Update README.md

README.md

@@ -6,10 +6,10 @@ tags:
 ---
 
 # MolGen-large-opt
-MolGen-large-opt was introduced in the paper ["Molecular Language Model as Multi-task Generator"](https://arxiv.org/pdf/2301.11259.pdf) and first released in [this repository](https://github.com/zjunlp/MolGen). It is a pre-trained molecular generative model built using the 100\% robust molecular language representation, SELFIES.
+MolGen-large-opt was introduced in the paper ["Molecular Language Model as Multi-task Generator"](https://arxiv.org/pdf/2301.11259.pdf) and first released in [this repository](https://github.com/zjunlp/MolGen). 
 
 ## Model description
-MolGen-large-opt is the first pre-trained model that only produces chemically valid molecules. 
+MolGen-large-opt is the fine-tuned version of MolGen-large. MolGen-large is the first pre-trained model that only produces chemically valid molecules. 
 With a training corpus of over 100 million molecules in SELFIES representation, MolGen-large learns the intrinsic structural patterns of molecules by mapping corrupted SELFIES to their original forms.
 Specifically, MolGen-large employs a bidirectional Transformer as its encoder and an autoregressive Transformer as its decoder.
 Through its carefully designed multi-task molecular prefix tuning (MPT), MolGen-large-opt can generate molecules with desired properties, making it a valuable tool for molecular optimization.
@@ -17,30 +17,30 @@ Through its carefully designed multi-task molecular prefix tuning (MPT), MolGen-
 ![image.png](./model.png)
 
 ## Intended uses
-You can use the raw model for molecule generation or fine-tune it to a downstream task. Please take note that the following examples only demonstrate the utilization of our pre-trained model for molecule generation. See the [repository](https://github.com/zjunlp/MolGen) to look for fine-tune details on a task that interests you.
+You can use the fine-tuned model for molecule optimization for downstream tasks. See the [repository](https://github.com/zjunlp/MolGen) to look for fine-tune details on a task that interests you.
 
 ### How to use
-Molecule generation example:
+Molecule optimization example:
 ```python
 >>> from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
 
 >>> tokenizer = AutoTokenizer.from_pretrained("zjunlp/MolGen-large-opt")
 >>> model = AutoModelForSeq2SeqLM.from_pretrained("zjunlp/MolGen-large-opt")
 
->>> sf_input = tokenizer("[C][=C][C][=C][C][=C][Ring1][=Branch1]", return_tensors="pt")
+>>> sf_input = tokenizer("[N][#C][C][C][C@@H1][C][C][C][C][C][C][C][C][C][C][C][Ring1][N][=O]", return_tensors="pt")
 >>> # beam search
 >>> molecules = model.generate(input_ids=sf_input["input_ids"],
                               attention_mask=sf_input["attention_mask"],
-                              max_length=15,
+                              max_length=35,
                               min_length=5,
                               num_return_sequences=5,
                               num_beams=5)
 >>> 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][=C][C][=C]',
-'[C][=C][C][=C][C][=C][Ring1][=Branch1][C@H1][C][=C][C]',
-'[C][=C][C][=C][C][=C][Ring1][=Branch1][C@H1][=C][C][=C]']
+['[N][#C][C][C][C@@H1][C][C][C][C][C][C][C][C][C][C][C][C][Ring1][N][=O]',
+'[N][#C][C][C][C@@H1][C][C][C][C][C][C][C][C][C][C][C][Ring1][N][=O]',
+'[N][#C][C][C][C@@H1][C][C][C][C][C][C][C][C][C][C][C][C][C][Ring1][N][=O]',
+'[N][#C][C][C][C@@H1][C][C][C][C][C][C][C][C][C][C][Ring1][N][=O]',
+'[N][#C][C][C][C@@H1][C][C][C][C][C][C][C][C][C][C][C][C][C][C][Ring1][N][=O]']
 ```