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README.md

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 # MonoQwen2-VL-2B-LoRA-Reranker
 
 ## Model Overview
-The **MonoQwen2-VL-2B-LoRA-Reranker** is a LoRA fine-tuned version of the Qwen2-VL-2B model, optimized for reranking image-query relevance.
+The **MonoQwen2-VL-v0.1** is a LoRA of the Qwen2-VL-2B model, optimized for reranking (i.e, asserting pointwise image-query relevance) using the [MonoT5](https://arxiv.org/pdf/2101.05667) objective.
+That is, given a couple of image and query fed into the prompt of the VLM, the model is tasked to generate "True" if the image is relevant to the query and "False" otherwise.
+During inference, a relevancy score can then be obtained by comparing the logits of the two tokens and this score can effectively be used to rerank the candidates generated by a first-stage retriever (such as DSE or ColPali) or filter them using a threshold.
 
-It was train using [ColPali train set](https://huggingface.co/datasets/vidore/colpali_train_set)
+The [ColPali train set](https://huggingface.co/datasets/vidore/colpali_train_set) was used to train this model with negatives mined using DSE.
 
 ## How to Use the Model
 Below is a quick example to rerank a single image against a user query using this model: