Update README.md

README.md

@@ -26,31 +26,6 @@ This version is the untrained base version to guarantee deterministic projection
 > This version should not be used: it is solely the base version useful for deterministic LoRA initialization.
 
 
-## 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 and most probably in the multimodal training.*
-
-### Parameters
-
-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 a 4 GPU setup with data parallelism, a learning rate of 5e-4 with linear decay with 2.5% warmup steps, and a batch size of 8.
-
-## Usage
-
-This should not be used as it is the base model, used only for initiliasation of the linear head weights of the model.
-
-## 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
 
 ColQwen2's vision language backbone model (Qwen2-VL) is under `apache2.0` license. The adapters attached to the model are under MIT license.