Datasets:
license: mit
tags:
- chemistry
- molecular-editing
- reasoning
configs:
- config_name: mol_edit
data_files:
- split: train
path: mol_edit/train.parquet
- split: test
path: mol_edit/test.parquet
- config_name: mol_opt
data_files:
- split: train
path: mol_opt/train.parquet
- split: test
path: mol_opt/test.parquet
- config_name: mol_und
data_files:
- split: train
path: mol_und/train.parquet
- split: test
path: mol_und/test.parquet
- config_name: reaction
data_files:
- split: train
path: reaction/train.parquet
- split: test
path: reaction/test.parquet
- config_name: all
data_files:
- split: train
path:
- mol_edit/train.parquet
- mol_opt/train.parquet
- mol_und/train.parquet
- reaction/train.parquet
- split: test
path:
- mol_edit/test.parquet
- mol_opt/test.parquet
- mol_und/test.parquet
- reaction/test.parquet
ChemCoTBench, hub-native
The deduplicated mtybilly/ChemCoTBench mirror (7b17148acba5e5aee716de1fe3d0db1a0bdf48e5), with the unified columns ground_truth, response and scoring_context added beside every original one. Six subtasks' train query is rendered in the full shape of that subtask's benchmark test prompts (add, delete, sub, equivalence, fs, rcr); every other subtask's train query keeps its own corpus wording with the benchmark's reply-format block appended. fs's train ground_truth is the reaction's main product (from meta.rxn_smiles), not upstream gt (the byproduct, kept as the original column it is), because that subtask's train rows are now rendered in the main-product shape its test prompts ask for. See scripts/datasets/README.md in the AutoDataSci repository for what those columns mean.
Row counts: test 1495, train 27494.
{
"mol_edit": {
"train": 4497,
"test": 100
},
"mol_opt": {
"train": 5025,
"test": 600
},
"mol_und": {
"train": 7690,
"test": 320
},
"reaction": {
"train": 10282,
"test": 475
}
}