Duplicate from Muennighoff/mbpp

Co-authored-by: Niklas Muennighoff <Muennighoff@users.noreply.huggingface.co>

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README.md

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+---
+annotations_creators:
+- crowdsourced
+- expert-generated
+language_creators:
+- crowdsourced
+- expert-generated
+language:
+- en
+license:
+- cc-by-4.0
+multilinguality:
+- monolingual
+size_categories:
+- n<1K
+source_datasets:
+- original
+task_categories:
+- text2text-generation
+task_ids: []
+pretty_name: Mostly Basic Python Problems
+tags:
+- code-generation
+---
+
+# Dataset Card for Mostly Basic Python Problems (mbpp)
+
+## Table of Contents
+- [Dataset Card for Mostly Basic Python Problems (mbpp)](#dataset-card-for-mostly-basic-python-problems-(mbpp))
+  - [Table of Contents](#table-of-contents)
+  - [Dataset Description](#dataset-description)
+    - [Dataset Summary](#dataset-summary)
+    - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+    - [Languages](#languages)
+  - [Dataset Structure](#dataset-structure)
+    - [Data Instances](#data-instances)
+    - [Data Fields](#data-fields)
+    - [Data Splits](#data-splits)
+  - [Dataset Creation](#dataset-creation)
+    - [Curation Rationale](#curation-rationale)
+    - [Source Data](#source-data)
+      - [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
+      - [Who are the source language producers?](#who-are-the-source-language-producers)
+    - [Annotations](#annotations)
+      - [Annotation process](#annotation-process)
+      - [Who are the annotators?](#who-are-the-annotators)
+    - [Personal and Sensitive Information](#personal-and-sensitive-information)
+  - [Considerations for Using the Data](#considerations-for-using-the-data)
+    - [Social Impact of Dataset](#social-impact-of-dataset)
+    - [Discussion of Biases](#discussion-of-biases)
+    - [Other Known Limitations](#other-known-limitations)
+  - [Additional Information](#additional-information)
+    - [Dataset Curators](#dataset-curators)
+    - [Licensing Information](#licensing-information)
+    - [Citation Information](#citation-information)
+    - [Contributions](#contributions)
+
+## Dataset Description
+- **Repository:** https://github.com/google-research/google-research/tree/master/mbpp
+- **Paper:** [Program Synthesis with Large Language Models](https://arxiv.org/abs/2108.07732)
+
+### Dataset Summary
+The benchmark consists of around 1,000 crowd-sourced Python programming problems, designed to be solvable by entry level programmers, covering programming fundamentals, standard library functionality, and so on. Each problem consists of a task description, code solution and 3 automated test cases. As described in the paper, a subset of the data has been hand-verified by us. 
+
+Released [here](https://github.com/google-research/google-research/tree/master/mbpp) as part of [Program Synthesis with Large Language Models, Austin et. al., 2021](https://arxiv.org/abs/2108.07732).
+
+### Supported Tasks and Leaderboards
+This dataset is used to evaluate code generations.
+
+### Languages
+English - Python code
+
+## Dataset Structure
+
+```python
+dataset_full = load_dataset("mbpp")
+DatasetDict({
+    test: Dataset({
+        features: ['task_id', 'text', 'code', 'test_list', 'test_setup_code', 'challenge_test_list'],
+        num_rows: 974
+    })
+})
+
+dataset_sanitized = load_dataset("mbpp", "sanitized")
+DatasetDict({
+    test: Dataset({
+        features: ['source_file', 'task_id', 'prompt', 'code', 'test_imports', 'test_list'],
+        num_rows: 427
+    })
+})
+```
+
+### Data Instances
+
+#### mbpp - full
+```
+{
+    'task_id': 1,
+    'text': 'Write a function to find the minimum cost path to reach (m, n) from (0, 0) for the given cost matrix cost[][] and a position (m, n) in cost[][].',
+    'code': 'R = 3\r\nC = 3\r\ndef min_cost(cost, m, n): \r\n\ttc = [[0 for x in range(C)] for x in range(R)] \r\n\ttc[0][0] = cost[0][0] \r\n\tfor i in range(1, m+1): \r\n\t\ttc[i][0] = tc[i-1][0] + cost[i][0] \r\n\tfor j in range(1, n+1): \r\n\t\ttc[0][j] = tc[0][j-1] + cost[0][j] \r\n\tfor i in range(1, m+1): \r\n\t\tfor j in range(1, n+1): \r\n\t\t\ttc[i][j] = min(tc[i-1][j-1], tc[i-1][j], tc[i][j-1]) + cost[i][j] \r\n\treturn tc[m][n]',
+    'test_list': [
+        'assert min_cost([[1, 2, 3], [4, 8, 2], [1, 5, 3]], 2, 2) == 8',
+        'assert min_cost([[2, 3, 4], [5, 9, 3], [2, 6, 4]], 2, 2) == 12',
+        'assert min_cost([[3, 4, 5], [6, 10, 4], [3, 7, 5]], 2, 2) == 16'],
+    'test_setup_code': '',
+    'challenge_test_list': []
+}
+```
+#### mbpp - sanitized
+```
+{
+    'source_file': 'Benchmark Questions Verification V2.ipynb',
+    'task_id': 2,
+    'prompt': 'Write a function to find the shared elements from the given two lists.',
+    'code': 'def similar_elements(test_tup1, test_tup2):\n  res = tuple(set(test_tup1) & set(test_tup2))\n  return (res) ',
+    'test_imports': [],
+    'test_list': [
+        'assert set(similar_elements((3, 4, 5, 6),(5, 7, 4, 10))) == set((4, 5))',
+        'assert set(similar_elements((1, 2, 3, 4),(5, 4, 3, 7))) == set((3, 4))',
+        'assert set(similar_elements((11, 12, 14, 13),(17, 15, 14, 13))) == set((13, 14))'
+        ]
+}
+```
+### Data Fields
+
+- `source_file`: unknown
+- `text`/`prompt`: description of programming task
+- `code`: solution for programming task
+- `test_setup_code`/`test_imports`: necessary code imports to execute tests
+- `test_list`: list of tests to verify solution
+- `challenge_test_list`: list of more challenging test to further probe solution
+
+### Data Splits
+There are two version of the dataset (full and sanitized) which only one split each (test).
+## Dataset Creation
+See section 2.1 of original [paper](https://arxiv.org/abs/2108.07732).
+
+### Curation Rationale
+In order to evaluate code generation functions a set of simple programming tasks as well as solutions is necessary which this dataset provides.
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+The dataset was manually created from scratch.
+
+#### Who are the source language producers?
+The dataset was created with an internal crowdsourcing effort at Google.
+
+### Annotations
+
+#### Annotation process
+The full dataset was created first and a subset then underwent a second round to improve the task descriptions.
+
+#### Who are the annotators?
+The dataset was created with an internal crowdsourcing effort at Google.
+
+### Personal and Sensitive Information
+None.
+
+## Considerations for Using the Data
+Make sure you execute generated Python code in a safe environment when evauating against this dataset as generated code could be harmful.
+
+### Social Impact of Dataset
+With this dataset code generating models can be better evaluated which leads to fewer issues introduced when using such models.
+
+### Discussion of Biases
+
+### Other Known Limitations
+Since the task descriptions might not be expressive enough to solve the task. The `sanitized` split aims at addressing this issue by having a second round of annotators improve the dataset.
+
+## Additional Information
+
+### Dataset Curators
+Google Research
+
+### Licensing Information
+CC-BY-4.0
+
+### Citation Information
+```
+@article{austin2021program,
+  title={Program Synthesis with Large Language Models},
+  author={Austin, Jacob and Odena, Augustus and Nye, Maxwell and Bosma, Maarten and Michalewski, Henryk and Dohan, David and Jiang, Ellen and Cai, Carrie and Terry, Michael and Le, Quoc and others},
+  journal={arXiv preprint arXiv:2108.07732},
+  year={2021}
+```
+### Contributions
+Thanks to [@lvwerra](https://github.com/lvwerra) for adding this dataset.
+

