Update README.md

README.md

@@ -50,17 +50,24 @@ Accuracy : 0.975
 **!IMPORTANT_HINT: The sentence given to pipe must not contain Turkish characters.**
 
 ```sh
-from transformers import AutoTokenizer, TextClassificationPipeline, TFBertForSequenceClassification
+from transformers import AutoTokenizer, TextClassificationPipeline, AutoModelForSequenceClassification
 
 tokenizer = AutoTokenizer.from_pretrained("nanelimon/bert-base-turkish-job-advertisement")
-model = TFBertForSequenceClassification.from_pretrained("nanelimon/bert-base-turkish-job-advertisement", from_pt=True)
+model = AutoModelForSequenceClassification.from_pretrained("nanelimon/bert-base-turkish-job-advertisement")
 pipe = TextClassificationPipeline(model=model, tokenizer=tokenizer)
 
-print(pipe('Bu bir denemedir hadi sende dene!')) 
+
+def set_sentence(sentence: str):
+    result = sentence.lower().replace('ö', 'o').replace('ı', 'i').replace('ü', 'u').replace('ç', 'c').replace('ğ', 'g').replace('ş', 's')
+    return result
+
+
+print(pipe(set_sentence('Fiziği düzgün 17 yaş kızlar aranıyor')))
+
 ```
 Result;
 ```sh
-[{'label': 'Is Ilani Degil', 'score': 0.9999035596847534}]
+output: [{'label': 'Mustehcen', 'score': 0.9992677569389343}]
 ```
 - label= It shows which class the sent Turkish text belongs to according to the model.
 - score= It shows the compliance rate of the Turkish text sent to the label found.