sagawa/ReactionT5v1-yield
Model Card for ReactionT5v1-yield
This is a ReactionT5 pre-trained to predict yields of reactions. You can use the demo here.
Model Sources
<!-- Provide the basic links for the model. -->
- Repository: https://github.com/sagawatatsuya/ReactionT5
- Paper: https://arxiv.org/abs/2311.06708
- Demo: https://huggingface.co/spaces/sagawa/ReactionT5taskyield
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. -->
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
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/ReactionT5v1-yield')
tokenizer = AutoTokenizer.from_pretrained('sagawa/ReactionT5v1-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. Following is the command used for training. For more information, please refer to the paper and GitHub repository.
python train.py
--data_path='all_ord_reaction_uniq_with_attr_v3.tsv'
--pretrained_model_name_or_path='sagawa/ZINC-t5'
--model='t5'
--epochs=100
--batch_size=50
--max_len=400
--num_workers=4
--weight_decay=0.05
--gradient_accumulation_steps=1
--batch_scheduler
--print_freq=100
--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. --> arxiv link: https://arxiv.org/abs/2311.06708
@misc{sagawa2023reactiont5,
title={ReactionT5: a large-scale pre-trained model towards application of limited reaction data},
author={Tatsuya Sagawa and Ryosuke Kojima},
year={2023},
eprint={2311.06708},
archivePrefix={arXiv},
primaryClass={physics.chem-ph}
}