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Cannot load the dataset split (in normal download mode) to extract the first rows.
Error code:   NormalRowsError
Exception:    NonMatchingChecksumError
Message:      Checksums didn't match for dataset source files:
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/responses/", line 337, in get_first_rows_response
                  rows = get_rows(dataset, config, split, streaming=True, rows_max_number=rows_max_number, hf_token=hf_token)
                File "/src/services/worker/src/worker/", line 123, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/responses/", line 77, in get_rows
                  rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/", line 718, in __iter__
                  for key, example in self._iter():
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/", line 708, in _iter
                  yield from ex_iterable
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/", line 112, in __iter__
                  yield from self.generate_examples_fn(**self.kwargs)
                File "/tmp/modules-cache/datasets_modules/datasets/competition_math/2a2a2995c2847186883ecd64f69be7d602b8a6f6b51950624d4dc2263f93333b/", line 87, in _generate_examples
                  with open(filepath, "rb") as fin:
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/", line 67, in wrapper
                  return function(*args, use_auth_token=use_auth_token, **kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/download/", line 453, in xopen
                  file_obj =, mode=mode, *args, **kwargs).open()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/", line 441, in open
                  return open_files(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/", line 273, in open_files
                  fs, fs_token, paths = get_fs_token_paths(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/", line 606, in get_fs_token_paths
                  fs = filesystem(protocol, **inkwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/", line 268, in filesystem
                  return cls(**storage_options)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/", line 76, in __call__
                  obj = super().__call__(*args, **kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/implementations/", line 91, in __init__
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/implementations/", line 96, in _index
                  for ti in self.tar:
                File "/usr/local/lib/python3.9/", line 2443, in __iter__
                  tarinfo =
                File "/usr/local/lib/python3.9/", line 2320, in next
                  if not
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/implementations/", line 574, in read
                  return super().read(length)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/", line 1575, in read
                  out = self.cache._fetch(self.loc, self.loc + length)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/", line 394, in _fetch
                  new = self.fetcher(self.end, bend)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/", line 111, in wrapper
                  return sync(self.loop, func, *args, **kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/", line 96, in sync
                  raise return_result
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/", line 53, in _runner
                  result[0] = await coro
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/implementations/", line 613, in async_fetch_range
                File "/src/services/worker/.venv/lib/python3.9/site-packages/aiohttp/", line 1004, in raise_for_status
                  raise ClientResponseError(
              aiohttp.client_exceptions.ClientResponseError: 429, message='Too Many Requests', url=URL('')
              During handling of the above exception, another exception occurred:
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/responses/", line 345, in get_first_rows_response
                  rows = get_rows(
                File "/src/services/worker/src/worker/", line 123, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/responses/", line 65, in get_rows
                  ds = load_dataset(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/", line 1746, in load_dataset
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/", line 704, in download_and_prepare
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/", line 1227, in _download_and_prepare
                  super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/", line 775, in _download_and_prepare
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/utils/", line 40, in verify_checksums
                  raise NonMatchingChecksumError(error_msg + str(bad_urls))
              datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:

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Dataset Card for Mathematics Aptitude Test of Heuristics (MATH) dataset

Dataset Summary

The Mathematics Aptitude Test of Heuristics (MATH) dataset consists of problems from mathematics competitions, including the AMC 10, AMC 12, AIME, and more. Each problem in MATH has a full step-by-step solution, which can be used to teach models to generate answer derivations and explanations.

Supported Tasks and Leaderboards

[More Information Needed]


[More Information Needed]

Dataset Structure

Data Instances

A data instance consists of a competition math problem and its step-by-step solution written in LaTeX and natural language. The step-by-step solution contains the final answer enclosed in LaTeX's \boxed tag.

An example from the dataset is:

{'problem': 'A board game spinner is divided into three parts labeled $A$, $B$  and $C$. The probability of the spinner landing on $A$ is $\\frac{1}{3}$ and the probability of the spinner landing on $B$ is $\\frac{5}{12}$.  What is the probability of the spinner landing on $C$? Express your answer as a common fraction.',
 'level': 'Level 1',
 'type': 'Counting & Probability',
 'solution': 'The spinner is guaranteed to land on exactly one of the three regions, so we know that the sum of the probabilities of it landing in each region will be 1. If we let the probability of it landing in region $C$ be $x$, we then have the equation $1 = \\frac{5}{12}+\\frac{1}{3}+x$, from which we have $x=\\boxed{\\frac{1}{4}}$.'}

Data Fields

  • problem: The competition math problem.
  • solution: The step-by-step solution.
  • level: The problem's difficulty level from 'Level 1' to 'Level 5', where a subject's easiest problems for humans are assigned to 'Level 1' and a subject's hardest problems are assigned to 'Level 5'.
  • type: The subject of the problem: Algebra, Counting & Probability, Geometry, Intermediate Algebra, Number Theory, Prealgebra and Precalculus.

Data Splits

  • train: 7,500 examples
  • test: 5,000 examples

Dataset Creation

Curation Rationale

[More Information Needed]

Source Data

Initial Data Collection and Normalization

[More Information Needed]

Who are the source language producers?

[More Information Needed]


Annotation process

[More Information Needed]

Who are the annotators?

[More Information Needed]

Personal and Sensitive Information

[More Information Needed]

Considerations for Using the Data

Social Impact of Dataset

[More Information Needed]

Discussion of Biases

[More Information Needed]

Other Known Limitations

[More Information Needed]

Additional Information

Dataset Curators

[More Information Needed]

Licensing Information

Citation Information

    title={Measuring Mathematical Problem Solving With the MATH Dataset},
    author={Dan Hendrycks
    and Collin Burns
    and Saurav Kadavath
    and Akul Arora
    and Steven Basart
    and Eric Tang
    and Dawn Song
    and Jacob Steinhardt},
    journal={arXiv preprint arXiv:2103.03874},


Thanks to @hacobe for adding this dataset.

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