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Update Space (evaluate main: 828c6327)
Browse files- README.md +110 -5
- app.py +6 -0
- competition_math.py +95 -0
- requirements.txt +4 -0
README.md
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---
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title:
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sdk: gradio
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sdk_version: 3.0.2
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app_file: app.py
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pinned: false
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---
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-
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---
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title: Competition MATH
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emoji: 🤗
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colorFrom: blue
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colorTo: red
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sdk: gradio
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sdk_version: 3.0.2
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app_file: app.py
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pinned: false
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tags:
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- evaluate
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- metric
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---
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# Metric Card for Competition MATH
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## Metric description
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This metric is used to assess performance on the [Mathematics Aptitude Test of Heuristics (MATH) dataset](https://huggingface.co/datasets/competition_math).
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It first canonicalizes the inputs (e.g., converting `1/2` to `\\frac{1}{2}`) and then computes accuracy.
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## How to use
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This metric takes two arguments:
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`predictions`: a list of predictions to score. Each prediction is a string that contains natural language and LaTeX.
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`references`: list of reference for each prediction. Each reference is a string that contains natural language and LaTeX.
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```python
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>>> from evaluate import load
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>>> math = load("competition_math")
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>>> references = ["\\frac{1}{2}"]
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>>> predictions = ["1/2"]
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>>> results = math.compute(references=references, predictions=predictions)
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```
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N.B. To be able to use Competition MATH, you need to install the `math_equivalence` dependency using `pip install git+https://github.com/hendrycks/math.git`.
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## Output values
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This metric returns a dictionary that contains the [accuracy](https://huggingface.co/metrics/accuracy) after canonicalizing inputs, on a scale between 0.0 and 1.0.
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### Values from popular papers
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The [original MATH dataset paper](https://arxiv.org/abs/2103.03874) reported accuracies ranging from 3.0% to 6.9% by different large language models.
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More recent progress on the dataset can be found on the [dataset leaderboard](https://paperswithcode.com/sota/math-word-problem-solving-on-math).
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## Examples
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Maximal values (full match):
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```python
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>>> from evaluate import load
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>>> math = load("competition_math")
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>>> references = ["\\frac{1}{2}"]
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>>> predictions = ["1/2"]
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>>> results = math.compute(references=references, predictions=predictions)
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>>> print(results)
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{'accuracy': 1.0}
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```
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Minimal values (no match):
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```python
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>>> from evaluate import load
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>>> math = load("competition_math")
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>>> references = ["\\frac{1}{2}"]
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>>> predictions = ["3/4"]
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>>> results = math.compute(references=references, predictions=predictions)
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>>> print(results)
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{'accuracy': 0.0}
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```
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Partial match:
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```python
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>>> from evaluate import load
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>>> math = load("competition_math")
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>>> references = ["\\frac{1}{2}","\\frac{3}{4}"]
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>>> predictions = ["1/5", "3/4"]
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>>> results = math.compute(references=references, predictions=predictions)
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>>> print(results)
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{'accuracy': 0.5}
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```
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## Limitations and bias
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This metric is limited to datasets with the same format as the [Mathematics Aptitude Test of Heuristics (MATH) dataset](https://huggingface.co/datasets/competition_math), and is meant to evaluate the performance of large language models at solving mathematical problems.
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N.B. The MATH dataset also assigns levels of difficulty to different problems, so disagregating model performance by difficulty level (similarly to what was done in the [original paper](https://arxiv.org/abs/2103.03874) can give a better indication of how a given model does on a given difficulty of math problem, compared to overall accuracy.
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## Citation
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```bibtex
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@article{hendrycksmath2021,
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title={Measuring Mathematical Problem Solving With the MATH Dataset},
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author={Dan Hendrycks
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and Collin Burns
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and Saurav Kadavath
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and Akul Arora
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and Steven Basart
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and Eric Tang
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and Dawn Song
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and Jacob Steinhardt},
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journal={arXiv preprint arXiv:2103.03874},
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year={2021}
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}
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```
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## Further References
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- [MATH dataset](https://huggingface.co/datasets/competition_math)
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- [MATH leaderboard](https://paperswithcode.com/sota/math-word-problem-solving-on-math)
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- [MATH paper](https://arxiv.org/abs/2103.03874)
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app.py
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import evaluate
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from evaluate.utils import launch_gradio_widget
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module = evaluate.load("competition_math")
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launch_gradio_widget(module)
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competition_math.py
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# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Accuracy metric for the Mathematics Aptitude Test of Heuristics (MATH) dataset."""
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import datasets
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import math_equivalence # From: git+https://github.com/hendrycks/math.git
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import evaluate
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_CITATION = """\
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@article{hendrycksmath2021,
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title={Measuring Mathematical Problem Solving With the MATH Dataset},
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author={Dan Hendrycks
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and Collin Burns
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and Saurav Kadavath
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and Akul Arora
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and Steven Basart
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and Eric Tang
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and Dawn Song
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and Jacob Steinhardt},
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journal={arXiv preprint arXiv:2103.03874},
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year={2021}
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}
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"""
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_DESCRIPTION = """\
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This metric is used to assess performance on the Mathematics Aptitude Test of Heuristics (MATH) dataset.
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It first canonicalizes the inputs (e.g., converting "1/2" to "\\frac{1}{2}") and then computes accuracy.
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"""
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_KWARGS_DESCRIPTION = r"""
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Calculates accuracy after canonicalizing inputs.
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Args:
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predictions: list of predictions to score. Each prediction
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is a string that contains natural language and LaTex.
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references: list of reference for each prediction. Each
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reference is a string that contains natural language
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and LaTex.
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Returns:
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accuracy: accuracy after canonicalizing inputs
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(e.g., converting "1/2" to "\\frac{1}{2}")
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Examples:
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>>> metric = evaluate.load("competition_math")
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>>> results = metric.compute(references=["\\frac{1}{2}"], predictions=["1/2"])
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>>> print(results)
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{'accuracy': 1.0}
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"""
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@datasets.utils.file_utils.add_end_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
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class CompetitionMathMetric(evaluate.EvaluationModule):
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"""Accuracy metric for the MATH dataset."""
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def _info(self):
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return evaluate.EvaluationModuleInfo(
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description=_DESCRIPTION,
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citation=_CITATION,
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inputs_description=_KWARGS_DESCRIPTION,
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features=datasets.Features(
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{
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"predictions": datasets.Value("string"),
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"references": datasets.Value("string"),
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}
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),
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# Homepage of the metric for documentation
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homepage="https://github.com/hendrycks/math",
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# Additional links to the codebase or references
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codebase_urls=["https://github.com/hendrycks/math"],
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)
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def _compute(self, predictions, references):
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"""Returns the scores"""
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n_correct = 0.0
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for i, j in zip(predictions, references):
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n_correct += 1.0 if math_equivalence.is_equiv(i, j) else 0.0
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accuracy = n_correct / len(predictions)
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return {
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"accuracy": accuracy,
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}
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requirements.txt
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# TODO: fix github to release
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git+https://github.com/huggingface/evaluate.git@b6e6ed7f3e6844b297bff1b43a1b4be0709b9671
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datasets~=2.0
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git+https://github.com/hendrycks/math.git
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