Update README.md

README.md

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 # FMMB-BE-EN: The Fairly Multilingual ModernBERT Embedding Model (Belgian Edition): Monolingual English version.
 
-🇳🇱 This monolingual English version of the [Fairly Multilingual ModernBERT Embedding Model (Belgian Edition)](https://huggingface.co/Parallia/Fairly-Multilingual-ModernBERT-Embed-BE) is the perfect model for embedding texts up to 8192 tokens written in English at the speed of light. It uses the exact same weights as the original FMMB-BE model, and therefore produces identical embeddings, but this version comes with only a English-optimized tokenizer and its associated embedding table, thereby improving performance.
+🇺🇸 This monolingual English version of the [Fairly Multilingual ModernBERT Embedding Model (Belgian Edition)](https://huggingface.co/Parallia/Fairly-Multilingual-ModernBERT-Embed-BE) is the perfect model for embedding texts up to 8192 tokens written in English at the speed of light. It uses the exact same weights as the original FMMB-BE model, and therefore produces identical embeddings, but this version comes with only a English-optimized tokenizer and its associated embedding table, thereby improving performance.
 
 🆘 This [sentence-transformers](https://www.SBERT.net) model was trained on a small parallel corpus containing English-French, English-Dutch, and English-German sentence pairs. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. The input texts can be used as-is, no need to use prefixes.