Update files from the datasets library (from 1.2.0)

Release notes: https://github.com/huggingface/datasets/releases/tag/1.2.0

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README.md

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+---
+annotations_creators:
+- no-annotations
+language_creators:
+- machine-generated
+languages:
+- en
+licenses:
+- apache-2-0
+multilinguality:
+- monolingual
+size_categories:
+- 100K<n<1M
+source_datasets:
+- original
+task_categories:
+- other
+task_ids:
+- other-other-contextual-embeddings
+---
+
+# Dataset Card for ethpy150open
+
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-instances)
+  - [Data Splits](#data-instances)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [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)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+
+## Dataset Description
+
+- **Homepage:** https://www.sri.inf.ethz.ch/py150
+- **Repository:** https://github.com/google-research-datasets/eth_py150_open
+- **Paper:** https://proceedings.icml.cc/static/paper_files/icml/2020/5401-Paper.pdf
+- **Leaderboard:** None
+- **Point of Contact:** Aditya Kanade <kanade@iisc.ac.in>, Petros Maniatis <maniatis@google.com> 
+
+### Dataset Summary
+
+A redistributable subset of the [ETH Py150 corpus](https://www.sri.inf.ethz.ch/py150), introduced in the ICML 2020 paper ['Learning and Evaluating Contextual Embedding of Source Code'](https://proceedings.icml.cc/static/paper_files/icml/2020/5401-Paper.pdf)
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed]
+
+### Languages
+
+English
+
+## Dataset Structure
+List of dicts of
+  {
+    "filepath": The relative URL containing the path to the file on GitHub
+    "license": The license used for that specific file or repository
+  }
+
+### Data Instances
+
+{
+  "filepath": "0rpc/zerorpc-python/setup.py",
+  "license": "mit"
+},
+{
+  "filepath": "0rpc/zerorpc-python/zerorpc/heartbeat.py",
+  "license": "mit"
+},
+
+### Data Fields
+
+- `filepath`: The relative URL containing the path to the file on GitHub
+- `license`: The license used for that specific file or repository
+
+### Data Splits
+
+|                            | Train   | Valid | Test  |
+| -----                      | ------- | ----- | ----- |
+| Dataset Split              | 74749   | 8302  | 41457 |
+
+## Dataset Creation
+The original dataset is at https://www.sri.inf.ethz.ch/py150
+### Curation Rationale
+
+To generate a more redistributable version of the dataset
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+All the urls are filepaths relative to GitHub and the master branch was used as available at the time
+
+#### Who are the source language producers?
+
+[More Information Needed]
+
+### Annotations
+
+#### Annotation process
+
+[More Information Needed]
+
+#### Who are the annotators?
+
+[More Information Needed]
+
+### Personal and Sensitive Information
+
+[More Information Needed]
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed]
+
+### Discussion of Biases
+
+[More Information Needed]
+
+### Other Known Limitations
+
+[More Information Needed]
+
+## Additional Information
+
+### Dataset Curators
+
+[More Information Needed]
+
+### Licensing Information
+
+Apache License 2.0
+
+### Citation Information
+
+@inproceedings{kanade2020learning,
+  title={Learning and Evaluating Contextual Embedding of Source Code},
+  author={Kanade, Aditya and Maniatis, Petros and Balakrishnan, Gogul and Shi, Kensen},
+  booktitle={International Conference on Machine Learning},
+  pages={5110--5121},
+  year={2020},
+  organization={PMLR}
+}

dataset_infos.json

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+{"eth_py150_open": {"description": "A redistributable subset of the ETH Py150 corpus, introduced in the ICML 2020 paper 'Learning and Evaluating Contextual Embedding of Source Code'\n", "citation": "@inproceedings{kanade2020learning,\n  title={Learning and Evaluating Contextual Embedding of Source Code},\n  author={Kanade, Aditya and Maniatis, Petros and Balakrishnan, Gogul and Shi, Kensen},\n  booktitle={International Conference on Machine Learning},\n  pages={5110--5121},\n  year={2020},\n  organization={PMLR}\n}\n", "homepage": "https://github.com/google-research-datasets/eth_py150_open", "license": "Apache License, Version 2.0", "features": {"filepath": {"dtype": "string", "id": null, "_type": "Value"}, "license": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": {"input": "filepath", "output": "license"}, "builder_name": "eth_py150_open", "config_name": "eth_py150_open", "version": {"version_str": "1.1.0", "description": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 5414978, "num_examples": 74749, "dataset_name": "eth_py150_open"}, "test": {"name": "test", "num_bytes": 3006199, "num_examples": 41457, "dataset_name": "eth_py150_open"}, "validation": {"name": "validation", "num_bytes": 598524, "num_examples": 8302, "dataset_name": "eth_py150_open"}}, "download_checksums": {"https://raw.githubusercontent.com/google-research-datasets/eth_py150_open/master/train__manifest.json": {"num_bytes": 8330299, "checksum": "faa632baf3a3e3ba234cc917dacd07fb646995990c930c6b86598d4d10484ce9"}, "https://raw.githubusercontent.com/google-research-datasets/eth_py150_open/master/dev__manifest.json": {"num_bytes": 922321, "checksum": "974426ff7448e7afd1fd26375814b264132b3eb62d4a995458c23f36857b4821"}, "https://raw.githubusercontent.com/google-research-datasets/eth_py150_open/master/eval__manifest.json": {"num_bytes": 4623051, "checksum": "b9a3235cb7457dac4bbb0cb7b31bc39186d78c318ec82c376bf1b61e66868554"}}, "download_size": 13875671, "post_processing_size": null, "dataset_size": 9019701, "size_in_bytes": 22895372}}

