metadata
license: mit
language:
- en
- hi
- ta
- te
- kn
- ml
metrics:
- accuracy
base_model:
- ai4bharat/indic-bert
pipeline_tag: text-classification
library_name: transformers
tags:
- moderation
- abuse_detection
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