Dataset Viewer
The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
id: string
ground_truth_smiles: string
formula: string
spectra: struct<1H: list<item: double>, 13C: list<item: double>>
child 0, 1H: list<item: double>
child 0, item: double
child 1, 13C: list<item: double>
child 0, item: double
meta: struct<id: string, dataset: string, ground_truth_smiles: string, formula: string, retrieval_setting: (... 175 chars omitted)
child 0, id: string
child 1, dataset: string
child 2, ground_truth_smiles: string
child 3, formula: string
child 4, retrieval_setting: string
child 5, db_topk: int64
child 6, beam_topk: int64
child 7, sample_topk: int64
child 8, pair_budget: int64
child 9, final_budget: int64
child 10, likelihood_weight: double
child 11, workflow: string
child 12, forward_batch_size: int64
db_candidates: list<item: string>
child 0, item: string
db_retrieval: struct<setting: string, exclusion_keys: int64, requested_candidates: int64, returned_candidates: int (... 138 chars omitted)
child 0, setting: string
child 1, exclusion_keys: int64
child 2, requested_candidates: int64
child 3, returned_candidates: int64
child 4, faiss_k: int64
child 5, server_mask_applied: bool
child 6, excluded_by_rule: int64
child 7, excluded_charged: int64
child 8, missing_metadata: int64
child 9, exhausted: bool
inverse_beam: list<item: string>
child 0, item: string
inverse_beam_scores: list<item: struct<mean_logprob: double, sum_logprob: double, token_count: int64>>
child 0, item: struct<mean_logp
...
rs omitted)
child 0, candidate_smiles: string
child 1, token_count: int64
child 2, sum_logprob: double
child 3, mean_logprob: double
child 4, nll: double
child 5, perplexity: double
child 6, fused_score: double
elapsed_seconds: double
input: string
system: string
samples: int64
topk: struct<1: struct<sequence_accuracy: double, token_accuracy: double, morgan_tanimoto: double, torsion (... 330 chars omitted)
child 0, 1: struct<sequence_accuracy: double, token_accuracy: double, morgan_tanimoto: double, torsion_tanimoto: (... 36 chars omitted)
child 0, sequence_accuracy: double
child 1, token_accuracy: double
child 2, morgan_tanimoto: double
child 3, torsion_tanimoto: double
child 4, atom_pair_tanimoto: double
child 1, 5: struct<sequence_accuracy: double, token_accuracy: double, morgan_tanimoto: double, torsion_tanimoto: (... 36 chars omitted)
child 0, sequence_accuracy: double
child 1, token_accuracy: double
child 2, morgan_tanimoto: double
child 3, torsion_tanimoto: double
child 4, atom_pair_tanimoto: double
child 2, 10: struct<sequence_accuracy: double, token_accuracy: double, morgan_tanimoto: double, torsion_tanimoto: (... 36 chars omitted)
child 0, sequence_accuracy: double
child 1, token_accuracy: double
child 2, morgan_tanimoto: double
child 3, torsion_tanimoto: double
child 4, atom_pair_tanimoto: double
view: string
input_format: string
to
{'system': Value('string'), 'input': Value('string'), 'view': Value('string'), 'input_format': Value('string'), 'samples': Value('int64'), 'topk': {'1': {'sequence_accuracy': Value('float64'), 'token_accuracy': Value('float64'), 'morgan_tanimoto': Value('float64'), 'torsion_tanimoto': Value('float64'), 'atom_pair_tanimoto': Value('float64')}, '5': {'sequence_accuracy': Value('float64'), 'token_accuracy': Value('float64'), 'morgan_tanimoto': Value('float64'), 'torsion_tanimoto': Value('float64'), 'atom_pair_tanimoto': Value('float64')}, '10': {'sequence_accuracy': Value('float64'), 'token_accuracy': Value('float64'), 'morgan_tanimoto': Value('float64'), 'torsion_tanimoto': Value('float64'), 'atom_pair_tanimoto': Value('float64')}}}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
return get_rows(
dataset=dataset,
...<4 lines>...
