student_chat_toxicity_classifier_model

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.

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🚀 Try the model live in this Hugging Face Space 🚀

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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.

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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)

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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.

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Use it with Gradio API:

from gradio_client import Client

client = Client("Sk1306/Student_Ethics_Chat_Classifier")
result = client.predict(
		text="you can copy in exam to pass!!",
		api_name="/predict"
)
print(result)

By loading Model

import torch
from transformers import RobertaTokenizer, RobertaForSequenceClassification

# Load the model and tokenizer
model_name = "Sk1306/student_chat_toxicity_classifier_model"
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"

# Test the model
message = "You can copy answers during the exam."
prediction = predict_toxicity(message)
print(f"Message: {message}\nPrediction: {prediction}")