Model Card for ReactionT5-product-prediction

This is a ReactionT5 pre-trained to predict the products of reactions. You can use the demo here.

Model Details

Model Sources

Repository: https://github.com/sagawatatsuya/ReactionT5
Paper: {{ paper | default("[More Information Needed]", true)}}
Demo: https://huggingface.co/spaces/sagawa/predictproduct-t5

Uses

How to Get Started with the Model

Download files and use the code below to get started with the model.

from transformers import AutoTokenizer, T5ForConditionalGeneration

tokenizer = AutoTokenizer.from_pretrained('sagawa/ReactionT5-product-prediction')
inp = tokenizer('REACTANT:COC(=O)C1=CCCN(C)C1.O.[Al+3].[H-].[Li+].[Na+].[OH-]REAGENT:C1CCOC1', return_tensors='pt')
model = T5ForConditionalGeneration.from_pretrained('sagawa/ReactionT5-product-prediction')
output = model.generate(**inp, min_length=6, max_length=109, num_beams=1, num_return_sequences=1, return_dict_in_generate=True, output_scores=True)
output = tokenizer.decode(output['sequences'][0], skip_special_tokens=True).replace(' ', '').rstrip('.')
output # 'O=S(=O)([O-])[O-].O=S(=O)([O-])[O-].O=S(=O)([O-])[O-].[Cr+3].[Cr+3]'

Training Details

Training Procedure

We used Open Reaction Database (ORD) dataset for model training. Following is the command used for training. For more information, please refer to the paper and GitHub repository.

python train.py \
    --epochs=100 \
    --batch_size=32 \
    --data_path='../data/all_ord_reaction_uniq_with_attr_v3.csv' \
    --use_reconstructed_data \
    --pretrained_model_name_or_path='sagawa/CompoundT5'

Results

Model	Training set	Test set	Top-1 [% acc.]	Top-2 [% acc.]	Top-3 [% acc.]	Top-5 [% acc.]
Sequence-to-sequence	USPTO	USPTO	80.3	84.7	86.2	87.5
WLDN	USPTO	USPTO	80.6 (85.6)	90.5	92.8	93.4
Molecular Transformer	USPTO	USPTO	88.8	92.6	–	94.4
T5Chem	USPTO	USPTO	90.4	94.2	–	96.4
CompoundT5	USPTO	USPTO	88.0	92.4	93.9	95.0
ReactionT5	ORD	USPTO	0.0 <85.0>	0.0 <90.6>	0.0 <92.3>	0.0 <93.8>

Performance comparison of Compound T5, ReactionT5, and other models in product prediction. The values enclosed in ‘<>’ in the table represent the scores of the model that was fine-tuned on 200 reactions from the USPTO dataset. The score enclosed in ‘()’ is the one reported in the original paper.

sagawa
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ReactionT5-product-prediction