mbpp.py

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+import json
+
+import datasets
+
+
+_DESCRIPTION = """\
+The MBPP (Mostly Basic Python Problems) dataset consists of around 1,000 crowd-sourced Python
+programming problems, designed to be solvable by entry level programmers, covering programming
+fundamentals, standard library functionality, and so on. Each problem consists of a task
+description, code solution and 3 automated test cases.
+"""
+
+_URLs = {
+    "full": "https://huggingface.co/datasets/Muennighoff/mbpp/resolve/main/data/mbpp.jsonl",
+    "sanitized": "https://huggingface.co/datasets/Muennighoff/mbpp/resolve/main/data/sanitized-mbpp.json",
+}
+
+_SPLITS = ["full", "sanitized"]
+
+_CITATION = """\
+@article{austin2021program,
+  title={Program Synthesis with Large Language Models},
+  author={Austin, Jacob and Odena, Augustus and Nye, Maxwell and Bosma, Maarten and Michalewski, Henryk and Dohan, David and Jiang, Ellen and Cai, Carrie and Terry, Michael and Le, Quoc and others},
+  journal={arXiv preprint arXiv:2108.07732},
+  year={2021}
+}"""
+
+_HOMEPAGE = "https://github.com/google-research/google-research/tree/master/mbpp"
+
+_LICENSE = "CC-BY-4.0"
+
+
+class MBPP(datasets.GeneratorBasedBuilder):
+    """MBPP: Mostly Basic Python Problems Dataset"""
+
+    VERSION = datasets.Version("1.0.0")
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(
+            name=f"{split}",
+            version=datasets.Version("1.0.0"),
+            description=_DESCRIPTION,
+        )
+        for split in _SPLITS
+    ]
+
+    DEFAULT_CONFIG_NAME = "full"
+
+    def _info(self):
+        if self.config.name == "full":
+            features = datasets.Features(
+                {
+                    "task_id": datasets.Value("int32"),
+                    "text": datasets.Value("string"),
+                    "code": datasets.Value("string"),
+                    "test_list": datasets.Sequence(datasets.Value("string")),
+                    "test_setup_code": datasets.Value("string"),
+                    "challenge_test_list": datasets.Sequence(datasets.Value("string")),
+                }
+            )
+        else:
+            features = datasets.Features(
+                {
+                    "source_file": datasets.Value("string"),
+                    "task_id": datasets.Value("int32"),
+                    "prompt": datasets.Value("string"),
+                    "code": datasets.Value("string"),
+                    "test_imports": datasets.Sequence(datasets.Value("string")),
+                    "test_list": datasets.Sequence(datasets.Value("string")),
+                }
+            )
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        config_urls = _URLs[self.config.name]
+        data_dir = dl_manager.download_and_extract(config_urls)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepath": data_dir,
+                },
+            )
+        ]
+
+    def _generate_examples(self, filepath):
+        """Yields examples."""
+        with open(filepath, encoding="utf-8") as file:
+            if self.config.name == "full":
+                data = [json.loads(line) for line in file]
+            else:
+                data = json.load(file)
+            id_ = 0
+            for sample in data:
+                yield id_, sample
+                id_ += 1