Create README.md

model_cards/gurkan08/bert-turkish-text-classification/README.md

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+---
+language: tr
+---
+# 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}]
+
+
+