---
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

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### Model Sources

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- **Repository:** https://github.com/sagawatatsuya/ReactionT5
- **Paper:** {{ paper | default("[More Information Needed]", true)}}
- **Demo:** https://huggingface.co/spaces/sagawa/predictproduct-t5

## Uses

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## 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]

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## Model Card Authors [optional]

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## Model Card Contact

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