Update README.md

README.md

@@ -15,46 +15,68 @@ should probably proofread and complete it, then remove this comment. -->
 
 # finetune_colqwen2-v1.0
 
-This model is a fine-tuned version of [vidore/colqwen2-base](https://huggingface.co/vidore/colqwen2-base) on the None dataset.
+This model is a fine-tuned version of [vidore/colqwen2-base](https://huggingface.co/vidore/colqwen2-base) on the custom dataset.
 
 ## Model description
 
-More information needed
-
-## Intended uses & limitations
-
-More information needed
-
-## Training and evaluation data
-
-More information needed
-
-## Training procedure
-
-### Training hyperparameters
+ColQwen 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 [Qwen2-VL-2B](https://huggingface.co/Qwen/Qwen2-VL-2B-Instruct) 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)
+
+This version is the untrained base version to guarantee deterministic projection layer initialization.
+<p align="center"><img width=800 src="https://github.com/illuin-tech/colpali/blob/main/assets/colpali_architecture.webp?raw=true"/></p>
+
+
+## Usage
+
+```
+model = ColQwen2.from_pretrained(
+    'toxic-pandas/finetune_colqwen2-v1.0',
+    torch_dtype=torch.bfloat16,
+    device_map=device,
+)
+```
+
+## 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.
+
+## Dataset
+
+With the help of the GT4-o mini model, a dataset was formed for the completion of the colqwen2-v1.0 model, containing the following fields:
+
+- document_filename: Filename of the document.
+- document_url: Original URL of the document.
+- search_query: The query used to fetch the document.
+- search_topic: Topic related to the document.
+- search_subtopic: Subtopic related to the document.
+- search_language: Language specified for the search.
+- search_filetype: Filetype filter applied during the search.
+- page_number: The page's number within the document.
+- page_description: A natural language description of the page.
+- page_language: Language used on the page.
+- page_contains_table: Boolean indicating the presence of tables.
+- page_contains_figure: Boolean indicating the presence of figures.
+- page_contains_paragraph: Boolean indicating the presence of paragraphs.
+- page_image: The image of the current page.
+- query_type: Type of query (e.g., Extractive, Open-ended, Boolean, Compare-contrast, Enumerative, Numerical).
+- query_answerability: Answerability level of the query (Fully answerable, Partially answerable, Unanswerable).
+- query_modality: Modality used for query generation.
+- query_language: Language of the query.
+- query_reasoning: Reasoning traces used in query generation.
+- query: The actual query text.
+- query_is_self_contained: Boolean indicating if the query is self-contained.
+- query_is_self_contained_reasoning: Reasoning traces for determining self-contained nature.
+- answer: Expected answer to the question from the "query" field.
+
+## Training hyperparameters
 
 The following hyperparameters were used during training:
 - learning_rate: 3e-05
-- train_batch_size: 2
-- eval_batch_size: 2
-- seed: 42
-- gradient_accumulation_steps: 8
-- total_train_batch_size: 16
-- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
-- lr_scheduler_type: linear
 - lr_scheduler_warmup_steps: 100
 - num_epochs: 1
 
-### Training results
-
-| Training Loss | Epoch  | Step | Validation Loss | Model Preparation Time |
-|:-------------:|:------:|:----:|:---------------:|:----------------------:|
-| No log        | 0.0150 | 1    | 0.0268          | 0.0318                 |
-
-
 ### Framework versions
 
-- Transformers 4.46.3
-- Pytorch 2.5.1+cu124
-- Datasets 3.1.0
-- Tokenizers 0.20.3
+- Transformers 4.46.3