Create README.md

README.md

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+---
+license: apache-2.0
+datasets:
+- qwedxzawsedr/emorag_defense
+metrics:
+- accuracy
+base_model:
+- google-bert/bert-base-uncased
+pipeline_tag: text-classification
+---
+# Malicious Text Detection Model for EmoRAG
+## Model Description
+This model is designed to detect malicious texts, particularly those containing emoticons, using a BERT-based architecture.
+## Intended Use
+- **Primary Use**: Detection of malicious texts containing emoticons.
+- **Applications**: 
+  - Content moderation for online platforms.
+  - Adversarial text filtering in natural language processing pipelines.
+  - Research on malicious text detection and adversarial attacks.
+
+Each data point contains up to eight emoticons, and the dataset was carefully curated to ensure diversity and balance.
+## How to Use the Model
+You can load and use the trained BERT-based model for malicious text detection with the following code:
+```python
+from transformers import BertTokenizer, BertForSequenceClassification
+import torch
+
+# Load the trained model and tokenizer
+model = BertForSequenceClassification.from_pretrained('path_to_your_model')
+tokenizer = BertTokenizer.from_pretrained('path_to_your_model')
+
+# Example text (malicious with emoticons)
+text = "However, there (●′ω`●) is any huge evidence ⊙︿⊙ that one single drug shot may induce a permanent ƪ(•̃͡ε•̃͡)∫ʃ psychotic disorder. ＋ˍ＋ The other hand is in regards of the the use of dopaminergic agonists in Parkinson desease, what did (ΘｏΘ) not ╰(*´︶`*)╯ show in that patients a ﾟヽ(●´ω｀●)ﾉ。 psychotic disorder but induce a hard psychotic effect in a normal subject mainly mixed 桃ｶﾗ≪(　＼(・ω・)/　)≫ｵﾊﾖｩ☆ with alcholl.", 
+
+# Tokenize the input text
+inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)
+
+# Make a prediction
+with torch.no_grad():
+    outputs = model(**inputs)
+    logits = outputs.logits
+    prediction = torch.argmax(logits, dim=-1)
+
+# Print the prediction
+print(f"Prediction: {'Malicious' if prediction.item() == 1 else 'Clean'}")