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README.md

@@ -1,179 +0,0 @@
----
-dataset_info:
-  features:
-  - name: task_id
-    dtype: string
-  - name: language
-    dtype: string
-  - name: prompt
-    dtype: string
-  - name: test
-    dtype: string
-  - name: entry_point
-    dtype: string
-  splits:
-  - name: multi-humaneval_python
-    num_bytes: 165716
-    num_examples: 164
-  download_size: 67983
-  dataset_size: 165716
-license: apache-2.0
-task_categories:
-- text-generation
-tags:
-- mxeval
-- code-generation
-- multi-humaneval
-- humaneval
-pretty_name: multi-humaneval
-language:
-- en
----
-# Multi-HumanEval
-
-## Table of Contents
-- [multi-humaneval](#multi-humaneval)
-  - [Table of Contents](#table-of-contents)
-  - [Dataset Description](#dataset-description)
-    - [Dataset Summary](#dataset-summary)
-    - [Supported Tasks and Leaderboards](#related-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)
-    - [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)
-  - [Additional Information](#additional-information)
-    - [Dataset Curators](#dataset-curators)
-    - [Licensing Information](#licensing-information)
-    - [Citation Information](#citation-information)
-    - [Contributions](#contributions)
-
-# multi-humaneval
-
-## Dataset Description
-
-- **Repository:** [GitHub Repository](https://github.com/amazon-science/mbxp-exec-eval)
-- **Paper:** [Multi-lingual Evaluation of Code Generation Models](https://openreview.net/forum?id=Bo7eeXm6An8)
-
-### Dataset Summary
-
-This repository contains data and code to perform execution-based multi-lingual evaluation of code generation capabilities and the corresponding data,
-namely, a multi-lingual benchmark MBXP, multi-lingual MathQA and multi-lingual HumanEval.
-<br>Results and findings can be found in the paper ["Multi-lingual Evaluation of Code Generation Models"](https://arxiv.org/abs/2210.14868).
-
-
-### Related Tasks and Leaderboards
-* [Multi-HumanEval](https://huggingface.co/datasets/mxeval/multi-humaneval)
-* [MBXP](https://huggingface.co/datasets/mxeval/mbxp)
-* [MathQAX](https://huggingface.co/datasets/mxeval/mathqax)
-
-### Languages
-The programming problems are written in multiple programming languages and contain English natural text in comments and docstrings.
-
-
-## Dataset Structure
-To lookup currently supported datasets
-```python
-get_dataset_config_names("mxeval/multi-humaneval")
-['python', 'csharp', 'go', 'java', 'javascript', 'kotlin', 'perl', 'php', 'ruby', 'scala', 'swift', 'typescript']
-
-```
-To load a specific dataset and language
-```python
-from datasets import load_dataset
-load_dataset("mxeval/multi-humaneval", "python")
-DatasetDict({
-    test: Dataset({
-        features: ['task_id', 'language', 'prompt', 'test', 'entry_point', 'canonical_solution', 'description'],
-        num_rows: 164
-    })
-})
-```
-
-### Data Instances
-
-An example of a dataset instance:
-
-```python
-{
-  "task_id": "HumanEval/0",
-  "language": "python",
-  "prompt": "from typing import List\n\n\ndef has_close_elements(numbers: List[float], threshold: float) -> bool:\n    \"\"\" Check if in given list of numbers, are any two numbers closer to each other than\n    given threshold.\n    >>> has_close_elements([1.0, 2.0, 3.0], 0.5)\n    False\n    >>> has_close_elements([1.0, 2.8, 3.0, 4.0, 5.0, 2.0], 0.3)\n    True\n    \"\"\"\n",
