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language: en
tags:
- clip-classifier
- e5
- classification
license: mit
---
# Clip Classifier
This model is fine-tuned from [intfloat/multilingual-e5-large-instruct](https://huggingface.co/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
```python
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]
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