sagawa/ReactionT5v2-forward-USPTO_MIT
Model Card for ReactionT5v2-forward
This is a ReactionT5 pre-trained to predict the products of reactions. You can use the demo here. This is a ReactionT5 pre-trained to predict the products of reactions and fine-tuned on USPOT_50k's train split. Base model before fine-tuning is 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 USPTO_MIT dataset for model finetuning. The command used for training is the following. For more information, please refer to the paper and GitHub repository.
cd task_forward
python finetune.py \
--output_dir='t5' \
--epochs=50 \
--lr=2e-5 \
--batch_size=32 \
--input_max_len=200 \
--target_max_len=150 \
--evaluation_strategy='epoch' \
--save_strategy='epoch' \
--logging_strategy='epoch' \
--save_total_limit=10 \
--train_data_path='../data/USPTO_MIT/MIT_separated/train.csv' \
--valid_data_path='../data/USPTO_MIT/MIT_separated/val.csv' \
--disable_tqdm \
--model_name_or_path='sagawa/ReactionT5v2-forward'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}
}