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

• 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

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

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

@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}
}