The dataset viewer is not available for this split.
Error code: FeaturesError
Exception: ArrowInvalid
Message: JSON parse error: Invalid value. in row 0
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 324, in _generate_tables
df = pandas_read_json(f)
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 38, in pandas_read_json
return pd.read_json(path_or_buf, **kwargs)
~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 791, in read_json
json_reader = JsonReader(
path_or_buf,
...<16 lines>...
engine=engine,
)
File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 905, in __init__
self.data = self._preprocess_data(data)
~~~~~~~~~~~~~~~~~~~~~^^^^^^
File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 917, in _preprocess_data
data = data.read()
File "/usr/local/lib/python3.14/site-packages/datasets/utils/file_utils.py", line 839, in read_with_retries
out = read(*args, **kwargs)
File "<frozen codecs>", line 325, in decode
UnicodeDecodeError: 'utf-8' codec can't decode byte 0xc7 in position 15: invalid continuation byte
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 244, in compute_first_rows_from_streaming_response
iterable_dataset = iterable_dataset._resolve_features()
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 4523, in _resolve_features
features = _infer_features_from_batch(self.with_format(None)._head())
~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2768, in _head
return next(iter(self.iter(batch_size=n)))
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2972, in iter
for key, pa_table in ex_iterable.iter_arrow():
~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2483, in _iter_arrow
yield from self.ex_iterable._iter_arrow()
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 547, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 430, 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 327, in _generate_tables
raise e
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 290, in _generate_tables
pa_table = paj.read_json(
io.BytesIO(batch), read_options=paj.ReadOptions(block_size=block_size)
)
File "pyarrow/_json.pyx", line 364, in pyarrow._json.read_json
File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
return check_status(status)
File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
raise convert_status(status)
pyarrow.lib.ArrowInvalid: JSON parse error: Invalid value. in row 0Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
Maple Bank Collections Hackathon dataset (fully synthetic)
Dataset for the CIBC Collections Hackathon build phase. One fictional bank ("Maple Bank"), 1,000,000 customers (1,020,000 CRM records), October 2016 to September 2026, snapshot date 2026-09-28. Every person, account, call, recording and document is synthetic.
Files
| File | Size | What |
|---|---|---|
maple_collections_release.zip |
see file list | Start here. 31 tables (CSV + Parquet), transcripts (JSON), policy documents, schema, data dictionary, 500 public labels each for notes and transcripts |
maple_collections_voice.zip |
see file list | Optional. 2,000 synthetic call recordings (WAV) for voice_samples; unzip and copy its files/ folder into the release folder |
After unzipping, read maple_collections_release/README.md first: it lists every table, file conventions (nulls, NA
codes, leading-zero IDs), loading snippets for DuckDB / pandas / Spark, and the customer key used by each source system.
The full column reference is maple_collections_release/DATA_DICTIONARY.xlsx.
Download
pip install -U huggingface_hub
hf download nuxsh/maple-collections-hackathon --repo-type dataset --local-dir maple_data
cd maple_data && unzip maple_collections_release.zip
or download the zips from the Files and versions tab.
Highlights
- Five source systems, each with its own customer key: building one customer view (C360) is part of the challenge.
- Deliberate data-quality issues (duplicates, missing values, inconsistent date formats, mismatched IDs, schema drift).
- Structured history (account snapshots, card statements, loan instalments, transactions, salary credits, bureau pulls) plus agent notes, call transcripts (English and French) and recordings.
- Metric definitions, benchmark questions (including ones that should be refused) and policy documents for question-answering and RAG.
Benchmark (Layer 2)
data/benchmark_questions.csv lists the released questions. Gold answers for the dev split are in
labels/benchmark_dev_answers.csv; test answers are hidden. More questions are released at 19:00 IST on 4 October.
Submit answers in the format of labels/benchmark_answers_template.csv (see the release README).
Rules
Protected attributes (gender, marital status, citizenship, household, newcomer, accessibility, vulnerability, accent) are included only to test fairness and must not be used in decisions. Phone numbers are random and not real.
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