README.md

---
library_name: transformers
tags:
- bert
- berturk
language:
- tr
pipeline_tag: text-classification
---

# Model Card for Model ID
Turkish news classifier. 


### Model Description

11 classes are present:
'turkiye': 0, 'ekonomi': 1, 'dunya': 2, 'spor': 3, 'magazin': 4, 'guncel': 5, 'genel': 6, 'siyaset': 7, 'saglik': 8, 'kultur-sanat': 9, 'teknoloji': 10, 'yasam': 11

The model is a finetuned bert-base-multilingual-uncased model. 
The model is not originally a classifier model, so classifier weights were trained completely using the turkish dataset. 🤗

Eval loss: train_loss': 0.8327703781731708
Train loss:0.8896290063858032
Eval train split: 0.2/0.8

- **Developed by:** [Ezel Bayraktar]
- **Model type:** [Classifier]
- **Language(s) (NLP):** [Turkish]
- **License:** [MIT License]
- **Finetuned from model [optional]:** [bert-base-multilingual-uncased]
## Training Details
I used rtx 3060 12gb card to tain the training took 245 minutes in total

learning_rate=5e-5,
per_device_train_batch_size=20,
per_device_eval_batch_size=20,
num_train_epochs=7,

### Training Data

I used the kemik 42bin haber data set which you can access from this link
http://www.kemik.yildiz.edu.tr/veri_kumelerimiz.html


## Model Card Contact

[email protected]