Load model directly

from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("iamrazi/text-moderation") #

model = AutoModelForSequenceClassification.from_pretrained("iamrazi/text-moderation")

model.eval() # Set model to evaluation mode

def predict_abuse(text: str, threshold: float = 0.5):

  """
  Predict if a text is abusive or not.
  
  Args:
      text (str): Input text.
      threshold (float): Probability threshold for classification.
  
  Returns:
      label (int): 0 for non-abusive, 1 for abusive
      proba (float): Probability of being abusive
  """
  # Tokenize
  inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=128)
  
  # Forward pass
  with torch.no_grad():
      outputs = model(**inputs)
      logits = outputs.logits
      probas = torch.sigmoid(logits)  # if your model output layer is logits
  
  # For binary classification, take the probability of class 1
  prob = probas[0][1].item() if probas.shape[1] > 1 else probas[0][0].item()
  
  # Determine label
  label = 1 if prob >= threshold else 0
  
  return label, prob

text = "तुम बहुत गंदे हो 😡"

label, proba = predict_abuse(text)

Output: Label: 0, Probability: 0.08