---
library_name: transformers
language:
- en
base_model:
- openai/clip-vit-base-patch16
license: mit  # or whatever license you want
tags:
- clip
- moral-foundations
- multimodal
- ethics
---
# MoralCLIP

MoralCLIP extends CLIP with explicit moral grounding based on Moral Foundations Theory (MFT). This model aligns image and text representations by shared moral meaning rather than purely semantic similarity.

## Model Details

- **Base Model**: openai/clip-vit-base-patch16
- **Training Data**: ~15k image-text pairs with MFT annotations
- **Moral Foundations**: Care, Fairness, Loyalty, Authority, Sanctity
- **Paper**: Under review

## Usage

```python
from transformers import CLIPModel, CLIPProcessor
from PIL import Image
import torch

model = CLIPModel.from_pretrained("anaaa2/moralclip-base")
processor = CLIPProcessor.from_pretrained("anaaa2/moralclip-base")

img = Image.open("image_path").convert("RGB")

inputs = processor(text=["a photo of care"], images=image, return_tensors="pt", padding=True)
outputs = model(**inputs)

image_embeds = outputs.image_embeds
text_embeds = outputs.text_embeds