Update README.md

README.md

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 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. 
 
-This model is based on [FND-CLIP](https://arxiv.org/pdf/2205.14304), proposed by Zhou et al. 2022.
+## 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