Convert dataset to Parquet

README.md

@@ -29,13 +29,20 @@ dataset_info:
     sequence: string
   splits:
   - name: validation
-    num_bytes: 379991
+    num_bytes: 379979
     num_examples: 755
   - name: test
-    num_bytes: 379711
+    num_bytes: 379699
     num_examples: 748
-  download_size: 731111
-  dataset_size: 759702
+  download_size: 289093
+  dataset_size: 759678
+configs:
+- config_name: default
+  data_files:
+  - split: validation
+    path: data/validation-*
+  - split: test
+    path: data/test-*
 ---
 
 # Dataset Card for JFLEG

data/test-00000-of-00001.parquet

@@ -0,0 +1,3 @@
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+oid sha256:c3e5328a79aebb6fd559d19291c055824ed1040c5038731b3449e0e9efb1d59b
+size 141012

data/validation-00000-of-00001.parquet

@@ -0,0 +1,3 @@
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+oid sha256:102d7bd261223c00e2a39f0dd239857de9e5f91f94ae2aefbdd50a38ac6fddf8
+size 148081

dataset_infos.json

@@ -1 +0,0 @@
-{"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}}

jfleg.py

@@ -1,144 +0,0 @@
-# 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."""
-
-
-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[f"ref{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
-                ), f"Sizes do not match: {num_source} vs {num_correction} for {source_file} vs {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_]}