zjunlp
/

MolGen-large

Text2Text Generation

molecular language model

molecule generation

Model card Files Files and versions Community

ZJU-Fangyin commited on Feb 1, 2023

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5d63c09

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1 Parent(s): 2a45f64

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@@ -28,14 +28,13 @@ Molecule generation example:
 >>> # 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)
 >>> 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]']
 ```

 >>> # 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,
                               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][=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]']
 ```