Update README

README.md

@@ -23,15 +23,15 @@ Training was done on type-level, where, given the historic form of a type, the m
 ## Demo Usage
 
 ```python
-from transformers import AutoTokenizer, AutoModel
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
 
 tokenizer = AutoTokenizer.from_pretrained('aehrm/dtaec-type-normalizer')
-model = AutoModel.from_pretrained('aehrm/dtaec-type-normalizer')
+model = AutoModelForSeq2SeqLM.from_pretrained('aehrm/dtaec-type-normalizer')
 
-model_in = tokenizer([['Freyheit'], ['seyn'], ['selbstthätig']])
-model_out = model(**model_in)
+model_in = tokenizer(['Freyheit', 'seyn', 'selbstthätig'], return_tensors='pt', padding=True)
+model_out = model.generate(**model_in)
 
-print(tokenizer.decode_batch(model_out))
+print(tokenizer.batch_decode(model_out))
 ```