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-annotation
+language_creators:
+- expert-generated
+- machine-generated
+languages:
+- en
+licenses:
+- ms-pl
+multilinguality:
+- monolingual
+- translation
+size_categories:
+- 1K<n<10K
+source_datasets:
+- extended|other-newstest2017
+task_categories:
+- conditional-text-generation
+task_ids:
+- machine-translation
+---
+
+# Dataset Card for msr_zhen_translation_parity
+
+## 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:**
+
+[Translator Human Parity Data](https://msropendata.com/datasets/93f9aa87-9491-45ac-81c1-6498b6be0d0b)
+
+- **Repository:**
+- **Paper:**
+
+[Achieving Human Parity on Automatic Chinese to English News Translation](https://www.microsoft.com/en-us/research/publication/achieving-human-parity-on-automatic-chinese-to-english-news-translation/)
+
+- **Leaderboard:**
+- **Point of Contact:**
+
+### Dataset Summary
+
+> Human evaluation results and translation output for the Translator Human Parity Data release,
+> as described in https://blogs.microsoft.com/ai/machine-translation-news-test-set-human-parity/ 
+ 
+> The Translator Human Parity Data release contains all human evaluation results and translations
+> related to our paper "Achieving Human Parity on Automatic Chinese to English News Translation",
+> published on March 14, 2018. We have released this data to 
+
+> 1) allow external validation of our claim of having achieved human parity
+> 2) to foster future research by releasing two additional human references 
+>   for the Reference-WMT test set. 
+>
+
+The dataset includes:
+
+1) two new references for Chinese-English language pair of WMT17, 
+   one based on human translation from scratch (Reference-HT),
+   the other based on human post-editing (Reference-PE); 
+
+2) human parity translations generated by our research systems Combo-4, Combo-5, and Combo-6, 
+   as well as translation output from online machine translation service Online-A-1710,
+   collected on October 16, 2017;
+
+The data package provided with the study also includes (but not parsed and provided as 
+workable features of this dataset) all data points collected in human evaluation campaigns.
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed]
+
+### Languages
+
+This dataset contains 6 extra English translations to Chinese-English language pair of WMT17.
+
+## Dataset Structure
+
+### Data Instances
+
+[More Information Needed]
+
+### Data Fields
+
+As mentioned in the summary, this dataset provides 6 extra English translations of 
+Chinese-English language pair of WMT17.
+
+Data fields are named exactly like the associated paper for easier cross-referenceing.
+
+- `Reference-HT`: human translation from scrach.
+- `Reference-PE`: human post-editing.
+- `Combo-4`, `Combo-5`, `Combo-6`: three translations by research systems.
+- `Online-A-1710`: a translation from an anonymous online machine translation service.
+
+All data fields of a record are translations for the same Chinese source sentence.
+
+### Data Splits
+
+[More Information Needed]
+
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed]
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed]
+
+#### 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
+
+[More Information Needed]
+
+### Citation Information
+
+Citation information is available at this link [Achieving Human Parity on Automatic Chinese to English News Translation](https://www.microsoft.com/en-us/research/publication/achieving-human-parity-on-automatic-chinese-to-english-news-translation/)

dataset_infos.json

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+{"default": {"description": "Translator Human Parity Data\n\nHuman evaluation results and translation output for the Translator Human Parity Data release, \nas described in https://blogs.microsoft.com/ai/machine-translation-news-test-set-human-parity/. \nThe Translator Human Parity Data release contains all human evaluation results and translations\nrelated to our paper \"Achieving Human Parity on Automatic Chinese to English News Translation\", \npublished on March 14, 2018.\n", "citation": "@misc{hassan2018achieving,\n      title={Achieving Human Parity on Automatic Chinese to English News Translation}, \n      author={ Hany Hassan and Anthony Aue and Chang Chen and Vishal Chowdhary and Jonathan Clark \n               and Christian Federmann and Xuedong Huang and Marcin Junczys-Dowmunt and William Lewis \n               and Mu Li and Shujie Liu and Tie-Yan Liu and Renqian Luo and Arul Menezes and Tao Qin \n               and Frank Seide and Xu Tan and Fei Tian and Lijun Wu and Shuangzhi Wu and Yingce Xia \n               and Dongdong Zhang and Zhirui Zhang and Ming Zhou},\n      year={2018},\n      eprint={1803.05567},\n      archivePrefix={arXiv},\n      primaryClass={cs.CL}\n}", "homepage": "https://msropendata.com/datasets/93f9aa87-9491-45ac-81c1-6498b6be0d0b", "license": "", "features": {"Reference-HT": {"dtype": "string", "id": null, "_type": "Value"}, "Reference-PE": {"dtype": "string", "id": null, "_type": "Value"}, "Combo-4": {"dtype": "string", "id": null, "_type": "Value"}, "Combo-5": {"dtype": "string", "id": null, "_type": "Value"}, "Combo-6": {"dtype": "string", "id": null, "_type": "Value"}, "Online-A-1710": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "msr_zhen_translation_parity", "config_name": "default", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 1797033, "num_examples": 2001, "dataset_name": "msr_zhen_translation_parity"}}, "download_checksums": {}, "download_size": 0, "post_processing_size": null, "dataset_size": 1797033, "size_in_bytes": 1797033}}

