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.gitattributes

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.gitignore

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-/__pycache__

MultiPL-E.py

@@ -1,121 +0,0 @@
-import json
-import datasets
-from pathlib import Path
-
-logger = datasets.logging.get_logger(__name__)
-
-_CITATION = """\
-@misc{multipl-e,
-  doi = {10.48550/ARXIV.2208.08227},
-  url = {https://arxiv.org/abs/2208.08227},
-  author = {Cassano, Federico and Gouwar, John and Nguyen, Daniel and
-            Nguyen, Sydney and Phipps-Costin, Luna and Pinckney, Donald and 
-            Yee, Ming-Ho and Zi, Yangtian and Anderson, Carolyn Jane and 
-            Feldman, Molly Q and Guha, Arjun and 
-            Greenberg, Michael and Jangda, Abhinav},
-  title = {A Scalable and Extensible Approach to Benchmarking NL2Code for 18
-           Programming Languages},
-  publisher = {arXiv},
-  year = {2022},
-}
-"""
-
-_DESCRIPTION = """\
-MultiPL-E is a dataset for evaluating large language models for code \
-generation that supports 18 programming languages. It takes the OpenAI \
-"HumanEval" and the MBPP Python benchmarks and uses little compilers to \
-translate them to other languages. It is easy to add support for new languages \
-and benchmarks.
-"""
-
-_SRCDATA = [ "humaneval", "mbpp" ]
-
-_LANGUAGES = [ 
-    "cpp", "cs", "d", "go", "java", "jl", "js", "lua", "php", "pl", "py", "r", 
-    "rb", "rkt", "rs", "scala", "sh", "swift", "ts"
-]
-
-_VARIATIONS = [ "keep", "transform", "reworded", "remove" ]
-
-class MultiPLEBuilderConfig(datasets.BuilderConfig):
-    """BuilderConfig for MultiPLEBuilderConfig."""
-
-    def __init__(
-        self,
-        srcdata,
-        language,
-        variation,
-        **kwargs,
-    ):
-        self.language = language
-        self.variation = variation
-        self.srcdata = srcdata
-        name = f"{srcdata}-{language}"
-        if variation != "reworded":
-            name = f"{name}-{variation}"
-        kwargs["name"] = name
-        super(MultiPLEBuilderConfig, self).__init__(**kwargs)
-
-def _is_interesting(srcdata: str, variation: str):
-    if srcdata == "humaneval":
-        return True
-    if srcdata == "mbpp":
-        # MBPP does not have doctests, so these are the only interesting
-        # variations
-        return variation in [ "keep", "reworded" ]
-
-class MultiPLE(datasets.GeneratorBasedBuilder):
-    BUILDER_CONFIG_CLASS = MultiPLEBuilderConfig
-
-    BUILDER_CONFIGS = [
-        MultiPLEBuilderConfig(
-            srcdata=srcdata,
-            language=language,
-            variation=variation,
-            version=datasets.Version("2.0.0"))
-        for srcdata in _SRCDATA
-        for language in _LANGUAGES 
-        for variation in _VARIATIONS
-        if _is_interesting(srcdata, variation)
-    ]
-
-    DEFAULT_CONFIG_NAME = "humaneval-cpp"
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            license="MIT",
-            features=datasets.Features({
-                "name": datasets.Value("string"),
-                "language": datasets.Value("string"),
-                "prompt": datasets.Value("string"),
-                "doctests": datasets.Value("string"),
-                "original": datasets.Value("string"),
-                "prompt_terminology": datasets.Value("string"),
-                "tests": datasets.Value("string"),
-                "stop_tokens": datasets.features.Sequence(datasets.Value("string")),
-            }),
-            supervised_keys=None,
-            homepage="https://nuprl.github.io/MultiPL-E/",
-            citation=_CITATION,
-            task_templates=[]
-        )
-
-    def _split_generators(self, dl_manager: datasets.DownloadManager):
-        files = dl_manager.download(
-            f"https://raw.githubusercontent.com/nuprl/MultiPL-E/1f21818a0f3265fd0a41c3954e30aab47f34063a/prompts/{self.config.srcdata}-{self.config.language}-{self.config.variation}.json"
-        )
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={
-                    "filepath": files,
-                }
-            )
-        ]
-
-    def _generate_examples(self, filepath):
-        with open(filepath, encoding="utf-8") as f:
-            data = json.load(f)
-            for id_, row in enumerate(data):
-                yield id_, row

