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
methodology: struct<labeled_set: string, n_samples: int64, n_fields: int64, fuzzy_threshold: double, max_new_toke (... 35 chars omitted)
child 0, labeled_set: string
child 1, n_samples: int64
child 2, n_fields: int64
child 3, fuzzy_threshold: double
child 4, max_new_tokens: int64
child 5, process_isolation: bool
host: struct<platform: string, processor: string, python: string, torch: string, total_ram_gb: double, cud (... 39 chars omitted)
child 0, platform: string
child 1, processor: string
child 2, python: string
child 3, torch: string
child 4, total_ram_gb: double
child 5, cuda_available: bool
child 6, mps_available: bool
results: list<item: struct<label: string, backend: string, model_id: string, constrained: bool, quantization: (... 390 chars omitted)
child 0, item: struct<label: string, backend: string, model_id: string, constrained: bool, quantization: string, sk (... 378 chars omitted)
child 0, label: string
child 1, backend: string
child 2, model_id: string
child 3, constrained: bool
child 4, quantization: string
child 5, skipped: bool
child 6, reason: string
child 7, n_samples: int64
child 8, n_schema_valid: int64
child 9, n_fields: int64
child 10, n_exact: int64
child 11, n_fuzzy: int64
child 12, n_repaired: int64
child 13, mean_seconds: double
child 14, outcomes: list<item: struct<sample_id: string, field: string, expected: string, got: string, exa
...
imated_blood_loss: string
child 9, operative_time: string
child 10, manifest_no: string
child 11, ship_date: string
child 12, origin_dc: string
child 13, destination_dc: string
child 14, carrier: string
child 15, temp_requirement: string
child 16, title: string
child 17, q1_revenue: string
child 18, q2_revenue: string
child 19, q3_revenue: string
child 20, q4_revenue: string
child 5, ground_truth: struct<patient_name: string, mrn: string, dob: timestamp[s], date_of_surgery: timestamp[s], surgeon: (... 351 chars omitted)
child 0, patient_name: string
child 1, mrn: string
child 2, dob: timestamp[s]
child 3, date_of_surgery: timestamp[s]
child 4, surgeon: string
child 5, procedure: string
child 6, diagnosis_code: string
child 7, asa_class: string
child 8, estimated_blood_loss: string
child 9, operative_time: string
child 10, manifest_no: string
child 11, ship_date: timestamp[s]
child 12, origin_dc: string
child 13, destination_dc: string
child 14, carrier: string
child 15, temp_requirement: string
child 16, title: string
child 17, q1_revenue: string
child 18, q2_revenue: string
child 19, q3_revenue: string
child 20, q4_revenue: string
_about: string
to
{'_about': Value('string'), 'samples': List({'id': Value('string'), 'image': Value('string'), 'category': Value('string'), 'prompt': Value('string'), 'schema': {'patient_name': Value('string'), 'mrn': Value('string'), 'dob': Value('string'), 'date_of_surgery': Value('string'), 'surgeon': Value('string'), 'procedure': Value('string'), 'diagnosis_code': Value('string'), 'asa_class': Value('string'), 'estimated_blood_loss': Value('string'), 'operative_time': Value('string'), 'manifest_no': Value('string'), 'ship_date': Value('string'), 'origin_dc': Value('string'), 'destination_dc': Value('string'), 'carrier': Value('string'), 'temp_requirement': Value('string'), 'title': Value('string'), 'q1_revenue': Value('string'), 'q2_revenue': Value('string'), 'q3_revenue': Value('string'), 'q4_revenue': Value('string')}, 'ground_truth': {'patient_name': Value('string'), 'mrn': Value('string'), 'dob': Value('timestamp[s]'), 'date_of_surgery': Value('timestamp[s]'), 'surgeon': Value('string'), 'procedure': Value('string'), 'diagnosis_code': Value('string'), 'asa_class': Value('string'), 'estimated_blood_loss': Value('string'), 'operative_time': Value('string'), 'manifest_no': Value('string'), 'ship_date': Value('timestamp[s]'), 'origin_dc': Value('string'), 'destination_dc': Value('string'), 'carrier': Value('string'), 'temp_requirement': Value('string'), 'title': Value('string'), 'q1_revenue': Value('string'), 'q2_revenue': Value('string'), 'q3_revenue': Value('string'), 'q4_revenue': Value('string')}})}
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
methodology: struct<labeled_set: string, n_samples: int64, n_fields: int64, fuzzy_threshold: double, max_new_toke (... 35 chars omitted)
child 0, labeled_set: string
child 1, n_samples: int64
child 2, n_fields: int64
child 3, fuzzy_threshold: double
child 4, max_new_tokens: int64
child 5, process_isolation: bool
host: struct<platform: string, processor: string, python: string, torch: string, total_ram_gb: double, cud (... 39 chars omitted)
child 0, platform: string
child 1, processor: string
child 2, python: string
child 3, torch: string
child 4, total_ram_gb: double
child 5, cuda_available: bool
child 6, mps_available: bool
results: list<item: struct<label: string, backend: string, model_id: string, constrained: bool, quantization: (... 390 chars omitted)
child 0, item: struct<label: string, backend: string, model_id: string, constrained: bool, quantization: string, sk (... 378 chars omitted)
child 0, label: string
child 1, backend: string
child 2, model_id: string
child 3, constrained: bool
child 4, quantization: string
child 5, skipped: bool
child 6, reason: string
child 7, n_samples: int64
child 8, n_schema_valid: int64
child 9, n_fields: int64
child 10, n_exact: int64
child 11, n_fuzzy: int64
child 12, n_repaired: int64
child 13, mean_seconds: double
child 14, outcomes: list<item: struct<sample_id: string, field: string, expected: string, got: string, exa
...
