describe

app.py

@@ -133,10 +133,18 @@ def generate_response(image):
         return {}
 
 
-title = "ColPali Query Generator"
-description = """This Space uses the Qwen2-VL model to generate queries for document retrieval tasks primarily focused on ColPali fine-tuning data.
+title = "ColPali fine-tuning Query Generator"
+description = """[ColPali](https://huggingface.co/papers/2407.01449) is a very exciting new approach to multimodal document retrieval which aims to replace existing document retrievers which often rely on an OCR step with an end-to-end multimodal approach. 
+To train ColPali models, we need a dataset of image-text pairs which represent the document images and the relevant text queries which those documents should match. 
+To make the ColPali models work even better we might want a dataset of query/image document pairs related to our domain or task. 
+One way in which we might go about generating such a dataset is to use an VLM to generate synthetic queries for us. 
+This space uses the [Qwen/Qwen2-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2-VL-7B-Instruct) to generate queries for a document, based on an input document image. 
+
 
 This [blog post](https://danielvanstrien.xyz/posts/post-with-code/colpali/2024-09-23-generate_colpali_dataset.html) gives an overview of how you can use this kind of approach to generate a full dataset for fine-tuning ColPali models. 
+
+If you want to convert a PDF(s) to a dataset of page images you can try out the [ PDFs to Page Images Converter](https://huggingface.co/spaces/Dataset-Creation-Tools/pdf-to-page-images-dataset) Space.
+
 """
 
 demo = gr.Interface(