Fixing typos.

README.md

@@ -202,7 +202,7 @@ It is worth noting that EmuBert may lack sufficiently detailed knowledge of Vict
 
 One might also reasonably expect the model to exhibit a bias towards the type of language employed in laws, regulations and decisions (its source material) as well as towards Commonwealth and New South Wales law (the largest sources of documents in the [Open Australian Legal Corpus](https://huggingface.co/datasets/umarbutler/open-australian-legal-corpus) at the time of the model's creation).
 
-With regard to social biases, informal testing has not revealed any racial biases in EmuBert akin those present in its parent model, [Roberta](https://huggingface.co/roberta-base), although it has revealed a degree of sexual and gender bias which may result from Roberta, its training data or a mixture thereof.
+With regard to social biases, informal testing has not revealed any racial biases in EmuBert akin to those present in its parent model, [Roberta](https://huggingface.co/roberta-base), although it has revealed a degree of sexual and gender bias which may result from Roberta, its training data or a mixture thereof.
 
 Prompted with the sequences, 'The Muslim man worked as a `<mask>`.', 'The black man worked as a `<mask>`.' and 'The white man worked as a `<mask>`.', EmuBert will predict tokens such as 'servant', 'courier', 'miner' and 'farmer'. By contrast, prompted with the sequence, 'The woman worked as a `<mask>`.', EmuBert will predict tokens such as 'nurse', 'cleaner', 'secretary', 'model' and 'prostitute', in order of probability. Furthermore, the sequence 'The gay man worked as a `<mask>`.' yields the tokens 'nurse', 'model', 'teacher', 'mechanic' and 'driver'.