Update parquet files

tldr-docs.jsonl → data/tldr-test.parquet

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data/tldr-train.parquet

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data/tldr-validation.parquet

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docs/tldr-train.parquet

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tldr.py

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-# coding=utf-8
-# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""CoNaLa dataset."""
-
-import json
-import datasets
-
-
-_CITATION = """\
-@article{zhou2022doccoder,
-  title={DocCoder: Generating Code by Retrieving and Reading Docs},
-  author={Zhou, Shuyan and Alon, Uri and Xu, Frank F and JIang, Zhengbao and Neubig, Graham},
-  journal={arXiv preprint arXiv:2207.05987},
-  year={2022}
-}
-"""
-
-_DESCRIPTION = """This is the re-split of CoNaLa dataset. For each code snippet in the dev and test set, at least one function is held out from the training set. This split aims at testing a code generation model's capacity in generating unseen functions.
-We further make sure that examples from the same StackOverflow post (same question_id before -) are in the same split."""
-
-_HOMEPAGE = "https://github.com/shuyanzhou/docprompting"
-_URLs = {
-    "docs": "tldr-docs.jsonl",
-    "data": {"train": "tldr-train.jsonl", "validation": "tldr-dev.jsonl", "test": "tldr-test.jsonl" },
-}
-
-class DocPromptingConala(datasets.GeneratorBasedBuilder):
-    """TLDR natural language to bash generation dataset."""
-
-    VERSION = datasets.Version("1.1.0")
-
-
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(
-            name="data",
-            version=datasets.Version("1.1.0"),
-            description=_DESCRIPTION,
-        ),
-        datasets.BuilderConfig(name="docs", version=datasets.Version("1.1.0"), description=_DESCRIPTION),
-    ]
-
-    DEFAULT_CONFIG_NAME = "data"
-    
-    
-    def _info(self):
-        if self.config.name == "data":
-            features=datasets.Features({"question_id": datasets.Value("string"),
-                                            "nl": datasets.Value("string"),
-                                            "cmd": datasets.Value("string"),
-                                            "oracle_man": datasets.Sequence(feature=datasets.Value("string")), 
-                                            "cmd_name": datasets.Value("string"), 
-                                            "tldr_cmd_name": datasets.Value("string"), 
-                                            "manual_exist": datasets.Value("bool"),
-                                            "matching_info": datasets.Sequence(
-                                                {
-                                                    'token': datasets.Value("string"),
-                                                    'oracle_man': datasets.Sequence(feature=datasets.Value("string"))
-                                                }
-                                                )
-                                            })
-        else:
-            features=datasets.Features({"doc_id": datasets.Value("string"),
-                                            "doc_content": datasets.Value("string"),
-                                            })
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=features,
-            supervised_keys=None,
-            citation=_CITATION,
-            homepage=_HOMEPAGE)
-
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-        config_urls = _URLs[self.config.name]
-        data_dir = dl_manager.download_and_extract(config_urls)
-        if self.config.name == "data":
-            return [
-                datasets.SplitGenerator(
-                    name=datasets.Split.TRAIN,
-                    gen_kwargs={"filepath": data_dir["train"], "split": "train"},
-                ),
-                datasets.SplitGenerator(
-                    name=datasets.Split.TEST,
-                    gen_kwargs={"filepath": data_dir["test"], "split": "test"},
-                ),
-                datasets.SplitGenerator(
-                    name=datasets.Split.VALIDATION,
-                    gen_kwargs={"filepath": data_dir["validation"], "split": "validation"},
-                ),
-                ]
-        else:
-            return [
-                datasets.SplitGenerator(
-                    name=datasets.Split.TRAIN,
-                    gen_kwargs={"filepath": data_dir, "split": "train"},
-                ),
-                ]
-
-
-    def _generate_examples(self, filepath, split):
-        key = 0
-        for line in open(filepath, encoding="utf-8"):
-            line = json.loads(line)
-            yield key, line   
-            key += 1