Datasets:
File size: 2,052 Bytes
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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.
```json
{
"mol_edit": {
"train": 4497,
"test": 100
},
"mol_opt": {
"train": 5025,
"test": 600
},
"mol_und": {
"train": 7690,
"test": 320
},
"reaction": {
"train": 10282,
"test": 475
}
}
```
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