-  "test": "\n\nMETADATA = {\n    \"author\": \"jt\",\n    \"dataset\": \"test\"\n}\n\n\ndef check(candidate):\n    assert candidate([1.0, 2.0, 3.9, 4.0, 5.0, 2.2], 0.3) == True\n    assert candidate([1.0, 2.0, 3.9, 4.0, 5.0, 2.2], 0.05) == False\n    assert candidate([1.0, 2.0, 5.9, 4.0, 5.0], 0.95) == True\n    assert candidate([1.0, 2.0, 5.9, 4.0, 5.0], 0.8) == False\n    assert candidate([1.0, 2.0, 3.0, 4.0, 5.0, 2.0], 0.1) == True\n    assert candidate([1.1, 2.2, 3.1, 4.1, 5.1], 1.0) == True\n    assert candidate([1.1, 2.2, 3.1, 4.1, 5.1], 0.5) == False\n\n",
-  "entry_point": "has_close_elements",
-  "canonical_solution": "    for idx, elem in enumerate(numbers):\n        for idx2, elem2 in enumerate(numbers):\n            if idx != idx2:\n                distance = abs(elem - elem2)\n                if distance < threshold:\n                    return True\n\n    return False\n",
-  "description": "Check if in given list of numbers, are any two numbers closer to each other than\n    given threshold.\n    >>> has_close_elements([1.0, 2.0, 3.0], 0.5)\n    False\n    >>> has_close_elements([1.0, 2.8, 3.0, 4.0, 5.0, 2.0], 0.3)\n    True"
-}
-```
-
-### Data Fields
-
-- `task_id`: identifier for the data sample
-- `prompt`: input for the model containing function header and docstrings
-- `canonical_solution`: solution for the problem in the `prompt`
-- `description`: task description
-- `test`: contains function to test generated code for correctness
-- `entry_point`: entry point for test
-- `language`: programming lanuage identifier to call the appropriate subprocess call for program execution
-
-
-### Data Splits
-
- - HumanXEval
-   - Python
-   - Csharp
-   - Go
-   - Java
-   - Javascript
-   - Kotlin
-   - Perl
-   - Php
-   - Ruby
-   - Scala
-   - Swift
-   - Typescript
-
-## Dataset Creation
-
-### Curation Rationale
-
-Since code generation models are often trained on dumps of GitHub a dataset not included in the dump was necessary to properly evaluate the model. However, since this dataset was published on GitHub it is likely to be included in future dumps.
-
-### Personal and Sensitive Information
-
-None.
-
-## Considerations for Using the Data
-Make sure to sandbox the execution environment.
-
-### Social Impact of Dataset
-With this dataset code generating models can be better evaluated which leads to fewer issues introduced when using such models.
-
-### Dataset Curators
-AWS AI Labs
-
-### Licensing Information
-
-[LICENSE](https://huggingface.co/datasets/mxeval/multi-humaneval/blob/main/multi-humaneval-LICENSE) <br>
-[THIRD PARTY LICENSES](https://huggingface.co/datasets/mxeval/multi-humaneval/blob/main/THIRD_PARTY_LICENSES)
-
-### Citation Information
-```
-@inproceedings{
-athiwaratkun2023multilingual,
-title={Multi-lingual Evaluation of Code Generation Models},
-author={Ben Athiwaratkun and Sanjay Krishna Gouda and Zijian Wang and Xiaopeng Li and Yuchen Tian and Ming Tan and Wasi Uddin Ahmad and Shiqi Wang and Qing Sun and Mingyue Shang and Sujan Kumar Gonugondla and Hantian Ding and Varun Kumar and Nathan Fulton and Arash Farahani and Siddhartha Jain and Robert Giaquinto and Haifeng Qian and Murali Krishna Ramanathan and Ramesh Nallapati and Baishakhi Ray and Parminder Bhatia and Sudipta Sengupta and Dan Roth and Bing Xiang},
-booktitle={The Eleventh International Conference on Learning Representations },
-year={2023},
-url={https://openreview.net/forum?id=Bo7eeXm6An8}
-}
-```
-
-### Contributions
-
-[skgouda@](https://github.com/sk-g) [benathi@](https://github.com/benathi)

