sagawa/ReactionT5v2-yield
Model Card for ReactionT5v2-yield
This is a ReactionT5 pre-trained to predict yields 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
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How to Get Started with the Model
Use the code below to get started with the model.
import torch
import torch.nn as nn
from transformers import AutoTokenizer, T5ForConditionalGeneration, AutoConfig, PreTrainedModel
import logging
logging.getLogger('transformers').setLevel(logging.ERROR)
class ReactionT5Yield(PreTrainedModel):
config_class = AutoConfig
def __init__(self, config):
super().__init__(config)
self.config = config
self.model = T5ForConditionalGeneration.from_pretrained(self.config._name_or_path)
self.model.resize_token_embeddings(self.config.vocab_size)
self.fc1 = nn.Linear(self.config.hidden_size, self.config.hidden_size//2)
self.fc2 = nn.Linear(self.config.hidden_size, self.config.hidden_size//2)
self.fc3 = nn.Linear(self.config.hidden_size//2*2, self.config.hidden_size)
self.fc4 = nn.Linear(self.config.hidden_size, self.config.hidden_size)
self.fc5 = nn.Linear(self.config.hidden_size, 1)
self._init_weights(self.fc1)
self._init_weights(self.fc2)
self._init_weights(self.fc3)
self._init_weights(self.fc4)
self._init_weights(self.fc5)
def _init_weights(self, module):
if isinstance(module, nn.Linear):
module.weight.data.normal_(mean=0.0, std=0.01)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.Embedding):
module.weight.data.normal_(mean=0.0, std=0.01)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
elif isinstance(module, nn.LayerNorm):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
def forward(self, inputs):
encoder_outputs = self.model.encoder(**inputs)
encoder_hidden_states = encoder_outputs[0]
outputs = self.model.decoder(input_ids=torch.full((inputs['input_ids'].size(0),1),
self.config.decoder_start_token_id,
dtype=torch.long), encoder_hidden_states=encoder_hidden_states)
last_hidden_states = outputs[0]
output1 = self.fc1(last_hidden_states.view(-1, self.config.hidden_size))
output2 = self.fc2(encoder_hidden_states[:, 0, :].view(-1, self.config.hidden_size))
output = self.fc3(torch.hstack((output1, output2)))
output = self.fc4(output)
output = self.fc5(output)
return output*100
model = ReactionT5Yield.from_pretrained('sagawa/ReactionT5v2-yield')
tokenizer = AutoTokenizer.from_pretrained('sagawa/ReactionT5v2-yield')
inp = tokenizer(['REACTANT:CC(C)n1ncnc1-c1cn2c(n1)-c1cnc(O)cc1OCC2.CCN(C(C)C)C(C)C.Cl.NC(=O)[C@@H]1C[C@H](F)CN1REAGENT: PRODUCT:O=C(NNC(=O)C(F)(F)F)C(F)(F)F'], return_tensors='pt')
print(model(inp)) # tensor([[19.1666]], grad_fn=<MulBackward0>)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 Open Reaction Database (ORD) dataset for model training. In addition, we used palladium-catalyzed Buchwald-Hartwig C-N cross-coupling reactions 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.
python train.py \
--train_data_path='../data/preprocessed_ord_train.csv' \
--valid_data_path='../data/preprocessed_ord_valid.csv' \
--test_data_path='../data/preprocessed_ord_test.csv' \
--CN_test_data_path='../data/C_N_yield/MFF_Test1/test.csv' \
--epochs=100 \
--batch_size=32 \
--output_dir='./'Results
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}
}