Convert dataset to Parquet (#2)

- Convert dataset to Parquet (89835d1cc6ea70d93839f6338a9d7dd6e69bedcf)
- Delete loading script (0feebfaaa053e2ba903133414784a568fcdbd273)
- Delete legacy dataset_infos.json (645577daa9c59eccafd6cad92543114be152476b)

README.md

@@ -43,16 +43,25 @@ dataset_info:
     dtype: string
   splits:
   - name: train
-    num_bytes: 4778409
+    num_bytes: 4778291
     num_examples: 20000
   - name: test
-    num_bytes: 493038
+    num_bytes: 492986
     num_examples: 2120
   - name: validation
-    num_bytes: 477964
+    num_bytes: 477935
     num_examples: 2051
-  download_size: 8135045
-  dataset_size: 5749411
+  download_size: 1843443
+  dataset_size: 5749212
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*
+  - split: test
+    path: data/test-*
+  - split: validation
+    path: data/validation-*
 ---
 
 # Dataset Card for Business Scene Dialogue 

bsd_ja_en.py

@@ -1,163 +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.
-"""Japanese-English Business Scene Dialogue (BSD) dataset. """
-
-
-import json
-
-import datasets
-
-
-_CITATION = """\
-@inproceedings{rikters-etal-2019-designing,
-    title = "Designing the Business Conversation Corpus",
-    author = "Rikters, Matīss  and
-      Ri, Ryokan  and
-      Li, Tong  and
-      Nakazawa, Toshiaki",
-    booktitle = "Proceedings of the 6th Workshop on Asian Translation",
-    month = nov,
-    year = "2019",
-    address = "Hong Kong, China",
-    publisher = "Association for Computational Linguistics",
-    url = "https://www.aclweb.org/anthology/D19-5204",
-    doi = "10.18653/v1/D19-5204",
-    pages = "54--61"
-}
-"""
-
-
-_DESCRIPTION = """\
-This is the Business Scene Dialogue (BSD) dataset,
-a Japanese-English parallel corpus containing written conversations
-in various business scenarios.
-
-The dataset was constructed in 3 steps:
-  1) selecting business scenes,
-  2) writing monolingual conversation scenarios according to the selected scenes, and
-  3) translating the scenarios into the other language.
-
-Half of the monolingual scenarios were written in Japanese
-and the other half were written in English.
-
-Fields:
-- id: dialogue identifier
-- no: sentence pair number within a dialogue
-- en_speaker: speaker name in English
-- ja_speaker: speaker name in Japanese
-- en_sentence: sentence in English
-- ja_sentence: sentence in Japanese
-- original_language: language in which monolingual scenario was written
-- tag: scenario
-- title: scenario title
-"""
-
-_HOMEPAGE = "https://github.com/tsuruoka-lab/BSD"
-
-_LICENSE = "CC BY-NC-SA 4.0"
-
-_REPO = "https://raw.githubusercontent.com/tsuruoka-lab/BSD/master/"
-
-_URLs = {
-    "train": _REPO + "train.json",
-    "dev": _REPO + "dev.json",
-    "test": _REPO + "test.json",
-}
-
-
-class BsdJaEn(datasets.GeneratorBasedBuilder):
-    """Japanese-English Business Scene Dialogue (BSD) dataset."""
-
-    VERSION = datasets.Version("1.0.0")
-
-    def _info(self):
-        features = datasets.Features(
-            {
-                "id": datasets.Value("string"),
-                "tag": datasets.Value("string"),
-                "title": datasets.Value("string"),
-                "original_language": datasets.Value("string"),
-                "no": datasets.Value("int32"),
-                "en_speaker": datasets.Value("string"),
-                "ja_speaker": datasets.Value("string"),
-                "en_sentence": datasets.Value("string"),
-                "ja_sentence": datasets.Value("string"),
-            }
-        )
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=features,
-            supervised_keys=None,
-            homepage=_HOMEPAGE,
-            license=_LICENSE,
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-        data_dir = dl_manager.download_and_extract(_URLs)
-
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "filepath": data_dir["train"],
-                    "split": "train",
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={"filepath": data_dir["test"], "split": "test"},
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                gen_kwargs={
-                    "filepath": data_dir["dev"],
-                    "split": "dev",
-                },
-            ),
-        ]
-
-    def _generate_examples(self, filepath, split):
-        """Yields examples."""
-
-        with open(filepath, encoding="utf-8") as f:
-            data = json.load(f)
-
-            for dialogue in data:
-                id_ = dialogue["id"]
-                tag = dialogue["tag"]
-                title = dialogue["title"]
-                original_language = dialogue["original_language"]
-                conversation = dialogue["conversation"]
-
-                for turn in conversation:
-                    sent_no = int(turn["no"])
-                    en_speaker = turn["en_speaker"]
-                    ja_speaker = turn["ja_speaker"]
-                    en_sentence = turn["en_sentence"]
-                    ja_sentence = turn["ja_sentence"]
-
-                    yield f"{id_}_{sent_no}", {
-                        "id": id_,
-                        "tag": tag,
-                        "title": title,
-                        "original_language": original_language,
-                        "no": sent_no,
-                        "en_speaker": en_speaker,
-                        "ja_speaker": ja_speaker,
-                        "en_sentence": en_sentence,
-                        "ja_sentence": ja_sentence,
-                    }

