Create docprompting-conala.py

docprompting-conala.py

@@ -0,0 +1,111 @@
+# 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": "conala-docs.json",
+    "data": {"train": "conala-train.json", "validation": "conala-dev.json", "test": "conala-test.json" },
+}
+
+class DocPromptingConala(datasets.GeneratorBasedBuilder):
+    """The resplit of CoNaLa Code 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.Value("list"), 
+                                            "canonical_cmd": datasets.Value("string"), 
+                                            "cmd_name": 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