Update README.md

README.md

@@ -14,19 +14,23 @@ should probably proofread and complete it, then remove this comment. -->
 
 # turkish-colpali
 
-This model is a fine-tuned version of [vidore/colpali-v1.3-hf](https://huggingface.co/vidore/colpali-v1.3-hf) on the None dataset.
+This model is a fine-tuned version of [vidore/colpali-v1.3-hf](https://huggingface.co/vidore/colpali-v1.3-hf) on the [selimc/tr-textbook-ColPali](https://huggingface.co/datasets/selimc/tr-textbook-ColPali) and [muhammetfatihaktug/bilim_teknik_mini_base_colpali](https://huggingface.co/datasets/muhammetfatihaktug/bilim_teknik_mini_base_colpali) datasets.
 
 ## Model description
 
-More information needed
+> 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
+This model is primarily designed for efficient indexing and retrieval of Turkish documents by leveraging both textual and visual features. While traditional RAG systems are limited to text-only retrieval, this model extends RAG capabilities by enabling both textual and visual retrieval, making it particularly effective for applications where visual context is as important as textual content. The model performs best with well-structured Turkish PDF like documents.
 
 ## Training and evaluation data
 
-More information needed
+The training data was created via the following steps:
+- Downloading PDF files of Turkish textbooks and science magazines that are publicly available on the internet.
+- 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 gemini-2.0-flash-exp to generate synthetic queries for these documents using the approach outlined [here](https://danielvanstrien.xyz/posts/post-with-code/colpali/2024-09-23-generate_colpali_dataset.html) with additional modifications. This results in [selimc/tr-textbook-ColPali](https://huggingface.co/datasets/selimc/tr-textbook-ColPali) and [muhammetfatihaktug/bilim_teknik_mini_base_colpali](https://huggingface.co/datasets/muhammetfatihaktug/bilim_teknik_mini_base_colpali).
+- Train the model using the fine tuning [notebook](https://github.com/merveenoyan/smol-vision/blob/main/Finetune_ColPali.ipynb?s=35) from [Merve Noyan](https://huggingface.co/merve). Data processing step was modified to include all 3 different types of queries. This approach not only adds variety to the training data but also effectively triples the dataset size, helping the model learn to handle diverse query types.
 
 ## Training procedure
 
@@ -46,8 +50,6 @@ The following hyperparameters were used during training:
 
 ### Training results
 
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 ### Framework versions
 
 - PEFT 0.11.1