Update README.md

README.md

@@ -30,7 +30,16 @@ The model is trained to perform a sequence classification task over phrase-level
 
 ## Intended uses & limitations
 
-More information needed
+The scope of the model is not to run lexical entailment (i.e., hypernym detection). The model is trained solely to perform a very specific subset of phrase-level entailment, based on adjective-nouns phrases. The type of question you should ask the model are limited, and should have one of three forms:
+- An adjective+Noun is a Noun (e.g. A red car is a car)
+- An adjective+Noun is a noun-hypernym (e.g. A red car is a vehicle)
+- An adjective+Noun is a adjective+noun-hypernym (e.g. A red car is a red vehicle)
+
+Linguistically speaking, adjectives belong to three macro classes (intersective, subsective, and intensional). From a linguistic and logical stand, these class shape the truth value of the three forms above. For instance, since red is an intersective adjective, the three from are all true. A subjective adjective like small allows just the first two, but not the last – that is, logically speaking, a small car is not a small vehicle.
+
+In other words, the model was built to study out-of-distribution compositional generalisation with respect to a very specific set of compositional phenomena.
+
+This poses clear limitations to the question you can ask the model. For instance, if you had to query the model with a basic (false) hypernym detection task (e.g., *A dog is a cat*), the model will consider it as true.
 
 ## Training and evaluation data