Update README.md

README.md

@@ -1,27 +1,74 @@
-'''Student Chat Toxicity Classifier
-This model is a fine-tuned version of the s-nlp/roberta_toxicity_classifier and is designed to classify text-based messages in student conversations as toxic or non-toxic. It is specifically tailored to detect and flag malpractice suggestions, unethical advice, or any toxic communication while encouraging ethical and positive interactions among students.
-
-Model Details
-Language: English (en)
-Base Model: s-nlp/roberta_toxicity_classifier
-Task: Text Classification (Binary)
-Class 0: Non-Toxic
-Class 1: Toxic
-Key Features:
-Detects messages promoting cheating or malpractice.
-Flags harmful or unethical advice in student chats.
-Encourages ethical and constructive communication.
-Training Details
-Dataset: The model was fine-tuned on a custom dataset containing examples of student conversations labeled as toxic (malpractice suggestions, harmful advice) or non-toxic (positive and constructive communication).
-Preprocessing:
-Tokenization using RobertaTokenizer.
-Truncation and padding applied for consistent input length (max_length=128).
-Framework: Hugging Face's transformers library.
-Optimizer: AdamW
-Loss Function: CrossEntropyLoss
-Epochs: 3 (adjusted for convergence)
-Intended Use
+# Student Chat Toxicity Classifier
+
+This model is a fine-tuned version of the `s-nlp/roberta_toxicity_classifier` and is designed to classify text-based messages in student conversations as **toxic** or **non-toxic**. It is specifically tailored to detect and flag malpractice suggestions, unethical advice, or any toxic communication while encouraging ethical and positive interactions among students.
+
+---
+
+## Model Details
+
+- **Language**: English (`en`)
+- **Base Model**: `s-nlp/roberta_toxicity_classifier`
+- **Task**: Text Classification (Binary)
+  - **Class 0**: Non-Toxic
+  - **Class 1**: Toxic
+
+### Key Features
+- Detects messages promoting cheating or malpractice.
+- Flags harmful or unethical advice in student chats.
+- Encourages ethical and constructive communication.
+
+---
+
+## Training Details
+
+- **Dataset**: The model was fine-tuned on a custom dataset containing examples of student conversations labeled as toxic (malpractice suggestions, harmful advice) or non-toxic (positive and constructive communication).
+- **Preprocessing**:
+  - Tokenization using `RobertaTokenizer`.
+  - Truncation and padding applied for consistent input length (`max_length=128`).
+- **Framework**: Hugging Face's `transformers` library.
+- **Optimizer**: `AdamW`
+- **Loss Function**: `CrossEntropyLoss`
+- **Epochs**: 3 (adjusted for convergence)
+
+---
+
+## Intended Use
+
 This model is intended for educational platforms, chat moderation tools, and student communication apps. Its purpose is to:
+1. Detect toxic messages, such as cheating suggestions, harmful advice, or unethical recommendations.
+2. Promote a positive and respectful chat environment for students.
+
+---
+
+## Example Usage
+
+```python
+import torch
+from transformers import RobertaTokenizer, RobertaForSequenceClassification
+
+# Load the model and tokenizer
+model_name = "path/to/your/model/directory"
+tokenizer = RobertaTokenizer.from_pretrained(model_name)
+model = RobertaForSequenceClassification.from_pretrained(model_name)
+
+# Function for toxicity prediction
+def predict_toxicity(text):
+    # Tokenize the input text
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=128)
+
+    # Run the text through the model
+    with torch.no_grad():
+        outputs = model(**inputs)
+    
+    # Extract logits and apply softmax to get probabilities
+    logits = outputs.logits
+    probabilities = torch.nn.functional.softmax(logits, dim=-1)
+
+    # Get the predicted class (0 = Non-Toxic, 1 = Toxic)
+    predicted_class = torch.argmax(probabilities, dim=-1).item()
+    return "Non-Toxic" if predicted_class == 0 else "Toxic"
 
-Detect toxic messages, such as cheating suggestions, harmful advice, or unethical recommendations.
-Promote a positive and respectful chat environment for students.
+# Test the model
+message = "You can copy answers during the exam."
+prediction = predict_toxicity(message)
+print(f"Message: {message}\nPrediction: {prediction}")