Update README.md

README.md

@@ -1,6 +1,68 @@
+Конечно, вот карточка на русском языке:
+
+```markdown
 ---
+language: ru
+tags:
+- spam-detection
+- text-classification
+- russian
 license: mit
-language:
-- ru
-pipeline_tag: text-classification
----
+datasets:
+- RUSpam/spam_dataset_v4
+metrics:
+- F1
+model-index:
+- name: spam_deberta_v4
+  results:
+  - task: 
+      name: Классификация текста
+      type: text-classification
+    dataset:
+      name: RUSpam/russian_spam_dataset
+      type: RUSpam/russian_spam_dataset
+    metrics:
+      - name: F1
+        type: F1
+        value: 0.9897 
+---
+
+# RUSpam/spam_deberta_v4
+
+## Описание
+
+Это модель определения спама, основанная на архитектуре Deberta, дообученная на русскоязычных данных о спаме. Она классифицирует текст как спам или не спам.
+
+## Использование
+
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+model_path = "RUSpam/spam_deberta_v4"
+tokenizer = AutoTokenizer.from_pretrained(model_path)
+model = AutoModelForSequenceClassification.from_pretrained(model_path)
+
+def predict(text):
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=256)
+    with torch.no_grad():
+        outputs = model(**inputs)
+        logits = outputs.logits
+        predicted_class = torch.argmax(logits, dim=1).item()
+    return "Спам" if predicted_class == 1 else "Не спам"
+
+text = "Ваш текст для проверки здесь"
+result = predict(text)
+print(f"Результат: {result}")
+```
+
+# Цитирование
+```
+@MISC{RUSpam/spam_deberta_v4,
+    author  = {Denis Petrov, Kirill,  Sergey Yalovegin},
+    title   = {Russian Spam Classification Model},
+    url     = {https://huggingface.co/RUSpam/spam_deberta_v4/},
+    year    = 2024
+}
+```