dummy/eth_py150_open/1.1.0/dummy_data.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:5ff1fea58d25d2f7f1f6d375ab344a179805d7b5149998a88138c257d9d42fcb
+size 955

eth_py150_open.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.
+"""A redistributable subset of the ETH Py150 corpus"""
+
+from __future__ import absolute_import, division, print_function
+
+import json
+import os
+
+import datasets
+
+
+_CITATION = """\
+@inproceedings{kanade2020learning,
+  title={Learning and Evaluating Contextual Embedding of Source Code},
+  author={Kanade, Aditya and Maniatis, Petros and Balakrishnan, Gogul and Shi, Kensen},
+  booktitle={International Conference on Machine Learning},
+  pages={5110--5121},
+  year={2020},
+  organization={PMLR}
+}
+"""
+
+
+_DESCRIPTION = """\
+A redistributable subset of the ETH Py150 corpus, introduced in the ICML 2020 paper 'Learning and Evaluating Contextual Embedding of Source Code'
+"""
+
+# TODO: Add a link to an official homepage for the dataset here
+_HOMEPAGE = "https://github.com/google-research-datasets/eth_py150_open"
+
+# TODO: Add the licence for the dataset here if you can find it
+_LICENSE = "Apache License, Version 2.0"
+
+# TODO: Add link to the official dataset URLs here
+# The HuggingFace dataset library don't host the datasets but only point to the original files
+# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
+_URL = "https://raw.githubusercontent.com/google-research-datasets/eth_py150_open/master/"
+
+
+# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
+class EthPy150Open(datasets.GeneratorBasedBuilder):
+    """A redistributable subset of the ETH Py150 corpus"""
+
+    VERSION = datasets.Version("1.1.0")
+
+    # This is an example of a dataset with multiple configurations.
+    # If you don't want/need to define several sub-sets in your dataset,
+    # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
+
+    # If you need to make complex sub-parts in the datasets with configurable options
+    # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
+    # BUILDER_CONFIG_CLASS = MyBuilderConfig
+
+    # You will be able to load one or the other configurations in the following list with
+    # data = datasets.load_dataset('my_dataset', 'first_domain')
+    # data = datasets.load_dataset('my_dataset', 'second_domain')
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(
+            name="eth_py150_open", version=VERSION, description="A subset of the original Py150 corpus"
+        ),
+    ]
+
+    def _info(self):
+        features = datasets.Features({"filepath": datasets.Value("string"), "license": datasets.Value("string")})
+        return datasets.DatasetInfo(
+            # This is the description that will appear on the datasets page.
+            description=_DESCRIPTION,
+            # This defines the different columns of the dataset and their types
+            features=features,  # Here we define them above because they are different between the two configurations
+            # If there's a common (input, target) tuple from the features,
+            # specify them here. They'll be used if as_supervised=True in
+            # builder.as_dataset.
+            supervised_keys=("filepath", "license"),
+            # Homepage of the dataset for documentation
+            homepage=_HOMEPAGE,
+            # License for the dataset if available
+            license=_LICENSE,
+            # Citation for the dataset
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
+        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
+
+        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLs
+        # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
+        # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
+        urls = {
+            "train": _URL + "train__manifest.json",
+            "dev": _URL + "dev__manifest.json",
+            "test": _URL + "eval__manifest.json",
+        }
+        data_dir = dl_manager.download_and_extract(urls)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={"filepath": os.path.join(data_dir["train"]), "split": "train"},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={"filepath": os.path.join(data_dir["test"]), "split": "test"},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={"filepath": os.path.join(data_dir["dev"]), "split": "dev"},
+            ),
+        ]
+
+    def _generate_examples(self, filepath, split):
+        """ Yields examples. """
+        # TODO: This method will receive as arguments the `gen_kwargs` defined in the previous `_split_generators` method.
+        # It is in charge of opening the given file and yielding (key, example) tuples from the dataset
+        # The key is not important, it's more here for legacy reason (legacy from tfds)
+
+        with open(filepath, encoding="utf-8") as f:
+            for id_, row in enumerate(json.load(f)):
+                yield id_, row