data/test-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:933f2d25d1c62fb563fcf67892230d6941879b3deaf888ed4f6bb252e89d0f9a
+size 171148

data/train-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:73d758c92c7d5e156d8f07d39912bd068317d9f396cab5b6fc53b435a70a1296
+size 1504882

data/validation-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:34d6d252efc7d078a5cff0bb3450011cc0b2d75aef1b81a86e0523f8e9866616
+size 167413

dataset_infos.json

@@ -1 +0,0 @@
-{"default": {"description": "This is the Business Scene Dialogue (BSD) dataset,\na Japanese-English parallel corpus containing written conversations\nin various business scenarios.\n\nThe dataset was constructed in 3 steps:\n  1) selecting business scenes,\n  2) writing monolingual conversation scenarios according to the selected scenes, and\n  3) translating the scenarios into the other language.\n\nHalf of the monolingual scenarios were written in Japanese\nand the other half were written in English.\n\nFields:\n- id: dialogue identifier\n- no: sentence pair number within a dialogue\n- en_speaker: speaker name in English\n- ja_speaker: speaker name in Japanese\n- en_sentence: sentence in English\n- ja_sentence: sentence in Japanese\n- original_language: language in which monolingual scenario was written\n- tag: scenario\n- title: scenario title\n", "citation": "@inproceedings{rikters-etal-2019-designing,\n    title = \"Designing the Business Conversation Corpus\",\n    author = \"Rikters, Mat\u012bss  and\n      Ri, Ryokan  and\n      Li, Tong  and\n      Nakazawa, Toshiaki\",\n    booktitle = \"Proceedings of the 6th Workshop on Asian Translation\",\n    month = nov,\n    year = \"2019\",\n    address = \"Hong Kong, China\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/D19-5204\",\n    doi = \"10.18653/v1/D19-5204\",\n    pages = \"54--61\"\n}\n", "homepage": "https://github.com/tsuruoka-lab/BSD", "license": "CC BY-NC-SA 4.0", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "tag": {"dtype": "string", "id": null, "_type": "Value"}, "title": {"dtype": "string", "id": null, "_type": "Value"}, "original_language": {"dtype": "string", "id": null, "_type": "Value"}, "no": {"dtype": "int32", "id": null, "_type": "Value"}, "en_speaker": {"dtype": "string", "id": null, "_type": "Value"}, "ja_speaker": {"dtype": "string", "id": null, "_type": "Value"}, "en_sentence": {"dtype": "string", "id": null, "_type": "Value"}, "ja_sentence": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "bsd_ja_en", "config_name": "default", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 4778409, "num_examples": 20000, "dataset_name": "bsd_ja_en"}, "test": {"name": "test", "num_bytes": 493038, "num_examples": 2120, "dataset_name": "bsd_ja_en"}, "validation": {"name": "validation", "num_bytes": 477964, "num_examples": 2051, "dataset_name": "bsd_ja_en"}}, "download_checksums": {"https://raw.githubusercontent.com/tsuruoka-lab/BSD/master/train.json": {"num_bytes": 6740756, "checksum": "e011d1b02ed1acdbed5b677b54a3ceae26baaf6cd2a4fea64e4af4ff699c5ffb"}, "https://raw.githubusercontent.com/tsuruoka-lab/BSD/master/dev.json": {"num_bytes": 687409, "checksum": "43c11e7d9bdb4ab8ecc83114c82a77c596f98c8845af92fb74c8ca12cc9cfa5c"}, "https://raw.githubusercontent.com/tsuruoka-lab/BSD/master/test.json": {"num_bytes": 706880, "checksum": "9da3f8907147b2424671058c93d9f41179ddbd8d0c8298a3e8546c2703174d31"}}, "download_size": 8135045, "post_processing_size": null, "dataset_size": 5749411, "size_in_bytes": 13884456}}