Instructions to use anilguven/albert_tr_turkish_news with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use anilguven/albert_tr_turkish_news with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="anilguven/albert_tr_turkish_news")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("anilguven/albert_tr_turkish_news") model = AutoModelForSequenceClassification.from_pretrained("anilguven/albert_tr_turkish_news", device_map="auto") - Notebooks
- Google Colab
- Kaggle
metadata
license: mit
datasets:
- anilguven/turkish_news_dataset
language:
- tr
metrics:
- accuracy
tags:
- news
- classification
- text
- turkish
Information
This model was developed/finetuned for news classification task for the Turkish Language. This model was finetuned via news dataset. This dataset contains 7 classes: economy, magazine, sport, politics, technology, health, and events.
- LABEL_0: economy
- LABEL_1: magazine
- LABEL_2: health
- LABEL_3: politics
- LABEL_4: sports
- LABEL_5: technology
- LABEL_6: events
Model Sources
- Dataset: https://huggingface.co/datasets/anilguven/turkish_news_dataset
- Paper: peer review (Springer)
- Finetuned from model:: https://huggingface.co/loodos/albert-base-turkish-uncased
Preprocessing
You must apply removing stopwords, stemming, or lemmatization process for Turkish.
Results
- Accuracy: %96.310
- F1-score: %96.316
Citation
BibTeX: Peer review process
APA: Peer review process