add more info

README.md

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 ### Overview
 
-BabyBERTA is a light-weight version of RoBERTa trained on 5M words of American-English child-directed input.
+BabyBERTa is a light-weight version of RoBERTa trained on 5M words of American-English child-directed input.
 It is intended for language acquisition research, on a single desktop with a single GPU - no high-performance computing infrastructure needed. 
 
 ### Performance
@@ -10,11 +10,15 @@ It is intended for language acquisition research, on a single desktop with a sin
 The provided model is the best-performing out of 10 that were evaluated on the [Zorro](https://github.com/phueb/Zorro) test suite.
 This model was trained for 400K steps, and achieves an overall accuracy of 80.3,
  comparable to RoBERTa-base, which achieves an overall accuracy of 82.6 on the latest version of Zorro (as of October, 2021).
-The latter value is slightly larger than that reported in the paper (Huebner et al., 2020) because the authors previously lower-cased all words in Zorro before evaluation.
-Lower-casing of proper nouns is detrimental to RoBERTa-base because RoBERTa-base has likely been exposed to proper nouns that are title-cased.
-Because BabyBERTa is not case-sensitive, performance is not influenced by this change.
  
-
+Both values differ slightly from those reported in the paper (Huebner et al., 2020). 
+There are two reasons for this:
+1. Performance of RoBERTa-base is slightly larger because the authors previously lower-cased all words in Zorro before evaluation.
+Lower-casing of proper nouns is detrimental to RoBERTa-base because RoBERTa-base has likely been trained on proper nouns that are primarily title-cased.
+In contrast, because BabyBERTa is not case-sensitive, its performance is not influenced by this change.
+2. The latest version of Zorro no longer contains ambiguous content words such as "Spanish" which can be both a noun and an adjective.
+ this resulted in a small reduction in the performance of BabyBERTa.
+ 
 
 ### Additional Information