Upload 3 files

app.py

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+import streamlit as st
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+# Model ve tokenizer'ı yükle
+model_path = 'microsoft/deberta-xlarge'
+tokenizer = AutoTokenizer.from_pretrained(model_path)
+model = AutoModelForSequenceClassification.from_pretrained(model_path)
+
+# Streamlit uygulaması
+st.title('DeBERTa-XLarge Model ile Metin Sınıflandırma')
+
+# Kullanıcıdan metin girişi al
+user_input = st.text_area("Metni Buraya Yazın:", height=200)
+
+if st.button("Tahmin Et"):
+    if user_input:
+        # Tokenizasyon
+        inputs = tokenizer(user_input, return_tensors='pt', padding=True, truncation=True)
+        
+        # Modeli çalıştır
+        with torch.no_grad():
+            outputs = model(**inputs)
+        
+        # Tahmini elde et
+        predictions = torch.argmax(outputs.logits, dim=-1)
+        
+        # Sonucu göster
+        st.success(f'Tahmin Sonucu: {predictions.item()}')
+    else:
+        st.warning("Lütfen bir metin giriniz.")

modelFile.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a3fce95a4eb327c940d97a05dd62265007f26c006f40c8ec0a480f9d82072481
+size 55310307

requirements.txt

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+streamlit
+tensorflow
+opencv-python
+scikit-learn
+torch
+torchvision
+matplotlib
+transformers
+sentencepiece
+plotly
+xgboost
+joblib