Convert dataset to Parquet (#6)

- Convert dataset to Parquet (ad0bf494765acb68bc670e6f5756770775803520)
- Add python data files (7f61525af75001bdcba4a36a15b870c2be051902)
- Delete loading script (4681785250b212273b3363aed349eaaf2f7168e4)
- Delete loading script auxiliary file (e7eebd26b27fe00e9097020c557157793fb6c5f4)
- Delete data file (fa1ce2a213d78018655640c19183e34a55b59ccc)
- Delete loading script auxiliary file (e5006b7e221f4c891ce8d8285ff8409d1d36cad9)
- Delete data file (dfef3bbb8a79c22c7c04278945a9fbd97543a699)
- Delete data file (be1d62668ddc3c16afc64e3269140bd48b834473)
- Delete data file (b9ec9e990cf69293f6bcd70c37d73f95879814e2)

README.md

@@ -29,16 +29,16 @@ dataset_info:
     sequence: string
   splits:
   - name: train
-    num_bytes: 128312061
+    num_bytes: 128312045
     num_examples: 12934
   - name: validation
-    num_bytes: 30259174
+    num_bytes: 30259166
     num_examples: 7189
   - name: test
-    num_bytes: 43027956
+    num_bytes: 43027948
     num_examples: 8268
-  download_size: 126856519
-  dataset_size: 201599191
+  download_size: 31320339
+  dataset_size: 201599159
 - config_name: python
   features:
   - name: id
@@ -49,13 +49,28 @@ dataset_info:
     sequence: string
   splits:
   - name: train
-    num_bytes: 684319575
+    num_bytes: 684319455
     num_examples: 100000
   - name: test
-    num_bytes: 333978088
+    num_bytes: 333978028
     num_examples: 50000
-  download_size: 199067128
-  dataset_size: 1018297663
+  download_size: 210143525
+  dataset_size: 1018297483
+configs:
+- config_name: java
+  data_files:
+  - split: train
+    path: java/train-*
+  - split: validation
+    path: java/validation-*
+  - split: test
+    path: java/test-*
+- config_name: python
+  data_files:
+  - split: train
+    path: python/train-*
+  - split: test
+    path: python/test-*
 ---
 # Dataset Card for "code_x_glue_cc_code_completion_token"
 

