| license: cc-by-4.0 | |
| task_categories: | |
| - text-generation | |
| - feature-extraction | |
| language: | |
| - en | |
| tags: | |
| - chemistry | |
| - chemoinformatics | |
| - reaction-prediction | |
| - RAG | |
| pretty_name: ORDerly Styrene Mizoroki-Heck RAG Dataset | |
| size_categories: | |
| - 10K<n<100K | |
| # ORDerly: Styrene Mizoroki-Heck RAG-Ready Dataset | |
| This repository contains chemical reaction data formatted for **Retrieval-Augmented Generation (RAG)** systems. | |
| The data is a processed version of the **ORDerly** benchmark, specifically focusing on reaction conditions and forward/retro prediction tasks. | |
| ## Dataset Structure | |
| The data is split into 10,000-row Parquet chunks to prevent Out-of-Memory (OOM) errors during ingestion into vector databases. | |
| It includes: | |
| - **orderly_condition**: Solvents and agents given reactants and products. | |
| - **orderly_forward**: Product prediction given reactants and agents. | |
| - **orderly_retro**: Retrosynthesis planning. | |
| ## How to Use | |
| ```python | |
| from datasets import load_dataset | |
| dataset = load_dataset("Azzindani/Open_Reaction_Data", streaming=True) | |
| ``` | |
| ## Citations and Data Source | |
| This dataset is based on the **ORDerly** framework. If you use this data, please cite the original authors: | |
| **Paper:** | |
| > Wigh, D. S., et al. (2024). ORDerly: Data Sets and Benchmarks for Chemical Reaction Data. *Journal of Chemical Information and Modeling*. [https://doi.org/10.1021/acs.jcim.4c00292](https://pubs.acs.org/doi/10.1021/acs.jcim.4c00292) | |
| **Original Dataset:** | |
| > Wigh, D. S., et al. (2023). ORDerly chemical reactions condition benchmarks. *Figshare*. [https://doi.org/10.6084/m9.figshare.23298467.v4](https://figshare.com/articles/dataset/ORDerly_chemical_reactions_condition_benchmarks/23298467/4) | |
| ## License | |
| Licensed under **CC BY 4.0**. | |