dummy/1.0.0/dummy_data.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:60c71290ad3a9ed3ac7ebf7c7ab6222e2e493afab8fd98439d938d04bd4343c4
+size 10458

msr_zhen_translation_parity.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.
+"""Microsoft Research: Translator Human Parity Data"""
+
+from __future__ import absolute_import, division, print_function
+
+import os
+
+import datasets
+
+
+_DESCRIPTION = """\
+Translator Human Parity Data
+
+Human evaluation results and translation output for the Translator Human Parity Data release,
+as described in https://blogs.microsoft.com/ai/machine-translation-news-test-set-human-parity/.
+The Translator Human Parity Data release contains all human evaluation results and translations
+related to our paper "Achieving Human Parity on Automatic Chinese to English News Translation",
+published on March 14, 2018.
+"""
+
+_LICENSE = """\
+See the Microsoft Research Data License Agreement.
+"""
+
+_CITATION = """\
+@misc{hassan2018achieving,
+      title={Achieving Human Parity on Automatic Chinese to English News Translation},
+      author={ Hany Hassan and Anthony Aue and Chang Chen and Vishal Chowdhary and Jonathan Clark
+               and Christian Federmann and Xuedong Huang and Marcin Junczys-Dowmunt and William Lewis
+               and Mu Li and Shujie Liu and Tie-Yan Liu and Renqian Luo and Arul Menezes and Tao Qin
+               and Frank Seide and Xu Tan and Fei Tian and Lijun Wu and Shuangzhi Wu and Yingce Xia
+               and Dongdong Zhang and Zhirui Zhang and Ming Zhou},
+      year={2018},
+      eprint={1803.05567},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}"""
+
+_FILE_PATHS = [
+    ["References", "Translator-HumanParityData-Reference-HT.txt"],
+    ["References", "Translator-HumanParityData-Reference-PE.txt"],
+    ["Translations", "Translator-HumanParityData-Combo-4.txt"],
+    ["Translations", "Translator-HumanParityData-Combo-5.txt"],
+    ["Translations", "Translator-HumanParityData-Combo-6.txt"],
+    ["Translations", "Translator-HumanParityData-Online-A-1710.txt"],
+]
+
+_FEATURE_NAMES = ["Reference-HT", "Reference-PE", "Combo-4", "Combo-5", "Combo-6", "Online-A-1710"]
+
+
+class MsrZhenTranslationParity(datasets.GeneratorBasedBuilder):
+    """Microsoft Research: Translator Human Parity Data"""
+
+    VERSION = datasets.Version("1.0.0")
+
+    @property
+    def manual_download_instructions(self):
+        return """\
+    You need to go to https://msropendata.com/datasets/93f9aa87-9491-45ac-81c1-6498b6be0d0b,
+    and manually download translatorhumanparitydata2.zip (or translatorhumanparitydata2.tar.gz).
+    Once it is completed, extract its content into a directory, <path/to/folder>.
+    Within this directory, there are three subdirectories, Translations, References, and Evaluations.
+    The <path/to/folder> can e.g. be "~/Downloads/translatorhumanparitydata2", if you just double click the .zip file.
+    msr_zhen_translation_parity can then be loaded using the following command
+    `datasets.load_dataset("msr_zhen_translation_parity", data_dir="<path/to/folder>")`.
+    """
+
+    def _info(self):
+        feature_names = _FEATURE_NAMES
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features({k: datasets.Value("string") for k in feature_names}),
+            supervised_keys=None,
+            homepage="https://msropendata.com/datasets/93f9aa87-9491-45ac-81c1-6498b6be0d0b",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        path_to_manual_file = os.path.abspath(os.path.expanduser(dl_manager.manual_dir))
+
+        if not os.path.exists(path_to_manual_file):
+            raise FileNotFoundError(
+                "{} does not exist. Make sure you insert a manual dir via `datasets.load_dataset('msr_zhen_translation_parity', data_dir=...)`. Manual download instructions: {})".format(
+                    path_to_manual_file, self.manual_download_instructions
+                )
+            )
+        return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"path": path_to_manual_file})]
+
+    def _generate_examples(self, path=None, title_set=None):
+        data = []
+        for fp in _FILE_PATHS:
+            filepath = os.path.join(path, fp[0], fp[1])
+            with open(filepath, encoding="utf-8-sig") as f:
+                data.append(f.readlines())
+        examples = len(data[0])
+        for i in range(examples):
+            record = [x[i].rstrip("\r\n") for x in data]
+            yield i, dict(zip(_FEATURE_NAMES, record))