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README.md
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@@ -19,14 +19,21 @@ You can use the raw model for molecular generation or fine-tune it to a downstre
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### How to use
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Molecule generation example:
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```python
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from transformers import AutoTokenizer, BartForConditionalGeneration
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tokenizer = AutoTokenizer.from_pretrained("zjunlp/MolGen")
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model = BartForConditionalGeneration.from_pretrained("zjunlp/MolGen", use_auth_token=True)
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sf_input = tokenizer("[C][=C][C][=C][C][=C][Ring1][=Branch1]", return_tensors="pt")
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```
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### How to use
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Molecule generation example:
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```python
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>>> from transformers import AutoTokenizer, BartForConditionalGeneration
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>>> tokenizer = AutoTokenizer.from_pretrained("zjunlp/MolGen")
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>>> model = BartForConditionalGeneration.from_pretrained("zjunlp/MolGen", use_auth_token=True)
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>>> sf_input = tokenizer("[C][=C][C][=C][C][=C][Ring1][=Branch1]", return_tensors="pt")
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>>> # beam search
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>>> 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)
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>>> # top-k search
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>>> 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)
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>>> sf_output = [tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=True).replace(" ","") for g in molecules]
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['[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]']
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```
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