Update README.md

README.md

@@ -1,3 +1,42 @@
 ---
 license: mit
+language:
+- tr
+pipeline_tag: text-classification
+tags:
+- text-classification
 ---
+
+## Model Description
+This model has been fine-tuned using [dbmdz/bert-base-turkish-128k-uncased](https://huggingface.co/dbmdz/bert-base-turkish-128k-uncased) model. 
+
+This model created for detecting gibberish sentences like "adssnfjnfjn" .
+It is a simple binary classification project that gives sentence is gibberish or real.
+
+## Usage
+
+```python
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+model = AutoModelForSequenceClassification.from_pretrained("TURKCELL/gibberish-detection-model-tr")
+tokenizer = AutoTokenizer.from_pretrained("TURKCELL/gibberish-detection-model-tr", do_lower_case=True, use_fast=True)
+
+model.to(device)
+
+def get_result_for_one_sample(model, tokenizer, device, sample):
+    d = {
+        1: 'gibberish',
+        0: 'real'
+    }
+    test_sample = tokenizer([sample], padding=True, truncation=True, max_length=256, return_tensors='pt').to(device)
+    # test_sample
+    output = model(**test_sample)
+    y_pred = np.argmax(output.logits.detach().to('cpu').numpy(), axis=1)
+    return d[y_pred[0]]
+
+sentence = "nabeer rdahdaajdajdnjnjf"
+result = get_result_for_one_sample(model, tokenizer, device, sentence)
+print(result)
+
+```
+