Datasets:

saraNour
/

compliments-reference-phase6

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
transition: large_string
input: large_string
input_rows: int64
output: large_string
output_rows: int64
rows_lost: int64
rows_added: int64
join_key: large_string
-- schema metadata --
pandas: '{"index_columns": [], "column_indexes": [], "columns": [{"name":' + 1003
to
{'phase': Value('large_string'), 'input_file': Value('large_string'), 'input_rows': Value('int64'), 'output_file': Value('large_string'), 'output_rows': Value('int64'), 'code_version': Value('large_string'), 'sha256': Value('large_string')}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 149, 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 129, 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 489, 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 2818, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2355, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2380, 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/parquet/parquet.py", line 220, in _generate_tables
                  yield Key(file_idx, batch_idx), self._cast_table(pa_table)
                                                  ~~~~~~~~~~~~~~~~^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/parquet/parquet.py", line 156, 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 2369, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              transition: large_string
              input: large_string
              input_rows: int64
              output: large_string
              output_rows: int64
              rows_lost: int64
              rows_added: int64
              join_key: large_string
              -- schema metadata --
              pandas: '{"index_columns": [], "column_indexes": [], "columns": [{"name":' + 1003
              to
              {'phase': Value('large_string'), 'input_file': Value('large_string'), 'input_rows': Value('int64'), 'output_file': Value('large_string'), 'output_rows': Value('int64'), 'code_version': Value('large_string'), 'sha256': Value('large_string')}
              because column names don't match

Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.

YAML Metadata Warning:empty or missing yaml metadata in repo card

Check out the documentation for more information.

Phase 6 — Nutri-Score 2023 + Agribalyse Mapping

Version: 1.0.0 Date: 2026-07-31 Status: IMPLEMENTED — Production Ready

Algorithm

Nutri-Score 2023 (updated algorithm) Source: Eurofins referencing Santé Publique France FAQ v21.Dec.2023 Reference: https://www.eurofins.de/food-analysis/other-services/nutri-score/

The Nutri-Score is a front-of-pack nutrition label that classifies products into 5 grades (A to E) based on their nutritional profile. The 2023 update modified point thresholds for sugars (0–15), salt (0–20), and protein (0–7), and updated grade boundaries.

Inputs

Input	Source	Rows	Cols
nutrition_per_100g	Phase 5	4,440	24
nutrition_cleaned	Phase 5	4,440	23
product_group_mapping	Phase 3	4,440	28
product_variant_mapping	Phase 4	4,440	11

Implementation

Nutri-Score Calculation

Converts calories (kcal) to energy (kJ) via × 4.184
Converts sodium (mg) to salt (g) via × 2.5 / 1000
Calculates negative points (energy, saturated fat, sugars, salt)
Calculates positive points (protein, fibre, fruit/vegetable/water content)
Raw score = negative − positive
Grade assigned via category-specific boundaries (general food vs beverages)

FVL Estimation

Fruits/vegetables/legumes (FVL) estimated from taxonomy
PRODUCE category: 90% FVL
All other categories: 0% FVL (conservative default)

Agribalyse Mapping

Category-level proxy mapping from taxonomy to Agribalyse v3.2 categories
Not a product-specific LCA — category-level approximation only

Eligibility

Products must be food domain
Products must have VALID nutrition quality status
Products must have ALL 6 required per-100g fields non-null

Outputs

File	Rows	Cols	Description
phase6_product_scores.parquet	4,440	31	Product-level scoring output
phase6_agribalyse_mapping.parquet	4,440	11	Agribalyse category mapping
phase6_score_exclusions.parquet	4,076	6	Products excluded from scoring
phase6_review_queue.parquet	0	6	Products flagged for review
phase6_summary.parquet	21	2	Summary statistics

Results

Metric	Value
Total products	4,440
Nutri-Score scored	364 (8.2%)
Nutri-Score excluded	4,076 (91.8%)

Grade Distribution (among scored)

Grade	Count	%
A	31	8.5%
B	42	11.5%
C	135	37.1%
D	83	22.8%
E	73	20.1%

Exclusion Reasons

Reason	Count
UNKNOWN_DOMAIN	1,232
MISSING_REQUIRED_FIELDS	2,012
MISSING_NUTRITION	425
NON_FOOD	394
SUSPICIOUS_NUTRITION	2

Agribalyse Confidence

Confidence	Count	%
HIGH	1,908	43.0%
MEDIUM	885	19.9%
LOW	1,310	29.5%
NONE	337	7.6%

Validation

All 364 scored products independently verified correct
Non-food scored: 0
Unknown scored: 0
Suspicious scored: 0
Deterministic: Yes (threshold-based, no randomness)
LLM usage: Zero
Reproducible: Yes (re-runs produce identical output)

Known Limitations

FVL estimated from taxonomy (not actual data)
Agribalyse is category-level proxy (not product-specific LCA)
Only general_food + beverages categories implemented
66.2% of products have no per-100g normalization (cannot be scored)
Non-food/unknown domain excluded from scoring

Downloads last month: 81