gurkan08/bert-turkish-text-classification

Turkish News Text Classification

Turkish text classification model obtained by fine-tuning the Turkish bert model (dbmdz/bert-base-turkish-cased)

Dataset

Dataset consists of 11 classes were obtained from https://www.trthaber.com/. The model was created using the most distinctive 6 classes.

Dataset can be accessed at https://github.com/gurkan08/datasets/tree/master/trt_11_category.

label_dict = {
    'LABEL_0': 'ekonomi',
    'LABEL_1': 'spor',
    'LABEL_2': 'saglik',
    'LABEL_3': 'kultur_sanat',
    'LABEL_4': 'bilim_teknoloji',
    'LABEL_5': 'egitim'
}

70% of the data were used for training and 30% for testing.

train f1-weighted score = %97

test f1-weighted score = %94

Usage

from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("gurkan08/bert-turkish-text-classification")
model = AutoModelForSequenceClassification.from_pretrained("gurkan08/bert-turkish-text-classification")

nlp = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)

text = ["Süper Lig'in 6. haftasında Sivasspor ile Çaykur Rizespor karşı karşıya geldi...",
"Son 24 saatte 69 kişi Kovid-19 nedeniyle yaşamını yitirdi, 1573 kişi iyileşti"]

out = nlp(text)

label_dict = {
 'LABEL_0': 'ekonomi',
 'LABEL_1': 'spor',
 'LABEL_2': 'saglik',
 'LABEL_3': 'kultur_sanat',
 'LABEL_4': 'bilim_teknoloji',
 'LABEL_5': 'egitim'
}

results = []
for result in out:
    result['label'] = label_dict[result['label']]
    results.append(result)
print(results)

# > [{'label': 'spor', 'score': 0.9992026090621948}, {'label': 'saglik', 'score': 0.9972177147865295}]