metadata

language: tr
Dataset: interpress_news_category_tr

INTERPRESS NEWS CLASSIFICATION

Dataset

The dataset downloaded from interpress. This dataset is real world data. Actually there are 273K data but I filtered them and used 108K data for this model. For more information about dataset please visit this link

Model

Model accuracy on train data and validation data is %97.

Usage for Torch

pip install transformers or pip install transformers==4.3.3

from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("serdarakyol/interpress-turkish-news-classification")
model = AutoModelForSequenceClassification.from_pretrained("serdarakyol/interpress-turkish-news-classification")

import torch
import numpy as np

if torch.cuda.is_available():    
    device = torch.device("cuda")
    model = model.cuda()
    print('There are %d GPU(s) available.' % torch.cuda.device_count())
    print('GPU name is:', torch.cuda.get_device_name(0))
else:
    print('No GPU available, using the CPU instead.')
    device = torch.device("cpu")

def prediction(news):
    news=[news]
    indices=tokenizer.batch_encode_plus(
    news,
    max_length=512,
    add_special_tokens=True,
    return_attention_mask=True,
    padding='max_length',
    truncation=True,
    return_tensors='pt')

    inputs = indices["input_ids"].clone().detach().to(device)
    masks = indices["attention_mask"].clone().detach().to(device)

    with torch.no_grad():
        output = model(inputs, token_type_ids=None,attention_mask=masks)

    logits = output[0]
    logits = logits.detach().cpu().numpy()
    pred = np.argmax(logits,axis=1)[0]
    return pred

news = r"ABD'den Prens Selman'a yaptırım yok Beyaz Saray Sözcüsü Psaki, Muhammed bin Selman'a yaptırım uygulamamanın \"doğru karar\" olduğunu savundu. Psaki, \"Tarihimizde, Demokrat ve Cumhuriyetçi başkanların yönetimlerinde diplomatik ilişki içinde olduğumuz ülkelerin liderlerine yönelik yaptırım getirilmemiştir\" dedi."

You can find the news in this link (news date: 02/03/2021)

labels = {
    0 : "Culture-Art",
    1 : "Economy",
    2 : "Politics",
    3 : "Education",
    4 : "World",
    5 : "Sport",
    6 : "Technology",
    7 : "Magazine",
    8 : "Health",
    9 : "Agenda"
}
pred = prediction(news)
print(labels[pred])
# > World

Usage for Tensorflow

pip install transformers or pip install transformers==4.3.3

import tensorflow as tf
from transformers import BertTokenizer, TFBertForSequenceClassification
import numpy as np

tokenizer = BertTokenizer.from_pretrained('serdarakyol/interpress-turkish-news-classification')
model = TFBertForSequenceClassification.from_pretrained("serdarakyol/interpress-turkish-news-classification")

inputs = tokenizer(news, return_tensors="tf")
inputs["labels"] = tf.reshape(tf.constant(1), (-1, 1)) # Batch size 1

outputs = model(inputs)
loss = outputs.loss
logits = outputs.logits
pred = np.argmax(logits,axis=1)[0]
labels[pred]
# > World

Thanks to @yavuzkomecoglu for contributes

If you have any question, please, don't hesitate to contact with me