Update files from the datasets library (from 1.2.0)

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

.gitattributes

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+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bin.* filter=lfs diff=lfs merge=lfs -text
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README.md

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+---
+annotations_creators:
+- expert-generated
+language_creators:
+- found
+languages:
+- en
+licenses:
+- cc-by-nc-sa-4-0
+multilinguality:
+- monolingual
+- other-language-learner
+size_categories:
+- 1K<n<10K
+source_datasets:
+- extended|other-GUG-grammaticality-judgements
+task_categories:
+- conditional-text-generation
+task_ids:
+- conditional-text-generation-other-grammatical-error-correction
+---
+
+# Dataset Card for JFLEG
+
+## 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:** [Github](https://github.com/keisks/jfleg)
+- **Repository:** [Github](https://github.com/keisks/jfleg)
+- **Paper:** [Napoles et al., 2020](https://www.aclweb.org/anthology/E17-2037/)
+- **Leaderboard:** [Leaderboard](https://github.com/keisks/jfleg#leader-board-published-results)
+- **Point of Contact:** Courtney Napoles, Keisuke Sakaguchi
+
+### Dataset Summary
+JFLEG (JHU FLuency-Extended GUG) is an English grammatical error correction (GEC) corpus. It is a gold standard benchmark for developing and evaluating GEC systems with respect to fluency (extent to which a text is native-sounding) as well as grammaticality. For each source document, there are four human-written corrections.
+
+### Supported Tasks and Leaderboards
+Grammatical error correction.
+
+### Languages
+English (native as well as L2 writers)
+
+## Dataset Structure
+
+### Data Instances
+Each instance contains a source sentence and four corrections. For example:
+```python
+{
+  'sentence': "They are moved by solar energy ."
+  'corrections': [
+    "They are moving by solar energy .",
+    "They are moved by solar energy .",
+    "They are moved by solar energy .",
+    "They are propelled by solar energy ." 
+  ]
+}
+ ```
+
+### Data Fields
+- sentence: original sentence written by an English learner
+- corrections: corrected versions by human annotators. The order of the annotations are consistent (eg first sentence will always be written by annotator "ref0").
+
+### Data Splits
+- This dataset contains 1511 examples in total and comprise a dev and test split. 
+- There are 754 and 747 source sentences for dev and test, respectively. 
+- Each sentence has 4 corresponding corrected versions. 
+
+## 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
+This work is licensed under a [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License](https://creativecommons.org/licenses/by-nc-sa/4.0/).
+
+### Citation Information
+This benchmark was proposed by [Napoles et al., 2020](https://www.aclweb.org/anthology/E17-2037/).
+
+```
+@InProceedings{napoles-sakaguchi-tetreault:2017:EACLshort,
+  author    = {Napoles, Courtney  and  Sakaguchi, Keisuke  and  Tetreault, Joel},
+  title     = {JFLEG: A Fluency Corpus and Benchmark for Grammatical Error Correction},
+  booktitle = {Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers},
+  month     = {April},
+  year      = {2017},
+  address   = {Valencia, Spain},
+  publisher = {Association for Computational Linguistics},
+  pages     = {229--234},
+  url       = {http://www.aclweb.org/anthology/E17-2037}
+}
+
+@InProceedings{heilman-EtAl:2014:P14-2,
+  author    = {Heilman, Michael  and  Cahill, Aoife  and  Madnani, Nitin  and  Lopez, Melissa  and  Mulholland, Matthew  and  Tetreault, Joel},
+  title     = {Predicting Grammaticality on an Ordinal Scale},
+  booktitle = {Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)},
+  month     = {June},
+  year      = {2014},
+  address   = {Baltimore, Maryland},
+  publisher = {Association for Computational Linguistics},
+  pages     = {174--180},
+  url       = {http://www.aclweb.org/anthology/P14-2029}
+}
+```

dataset_infos.json

