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--- |
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license: mit |
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language: |
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- en |
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tags: |
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- vidore |
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--- |
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# BiSigLip: Visual Retriever based on PaliGemma-3B with ColBERT strategy |
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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. |
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It is a [PaliGemma-3B](https://huggingface.co/google/paligemma-3b-mix-448) extension that generates [ColBERT](https://arxiv.org/abs/2004.12832)- style multi-vector representations of text and images. |
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It was introduced in the paper [ColPali: Efficient Document Retrieval with Vision Language Models[add link]]() and first released in [this repository](https://github.com/ManuelFay/colpali) |
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## Model Description |
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This model is built iteratively, starting from an off-the-shelf [Siglip](https://huggingface.co/google/siglip-so400m-patch14-384) model. We finetuned it to create *BiSigLip*. |
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## Model Training |
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### Dataset |
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Our training dataset of 127,460 query-page pairs is comprised of train sets of openly available academic datasets (63%) and a synthetic dataset made up of pages from web-crawled PDF documents and augmented with VLM-generated (Claude-3 Sonnet) pseudo-questions (37%). |
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Our training set is fully English by design, enabling us to study zero-shot generalization to non-English languages. We explicitly verify no multi-page PDF document is used both [*ViDoRe*](https://huggingface.co/collections/vidore/vidore-benchmark-667173f98e70a1c0fa4db00d) and in the train set to prevent evaluation contamination. |
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A validation set is created with 2% of the samples to tune hyperparameters. |
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*Note: Multilingual data is present in the pretraining corpus of the language model (Gemma-2B) and potentially occurs during PaliGemma-3B's multimodal training.* |
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### Parameters |
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All models are trained for 1 epoch on the train set. Unless specified otherwise, we train models in `bfloat16` format, use low-rank adapters ([LoRA](https://arxiv.org/abs/2106.09685)) |
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with `alpha=32` and `r=32` on the transformer layers from the language model, |
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as well as the final randomly initialized projection layer, and use a `paged_adamw_8bit` optimizer. |
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We train on an 8 GPU setup with data parallelism, a learning rate of 5e-5 with linear decay with 2.5% warmup steps, and a batch size of 32. |
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## Intended uses |
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#TODO |
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## Limitations |
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- **Focus**: The model primarily focuses on PDF-type documents and high-ressources languages, potentially limiting its generalization to other document types or less represented languages. |
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- **Support**: The model relies on multi-vector retreiving derived from the ColBERT late interaction mechanism, which may require engineering efforts to adapt to widely used vector retrieval frameworks that lack native multi-vector support. |
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## License |
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ColPali based model (PaliGemma) is under `gemma` license as specified in its [model card](https://huggingface.co/google/paligemma-3b-mix-448). The adapters attached to the model are under MIT license. |
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## Contact |
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- Manuel Faysse: [email protected] |
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- Hugues Sibille: [email protected] |
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- Tony Wu: [email protected] |
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## Citation |
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If you use any datasets or models from this organization in your research, please cite the original dataset as follows: |
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```bibtex |
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@misc{faysse2024colpaliefficientdocumentretrieval, |
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title={ColPali: Efficient Document Retrieval with Vision Language Models}, |
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author={Manuel Faysse and Hugues Sibille and Tony Wu and Gautier Viaud and Céline Hudelot and Pierre Colombo}, |
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year={2024}, |
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eprint={2407.01449}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.IR}, |
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url={https://arxiv.org/abs/2407.01449}, |
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} |
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``` |
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