Update README.md

README.md

@@ -2,7 +2,7 @@
 license: mit
 ---
 
-This is a many-to-many model for Creole-English, English-Creole and Creole-Creole MT, fine-tuned on top of facebook/mbart-large-50-many-to-many-mmt, with all data.
+This is a many-to-many model for Creole-English, English-Creole and Creole-Creole MT, fine-tuned on top of `facebook/mbart-large-50-many-to-many-mmt`, with all data.
 
 Usage:
 
@@ -10,13 +10,13 @@ Usage:
 from transformers import MBartForConditionalGeneration, AutoModelForSeq2SeqLM
 from transformers import MbartTokenizer, AutoTokenizer
 
-tokenizer = AutoTokenizer.from_pretrained("n8rob/kreyol-mt", do_lower_case=False, use_fast=False, keep_accents=True)
+tokenizer = AutoTokenizer.from_pretrained("jhu-clsp/kreyol-mt", do_lower_case=False, use_fast=False, keep_accents=True)
 
-# Or use tokenizer = MbartTokenizer.from_pretrained("n8rob/kreyol-mt", use_fast=False)
+# Or use tokenizer = MbartTokenizer.from_pretrained("jhu-clsp/kreyol-mt", use_fast=False)
 
-model = AutoModelForSeq2SeqLM.from_pretrained("n8rob/kreyol-mt")
+model = AutoModelForSeq2SeqLM.from_pretrained("jhu-clsp/kreyol-mt")
 
-# Or use model = MBartForConditionalGeneration.from_pretrained("n8rob/kreyol-mt")
+# Or use model = MBartForConditionalGeneration.from_pretrained("jhu-clsp/kreyol-mt")
 
 # First tokenize the input and outputs. The format below is how the model was trained so the input should be "Sentence </s> SRCCODE". Similarly, the output should be "TGTCODE Sentence </s>". 
 # Example: For Saint Lucian Patois to English translation, we need to use language indicator tags: <2acf> and <2eng> where acf represents Saint Lucian Patois and eng represents English.