metadata
license: apache-2.0
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.
How to Use the Model
Below is a quick example to rerank a single image against a user query using this model:
import torch
from PIL import Image
from transformers import AutoProcessor, Qwen2VLForConditionalGeneration
# Load processor and model
processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-2B-Instruct")
model = Qwen2VLForConditionalGeneration.from_pretrained("lightonai/MonoQwen2-VL-2B-LoRA-Reranker")
# Define the query and the image
query = "What is the value of the thing in the document"
image = Image.open("path_to_image.jpg")
# Prepare the inputs
prompt = f"Assert the relevance of the previous image document to the following query, answer True or False. The query is: {query}"
inputs = processor(text=prompt, images=image, return_tensors="pt")
# Run the model and obtain results
with torch.no_grad():
outputs = model(**inputs)
logits = outputs.logits
logits_for_last_token = logits[:, -1, :]
true_token_id = processor.tokenizer.convert_tokens_to_ids("True")
false_token_id = processor.tokenizer.convert_tokens_to_ids("False")
relevance_score = torch.softmax(logits_for_last_token[:, [true_token_id, false_token_id]], dim=-1)
# Print the True/False probabilities
true_prob = relevance_score[:, 0].item()
false_prob = relevance_score[:, 1].item()
print(f"True probability: {true_prob}, False probability: {false_prob}")
This example demonstrates how to use the model to assess the relevance of an image with respect to a query. It outputs the probability that the image is relevant ("True") or not relevant ("False").
Performance Metrics
The model has been evaluated on ViDoRe Benchmark, by retrieving 10 elements with MrLight_dse-qwen2-2b-mrl-v1 and reranking them. The table below summarizes its ndcg@5 scores:
| Dataset | NDCG@5 Before Reranking | NDCG@5 After Reranking |
|---|---|---|
| Mean | 87.6 | 91.8 |
| vidore/arxivqa_test_subsampled | 85.6 | 89.01 |
| vidore/docvqa_test_subsampled | 57.1 | 59.71 |
| vidore/infovqa_test_subsampled | 88.1 | 93.49 |
| vidore/tabfquad_test_subsampled | 93.1 | 95.96 |
| vidore/shiftproject_test | 82.0 | 92.98 |
| vidore/syntheticDocQA_artificial_intelligence_test | 97.5 | 100.00 |
| vidore/syntheticDocQA_energy_test | 92.9 | 97.65 |
| vidore/syntheticDocQA_government_reports_test | 96.0 | 98.04 |
| vidore/syntheticDocQA_healthcare_industry_test | 96.4 | 99.27 |
License
This LoRA model is licensed under the Apache 2.0 license.