Instructions to use anilguven/electra_tr_turkish_news with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use anilguven/electra_tr_turkish_news with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="anilguven/electra_tr_turkish_news")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("anilguven/electra_tr_turkish_news") model = AutoModelForSequenceClassification.from_pretrained("anilguven/electra_tr_turkish_news", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 966 Bytes
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license: mit
datasets:
- anilguven/turkish_news_dataset
language:
- tr
metrics:
- accuracy
- f1
tags:
- electra
- news
- classification
- text
---
### 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/dbmdz/electra-base-turkish-cased-discriminator
### Preprocessing
You must apply removing stopwords, stemming, or lemmatization process for Turkish.
### Results
- Accuracy: %97.619
- F1-score: %97.617
### Citation
BibTeX:
Peer review process |