Update README.md

README.md

@@ -31,15 +31,22 @@ The model achieves the following results on the evaluation set:
 
 ## Model description
 
-More information needed
+This model is a fine tune of a ColPali [vidore/colpaligemma-3b-pt-448-base](https://huggingface.co/vidore/colpaligemma-3b-pt-448-base):
+
+> ColPali is a model based on a novel model architecture and training strategy based on Vision Language Models (VLMs) to efficiently index documents from their visual features. It is a PaliGemma-3B extension that generates ColBERT- style multi-vector representations of text and images. It was introduced in the paper [ColPali: Efficient Document Retrieval with Vision Language Models](https://huggingface.co/papers/2407.01449). 
 
 ## Intended uses & limitations
 
-More information needed
+For retrieving UFO newsletters documents. 
 
 ## Training and evaluation data
 
-More information needed
+The training data was created via the following steps:
+
+- Downloading a sample of UFO newsletters from this Internet archive [Collection](https://archive.org/details/ufonewsletters).
+- Using the [pdf-to-page-images-dataset](https://huggingface.co/spaces/Dataset-Creation-Tools/pdf-to-page-images-dataset) Space to convert the PDF documents into a single page image dataset
+- Use a VLM to generate synthetic queries for these documents using the approach outlines [here](https://danielvanstrien.xyz/posts/post-with-code/colpali/2024-09-23-generate_colpali_dataset.html). This results in [davanstrien/ufo-ColPali](https://huggingface.co/datasets/davanstrien/ufo-ColPali).
+- Train the model using the fine tuning [notebook](https://github.com/tonywu71/colpali-cookbooks/blob/main/examples/finetune_colpali.ipynb) from [tonywu71](https://huggingface.co/tonywu71). I changed almost nothing except the data processing steps. 
 
 ## Training procedure