umarfarzan/clip-classifier-v9_50_50

Clip Classifier

This model is fine-tuned from intfloat/multilingual-e5-large-instruct for classifying text as "Clip Worthy" or "Not Clip Worthy".

Model Details

• Model Type: Sequence classification fine-tuned from E5 Large Instruct
• Task: Binary classification (Clip worthy vs Not clip worthy)
• Training Data: Custom dataset

Usage

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
# Load model and tokenizer
model_name = "umarfarzan/clip-classifier-v9_50_50"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
# Example text
text = "Your text here"
# Tokenize and predict
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512, padding="max_length")
with torch.no_grad():
    outputs = model(**inputs)
# Get prediction
probabilities = torch.nn.functional.softmax(outputs.logits, dim=1)
prediction = torch.argmax(probabilities, dim=1).item()
confidence = probabilities[0][prediction].item()
# Map prediction to label
id2label = {0: "Not Clip Worthy", 1: "Clip Worthy"}
result = id2label[prediction]
print(f"Prediction: {result}")
print(f"Confidence: {confidence:.2f}")

Limitations

[Describe any limitations here]

Citation

If you use this model, please cite: [Your citation information]