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README.md

@@ -125,29 +125,38 @@ The easiest way to starting using `jina-embeddings-v3` is to use Jina AI's [Embe
 
 ## Intended Usage & Model Info
 
-`jina-embeddings-v3` is a multilingual **text embedding model** supporting **8192 sequence length**.
-It is based on a XLMRoBERTa architecture (JinaXLMRoBERTa) that supports the Rotary Position Embeddings to allow longer sequence length.
-The backbone `JinaXLMRoBERTa ` is pretrained on variable length textual data on Mask Language Modeling objective for 160k steps on 89 languages.
-The model is further trained on Jina AI's collection of more than 500 millions of multilingual sentence pairs and hard negatives.
-These pairs were obtained from various domains and were carefully selected through a thorough cleaning process.
 
-`jina-embeddings-v3` has 5 task-specific LoRA adapters tuned on top of our backbone, add `task_type` as additional parameter when using the model:
+`jina-embeddings-v3` is a **multilingual multi-task text embedding model** designed for a variety of NLP applications.
+Based on the [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 sequences up to **8192 tokens**.
+Additionally, it features [LoRA](https://arxiv.org/abs/2106.09685) adapters to generate task-specific embeddings efficiently.
 
-TODO UPDATE THIS
+### Key Features:
+- **Extended Sequence Length:** Supports up to 8192 tokens with RoPE.
+- **Task-Specific Embedding:** Customize embeddings through the `task_type` argument with the following options:
+    - `retrieval.query`: Used for query embeddings in asymmetric retrieval tasks
+    - `retrieval.passage`: Used for passage embeddings in asymmetric retrieval tasks
+    - `separation`: Used for embeddings in clustering and re-ranking applications
+    - `classification`: Used for embeddings in classification tasks
+    - `text-matching`: Used for embeddings in tasks that quantify similarity between two texts, such as STS or symmetric retrieval tasks
+- **Matryoshka Embeddings**: Supports flexible embedding sizes (`32, 64, 128, 256, 512, 768, 1024`), allowing for truncating embeddings to fit your application.
 
-1. **query**: Handles user incoming queries at search time.
-2. **index**: Manages user documents submitted for indexing.
-3. **text-matching**: Processes symmetric text similarity tasks, whether short or long, such as STS (Semantic Textual Similarity).
-4. **classification**: Classifies user inputs into predefined categories.
-5. **clustering**: Facilitates the clustering of embeddings for further analysis.
+### Model Lineage:
 
-`jina-embeddings-v3` supports Matryoshka representation learning. We recommend using an embedding size of 128 or higher (1024 provides optimal performance) for storing your embeddings.
+`jina-embeddings-v3` builds upon the [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) model, which was originally trained on 100 languages. 
+We extended its capabilities with an extra pretraining phase on the [CulturaX](https://huggingface.co/datasets/uonlp/CulturaX) dataset, 
+then contrastively fine-tuned it on 30 languages for enhanced performance on embedding tasks in both monolingual and cross-lingual setups.
 
+### Supported Languages:
+While the base model supports 100 languages, we've focused our tuning efforts on the following 30 languages: 
+**Arabic, Bengali, Chinese, Danish, Dutch, English, Finnish, French, Georgian, German, Greek, 
+Hindi, Indonesian, Italian, Japanese, Korean, Latvian, Norwegian, Polish, Portuguese, Romanian, 
+Russian, Slovak, Spanish, Swedish, Thai, Turkish, Ukrainian, Urdu,** and **Vietnamese.**
 
 
 ## Data & Parameters
 
-coming soon.
+The data and training details are described in the technical report (coming soon).
 
 ## Usage