README.md

---
license: mit
language:
- en
base_model:
- google-bert/bert-base-uncased
- microsoft/resnet-34
tags:
- Social Bias
- Fairness
- Fake News
metrics:
- f1(0.698616087436676)
- precision(0.6369158625602722)
- recall(0.7735527753829956)
- accuracy(0.6247606873512268)
datasets:
- vector-institute/newsmediabias-plus
library_name: transformers
co2_eq_emissions:
  emissions: 8
  source: Code Carbon
  training_type: fine-tuning
  geographical_location: Albany, New York
  hardware_used: T4
---
  
# Multimodal Bias Classifier

This model is a multimodal classifier that combines text and image inputs to detect potential bias in content. It uses a BERT-based text encoder and a ResNet-34 image encoder, which are fused for classification purposes. A contrastive learning approach was used during training, leveraging CLIP embeddings as guidance to align the text and image representations. 

## Resources:

- This model is based on [FND-CLIP](https://arxiv.org/pdf/2205.14304), proposed by Zhou et al. 2022.
- It was trained on the [News Media Bias Plus dataset](https://huggingface.co/datasets/vector-institute/newsmediabias-plus), here are the offical [dataset docs](https://vectorinstitute.github.io/Newsmediabias-plus/).
- [Model training ipynb](https://github.com/VectorInstitute/news-media-bias-plus/blob/main/benchmarking/multi-modal-classifiers/baselines-and-notebooks/training-notebooks/slm/fnd-clip-bias-training.ipynb)

## Model Details

- **Text Encoder**: BERT (`bert-base-uncased`)
- **Image Encoder**: ResNet-34 (`microsoft/resnet-34`)
- **Projection Dimensionality**: 768
- **Fusion Method**: Concatenation (default), Alignment, or Cosine Similarity
- **Loss Functions**: Binary Cross-Entropy for classification, Cosine Embedding Loss for contrastive learning
- **Purpose**: Detecting bias in multimodal content (text + image)

## Training

The model was trained using a multimodal dataset with labeled instances of biased and unbiased content. The training process incorporated both classification and contrastive loss to help align the text and image representations in a shared latent space.

### Training Losses
- **Classification Loss**: Binary Cross-Entropy (BCEWithLogitsLoss) to classify content as biased or unbiased.
- **Contrastive Loss**: CosineEmbeddingLoss, which uses CLIP text and image embeddings as ground truth guidance to align text and image features.

### Excluding CLIP
While the CLIP model was used during training to guide the alignment of the image and text embeddings, the final model does **not** retain CLIP weights, as it is designed to function independently once training is complete.

## How to Load the Model

You can load this model for bias classification by following the code below. The model accepts text input and an image input, processing them through BERT and ResNet-34 encoders, respectively. The final prediction indicates whether the content is likely biased or unbiased.

```python
import torch
from torch import nn
from transformers import AutoModel
from huggingface_hub import hf_hub_download
from typing import Literal
import json

class MultimodalClassifier(nn.Module):
    def __init__(
            self,
            text_encoder_id_or_path: str,
            image_encoder_id_or_path: str,
            projection_dim: int,
            fusion_method: Literal["concat", "align", "cosine_similarity"] = "concat",
            proj_dropout: float = 0.1,
            fusion_dropout: float = 0.1,
            num_classes: int = 1,
        ) -> None:
        super().__init__()

        self.fusion_method = fusion_method
        self.projection_dim = projection_dim
        self.num_classes = num_classes

        ##### Text Encoder
        self.text_encoder = AutoModel.from_pretrained(text_encoder_id_or_path)
        self.text_projection = nn.Sequential(
            nn.Linear(self.text_encoder.config.hidden_size, self.projection_dim),
            nn.Dropout(proj_dropout),
        )

        ##### Image Encoder (using ResNet34 from AutoModel with timm)
        self.image_encoder = AutoModel.from_pretrained(image_encoder_id_or_path, trust_remote_code=True)
        self.image_encoder.classifier = nn.Identity()  # rm the classification head
        self.image_projection = nn.Sequential(
            nn.Linear(512, self.projection_dim),
            nn.Dropout(proj_dropout),
        )

        ##### Fusion Layer
        fusion_input_dim = self.projection_dim * 2 if fusion_method == "concat" else self.projection_dim
        self.fusion_layer = nn.Sequential(
            nn.Dropout(fusion_dropout),
            nn.Linear(fusion_input_dim, self.projection_dim),
            nn.GELU(),
            nn.Dropout(fusion_dropout),
        )

        ##### Classification Layer
        self.classifier = nn.Linear(self.projection_dim, self.num_classes)

    def forward(self, pixel_values: torch.Tensor, input_ids: torch.Tensor, attention_mask: torch.Tensor) -> torch.Tensor:
        ##### Text Encoder Projection #####
        full_text_features = self.text_encoder(input_ids=input_ids, attention_mask=attention_mask, return_dict=True).last_hidden_state
        full_text_features = full_text_features[:, 0, :]  # using cls token
        full_text_features = self.text_projection(full_text_features)

        ##### Image Encoder Projection #####
        resnet_image_features = self.image_encoder(pixel_values=pixel_values).last_hidden_state
        
        # global average pooling for resent image features (bad idea? dim problems)
        resnet_image_features = resnet_image_features.mean(dim=[-2, -1])
        resnet_image_features = self.image_projection(resnet_image_features)

        ##### Fusion and Classification #####
        if self.fusion_method == "concat":
            fused_features = torch.cat([full_text_features, resnet_image_features], dim=-1)
        else:
            fused_features = full_text_features * resnet_image_features # don't think this works atm (should be dot prod)

        # fusion and classifier layers
        fused_features = self.fusion_layer(fused_features)
        classification_output = self.classifier(fused_features)

        return classification_output

def load_model():
    config_path = hf_hub_download(repo_id="maximuspowers/multimodal-bias-classifier", filename="config.json")
    with open(config_path, "r") as f:
        config = json.load(f)

    model = MultimodalClassifier(
        text_encoder_id_or_path=config["text_encoder_id_or_path"],
        image_encoder_id_or_path="microsoft/resnet-34",
        projection_dim=config["projection_dim"],
        fusion_method=config["fusion_method"],
        proj_dropout=config["proj_dropout"],
        fusion_dropout=config["fusion_dropout"],
        num_classes=config["num_classes"]
    )

    model_weights_path = hf_hub_download(repo_id="maximuspowers/multimodal-bias-classifier", filename="model_weights.pth")
    checkpoint = torch.load(model_weights_path, map_location=torch.device('cpu'))
    model.load_state_dict(checkpoint, strict=False)

    return model
```

## How to Run the Model

```python
import torch
from transformers import AutoTokenizer
from PIL import Image
import requests
from torchvision import transforms

model = load_model()
model.eval()

# text input
text_tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
sample_text = "This is a sample sentence for bias classification."
text_inputs = text_tokenizer(
    sample_text, 
    return_tensors="pt", 
    padding="max_length", 
    truncation=True, 
    max_length=512
)

# image input
image = Image.open("./random_image.jpg").convert("RGB")
image_transform = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])
image_input = image_transform(image).unsqueeze(0) # add batch dim

# run
with torch.no_grad():
    classification_output = model(
        pixel_values=image_input, 
        input_ids=text_inputs["input_ids"], 
        attention_mask=text_inputs["attention_mask"]
    )
    predicted_class = torch.sigmoid(classification_output).round().item()
print("Predicted class:", "Biased" if predicted_class == 1 else "Unbiased")
```