metadata
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
news_classifier_ft
This model is a fine-tuned version of sentence-transformers/LaBSE on my rus-news-classifier dataset. 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. 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)
How to use
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
Enjoy to use in your purpose
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