Create README.md

README.md

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+---
+tags:
+- molecular language model
+- SELFIES
+- molecule optimization
+---
+
+# 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.
+
+## Model description
+MolGen-large-opt 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.
+
+![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.
+
+### How to use
+Molecule generation 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")
+>>> # beam search
+>>> molecules = model.generate(input_ids=sf_input["input_ids"],
+                              attention_mask=sf_input["attention_mask"],
+                              max_length=15,
+                              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]']
+```
+
+
+### BibTeX entry and citation info
+```bibtex
+@article{fang2023molecular,
+  title={Molecular Language Model as Multi-task Generator},
+  author={Fang, Yin and Zhang, Ningyu and Chen, Zhuo and Fan, Xiaohui and Chen, Huajun},
+  journal={arXiv preprint arXiv:2301.11259},
+  year={2023}
+}
+```