README.md

metadata

configs:
  - config_name: default
    data_files:
      - split: train
        path: chanlam_train_final.csv
      - split: test
        path: chanlam_test_final.csv
      - split: val
        path: chanlam_val_final.csv
      - split: train_augmented
        path: chanlam_train_augmented_final.csv
license: mit
task_categories:
  - translation
language:
  - en
tags:
  - chemistry
pretty_name: Chan Lam Dataset from Open Reaction Database
size_categories:
  - 10K<n<100K

Dataset Card for [Dataset Name]

Table of Contents

• Table of Contents
• Dataset Description
  • Dataset Summary
  • Supported Tasks and Leaderboards
  • Languages
• Dataset Structure
  • Data Instances
  • Data Fields
  • Data Splits
• Dataset Creation
  • Curation Rationale
  • Source Data
  • Annotations
  • Personal and Sensitive Information
• Considerations for Using the Data
  • Social Impact of Dataset
  • Discussion of Biases
  • Other Known Limitations
• Additional Information
  • Dataset Curators
  • Licensing Information
  • Citation Information
  • Contributions

Dataset Description

• Original Authors:
• Original Paper:
• Open Reaction Database Repo: https://

Dataset Summary

The order of addition is as follows:

Reactants>/.Reagents i.e. Sulfonamide.Boronic acid>/.Catalyst.Base

I processed the original dataset to extract only the reactants, reagents and products (yields are in another dataset)

Supported Tasks and Leaderboards

Any SMILES-based Seq2Seq model can use this dataset.

Languages

SMILES strings.

Dataset Structure

Data Instances

[More Information Needed]

Data Fields

Input: Reactants + Reagents Target: Products

Data Splits

train: Default train split val: Used in eval loop of Trainer test: Hold out set for separate evaluation train_augmented: train set but augmented with 4 extra random SMILES per row, totalling a dataset with 5x more rows

Dataset Creation

Curation Rationale

This is a small dataset, good for testing out the effectiveness of LoRA vs full finetuning

Source Data

Initial Data Collection and Normalization

[More Information Needed]

Who are the source language producers?

[More Information Needed]

Annotations

Annotation process

[More Information Needed]

Who are the annotators?

[More Information Needed]

Personal and Sensitive Information

[More Information Needed]

Considerations for Using the Data

Social Impact of Dataset

[More Information Needed]

Discussion of Biases

[More Information Needed]

Other Known Limitations

[More Information Needed]

Additional Information

Dataset Curators

[More Information Needed]

Licensing Information

[More Information Needed]

Citation Information

[More Information Needed]

Contributions

Thanks to @github-username for adding this dataset.