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+{"default": {"description": "JFLEG (JHU FLuency-Extended GUG) is an English grammatical error correction (GEC) corpus.\nIt is a gold standard benchmark for developing and evaluating GEC systems with respect to\nfluency (extent to which a text is native-sounding) as well as grammaticality.\n\nFor each source document, there are four human-written corrections (ref0 to ref3).\n", "citation": "@InProceedings{napoles-sakaguchi-tetreault:2017:EACLshort,\n  author    = {Napoles, Courtney\n               and  Sakaguchi, Keisuke\n               and  Tetreault, Joel},\n  title     = {JFLEG: A Fluency Corpus and Benchmark for Grammatical Error Correction},\n  booktitle = {Proceedings of the 15th Conference of the European Chapter of the\n               Association for Computational Linguistics: Volume 2, Short Papers},\n  month     = {April},\n  year      = {2017},\n  address   = {Valencia, Spain},\n  publisher = {Association for Computational Linguistics},\n  pages     = {229--234},\n  url       = {http://www.aclweb.org/anthology/E17-2037}\n}\n@InProceedings{heilman-EtAl:2014:P14-2,\n  author    = {Heilman, Michael\n               and  Cahill, Aoife\n               and  Madnani, Nitin\n               and  Lopez, Melissa\n               and  Mulholland, Matthew\n               and  Tetreault, Joel},\n  title     = {Predicting Grammaticality on an Ordinal Scale},\n  booktitle = {Proceedings of the 52nd Annual Meeting of the\n               Association for Computational Linguistics (Volume 2: Short Papers)},\n  month     = {June},\n  year      = {2014},\n  address   = {Baltimore, Maryland},\n  publisher = {Association for Computational Linguistics},\n  pages     = {174--180},\n  url       = {http://www.aclweb.org/anthology/P14-2029}\n}\n", "homepage": "https://github.com/keisks/jfleg", "license": "CC BY-NC-SA 4.0", "features": {"sentence": {"dtype": "string", "id": null, "_type": "Value"}, "corrections": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "builder_name": "jfleg", "config_name": "default", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"validation": {"name": "validation", "num_bytes": 379991, "num_examples": 755, "dataset_name": "jfleg"}, "test": {"name": "test", "num_bytes": 379711, "num_examples": 748, "dataset_name": "jfleg"}}, "download_checksums": {"https://raw.githubusercontent.com/keisks/jfleg/master/dev/dev.src": {"num_bytes": 72726, "checksum": "4a0e8b86d18a1058460ff0a592dac1ba68986d135256efbd27e997ac43f295f8"}, "https://raw.githubusercontent.com/keisks/jfleg/master/dev/dev.ref0": {"num_bytes": 73216, "checksum": "adea6287c6e2240b7777e63cd56f8e228e742bbfb42c5152bc0bd2bc91f4e53e"}, "https://raw.githubusercontent.com/keisks/jfleg/master/dev/dev.ref1": {"num_bytes": 73129, "checksum": "d40d56ec7468ddab03fdcca97065ab3f9d391d749dbc7097b7c777a19ce4242e"}, "https://raw.githubusercontent.com/keisks/jfleg/master/dev/dev.ref2": {"num_bytes": 73394, "checksum": "b070691d633e0c4143d96ba21299ae71cb126086517d2970df47420842067793"}, "https://raw.githubusercontent.com/keisks/jfleg/master/dev/dev.ref3": {"num_bytes": 73164, "checksum": "9187fd834693fa77d07957991282d32d61ff84a207c25cbfab318c871bacdbc4"}, "https://raw.githubusercontent.com/keisks/jfleg/master/test/test.src": {"num_bytes": 72684, "checksum": "893db119162487aa7f956b65978453576919e6797cd6c1955f93b7a8b9f4bbd8"}, "https://raw.githubusercontent.com/keisks/jfleg/master/test/test.ref0": {"num_bytes": 73090, "checksum": "875953280a3ea1dea2827337b1778c0105f0c0aa79f2517a6e0e42db5e5e170c"}, "https://raw.githubusercontent.com/keisks/jfleg/master/test/test.ref1": {"num_bytes": 73325, "checksum": "190d3398f2765f54a39b5489d1e96c483412a656086c731f8712ad0591087d80"}, "https://raw.githubusercontent.com/keisks/jfleg/master/test/test.ref2": {"num_bytes": 73018, "checksum": "0e3c6abe934ccd16c9dffb2fd889d6f55afc3ad13a63c1e148c720bb4e99046b"}, "https://raw.githubusercontent.com/keisks/jfleg/master/test/test.ref3": {"num_bytes": 73365, "checksum": "19f49de6eff813b26505ecf756c20dc301aeb80696696b01ca950298f6e58441"}}, "download_size": 731111, "post_processing_size": null, "dataset_size": 759702, "size_in_bytes": 1490813}}

dummy/1.0.0/dummy_data.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e68f341e896df513b6f5df1c388b2d8348674e68e41c1e4d0f35a6bc64c9a1a7
+size 4859

jfleg.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.
+"""JFLEG dataset."""
