chore: update readme

README.md

@@ -21528,7 +21528,7 @@ model-index:
 </p>
 
 <p align="center">
-<b>Jina Embedding V3: A Multilingual Multi-Task Embedding Model</b>
+<b>jina-embeddings-v3: Multilingual Embeddings With Task LoRA</b>
 </p>
 
 ## Quick Start
@@ -21541,8 +21541,8 @@ The easiest way to start using `jina-embeddings-v3` is with the [Jina Embedding
 
 `jina-embeddings-v3` is a **multilingual multi-task text embedding model** designed for a variety of NLP applications.
 Based on the [Jina-XLM-RoBERTa architecture](https://huggingface.co/jinaai/xlm-roberta-flash-implementation), 
-this model supports [Rotary Position Embeddings (RoPE)](https://arxiv.org/abs/2104.09864) to handle long input sequences up to **8192 tokens**.
-Additionally, it features 5 [LoRA](https://arxiv.org/abs/2106.09685) adapters to generate task-specific embeddings efficiently.
+this model supports Rotary Position Embeddings to handle long input sequences up to **8192 tokens**.
+Additionally, it features 5 LoRA adapters to generate task-specific embeddings efficiently.
 
 ### Key Features:
 - **Extended Sequence Length:** Supports up to 8192 tokens with RoPE.
@@ -21560,11 +21560,6 @@ While the foundation model supports 89 languages, we've focused our tuning effor
 Hindi, Indonesian, Italian, Japanese, Korean, Latvian, Norwegian, Polish, Portuguese, Romanian, 
 Russian, Slovak, Spanish, Swedish, Thai, Turkish, Ukrainian, Urdu,** and **Vietnamese.**
 
-
-## Data & Parameters
-
-The data and training details are described in the technical report (coming soon).
-
 ## Usage
 
 **<details><summary>Apply mean pooling when integrating the model.</summary>**