THIRD_PARTY_LICENSES

@@ -1,23 +0,0 @@
-The amazon/multi-humaneval repository includes the following third-party software/licensing:
-
-** (1) In multi-humaneval, we release Multi-lingual HumanEval where we adapt the HumanEval dataset by OpenAI into multiple datasets in different programming languages. The original code and dataset are from https://github.com/openai/human-eval/tree/master/data.
-
-The MIT License
-
-Permission is hereby granted, free of charge, to any person obtaining a copy
-of this software and associated documentation files (the "Software"), to deal
-in the Software without restriction, including without limitation the rights
-to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-copies of the Software, and to permit persons to whom the Software is
-furnished to do so, subject to the following conditions:
-
-The above copyright notice and this permission notice shall be included in
-all copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
-THE SOFTWARE.

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multi-humaneval-LICENSE

@@ -1,198 +0,0 @@
-
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-
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multi-humaneval.py

@@ -1,128 +0,0 @@
-import json
-import os
-import requests
-import datasets
-
-import os
-from collections import defaultdict
-
-_CITATION = """\
-@inproceedings{
-    athiwaratkun2023multilingual,
-    title={Multi-lingual Evaluation of Code Generation Models},
-    author={Ben Athiwaratkun and Sanjay Krishna Gouda and Zijian Wang and Xiaopeng Li and Yuchen Tian and Ming Tan and Wasi Uddin Ahmad and Shiqi Wang and Qing Sun and Mingyue Shang and Sujan Kumar Gonugondla and Hantian Ding and Varun Kumar and Nathan Fulton and Arash Farahani and Siddhartha Jain and Robert Giaquinto and Haifeng Qian and Murali Krishna Ramanathan and Ramesh Nallapati and Baishakhi Ray and Parminder Bhatia and Sudipta Sengupta and Dan Roth and Bing Xiang},
-    booktitle={The Eleventh International Conference on Learning Representations },
-    year={2023},
-    url={https://openreview.net/forum?id=Bo7eeXm6An8}
-}"""
-
-VERSION=f"1.1.0"
-
-_HOMEPAGE = "https://github.com/amazon-science/mbxp-exec-eval"
-
-_LICENSE = "Apache License 2.0"
-
-_DESCRIPTION = """\
-A collection of execution-based multi-lingual benchmark for code generation.
-"""
-
-_LICENSES = defaultdict(lambda: _LICENSE)
-_LICENSES["python"] = "MIT License"
-
-_CITATIONS = defaultdict(lambda: _CITATION)
-
-_CITATIONS["python"] = """\
-@misc{chen2021evaluating,
-      title={Evaluating Large Language Models Trained on Code},
-      author={Mark Chen and Jerry Tworek and Heewoo Jun and Qiming Yuan and Henrique Ponde de Oliveira Pinto and Jared Kaplan and Harri Edwards and Yuri Burda and Nicholas Joseph and Greg Brockman and Alex Ray and Raul Puri and Gretchen Krueger and Michael Petrov and Heidy Khlaaf and Girish Sastry and Pamela Mishkin and Brooke Chan and Scott Gray and Nick Ryder and Mikhail Pavlov and Alethea Power and Lukasz Kaiser and Mohammad Bavarian and Clemens Winter and Philippe Tillet and Felipe Petroski Such and Dave Cummings and Matthias Plappert and Fotios Chantzis and Elizabeth Barnes and Ariel Herbert-Voss and William Hebgen Guss and Alex Nichol and Alex Paino and Nikolas Tezak and Jie Tang and Igor Babuschkin and Suchir Balaji and Shantanu Jain and William Saunders and Christopher Hesse and Andrew N. Carr and Jan Leike and Josh Achiam and Vedant Misra and Evan Morikawa and Alec Radford and Matthew Knight and Miles Brundage and Mira Murati and Katie Mayer and Peter Welinder and Bob McGrew and Dario Amodei and Sam McCandlish and Ilya Sutskever and Wojciech Zaremba},
-      year={2021},
-      eprint={2107.03374},
-      archivePrefix={arXiv},
-      primaryClass={cs.LG}
-}"""
-
-_GITHUB_ROOT = "https://raw.githubusercontent.com/amazon-science/mbxp-exec-eval/verify_files/data/multilingual_humaneval/"
-
-metadata_dict_path = requests.get(os.path.join(_GITHUB_ROOT, "metadata.json"))
-metadata = json.loads(metadata_dict_path.text)
-
-class MultiHumanEvalConfig(datasets.BuilderConfig):
-    """BuilderConfig for MultiHumanEval."""
-
-    def __init__(
-        self,
-        language,
-        data_url,
-        citation,
-        version,
-        **kwargs,
-    ):
-        super(MultiHumanEvalConfig, self).__init__(version=datasets.Version(f"{version}", ""), **kwargs)
-        self.name = language
-        self.data_url = data_url
-        self.citation = citation
-
-
-class MultiHumanEval(datasets.GeneratorBasedBuilder):
-    """MultiHumanEval: An execution-based multi-lingual HumanEval benchmark for code generation."""
-
-    BUILDER_CONFIGS = [
-        MultiHumanEvalConfig(
-            name=f"{language}",
-            language=f"{language}",
-            version=VERSION,
-            citation=_CITATIONS[f"{language}"],
-            description=f"HumanEval benchmark in {language}",
-            data_url=os.path.join(_GITHUB_ROOT, language_path)
-        ) for language, language_path in metadata.items()
-    ]
-
-    def _info(self):
-        self.build_name = self.name
-        features = datasets.Features(
-            {
-                "task_id": datasets.Value("string"),
-                "language": datasets.Value("string"),
-                "prompt": datasets.Value("string"),
-                "test": datasets.Value("string"),
-                "entry_point": datasets.Value("string"),
-                "canonical_solution": datasets.Value("string"),
-                "description": datasets.Value("string"),
-            }
-        )
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=features,
-            supervised_keys=None,
-            homepage=_HOMEPAGE,
-            license=_LICENSES[self.config.name],
-            citation=_CITATIONS[self.config.name],
-        )
-
-
-    def _split_generators(
-            self, dl_manager
-    ):
-        """Returns SplitGenerators."""
-        data_file = dl_manager.download_and_extract(url_or_urls=self.config.data_url)
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={
-                    "filepath": data_file,
-                },
-            )
-        ]
-
-    
-    def _generate_examples(self, filepath):
-        """Yields examples."""
-        with open(filepath) as file:
-            data = []
-            for line in file:
-                jd = json.loads(line)
-                data.append(jd)
-            id_ = 0
-            for sample in data:
-                yield id_, sample
-                id_ += 1

perl/multi-humaneval-test.parquet

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