Update README.md

README.md

@@ -7,10 +7,126 @@ datasets:
 - vidore/colpali_train_set
 language:
 - en
-- fr
 base_model:
-- google/paligemma-3b-mix-448
+- vidore/colpaligemma-3b-pt-448-base
 ---
 
 > [!WARNING]
-> DEV ONLY: DO NOT USE!
+> DEV ONLY: do not use until the official transformers 🤗 release!
+> 
+
+# 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](https://arxiv.org/abs/2407.01449) and first released in [this repository](https://github.com/ManuelFay/colpali)
+
+<p align="center"><img width=800 src="https://github.com/illuin-tech/colpali/blob/main/assets/colpali_architecture.webp?raw=true"/></p>
+
+## Version specificity
+
+This version is trained with `colpali-engine==0.2.0` but can be loaded for any version `>=0.2.0`.
+
+Compared to [`vidore/colpali`](https://huggingface.co/vidore/colpali), this version is trained with right padding for queries to fix unwanted tokens in the query encoding.
+It also stems from the fixed `vidore/colpaligemma-3b-pt-448-base` to guarantee deterministic projection layer initialization.
+It was trained for 5 epochs, with in-batch negatives and hard mined negatives and a warmup of 1000 steps (10x longer) to help reduce non-english language collapse.
+
+Data is the same as the ColPali data described in the paper.
+
+## 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.
+
+## Usage
+
+```python
+import torch
+from PIL import Image
+
+from transformers import ColPali, ColPaliProcessor
+
+model_name = "vidore/colpali-v1.2-hf"
+
+model = ColPali.from_pretrained(
+    model_name,
+    torch_dtype=torch.bfloat16,
+    device_map="cuda:0",  # or "mps" if on Apple Silicon
+).eval()
+
+processor = ColPaliProcessor.from_pretrained(model_name)
+
+# Your inputs
+images = [
+    Image.new("RGB", (32, 32), color="white"),
+    Image.new("RGB", (16, 16), color="black"),
+]
+queries = [
+    "What is the organizational structure for our R&D department?",
+    "Can you provide a breakdown of last year’s financial performance?",
+]
+
+# Process the inputs
+batch_images = processor(images=images).to(model.device)
+batch_queries = processor(text=queries).to(model.device)
+
+# Forward pass
+with torch.no_grad():
+    image_embeddings = model(**batch_images)
+    query_embeddings = model(**batch_queries)
+
+scores = processor.score_retrieval(query_embeddings, image_embeddings)
+```
+
+## 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's vision language backbone 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]
+
+The HuggingFace transformers 🤗 implementation was contributed by Tony Wu (@tonywu71) and Yoni Gozlan (@yonigozlan).
+
+
+## Citation
+
+If you use any datasets or models from this organization in your research, please cite the original dataset as follows:
+
+```bibtex
+@misc{faysse2024colpaliefficientdocumentretrieval,
+  title={ColPali: Efficient Document Retrieval with Vision Language Models}, 
+  author={Manuel Faysse and Hugues Sibille and Tony Wu and Bilel Omrani and Gautier Viaud and Céline Hudelot and Pierre Colombo},
+  year={2024},
+  eprint={2407.01449},
+  archivePrefix={arXiv},
+  primaryClass={cs.IR},
+  url={https://arxiv.org/abs/2407.01449}, 
+}
+```