+
+from __future__ import absolute_import, division, print_function
+
+import datasets
+
+
+_CITATION = """\
+@InProceedings{napoles-sakaguchi-tetreault:2017:EACLshort,
+  author    = {Napoles, Courtney
+               and  Sakaguchi, Keisuke
+               and  Tetreault, Joel},
+  title     = {JFLEG: A Fluency Corpus and Benchmark for Grammatical Error Correction},
+  booktitle = {Proceedings of the 15th Conference of the European Chapter of the
+               Association for Computational Linguistics: Volume 2, Short Papers},
+  month     = {April},
+  year      = {2017},
+  address   = {Valencia, Spain},
+  publisher = {Association for Computational Linguistics},
+  pages     = {229--234},
+  url       = {http://www.aclweb.org/anthology/E17-2037}
+}
+@InProceedings{heilman-EtAl:2014:P14-2,
+  author    = {Heilman, Michael
+               and  Cahill, Aoife
+               and  Madnani, Nitin
+               and  Lopez, Melissa
+               and  Mulholland, Matthew
+               and  Tetreault, Joel},
+  title     = {Predicting Grammaticality on an Ordinal Scale},
+  booktitle = {Proceedings of the 52nd Annual Meeting of the
+               Association for Computational Linguistics (Volume 2: Short Papers)},
+  month     = {June},
+  year      = {2014},
+  address   = {Baltimore, Maryland},
+  publisher = {Association for Computational Linguistics},
+  pages     = {174--180},
+  url       = {http://www.aclweb.org/anthology/P14-2029}
+}
+"""
+
+_DESCRIPTION = """\
+JFLEG (JHU FLuency-Extended GUG) is an English grammatical error correction (GEC) corpus.
+It is a gold standard benchmark for developing and evaluating GEC systems with respect to
+fluency (extent to which a text is native-sounding) as well as grammaticality.
+
+For each source document, there are four human-written corrections (ref0 to ref3).
+"""
+
+_HOMEPAGE = "https://github.com/keisks/jfleg"
+
+_LICENSE = "CC BY-NC-SA 4.0"
+
+_URLs = {
+    "dev": {
+        "src": "https://raw.githubusercontent.com/keisks/jfleg/master/dev/dev.src",
+        "ref0": "https://raw.githubusercontent.com/keisks/jfleg/master/dev/dev.ref0",
+        "ref1": "https://raw.githubusercontent.com/keisks/jfleg/master/dev/dev.ref1",
+        "ref2": "https://raw.githubusercontent.com/keisks/jfleg/master/dev/dev.ref2",
+        "ref3": "https://raw.githubusercontent.com/keisks/jfleg/master/dev/dev.ref3",
+    },
+    "test": {
+        "src": "https://raw.githubusercontent.com/keisks/jfleg/master/test/test.src",
+        "ref0": "https://raw.githubusercontent.com/keisks/jfleg/master/test/test.ref0",
+        "ref1": "https://raw.githubusercontent.com/keisks/jfleg/master/test/test.ref1",
+        "ref2": "https://raw.githubusercontent.com/keisks/jfleg/master/test/test.ref2",
+        "ref3": "https://raw.githubusercontent.com/keisks/jfleg/master/test/test.ref3",
+    },
+}
+
+
+class Jfleg(datasets.GeneratorBasedBuilder):
+    """JFLEG (JHU FLuency-Extended GUG) grammatical error correction dataset."""
+
+    VERSION = datasets.Version("1.0.0")
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {"sentence": datasets.Value("string"), "corrections": datasets.Sequence(datasets.Value("string"))}
+            ),
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+
+        downloaded_dev = dl_manager.download_and_extract(_URLs["dev"])
+        downloaded_test = dl_manager.download_and_extract(_URLs["test"])
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "filepath": downloaded_dev,
+                    "split": "dev",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={"filepath": downloaded_test, "split": "test"},
+            ),
+        ]
+
+    def _generate_examples(self, filepath, split):
+        """ Yields examples. """
+
+        source_file = filepath["src"]
+        with open(source_file, encoding="utf-8") as f:
+            source_sentences = f.read().split("\n")
+            num_source = len(source_sentences)
+
+        corrections = []
+        for n in range(0, 4):
+            correction_file = filepath["ref{n}".format(n=n)]
+            with open(correction_file, encoding="utf-8") as f:
+                correction_sentences = f.read().split("\n")
+                num_correction = len(correction_sentences)
+
+                assert len(correction_sentences) == len(
+                    source_sentences
+                ), "Sizes do not match: {ns} vs {nr} for {sf} vs {cf}.".format(
+                    ns=num_source, nr=num_correction, sf=source_file, cf=correction_file
+                )
+                corrections.append(correction_sentences)
+
+        corrected_sentences = list(zip(*corrections))
+        for id_, source_sentence in enumerate(source_sentences):
+            yield id_, {"sentence": source_sentence, "corrections": corrected_sentences[id_]}