readme: minor changes

README.md

@@ -120,7 +120,7 @@ library_name: transformers
 
 ## Quick Start
 
-The easiest way to start using `jina-embeddings-v3` is Jina AI's [Embedding API](https://jina.ai/embeddings/).
+The easiest way to start using `jina-embeddings-v3` is with the [Jina Embedding API](https://jina.ai/embeddings/).
 
 
 ## Intended Usage & Model Info
@@ -128,7 +128,7 @@ The easiest way to start using `jina-embeddings-v3` is Jina AI's [Embedding API]
 
 `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**.
+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 [LoRA](https://arxiv.org/abs/2106.09685) adapters to generate task-specific embeddings efficiently.
 
 ### Key Features:
@@ -201,7 +201,7 @@ embeddings = F.normalize(embeddings, p=2, dim=1)
 </p>
 </details>
 
-The easiest way to start using `jina-embeddings-v3` is Jina AI's [Embedding API](https://jina.ai/embeddings/).
+The easiest way to start using `jina-embeddings-v3` is with the [Jina Embedding API](https://jina.ai/embeddings/).
 
 Alternatively, you can use `jina-embeddings-v3` directly via Transformers package:
 ```python
@@ -254,12 +254,7 @@ The latest version (#todo: specify version) of SentenceTransformers also support
 from sentence_transformers import SentenceTransformer
 
 model = SentenceTransformer(
-    "jinaai/jina-embeddings-v3",
-    prompts={
-        "retrieval.query": "Represent the query for retrieving evidence documents: ",
-        "retrieval.passage": "Represent the document for retrieval: ",
-    },
-    trust_remote_code=True
+    "jinaai/jina-embeddings-v3", trust_remote_code=True
 )
 
 embeddings = model.encode(['What is the weather like in Berlin today?'], task_type='retrieval.query')