Reaction SFT Dataset
Collection
SFT dataset collections for Chemical Reaction related tasks
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7 items
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Updated
Error code: DatasetGenerationError
Exception: ArrowNotImplementedError
Message: Cannot write struct type '_format_kwargs' with no child field to Parquet. Consider adding a dummy child field.
Traceback: Traceback (most recent call last):
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1870, in _prepare_split_single
writer.write_table(table)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 620, in write_table
self._build_writer(inferred_schema=pa_table.schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 441, in _build_writer
self.pa_writer = self._WRITER_CLASS(self.stream, schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/pyarrow/parquet/core.py", line 1010, in __init__
self.writer = _parquet.ParquetWriter(
File "pyarrow/_parquet.pyx", line 2157, in pyarrow._parquet.ParquetWriter.__cinit__
File "pyarrow/error.pxi", line 154, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 91, in pyarrow.lib.check_status
pyarrow.lib.ArrowNotImplementedError: Cannot write struct type '_format_kwargs' with no child field to Parquet. Consider adding a dummy child field.
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1886, in _prepare_split_single
num_examples, num_bytes = writer.finalize()
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 639, in finalize
self._build_writer(self.schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 441, in _build_writer
self.pa_writer = self._WRITER_CLASS(self.stream, schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/pyarrow/parquet/core.py", line 1010, in __init__
self.writer = _parquet.ParquetWriter(
File "pyarrow/_parquet.pyx", line 2157, in pyarrow._parquet.ParquetWriter.__cinit__
File "pyarrow/error.pxi", line 154, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 91, in pyarrow.lib.check_status
pyarrow.lib.ArrowNotImplementedError: Cannot write struct type '_format_kwargs' with no child field to Parquet. Consider adding a dummy child field.
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1438, in compute_config_parquet_and_info_response
parquet_operations = convert_to_parquet(builder)
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1050, in convert_to_parquet
builder.download_and_prepare(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 924, in download_and_prepare
self._download_and_prepare(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1000, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1741, in _prepare_split
for job_id, done, content in self._prepare_split_single(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1897, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the datasetNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
_data_files
list | _fingerprint
string | _format_columns
null | _format_kwargs
dict | _format_type
null | _output_all_columns
bool | _split
string |
|---|---|---|---|---|---|---|
[
{
"filename": "data-00000-of-00001.arrow"
}
] |
3582eb4d53a66cfc
| null |
{}
| null | false
|
train
|
Suzuki-Miyaura Dataset. Converted from ChemLLMBench, which can be referred to Suzuki High-Throughput Experiment (HTE) dataset.
We build the instruction dataset strictly following ChemLLMBench instruction template. But We convert the output into 'integer' (as a ranking task).
In general, three different tasks involved:
reagent selection (train: #2387, test: #100)solvent selection (train:#1155, test: #100)ligand selection (train:#413, test: #100)Train: 3955
Test: 300
Example:
{
'task': 'reagent_selection',
'molecules': {'selfies': LIST[SELFIES]], 'smiles': LIST[SMILES]},
'messages': [
{
'content': 'You are an expert chemist. Given selected one reactant, two reagents and solvent of a Suzuki reaction, predict the optimal reactant that maximize the yield with the rest of reaction components by using your experienced chemical reactant selection knowledge. No explanations and other information. Only return the reactant smiles from the given list. Please strictly follow the format, the template are provided as follows:\nGiven the rest of reaction components:\nreactant: reactant 1\n ligand: ligand 1\n base: base 1\n solvent: solvent 1 \nReactant list: reactant1,reactant2,reactant3,.etc\nOptimal reactant: reactant2 ',
'role': 'system'
},
{
'content': 'Given the rest of reaction components:\nreactant: <molecule_2d>\nligand: <molecule_2d>\nsolvent: <molecule_2d>\nbase: <molecule_2d> \nReactants list for selection:\n<molecule_2d>,<molecule_2d>,<molecule_2d>\nOptimal reactant:\n',
'role': 'user'
},
{
'content': '2',
'role': 'assistant'
}
],
'id': 0
}