Downloads · 30 days
230
0% of all-time downloads
molt5-base-caption2smiles
laituan245/molt5-base-caption2smiles
molt5-base-caption2smiles is a machine learning model from laituan245. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as apache-2.0.
This model can be used to generate a SMILES string from an input caption.
All-time downloads
56.9K
Public
Repo size
2 GB
Likes
1
Public
Repo makeup
Click a slice to open those files.
.bin990 MB · 100%
At a glance
- Library
- transformers
- License
- apache-2.0
- Model type
- t5
- Access
- Public
- Created
- May 3, 2022
- Updated
- May 3, 2022
- SHA
- c7689836
Spaces
Model card
From the Hugging Face model README
This model can be used to generate a SMILES string from an input caption.
Example Usage
from transformers import T5Tokenizer, T5ForConditionalGeneration
tokenizer = T5Tokenizer.from_pretrained("laituan245/molt5-base-caption2smiles", model_max_length=512)
model = T5ForConditionalGeneration.from_pretrained('laituan245/molt5-base-caption2smiles')
input_text = 'The molecule is a monomethoxybenzene that is 2-methoxyphenol substituted by a hydroxymethyl group at position 4. It has a role as a plant metabolite. It is a member of guaiacols and a member of benzyl alcohols.'
input_ids = tokenizer(input_text, return_tensors="pt").input_ids
outputs = model.generate(input_ids, num_beams=5, max_length=512)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
# The model will generate "COC1=C(C=CC(=C1)CCCO)O". The ground-truth is "COC1=C(C=CC(=C1)CO)O".
Paper
For more information, please take a look at our paper.
Paper: Translation between Molecules and Natural Language
Authors: Carl Edwards*, Tuan Lai*, Kevin Ros, Garrett Honke, Heng Ji
- Library
- transformers
- Type
- t5
- License
- apache-2.0
- Created
- May 3, 2022
- Updated
- May 3, 2022