Update README.md

README.md

@@ -288,10 +288,11 @@ The successor of German_Semantic_STS_V2 is here!
 
 ## Major updates and USPs:
 
-- **Sequence length:** 8192, (16 times more than V2 and other models) => thanks to the ALiBi implementation of Jina-Team!
+- **Sequence length:** 8192, (16 times more than V2 and other models) -> thanks to the ALiBi implementation of Jina-Team!
 - **Matryoshka Embeddings:** The model is trained for embedding sizes from 1024 down to 64, allowing you to store much smaller embeddings with little quality loss.
 - **License:** Apache 2.0
-- **German only:** This model is German-only, causing the model to learn more efficient and deal better with shorter queries.
+- **German only:** This model is German-only, causing the model to learn more efficient thanks to its tokenizer, deal better with shorter queries and generally be more nuanced.
+- **Updated knowledge and quality data:** The backbone of this model is gbert-large by deepset. With Stage-2 pretraining on German fineweb by occiglot (newest only), up-to-date knowledge is ensured.
 - **Flexibility:** Trained with flexible sequence-length and embedding truncation, flexibility is a core feature of the model, while improving on V2-performance. 
 
 ## Usage:
@@ -300,7 +301,7 @@ The successor of German_Semantic_STS_V2 is here!
 from sentence_transformers import SentenceTransformer
 
 
-matryoshka_dim = 1024 # How big your embeddings should be, choose from: 64, 128, 256, 512, 1024
+matryoshka_dim = 1024 # How big your embeddings should be, choose from: 64, 128, 256, 512, 768, 1024
 model = SentenceTransformer("aari1995/German_Semantic_V3", trust_remote_code=True, truncate_dim=matryoshka_dim)
 
 # model.truncate_dim = 64 # truncation dimensions can also be changed after loading