Fix the usage example: fetch the processor before importing it

README.md

@@ -52,16 +52,21 @@ from processing_char_bert import CharBertProcessor
 
 proc = CharBertProcessor()
 
-print(proc.restore_respaced(model, "ἔδοξεν τῇ βου-- καὶ τῷ δήμῳ"))
+# note the input: no accents on τηβου, and no word division either -- the model
+# recovers the missing letters, the accents and the spacing together
+print(proc.restore_respaced(model, "ἔδοξεν τηβου-- καὶ τῷ δήμῳ"))
 # ἔδοξεν τῇ βουλῇ καὶ τῷ δήμῳ
 
-print(proc.restore_respaced(model, "στεφανῶσαι αὐτὸν χρυσῷ στεφά[3±1] ἀρετῆς ἕνεκα"))
+print(proc.restore_respaced(model, "στεφανῶσαι αὐτὸν χρυσῷ στεφα[3±1]ετης ἕνεκα"))
 # στεφανῶσαι αὐτὸν χρυσῷ στεφάνῳ ἀρετῆς ἕνεκα
 ```
 
 `restore_respaced` works in the order an editor would: it fills the letters first, then throws
 the spacing away and runs the model again over the resulting *scriptio continua* with word
-division and accents unknown everywhere -- the regime it was pretrained on. Deciding the letters
+division and accents unknown everywhere -- the regime it was pretrained on. Nothing in the
+input has to be normalized first: accents and word division are predictions, not requirements,
+so a bare majuscule transcript is as readable to this model as a modern critical text, and the
+gap is filled in the same pass that decides where the words end. Deciding the letters
 and the segmentation in a single pass, which `decode_restoration` does, leaves the boundary head
 hedging against a half-known segmentation, and a correctly restored word can come back cut in
 two. The evaluation harness in the code repository goes further still, enumerating the division