Create README.md

README.md

@@ -0,0 +1,115 @@
+---
+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](https://huggingface.co/datasets/interpress_news_category_tr)
+
+## Model
+Model accuracy on train data and validation data is %97.
+
+## Usage
+```sh
+pip install transformers or pip install transfomers==4.3.3
+```
+```sh
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+tokenizer = AutoTokenizer.from_pretrained("Serdar/bert-model")
+
+model = AutoModelForSequenceClassification.from_pretrained("Serdar/bert-model")
+```
+```sh
+# PREPROCESSING
+import re
+my_punc = r"#$%&()*+-/:;<=>@[\]^_{|}~"
+
+def clean_url(content):
+    reg_url=r'[\S]+\.(net|com|org|info|edu|gov|uk|de|ca|jp|fr|au|us|ru|ch|it|nel|se|no|es|mil)[\S]*\s?'
+    pattern_url = re.compile(reg_url)
+    content = pattern_url.sub('',content)
+    return content
+    
+def clean_email(content):
+    reg_email='\S*@\S*\s?'
+    pattern_email = re.compile(reg_email)
+    content = pattern_email.sub('',content)
+    return content
+    
+def clean_punctuation(content):
+    content = content.translate(content.maketrans("", "", my_punc))
+    return content
+
+def clean_data(text):
+    text = clean_url(text)
+    text = clean_email(text)
+    text = clean_punctuation(text)
+
+    filtered_sentence = []
+    for word in text.split(" "):
+        if len(word) > 2:
+            filtered_sentence.append(word)
+    
+    text = ' '.join(filtered_sentence)
+    return text
+```
+```sh
+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")
+```
+```sh
+def prediction(news):
+    news=clean_data(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') # for tf tensors, switch pt to tf
+
+    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
+```
+
+```sh
+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])
+```
+Thanks to @yavuzkomecoglu for contributes
+
+If you have any question, please, don't hesitate to contact with me
+[![linkedin](https://img.shields.io/badge/LinkedIn-0077B5?style=for-the-badge&logo=linkedin&logoColor=white)](https://www.linkedin.com/in/serdarakyol55/)
+[![Github](https://img.shields.io/badge/GitHub-100000?style=for-the-badge&logo=github&logoColor=white)](https://github.com/serdarakyol)