
### dataset description
We downloaded open-reaction-database(ORD) dataset from [here](https://github.com/open-reaction-database/ord-data). As a preprocess, we removed overlapping data and canonicalized them using RDKit.
We used the following function to canonicalize the data and removed some SMILES that cannot be read by RDKit.
    
```python:
from rdkit import Chem
def canonicalize(mol):
    mol = Chem.MolToSmiles(Chem.MolFromSmiles(mol),True)
    return mol 
```

We randomly split the preprocessed data into train, validation and test. The ratio is 8:1:1.
task_categories:text2text-generation task_categories:translation multilinguality:monolingual size_categories:1M<n<10M source_datasets:original license:apache-2.0 ord chemical reaction region:us
{"annotations_creators": [], "language_creators": [], "license": ["apache-2.0"], "multilinguality": ["monolingual"], "size_categories": ["1M<n<10M"], "source_datasets": ["original"], "task_categories": ["text2text-generation", "translation"], "task_ids": [], "pretty_name": "canonicalized ORD", "tags": ["ord", "chemical", "reaction"]}
TAGS
#task_categories-text2text-generation #task_categories-translation #multilinguality-monolingual #size_categories-1M<n<10M #source_datasets-original #license-apache-2.0 #ord #chemical #reaction #region-us 


### dataset description
We downloaded open-reaction-database(ORD) dataset from here. As a preprocess, we removed overlapping data and canonicalized them using RDKit.
We used the following function to canonicalize the data and removed some SMILES that cannot be read by RDKit.
    


We randomly split the preprocessed data into train, validation and test. The ratio is 8:1:1.
### dataset description
We downloaded open-reaction-database(ORD) dataset from here. As a preprocess, we removed overlapping data and canonicalized them using RDKit.
We used the following function to canonicalize the data and removed some SMILES that cannot be read by RDKit.
    


We randomly split the preprocessed data into train, validation and test. The ratio is 8:1:1.
TAGS
#task_categories-text2text-generation #task_categories-translation #multilinguality-monolingual #size_categories-1M<n<10M #source_datasets-original #license-apache-2.0 #ord #chemical #reaction #region-us 
 ### dataset description
We downloaded open-reaction-database(ORD) dataset from here. As a preprocess, we removed overlapping data and canonicalized them using RDKit.
We used the following function to canonicalize the data and removed some SMILES that cannot be read by RDKit.
    


We randomly split the preprocessed data into train, validation and test. The ratio is 8:1:1.
passage: TAGS
#task_categories-text2text-generation #task_categories-translation #multilinguality-monolingual #size_categories-1M<n<10M #source_datasets-original #license-apache-2.0 #ord #chemical #reaction #region-us 
### dataset description
We downloaded open-reaction-database(ORD) dataset from here. As a preprocess, we removed overlapping data and canonicalized them using RDKit.
We used the following function to canonicalize the data and removed some SMILES that cannot be read by RDKit.
    


We randomly split the preprocessed data into train, validation and test. The ratio is 8:1:1.