 0.9022   | 0.9018 | 0.9024    | 0.9022 |


### Framework versions

- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1