imated_blood_loss: string
child 9, operative_time: string
child 10, manifest_no: string
child 11, ship_date: string
child 12, origin_dc: string
child 13, destination_dc: string
child 14, carrier: string
child 15, temp_requirement: string
child 16, title: string
child 17, q1_revenue: string
child 18, q2_revenue: string
child 19, q3_revenue: string
child 20, q4_revenue: string
child 5, ground_truth: struct<patient_name: string, mrn: string, dob: timestamp[s], date_of_surgery: timestamp[s], surgeon: (... 351 chars omitted)
child 0, patient_name: string
child 1, mrn: string
child 2, dob: timestamp[s]
child 3, date_of_surgery: timestamp[s]
child 4, surgeon: string
child 5, procedure: string
child 6, diagnosis_code: string
child 7, asa_class: string
child 8, estimated_blood_loss: string
child 9, operative_time: string
child 10, manifest_no: string
child 11, ship_date: timestamp[s]
child 12, origin_dc: string
child 13, destination_dc: string
child 14, carrier: string
child 15, temp_requirement: string
child 16, title: string
child 17, q1_revenue: string
child 18, q2_revenue: string
child 19, q3_revenue: string
child 20, q4_revenue: string
_about: string
to
{'_about': Value('string'), 'samples': List({'id': Value('string'), 'image': Value('string'), 'category': Value('string'), 'prompt': Value('string'), 'schema': {'patient_name': Value('string'), 'mrn': Value('string'), 'dob': Value('string'), 'date_of_surgery': Value('string'), 'surgeon': Value('string'), 'procedure': Value('string'), 'diagnosis_code': Value('string'), 'asa_class': Value('string'), 'estimated_blood_loss': Value('string'), 'operative_time': Value('string'), 'manifest_no': Value('string'), 'ship_date': Value('string'), 'origin_dc': Value('string'), 'destination_dc': Value('string'), 'carrier': Value('string'), 'temp_requirement': Value('string'), 'title': Value('string'), 'q1_revenue': Value('string'), 'q2_revenue': Value('string'), 'q3_revenue': Value('string'), 'q4_revenue': Value('string')}, 'ground_truth': {'patient_name': Value('string'), 'mrn': Value('string'), 'dob': Value('timestamp[s]'), 'date_of_surgery': Value('timestamp[s]'), 'surgeon': Value('string'), 'procedure': Value('string'), 'diagnosis_code': Value('string'), 'asa_class': Value('string'), 'estimated_blood_loss': Value('string'), 'operative_time': Value('string'), 'manifest_no': Value('string'), 'ship_date': Value('timestamp[s]'), 'origin_dc': Value('string'), 'destination_dc': Value('string'), 'carrier': Value('string'), 'temp_requirement': Value('string'), 'title': Value('string'), 'q1_revenue': Value('string'), 'q2_revenue': Value('string'), 'q3_revenue': Value('string'), 'q4_revenue': Value('string')}})}
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.
VisionFlow evaluation set and benchmark results
Companion data for VisionFlow, an edge-first quantized VLM pipeline for local document intelligence.
Contents
| Path | What it is |
|---|---|
sample_images/ |
Three synthetic document images: a surgical report, a cold-chain shipping manifest, and a bar chart |
labeled_set.json |
Ground-truth field labels for those images |
results/ |
Raw output from vf bench, vf accuracy, and the ONNX provider benchmark |
Ground truth
Labels are the literal strings drawn into each image by the generator script, so they are exact by construction rather than human-annotated. The trade-off is scope: this is 3 images and 21 fields, sized to expose relative differences between quantization levels — not to support an absolute accuracy claim.
Synthetic data notice
Every name, MRN, date, diagnosis code, SKU, lot number, and revenue figure here is fabricated. No real patient, shipment, or company data is present. The medical image is a plausible-looking surgical report and is not a real medical record.
Reproducing
pip install visionflow
vf bench
vf accuracy
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