sagawa/ReactionT5v2-forward
Model Card for ReactionT5v2-forward
This is a ReactionT5 pre-trained to predict the products of reactions. You can use the demo here.
Model Sources
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- Repository: https://github.com/sagawatatsuya/ReactionT5v2
- Paper: https://jcheminf.biomedcentral.com/articles/10.1186/s13321-025-01075-4
- Demo: https://huggingface.co/spaces/sagawa/ReactionT5
Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> You can use this model for forward reaction prediction or fine-tune this model with your dataset.
How to Get Started with the Model
Use the code below to get started with the model.
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("sagawa/ReactionT5v2-forward", return_tensors="pt")
model = AutoModelForSeq2SeqLM.from_pretrained("sagawa/ReactionT5v2-forward")
inp = tokenizer('REACTANT:COC(=O)C1=CCCN(C)C1.O.[Al+3].[H-].[Li+].[Na+].[OH-]REAGENT:C1CCOC1', return_tensors='pt')
output = model.generate(**inp, 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 # 'CN1CCC=C(CO)C1'Training Details
Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> We used the Open Reaction Database (ORD) dataset for model training. In addition, we used USPTO_MIT dataset's test split to prevent data leakage. The command used for training is the following. For more information about data preprocessing and training, please refer to the paper and GitHub repository.
cd task_forward
python train.py \
--output_dir='t5' \
--epochs=100 \
--lr=1e-3 \
--batch_size=32 \
--input_max_len=150 \
--target_max_len=100 \
--weight_decay=0.01 \
--evaluation_strategy='epoch' \
--save_strategy='epoch' \
--logging_strategy='epoch' \
--train_data_path='../data/preprocessed_ord_train.csv' \
--valid_data_path='../data/preprocessed_ord_valid.csv' \
--test_data_path='../data/preprocessed_ord_test.csv' \
--USPTO_test_data_path='../data/USPTO_MIT/MIT_separated/test.csv' \
--disable_tqdm \
--pretrained_model_name_or_path='sagawa/CompoundT5'Results
Performance comparison of Compound T5, ReactionT5, and other models in product prediction.
Citation
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
@article{Sagawa2025,
title = {ReactionT5: a pre-trained transformer model for accurate chemical reaction prediction with limited data},
author = {Sagawa, Tatsuya and Kojima, Ryosuke},
journal = {Journal of Cheminformatics},
year = {2025},
volume = {17},
number = {1},
pages = {126},
doi = {10.1186/s13321-025-01075-4},
url = {https://doi.org/10.1186/s13321-025-01075-4}
}