Update README.md

README.md

@@ -38,19 +38,20 @@ It achieves the following results on the evaluation set:
 This model is a fine-tuned version of t5-small on a custom dataset derived from the Dictionary of Middle Egyptian.
 
 The Inference Api on the hugging face model page doesn't work well, load the model in jupyter notebook using the following code snippet:
-  text = "" # add your hieroglyph gardiner code combination in the string
   
-  from transformers import AutoTokenizer
-  
-  tokenizer = AutoTokenizer.from_pretrained("AnushS/hieroglyph_unicode_translator_t5_small")
-  inputs = tokenizer(text, return_tensors="pt").input_ids
-  
-  from transformers import AutoModelForSeq2SeqLM
-  
-  
-  model = AutoModelForSeq2SeqLM.from_pretrained("AnushS/hieroglyph_unicode_translator_t5_small")
-  outputs = model.generate(inputs, max_new_tokens=40, do_sample=True, top_k=30, top_p=0.95)
-  translated_keywords = str(tokenizer.decode(outputs[0], skip_special_tokens=True))
+    text = "" # add your hieroglyph gardiner code combination in the string
+    
+    from transformers import AutoTokenizer
+    
+    tokenizer = AutoTokenizer.from_pretrained("AnushS/hieroglyph_unicode_translator_t5_small")
+    inputs = tokenizer(text, return_tensors="pt").input_ids
+    
+    from transformers import AutoModelForSeq2SeqLM
+    
+    
+    model = AutoModelForSeq2SeqLM.from_pretrained("AnushS/hieroglyph_unicode_translator_t5_small")
+    outputs = model.generate(inputs, max_new_tokens=40, do_sample=True, top_k=30, top_p=0.95)
+    translated_keywords = str(tokenizer.decode(outputs[0], skip_special_tokens=True))
 
 
 Intended uses & limitations