column_names=column_names,
)
File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
return func(*args, **kwargs)
File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
File "/src/services/worker/src/worker/utils.py", line 483, in safe_iter
yield from ds.decode(False) if ds.features else ds
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2840, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2373, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2398, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
id: string
ground_truth_smiles: string
formula: string
spectra: struct<1H: list<item: double>, 13C: list<item: double>>
child 0, 1H: list<item: double>
child 0, item: double
child 1, 13C: list<item: double>
child 0, item: double
meta: struct<id: string, dataset: string, ground_truth_smiles: string, formula: string, retrieval_setting: (... 175 chars omitted)
child 0, id: string
child 1, dataset: string
child 2, ground_truth_smiles: string
child 3, formula: string
child 4, retrieval_setting: string
child 5, db_topk: int64
child 6, beam_topk: int64
child 7, sample_topk: int64
child 8, pair_budget: int64
child 9, final_budget: int64
child 10, likelihood_weight: double
child 11, workflow: string
child 12, forward_batch_size: int64
db_candidates: list<item: string>
child 0, item: string
db_retrieval: struct<setting: string, exclusion_keys: int64, requested_candidates: int64, returned_candidates: int (... 138 chars omitted)
child 0, setting: string
child 1, exclusion_keys: int64
child 2, requested_candidates: int64
child 3, returned_candidates: int64
child 4, faiss_k: int64
child 5, server_mask_applied: bool
child 6, excluded_by_rule: int64
child 7, excluded_charged: int64
child 8, missing_metadata: int64
child 9, exhausted: bool
inverse_beam: list<item: string>
child 0, item: string
inverse_beam_scores: list<item: struct<mean_logprob: double, sum_logprob: double, token_count: int64>>
child 0, item: struct<mean_logp
...
rs omitted)
child 0, candidate_smiles: string
child 1, token_count: int64
child 2, sum_logprob: double
child 3, mean_logprob: double
child 4, nll: double
child 5, perplexity: double
child 6, fused_score: double
elapsed_seconds: double
input: string
system: string
samples: int64
topk: struct<1: struct<sequence_accuracy: double, token_accuracy: double, morgan_tanimoto: double, torsion (... 330 chars omitted)
child 0, 1: struct<sequence_accuracy: double, token_accuracy: double, morgan_tanimoto: double, torsion_tanimoto: (... 36 chars omitted)
child 0, sequence_accuracy: double
child 1, token_accuracy: double
child 2, morgan_tanimoto: double
child 3, torsion_tanimoto: double
child 4, atom_pair_tanimoto: double
child 1, 5: struct<sequence_accuracy: double, token_accuracy: double, morgan_tanimoto: double, torsion_tanimoto: (... 36 chars omitted)
child 0, sequence_accuracy: double
child 1, token_accuracy: double
child 2, morgan_tanimoto: double
child 3, torsion_tanimoto: double
child 4, atom_pair_tanimoto: double
child 2, 10: struct<sequence_accuracy: double, token_accuracy: double, morgan_tanimoto: double, torsion_tanimoto: (... 36 chars omitted)
child 0, sequence_accuracy: double
child 1, token_accuracy: double
child 2, morgan_tanimoto: double
child 3, torsion_tanimoto: double
child 4, atom_pair_tanimoto: double
view: string
input_format: string
to
{'system': Value('string'), 'input': Value('string'), 'view': Value('string'), 'input_format': Value('string'), 'samples': Value('int64'), 'topk': {'1': {'sequence_accuracy': Value('float64'), 'token_accuracy': Value('float64'), 'morgan_tanimoto': Value('float64'), 'torsion_tanimoto': Value('float64'), 'atom_pair_tanimoto': Value('float64')}, '5': {'sequence_accuracy': Value('float64'), 'token_accuracy': Value('float64'), 'morgan_tanimoto': Value('float64'), 'torsion_tanimoto': Value('float64'), 'atom_pair_tanimoto': Value('float64')}, '10': {'sequence_accuracy': Value('float64'), 'token_accuracy': Value('float64'), 'morgan_tanimoto': Value('float64'), 'torsion_tanimoto': Value('float64'), 'atom_pair_tanimoto': Value('float64')}}}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
NMR-CRAFT Results
This dataset contains full workflow outputs from NMR-CRAFT on the NMRGym and NMRSpec test sets. Each result file is JSONL with one record per test sample; the corresponding metrics file contains aggregate evaluation results.
Files
nmrgym/
results.jsonl
metrics.json
nmrspec/
results.jsonl
metrics.json
NMRGym
The NMRGym test set contains 27,054 samples. Retrieval uses the
exclude_scaffold setting.
| Metric | Top-1 | Top-5 | Top-10 |
|---|---|---|---|
| Sequence accuracy | 8.23% | 15.14% | 17.34% |
NMRSpec
The NMRSpec test set contains 21,298 samples. Retrieval uses the
exclude_molecule setting and preserves detailed proton-peak information.
| Metric | Top-1 | Top-5 | Top-10 |
|---|---|---|---|
| Sequence accuracy | 58.51% | 77.66% | 79.67% |
The JSONL files contain the full per-sample workflow outputs. The metrics files contain the aggregate evaluation reports used to produce the tables above.
- Downloads last month
- 36