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---
language:
- en
license: mit
tags:
- chemistry
- SMILES
- product
datasets:
- ORD
metrics:
- accuracy
---
# Model Card for ReactionT5-product-prediction
This is a ReactionT5 pre-trained to predict the products of reactions. You can use the demo [here](https://huggingface.co/spaces/sagawa/predictproduct-t5).
## Model Details
<!-- Provide a longer summary of what this model is. -->
### Model Sources
<!-- Provide the basic links for the model. -->
- **Repository:** https://github.com/sagawatatsuya/ReactionT5
- **Paper:** {{ paper | default("[More Information Needed]", true)}}
- **Demo:** https://huggingface.co/spaces/sagawa/predictproduct-t5
## 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
Download files and use the code below to get started with the model.
```python
from transformers import AutoTokenizer, T5ForConditionalGeneration
tokenizer = AutoTokenizer.from_pretrained('sagawa/ReactionT5-product-prediction')
inp = tokenizer('REACTANT:COC(=O)C1=CCCN(C)C1.O.[Al+3].[H-].[Li+].[Na+].[OH-]REAGENT:C1CCOC1', return_tensors='pt')
model = T5ForConditionalGeneration.from_pretrained('sagawa/ReactionT5-product-prediction')
output = model.generate(**inp, min_length=6, max_length=109, 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 # 'O=S(=O)([O-])[O-].O=S(=O)([O-])[O-].O=S(=O)([O-])[O-].[Cr+3].[Cr+3]'
```
## 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
python train.py \
--epochs=100 \
--batch_size=32 \
--data_path='../data/all_ord_reaction_uniq_with_attr_v3.csv' \
--use_reconstructed_data \
--pretrained_model_name_or_path='sagawa/CompoundT5'
```
### Results
| Model | Training set | Test set | Top-1 [% acc.] | Top-2 [% acc.] | Top-3 [% acc.] | Top-5 [% acc.] |
|----------------------|---------------------------|----------|----------------|----------------|----------------|----------------|
| Sequence-to-sequence | USPTO | USPTO | 80.3 | 84.7 | 86.2 | 87.5 |
| WLDN | USPTO | USPTO | 80.6 (85.6) | 90.5 | 92.8 | 93.4 |
| Molecular Transformer| USPTO | USPTO | 88.8 | 92.6 | – | 94.4 |
| T5Chem | USPTO | USPTO | 90.4 | 94.2 | – | 96.4 |
| CompoundT5 | USPTO | USPTO | 88.0 | 92.4 | 93.9 | 95.0 |
| ReactionT5 | ORD | USPTO | 0.0 <85.0> | 0.0 <90.6> | 0.0 <92.3> | 0.0 <93.8> |
Performance comparison of Compound T5, ReactionT5, and other models in product prediction. The values enclosed in ‘<>’ in the table represent the scores of the model that was fine-tuned on 200 reactions from the USPTO dataset. The score enclosed in ‘()’ is the one reported in the original paper.
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
## Model Card Authors [optional]
{{ model_card_authors | default("[More Information Needed]", true)}}
## Model Card Contact
{{ model_card_contact | default("[More Information Needed]", true)}} |