fwieo

README.md

@@ -45,12 +45,35 @@ The following variants are available, pre-trained on the specified proportion of
 
 ## Basic usage
 
+BORT was intended to be fine-tuned to a specific task, but for a basic demonstration of what distinguishes it from 
+other LLMs, please consider the following example. The pre-trained model has no issue translating "Long /aɪlənd/" to
+"Long Island", or "Long /biʧ/" to "Long Beach". The next two texts demonstrate the effect of context. While 
+"lɔŋ ·aɪlən·d" still translates to "Long Island", "lɔŋ ·b·iʧ" bumps up against a homonym, and the model produces
+"long beech". (Note: the bullet character `·` is used to prevent the BPE tokenizer 
+from combining phonemes.)
+
 ```python
+from transformers import AutoTokenizer, BartForConditionalGeneration
+
+tokenizer = AutoTokenizer.from_pretrained("rcgale/bort-test")
+model = BartForConditionalGeneration.from_pretrained("rcgale/bort-test")
+
 in_texts = [
+    "Due to its coastal location, Long ·aɪlən·d winter temperatures are milder than most of the state.",
+    # Output:
+    # Due to its coastal location, Long Island winter temperatures are milder than most of the state.",
+    
+    "Due to its coastal location, Long ·b·iʧ winter temperatures are milder than most of the state.",
+    # Output:
+    # Due to its coastal location, Long Beach winter temperatures are milder than most of the state.",
+
     "Due to its coastal location, lɔŋ ·aɪlən·d winter temperatures are milder than most of the state.",
+    # Output:
+    # Due to its coastal location, Long Island winter temperatures are milder than most of the state.",
+
     "Due to its coastal location, lɔŋ ·b·iʧ winter temperatures are milder than most of the state.",
-    "Due to its coastal location, Long ·b·iʧ winter temperatures are milder than most of the state.",
-    "Due to its coastal location, lɔŋfɝd winter temperatures are milder than most of the state.",
+    # Output:
+    # Due to its coastal location, long beech winter temperatures are milder than most of the state.",
 ]
 
 inputs = tokenizer(in_texts, return_tensors="pt", padding=True)