multimodal problem

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I am a student from China, and I really appreciate your project. I am now trying to do some interesting work, but I have encountered some problems. My idea is to perform topic modeling using product images and text reviews. Since the clip-ViT-B-32 encoder does not support Chinese, I am using another CLIP model trained on Chinese data to generate image_features and text_features. Then, I perform a concatenation operation to generate combined_image_features as the embeddings for BERTopic, and pass each image's corresponding review as the docs to the model. The good news is that the model works, but there is a problem with the topic representation: it only produces some meaningless English words and numbers. Since I am not an expert in the field of multimodal computing, I don't know which part of the model has gone wrong.

The report names no repository files, tests, or entry points. Start by isolating the multimodal BERTopic setup described in the issue and tracing how the Chinese image and text features become topic representations. Done means identifying why the output is meaningless and documenting a reproducible diagnosis or fix.

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