Update README.md

README.md

@@ -19,14 +19,21 @@ 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, BartForConditionalGeneration
 
-tokenizer = AutoTokenizer.from_pretrained("zjunlp/MolGen")
-model = BartForConditionalGeneration.from_pretrained("zjunlp/MolGen", use_auth_token=True)
+>>> tokenizer = AutoTokenizer.from_pretrained("zjunlp/MolGen")
+>>> model = BartForConditionalGeneration.from_pretrained("zjunlp/MolGen", use_auth_token=True)
 
-sf_input = tokenizer("[C][=C][C][=C][C][=C][Ring1][=Branch1]", return_tensors="pt")
-molecules = model.generate(input_ids=sf_input["input_ids"],attention_mask=sf_input["attention_mask"],max_length=20,min_length=5,num_return_sequences=5,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]
+>>> sf_input = tokenizer("[C][=C][C][=C][C][=C][Ring1][=Branch1]", return_tensors="pt")
+
+>>> # beam search
+>>> molecules = model.generate(input_ids=sf_input["input_ids"],attention_mask=sf_input["attention_mask"],max_length=20,min_length=5,num_return_sequences=5,num_beams=5,past_prompt=None)
+
+>>> # top-k search
+>>> molecules = model.generate(input_ids=sf_input["input_ids"],attention_mask=sf_input["attention_mask"],do_sample=True,max_length=20,min_length=5,top_k=5,num_return_sequences=3,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]']
 ```