code_x_glue_cc_code_completion_token.py

@@ -1,220 +0,0 @@
-import os
-import os.path
-from typing import List
-
-import datasets
-
-from .common import Child
-from .generated_definitions import DEFINITIONS
-
-
-_DESCRIPTION = """Predict next code token given context of previous tokens. Models are evaluated by token level accuracy.
-Code completion is a one of the most widely used features in software development through IDEs. An effective code completion tool could improve software developers' productivity. We provide code completion evaluation tasks in two granularities -- token level and line level. Here we introduce token level code completion. Token level task is analogous to language modeling. Models should have be able to predict the next token in arbitary types.
-"""
-
-_CITATION = """@article{raychev2016probabilistic,
-    title={Probabilistic Model for Code with Decision Trees},
-    author={Raychev, Veselin and Bielik, Pavol and Vechev, Martin},
-    journal={ACM SIGPLAN Notices},
-    pages={731--747},
-    year={2016},
-    publisher={ACM New York, NY, USA}
-}
-@inproceedings{allamanis2013mining,
-    title={Mining Source Code Repositories at Massive Scale using Language Modeling},
-    author={Allamanis, Miltiadis and Sutton, Charles},
-    booktitle={2013 10th Working Conference on Mining Software Repositories (MSR)},
-    pages={207--216},
-    year={2013},
-    organization={IEEE}
-}
-@dataset{rafael_michael_karampatsis_2020_3628665,
-  author       = {Rafael - Michael Karampatsis and
-                  Hlib Babii and
-                  Romain Robbes and
-                  Charles Sutton and
-                  Andrea Janes},
-  title        = {Preprocessed Java Code Corpus},
-  month        = jan,
-  year         = 2020,
-  publisher    = {Zenodo},
-  version      = {1.0},
-  doi          = {10.5281/zenodo.3628665},
-  url          = {https://doi.org/10.5281/zenodo.3628665}
-}"""
-
-
-class CodeXGlueCcCodeCompletionTokenImpl(Child):
-    _DESCRIPTION = _DESCRIPTION
-    _CITATION = _CITATION
-
-
-class CodeXGlueCcCodeCompletionTokenJavaImpl(CodeXGlueCcCodeCompletionTokenImpl):
-    SPLITS = {
-        "training": datasets.Split.TRAIN,
-        "validation": datasets.Split.VALIDATION,
-        "test": datasets.Split.TEST,
-    }
-
-    _FEATURES = {
-        "id": datasets.Value("int32"),  # Index of the sample
-        "code": datasets.features.Sequence(datasets.Value("string")),  # Code Tokens
-    }
-
-    def generate_urls(self, split_name):
-        language = self.info["parameters"]["language"]
-        if language != "java":
-            raise RuntimeError(f"Unknown language {language}: should be java.")
-
-        yield "data", f"https://huggingface.co/datasets/code_x_glue_cc_code_completion_token/resolve/main/data/java/java_{split_name}_pre"
-
-    def _generate_examples(self, split_name, file_paths):
-        with open(file_paths["data"], encoding="utf-8") as f:
-            for idx, line in enumerate(f):
-                new_data = []
-                for token in line.strip().split():
-                    if len(token) > 100:
-                        continue
-                    new_data.append(token)
-                entry = dict(id=idx, code=new_data)
-                yield idx, entry
-
-
-class CodeXGlueCcCodeCompletionTokenPythonImpl(CodeXGlueCcCodeCompletionTokenImpl):
-    SPLITS = {"train": datasets.Split.TRAIN, "test": datasets.Split.TEST}
-
-    _FEATURES = {
-        "id": datasets.Value("int32"),  # Index of the sample
-        "path": datasets.Value("string"),  # Original path in the dataset
-        "code": datasets.features.Sequence(datasets.Value("string")),  # Code Tokens
-    }
-
-    PYTHON_FILE_MAPPING = dict(train="python100k_train.txt", test="python50k_eval.txt")
-
-    def generate_urls(self, split_name):
-        language = self.info["parameters"]["language"]
-        if language != "python":
-            raise RuntimeError(f"Unknown language {language}")
-
-        yield "data", "https://huggingface.co/datasets/code_x_glue_cc_code_completion_token/resolve/main/data/python/py150_files.zip"
-
-    def process_string(self, token):
-        # Copyright (c) Microsoft Corporation.
-        # Licensed under the MIT License.
-        import re
-
-        str_quote_options = ["'''", '"""', "'", '"']
-        start_quote = ""
-        end_quote = ""
-        qualifier_regex = r"^[a-z]+"
-        qualifier_match = re.search(qualifier_regex, token)
-        # string qualifiers like 'r' for regex, 'f' for formatted string, 'b' for bytes, 'u' for unicode, etc (or combination of them)
