Update README.md

README.md

@@ -50,7 +50,8 @@ As an alternative to downloading the already precomputed model (much storage). Y
 and compute the embeddings for the dataset using:
 
 ```python
-linker = EntityMentionLinker.build("cambridgeltl/SapBERT-from-PubMedBERT-fulltext", dictionary_name_or_path="ncbi-taxonomy", entity_type="species", hybrid_search=False)
+from flair.models.entity_mention_linking import BioSynEntityPreprocessor
+linker = EntityMentionLinker.build("cambridgeltl/SapBERT-from-PubMedBERT-fulltext", dictionary_name_or_path="ncbi-taxonomy", entity_type="species", preprocessor=BioSynEntityPreprocessor(), hybrid_search=False)
 ```
 
 This will reduce the download requirements, at the cost of computation. Note `hybrid_search=False` as SapBERT unlike BioSyn is trained only for dense retrieval.