Update README.md

README.md

@@ -19,10 +19,10 @@ You can use the raw model for molecular generation or fine-tune it to a downstre
 ### How to use
 Molecule generation example:
 ```python
->>> from transformers import AutoTokenizer, BartForConditionalGeneration
+>>> from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
 
 >>> tokenizer = AutoTokenizer.from_pretrained("zjunlp/MolGen")
->>> model = BartForConditionalGeneration.from_pretrained("zjunlp/MolGen")
+>>> model = AutoModelForSeq2SeqLMq.from_pretrained("zjunlp/MolGen")
 
 >>> sf_input = tokenizer("[C][=C][C][=C][C][=C][Ring1][=Branch1]", return_tensors="pt")
 >>> # beam search
@@ -34,7 +34,8 @@ Molecule generation example:
                               num_beams=5,
                               past_prompt=None)
 >>> sf_output = [tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=True).replace(" ","") for g in molecules]
-['[C][=C][C][=C][C][=C][Ring1][=Branch1]', '[C][=C][C][=C][C][=C][C][=C][Ring1][=Branch1]', '[C][=C][C][=C][C][=C][Ring1][=Branch1][C@H1][C][=C][C][=C][C][=C][Ring1][=Branch1]', '[C][=C][C][=C][C][=C][Ring1][=Branch1][C][=C][C][=C][C][=C][Ring1][=Branch1]', '[C][=C][C][=C][C][=C][Ring1][=Branch1][C@H1][=C][C][=C][Ring1][=Branch1]']
+['[C][=C][C][=C][C][=C][Ring1][=Branch1]', '[C][=C][C][=C][C][=C][C][=C][Ring1][=Branch1]', '[C][=C][C][=C][C][=C][Ring1][=Branch1][C@H1][C][=C][C][=C][C][=C][Ring1][=Branch1]', '[C][=C][C][=C][C][=C][Ring1][=Branch1][C][=C][C][=C][C][=C][Ring1][=Branch1]
+', '[C][=C][C][=C][C][=C][Ring1][=Branch1][C@H1][=C][C][=C][Ring1][=Branch1]']
 ```