Update README.md

README.md

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 license: mit
 language:
 - en
+- fr
 tags:
 - vidore
----
+---
+# ColPali: Visual Retriever based on PaliGemma-3B with ColBERT strategy
+
+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.
+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. 
+It was introduced in the paper [ColPali: Efficient Document Retrieval with Vision Language Models[LINK]]() and first released in [this repository](https://github.com/ManuelFay/colpali)
+
+## Model Description
+
+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](https://huggingface.co/vidore/bisiglip) and fed the patch-embeddings output by SigLip to an LLM, [PaliGemma-3B](https://huggingface.co/google/paligemma-3b-mix-448) to create [BiPali](https://huggingface.co/vidore/bipali). 
+
+One benefit of inputting image patch embeddings through a language model is that they are natively mapped to a latent space similar to textual input (query). 
+This enables leveraging the [ColBERT](https://arxiv.org/abs/2004.12832) strategy to compute interactions between text tokens and image patches, which enables a step-change improvement in performance compared to BiPali. 
+
+## Model Training
+
+### Dataset
+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%). 
+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. 
+A validation set is created with 2% of the samples to tune hyperparameters.
+
+*Note: Multilingual data is present in the pretraining corpus of the language model (Gemma-2B) and potentially occurs during PaliGemma-3B's multimodal training.*
+
+### Parameters
+
+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)) 
+with `alpha=32`  and `r=32` on the transformer layers from the language model, 
+as well as the final randomly initialized projection layer, and use a `paged_adamw_8bit` optimizer. 
+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.
+
+
+## Intended uses & limitations
+ - **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.
+ - **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.
+
+## License
+
+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.
+
+## Contact
+
+- Manuel Faysse: [email protected]
+- Hugues Sibille: [email protected]
+- Tony Wu: [email protected]
+
+## Citation
+
+If you use any datasets or models from this organization in your research, please cite the original dataset as follows:
+
+```bibtex
+    [include BibTeX]
+```