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--- |
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license: apache-2.0 |
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tags: |
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- vidore |
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- reranker |
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- qwen2_vl |
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datasets: |
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- vidore/colpali_train_set |
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base_model: |
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- Qwen/Qwen2-VL-2B-Instruct |
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--- |
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# MonoQwen2-VL-v0.1 |
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## Model Overview |
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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. |
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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. |
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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. |
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The [ColPali train set](https://huggingface.co/datasets/vidore/colpali_train_set) was used to train this model with negatives mined using DSE. |
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## How to Use the Model |
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Below is a quick example to rerank a single image against a user query using this model: |
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```python |
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import torch |
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from PIL import Image |
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from transformers import AutoProcessor, Qwen2VLForConditionalGeneration |
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# Load processor and model |
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processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-2B-Instruct") |
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model = Qwen2VLForConditionalGeneration.from_pretrained( |
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"lightonai/MonoQwen2-VL-v0.1", |
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device_map="auto", |
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# attn_implementation="flash_attention_2", |
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# torch_dtype=torch.bfloat16, |
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) |
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# Define query and load image |
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query = "What is ColPali?" |
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image_path = "your/path/to/image.png" |
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image = Image.open(image_path) |
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# Construct the prompt and prepare input |
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prompt = ( |
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"Assert the relevance of the previous image document to the following query, " |
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"answer True or False. The query is: {query}" |
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).format(query=query) |
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messages = [ |
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{ |
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"role": "user", |
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"content": [ |
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{"type": "image", "image": image}, |
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{"type": "text", "text": prompt}, |
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], |
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} |
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] |
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# Apply chat template and tokenize |
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text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) |
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inputs = processor(text=text, images=image, return_tensors="pt").to("cuda") |
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# Run inference to obtain logits |
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with torch.no_grad(): |
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outputs = model(**inputs) |
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logits_for_last_token = outputs.logits[:, -1, :] |
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# Convert tokens and calculate relevance score |
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true_token_id = processor.tokenizer.convert_tokens_to_ids("True") |
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false_token_id = processor.tokenizer.convert_tokens_to_ids("False") |
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relevance_score = torch.softmax(logits_for_last_token[:, [true_token_id, false_token_id]], dim=-1) |
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# Extract and display probabilities |
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true_prob = relevance_score[0, 0].item() |
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false_prob = relevance_score[0, 1].item() |
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print(f"True probability: {true_prob:.4f}, False probability: {false_prob:.4f}") |
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``` |
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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"). |
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## Performance Metrics |
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The model has been evaluated on [ViDoRe Benchmark](https://huggingface.co/spaces/vidore/vidore-leaderboard), by retrieving 10 elements with [MrLight_dse-qwen2-2b-mrl-v1](https://huggingface.co/MrLight/dse-qwen2-2b-mrl-v1) and reranking them. The table below summarizes its `ndcg@5` scores: |
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| Dataset | [MrLight_dse-qwen2-2b-mrl-v1](https://huggingface.co/MrLight/dse-qwen2-2b-mrl-v1) | MonoQwen2-VL-v0.1 reranking | |
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|---------------------------------------------------|--------------------------|------------------------| |
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| vidore/arxivqa_test_subsampled | 85.6 | 89.0 | |
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| vidore/docvqa_test_subsampled | 57.1 | 59.7 | |
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| vidore/infovqa_test_subsampled | 88.1 | 93.2 | |
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| vidore/tabfquad_test_subsampled | 93.1 | 96.0 | |
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| vidore/shiftproject_test | 82.0 | 93.0 | |
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| vidore/syntheticDocQA_artificial_intelligence_test| 97.5 | 100.0 | |
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| vidore/syntheticDocQA_energy_test | 92.9 | 97.7 | |
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| vidore/syntheticDocQA_government_reports_test | 96.0 | 98.0 | |
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| vidore/syntheticDocQA_healthcare_industry_test | 96.4 | 99.3 | |
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| vidore/tatdqa_test | 69.4 | 79.0 | |
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| **Mean** | 85.8 | 90.5 | |
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## License |
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This LoRA model is licensed under the Apache 2.0 license. |