README.md

@@ -1,107 +0,0 @@
----
-annotations_creators:
-- machine-generated
-language:
-- en
-language_creators:
-- machine-generated
-- expert-generated
-license:
-- mit
-multilinguality:
-- monolingual
-pretty_name: MultiPLE-E
-size_categories:
-- 1K<n<10K
-source_datasets:
-- original
-- extended|openai_humaneval
-- extended|mbpp
-tags: []
-task_categories: []
-task_ids: []
----
-
-# Dataset Card for MultiPL-E
-
-## Dataset Description
-
-- **Homepage:**  https://nuprl.github.io/MultiPL-E/
-- **Repository:**  https://github.com/nuprl/MultiPL-E
-- **Paper:** https://arxiv.org/abs/2208.08227
-- **Point of Contact:** carolyn.anderson@wellesley.edu, mfeldman@oberlin.edu, a.guha@northeastern.edu
-
-## Dataset Summary
-
-MultiPL-E is a dataset for evaluating large language models for code
-generation that supports 18 programming languages. It takes the OpenAI 
-"HumanEval" and the MBPP Python benchmarks and uses little compilers to
-translate them  to other languages. It is easy to add support for new languages 
-and benchmarks.
-
-## Subsets
-
-For most purposes, you should use the variations called *SRCDATA-LANG*, where
-*SRCDATA* is either "humaneval" or "mbpp" and *LANG* is one of the supported
-languages. We use the canonical file extension for each language to identify
-the language, e.g., "py" for Python, "cpp" for C++, "lua" for Lua, and so on.
-
-We also provide a few other variations:
-
-- *SRCDATA-LANG-keep* is the same as *SRCDATA-LANG*, but the text of the prompt
-  is totally unchanged. If the original prompt had Python doctests, they remain
-  as Python instead of being translated to *LANG*. If the original prompt had 
-  Python-specific terminology, e.g., "list", it remains "list", instead of 
-  being translated, e.g., to "vector" for C++.
-
-- *SRCDATA-LANG-transform* transforms the doctests to *LANG* but leaves
-  the natural language text of the prompt unchanged.
-
-- *SRCDATA-LANG-removed* removes the doctests from the prompt.
-
-Note that MBPP does not have any doctests, so the "removed" and "transform"
-variations are not available for MBPP.
-
-## Example
-
-The following script uses the Salesforce/codegen model to generate Lua
-and MultiPL-E to produce a script with unit tests for luaunit.
-
-```python
-import datasets
-from transformers import AutoTokenizer, AutoModelForCausalLM
-
-LANG = "lua"
-MODEL_NAME = "Salesforce/codegen-350M-multi"
-tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
-model = AutoModelForCausalLM.from_pretrained(MODEL_NAME).half().cuda()
-problems = datasets.load_dataset("nuprl/MultiPL-E", f"humaneval-{LANG}")
-
-def stop_at_stop_token(decoded_string, problem):
-    """
-    Truncates the output at stop tokens, taking care to skip the prompt
-    which may have stop tokens.
-    """
-    min_stop_index = len(decoded_string)
-    for stop_token in problem["stop_tokens"]:
-        stop_index = decoded_string.find(stop_token)
-        if stop_index != -1 and stop_index > len(problem["prompt"]) and stop_index < min_stop_index:
-            min_stop_index = stop_index
-    return decoded_string[:min_stop_index]
-
-for problem in problems["test"]:
-    input_ids = tokenizer(
-        problem["prompt"],
-        return_tensors="pt",
-    ).input_ids.cuda()
-    generated_ids = model.generate(
-        input_ids, max_length=512, pad_token_id=tokenizer.eos_token_id + 2
-    )
-    truncated_string = stop_at_stop_token(tokenizer.decode(generated_ids[0]), problem)
-    filename = problem["name"] + "." + LANG
-    with open(filename, "w") as f:
-        print(f"Created {filename}")
-        f.write(truncated_string)
-        f.write("\n")
-        f.write(problem["tests"])
-```

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