Update README.md

README.md

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 **A:** In terms of flexibility, this model is better. Performance wise, in most of the experiments this model is also better.
 
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 **Q: What is the difference between V3 and V3b?**
-A: V3 is slightly worse on benchmarks, while V3b has a knowledge cutoff by 2020, so it really depends on your use-case what model to use.
+**A:** V3 is slightly worse on benchmarks, while V3b has a knowledge cutoff by 2020, so it really depends on your use-case what model to use.
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 If you want peak performance and do not worry too much about recent developments, take this one (V3b).
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 If you are fine with sacrificing a few points on benchmarks and want the model to know what happened from 2020 on (elections, covid, other cultural events etc.), I'd suggest you use [German_Semantic_V3](https://huggingface.co/aari1995/German_Semantic_V3).
 
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 **Q: How does the model perform vs. multilingual models?**
 
 **A:** There are really great multilingual models that will be very useful for many use-cases. This model shines with its cultural knowledge and knowledge about German people and behaviour. 
 
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 **Q: What is the trade-off when reducing the embedding size?**
 
 **A:** Broadly speaking, when going from 1024 to 512 dimensions, there is very little trade-off (1 percent). When going down to 64 dimensions, you may face a decrease of up to 3 percent.
 
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 ## Up next:
 German_Semantic_V3_Instruct: Guiding your embeddings towards self-selected aspects