-        qualifier = "" if not qualifier_match else qualifier_match[0]
-        # token string without qualifiers
-        token_string = re.sub(qualifier_regex, "", token)
-        # string literal without quotes
-        str_lit = token_string
-        for q in str_quote_options:
-            if token_string.startswith(q):
-                start_quote = q
-                str_lit = str_lit[len(q) :]
-                if token_string.endswith(q):
-                    end_quote = q
-                    str_lit = str_lit[: -len(q)]
-                break
-        if start_quote in str_quote_options[:2]:
-            return ""
-        return (
-            f"{qualifier}{start_quote}{str_lit}{end_quote}"
-            if len(str_lit) < 15
-            and "\n" not in str_lit
-            and "</s>" not in str_lit
-            and "<s>" not in str_lit
-            and "<pad>" not in str_lit
-            and "<EOL>" not in str_lit
-            else f"{qualifier}{start_quote}{end_quote}"
-        )
-
-    def py_tokenize(self, base_dir, file_name):
-        # Copyright (c) Microsoft Corporation.
-        # Licensed under the MIT License.
-        from io import BytesIO
-        from tokenize import COMMENT, ENCODING, ENDMARKER, INDENT, NEWLINE, NL, NUMBER, STRING, tokenize
-
-        file_paths = open(os.path.join(base_dir, file_name), encoding="utf-8").readlines()
-        for ct, path in enumerate(file_paths):
-            try:
-                code = open(os.path.join(base_dir, path.strip()), encoding="utf-8").read()
-                token_gen = tokenize(BytesIO(bytes(code, "utf8")).readline)
-                out_tokens = []
-                prev_eol = False
-                for toknum, tokval, _, _, _ in token_gen:
-                    tokval = " ".join(tokval.split())
-                    if len(tokval) > 100:
-                        continue
-                    if toknum == STRING:
-                        add_token = self.process_string(tokval)
-                        if len(add_token) > 0:
-                            out_tokens.append(add_token)
-                            prev_eol = False
-                    elif toknum == NUMBER:
-                        if len(tokval) < 50:
-                            out_tokens.append(tokval)
-                            prev_eol = False
-                    elif toknum in [NEWLINE, NL]:
-                        if not prev_eol:
-                            out_tokens.append("<EOL>")
-                            prev_eol = True
-                    elif toknum in [COMMENT, INDENT, ENCODING, ENDMARKER] or len(tokval) == 0:
-                        continue
-                    else:
-                        out_tokens.append(tokval)
-                        prev_eol = False
-                if out_tokens[0] == "<EOL>":
-                    out_tokens = out_tokens[1:]
-                if out_tokens[-1] == "<EOL>":
-                    out_tokens = out_tokens[:-1]
-            except Exception:
-                out_tokens = []
-            out_tokens = ["<s>"] + out_tokens + ["</s>"]
-            yield path, out_tokens
-
-    def _generate_examples(self, split_name, file_paths):
-        base_dir = file_paths["data"]
-        filename = self.PYTHON_FILE_MAPPING[split_name]
-
-        idx = 0
-        for entry in self.py_tokenize(base_dir=base_dir, file_name=filename):
-            path, out_tokens = entry
-            path = path[len("data/") :]
-            yield idx, dict(id=idx, path=path, code=out_tokens)
-            idx += 1
-
-
-CLASS_MAPPING = {
-    "CodeXGlueCcCodeCompletionTokenJava": CodeXGlueCcCodeCompletionTokenJavaImpl,
-    "CodeXGlueCcCodeCompletionTokenPython": CodeXGlueCcCodeCompletionTokenPythonImpl,
-}
-
-
-class CodeXGlueCcCodeCompletionToken(datasets.GeneratorBasedBuilder):
-    BUILDER_CONFIG_CLASS = datasets.BuilderConfig
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(name=name, description=info["description"]) for name, info in DEFINITIONS.items()
-    ]
-
-    def _info(self):
-        name = self.config.name
-        info = DEFINITIONS[name]
-        if info["class_name"] in CLASS_MAPPING:
-            self.child = CLASS_MAPPING[info["class_name"]](info)
-        else:
-            raise RuntimeError(f"Unknown python class for dataset configuration {name}")
-        ret = self.child._info()
-        return ret
-
-    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
-        return self.child._split_generators(dl_manager=dl_manager)
-
-    def _generate_examples(self, split_name, file_paths):
-        return self.child._generate_examples(split_name, file_paths)

