fixed model card

README.md

@@ -1,14 +1,21 @@
 ---
 license: mit
-datasets:
-  - vector-institute/newsmediabias-plus
 language:
-  - en
+- 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)
+- 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
@@ -16,16 +23,8 @@ co2_eq_emissions:
   training_type: fine-tuning
   geographical_location: Albany, New York
   hardware_used: T4
-base_model:
-  - google-bert/bert-base-uncased
-  - microsoft/resnet-34
-pipeline_tag: custom
-tags:
-  - Social Bias
-  - Multimodal
-  
 ---
-
+  
 # 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. 
@@ -193,4 +192,4 @@ with torch.no_grad():
     )
     predicted_class = torch.sigmoid(classification_output).round().item()
 print("Predicted class:", "Biased" if predicted_class == 1 else "Unbiased")
-```
+```