Update README.md

README.md

@@ -13,7 +13,7 @@ models-in particular, in low-resource situations. Considering the fact that the
 which is different from the one that would be expected to be found in free natural language, we explore whether our affiliation span identification and 
 NER models would benefit from being fine-tuned from models that have been *further pre-trained* on raw affiliation strings for the masked token prediction task.
 
-We adatap RoBERTa-ase to 10 million random raw affiliation strings from OpenAlex, reporting perplexity on 50k randomly held-out affiliation strings. 
+We adapt models to 10 million random raw affiliation strings from OpenAlex, reporting perplexity on 50k randomly held-out affiliation strings. 
 In what follows, we refer to our adapted models as AffilRoBERTa (adapted RoBERTa model) and AffilXLM (adapted XLM-RoBERTa).
 
 Specific details of the adaptive pre-training procedure can be found in [Duran-Silva *et al.* (2024)](https://aclanthology.org/2024.sdp-1.13.pdf).