common.py

@@ -1,75 +0,0 @@
-from typing import List
-
-import datasets
-
-
-# Citation, taken from https://github.com/microsoft/CodeXGLUE
-_DEFAULT_CITATION = """@article{CodeXGLUE,
-         title={CodeXGLUE: A Benchmark Dataset and Open Challenge for Code Intelligence},
-         year={2020},}"""
-
-
-class Child:
-    _DESCRIPTION = None
-    _FEATURES = None
-    _CITATION = None
-    SPLITS = {"train": datasets.Split.TRAIN}
-    _SUPERVISED_KEYS = None
-
-    def __init__(self, info):
-        self.info = info
-
-    def homepage(self):
-        return self.info["project_url"]
-
-    def _info(self):
-        # This is the description that will appear on the datasets page.
-        return datasets.DatasetInfo(
-            description=self.info["description"] + "\n\n" + self._DESCRIPTION,
-            features=datasets.Features(self._FEATURES),
-            homepage=self.homepage(),
-            citation=self._CITATION or _DEFAULT_CITATION,
-            supervised_keys=self._SUPERVISED_KEYS,
-        )
-
-    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
-        SPLITS = self.SPLITS
-        _URL = self.info["raw_url"]
-        urls_to_download = {}
-        for split in SPLITS:
-            if split not in urls_to_download:
-                urls_to_download[split] = {}
-
-            for key, url in self.generate_urls(split):
-                if not url.startswith("http"):
-                    url = _URL + "/" + url
-                urls_to_download[split][key] = url
-
-        downloaded_files = {}
-        for k, v in urls_to_download.items():
-            downloaded_files[k] = dl_manager.download_and_extract(v)
-
-        return [
-            datasets.SplitGenerator(
-                name=SPLITS[k],
-                gen_kwargs={"split_name": k, "file_paths": downloaded_files[k]},
-            )
-            for k in SPLITS
-        ]
-
-    def check_empty(self, entries):
-        all_empty = all([v == "" for v in entries.values()])
-        all_non_empty = all([v != "" for v in entries.values()])
-
-        if not all_non_empty and not all_empty:
-            raise RuntimeError("Parallel data files should have the same number of lines.")
-
-        return all_empty
-
-
-class TrainValidTestChild(Child):
-    SPLITS = {
-        "train": datasets.Split.TRAIN,
-        "valid": datasets.Split.VALIDATION,
-        "test": datasets.Split.TEST,
-    }

generated_definitions.py

@@ -1,24 +0,0 @@
-DEFINITIONS = {
-    "java": {
-        "class_name": "CodeXGlueCcCodeCompletionTokenJava",
-        "dataset_type": "Code-Code",
-        "description": "CodeXGLUE CodeCompletion-token dataset, available at https://github.com/microsoft/CodeXGLUE/tree/main/Code-Code/CodeCompletion-token",
-        "dir_name": "CodeCompletion-token",
-        "name": "java",
-        "parameters": {"language": "java", "original_language_name": "javaCorpus"},
-        "project_url": "https://github.com/madlag/CodeXGLUE/tree/main/Code-Code/CodeCompletion-token",
-        "raw_url": "https://raw.githubusercontent.com/madlag/CodeXGLUE/main/Code-Code/CodeCompletion-token/dataset/javaCorpus",
-        "sizes": {"test": 8268, "train": 12934, "validation": 7189},
-    },
-    "python": {
-        "class_name": "CodeXGlueCcCodeCompletionTokenPython",
-        "dataset_type": "Code-Code",
-        "description": "CodeXGLUE CodeCompletion-token dataset, available at https://github.com/microsoft/CodeXGLUE/tree/main/Code-Code/CodeCompletion-token",
-        "dir_name": "CodeCompletion-token",
-        "name": "python",
-        "parameters": {"language": "python", "original_language_name": "py150"},
-        "project_url": "https://github.com/madlag/CodeXGLUE/tree/main/Code-Code/CodeCompletion-token",
-        "raw_url": "https://raw.githubusercontent.com/madlag/CodeXGLUE/main/Code-Code/CodeCompletion-token/dataset/py150",
-        "sizes": {"test": 50000, "train": 100000},
-    },
-}

data/java/java_validation_pre → java/test-00000-of-00001.parquet

@@ -1,3 +1,3 @@
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data/java/java_test_pre → java/train-00000-of-00001.parquet

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+size 19617199

data/java/java_training_pre → java/validation-00000-of-00001.parquet

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+size 5160037

data/python/py150_files.zip → python/test-00000-of-00001.parquet

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python/train-00000-of-00002.parquet

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python/train-00001-of-00002.parquet

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