ColPali: A new approach to efficient and intelligent document retrieval 🚀
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ColPali: A new approach to efficient and intelligent document retrieval 🚀
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Our latest research paper, "ColPali: Efficient Document Retrieval with Vision Language Models," introduces a groundbreaking approach to large-scale visual document analysis. By leveraging Vision Language Models (VLMs), we have created a new framework for document retrieval that's both powerful and efficient.
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Our latest research paper, "ColPali: Efficient Document Retrieval with Vision Language Models," introduces a groundbreaking approach to large-scale visual document analysis. By leveraging Vision Language Models (VLMs), we have created a new framework for document retrieval that's both powerful and efficient.
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Key Insights:
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Key Insights:
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💡 ColPali combines ColBERT's multi-vector strategy with VLMs' document understanding capabilities
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💡 ColPali combines ColBERT's multi-vector strategy with VLMs' document understanding capabilities
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⚙️ ColPali is based on PaliGemma-3B (SigLIP, Gemma-2B) + a linear projection layer and is trained to maximize the similarity between the document and the query embeddings
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⚙️ ColPali is based on PaliGemma-3B (SigLIP, Gemma-2B) + a linear projection layer and is trained to maximize the similarity between the document and the query embeddings
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📊 The Vision Document Retrieval benchmark (ViDoRe) is a challenging dataset that spans various industry topics and aims at matching real-life retrieval scenarios
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📊 The Vision Document Retrieval benchmark (ViDoRe) is a challenging dataset that spans various industry topics and aims at matching real-life retrieval scenarios
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🏆 ColPali outperforms existing models on all datasets in ViDoRe (average NDCG@5 of 81.3% vs 67.0% for the best baseline model)
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🏆 ColPali outperforms existing models on all datasets in ViDoRe (average NDCG@5 of 81.3% vs 67.0% for the best baseline model)
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⚡ ColPali is faster at document embedding compared to traditional PDF parser pipelines, making ColPali viable for industrial use
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⚡ ColPali is faster at document embedding compared to traditional PDF parser pipelines, making ColPali viable for industrial use
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🔍 ColPali is highly interpretable thanks to patch-based similarity maps
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🔍 ColPali is highly interpretable thanks to patch-based similarity maps
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Dive deeper into ColPali and explore our resources:
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Dive deeper into ColPali and explore our resources:
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📑 Full paper: arxiv.org/abs/2407.01449
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📑 Full paper: arxiv.org/abs/2407.01449
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🛠️ Datasets, model weights, evaluation code, leaderboard, demos: huggingface.co/vidore
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🛠️ Datasets, model weights, evaluation code, leaderboard, demos: huggingface.co/vidore
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Shoutout to my amazing co-authors Manuel Faysse (
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Shoutout to my amazing co-authors Manuel Faysse (
@manu
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manu
) and Hugues Sibille (
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) and Hugues Sibille (
@HugSib
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HugSib
). We are grateful for the invaluable feedback from Bilel Omrani, Gautier Viaud, Celine Hudelot, and Pierre Colombo. This work is sponsored by ILLUIN Technology. ✨ 
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). We are grateful for the invaluable feedback from Bilel Omrani, Gautier Viaud, Celine Hudelot, and Pierre Colombo. This work is sponsored by ILLUIN Technology. ✨ 
ColPali: A new approach to efficient and intelligent document retrieval 🚀

Our latest research paper, "ColPali: Efficient Document Retrieval with Vision Language Models," introduces a groundbreaking approach to large-scale visual document analysis. By leveraging Vision Language Models (VLMs), we have created a new framework for document retrieval that's both powerful and efficient.

Key Insights:
💡 ColPali combines ColBERT's multi-vector strategy with VLMs' document understanding capabilities
⚙️ ColPali is based on PaliGemma-3B (SigLIP, Gemma-2B) + a linear projection layer and is trained to maximize the similarity between the document and the query embeddings
📊 The Vision Document Retrieval benchmark (ViDoRe) is a challenging dataset that spans various industry topics and aims at matching real-life retrieval scenarios
🏆 ColPali outperforms existing models on all datasets in ViDoRe (average NDCG@5 of 81.3% vs 67.0% for the best baseline model)
⚡ ColPali is faster at document embedding compared to traditional PDF parser pipelines, making ColPali viable for industrial use
🔍 ColPali is highly interpretable thanks to patch-based similarity maps

Dive deeper into ColPali and explore our resources:
📑 Full paper: arxiv.org/abs/2407.01449
🛠️ Datasets, model weights, evaluation code, leaderboard, demos: huggingface.co/vidore

Shoutout to my amazing co-authors Manuel Faysse (@manu) and Hugues Sibille (@HugSib). We are grateful for the invaluable feedback from Bilel Omrani, Gautier Viaud, Celine Hudelot, and Pierre Colombo. This work is sponsored by ILLUIN Technology. ✨ 
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