Convert dataset to Parquet (#4)

- Convert dataset to Parquet (c29c92db214250cfc9cd1dbd6c9d39326bbfe5d5)
- Delete loading script (01bd059da26164e7f76995b65857e975b025fd99)

README.md

@@ -12,7 +12,6 @@ license:
 multilinguality:
 - multilingual
 - translation
-pretty_name: CMU Document Grounded Conversations
 size_categories:
 - 1K<n<10K
 source_datasets:
@@ -20,6 +19,7 @@ source_datasets:
 task_categories:
 - translation
 task_ids: []
+pretty_name: CMU Document Grounded Conversations
 dataset_info:
   features:
   - name: date
@@ -64,16 +64,25 @@ dataset_info:
     dtype: int64
   splits:
   - name: train
-    num_bytes: 3142398
+    num_bytes: 3140818
     num_examples: 8060
   - name: test
-    num_bytes: 379521
+    num_bytes: 379465
     num_examples: 960
   - name: validation
-    num_bytes: 368726
+    num_bytes: 368670
     num_examples: 942
-  download_size: 8749685
-  dataset_size: 3890645
+  download_size: 1039828
+  dataset_size: 3888953
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*
+  - split: test
+    path: data/test-*
+  - split: validation
+    path: data/validation-*
 ---
 
 # Dataset Card for CMU Document Grounded Conversations

cmu_hinglish_dog.py

@@ -1,190 +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.
-
-import json
-import os
-import re
-
-import datasets
-
-
-_CITATION = """\
-@inproceedings{cmu_dog_emnlp18,
-    title={A Dataset for Document Grounded Conversations},
-    author={Zhou, Kangyan and Prabhumoye, Shrimai and Black, Alan W},
-    year={2018},
-    booktitle={Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing}
-}
-
-@inproceedings{khanuja-etal-2020-gluecos,
-    title = "{GLUEC}o{S}: An Evaluation Benchmark for Code-Switched {NLP}",
-    author = "Khanuja, Simran  and
-      Dandapat, Sandipan  and
-      Srinivasan, Anirudh  and
-      Sitaram, Sunayana  and
-      Choudhury, Monojit",
-    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
-    month = jul,
-    year = "2020",
-    address = "Online",
-    publisher = "Association for Computational Linguistics",
-    url = "https://www.aclweb.org/anthology/2020.acl-main.329",
-    pages = "3575--3585"
-}
-"""
-
-_DESCRIPTION = """\
-This is a collection of text conversations in Hinglish (code mixing between Hindi-English) and their corresponding English only versions. Can be used for Translating between the two.
-"""
-
-_HOMEPAGE = "http://festvox.org/cedar/data/notyet/"
-_URL_HINGLISH = "http://festvox.org/cedar/data/notyet/CMUHinglishDoG.zip"
-# From: https://github.com/festvox/datasets-CMU_DoG/archive/master/Conversations.zip
-_URL_ENGLISH = "data-english.zip"
-
-
-class CMUHinglishDoG(datasets.GeneratorBasedBuilder):
-    """Load the CMU Hinglish DoG Data for MT"""
-
-    def _info(self):
-        features = datasets.Features(
-            {
-                "date": datasets.Value("string"),
-                "docIdx": datasets.Value("int64"),
-                "translation": datasets.Translation(languages=["en", "hi_en"]),
-                "uid": datasets.Value("string"),
-                "utcTimestamp": datasets.Value("string"),
-                "rating": datasets.Value("int64"),
-                "status": datasets.Value("int64"),
-                "uid1LogInTime": datasets.Value("string"),
-                "uid1LogOutTime": datasets.Value("string"),
-                "uid1response": {
-                    "response": datasets.Sequence(datasets.Value("int64")),
-                    "type": datasets.Value("string"),
-                },
-                "uid2response": {
-                    "response": datasets.Sequence(datasets.Value("int64")),
-                    "type": datasets.Value("string"),
-                },
-                "user2_id": datasets.Value("string"),
-                "whoSawDoc": datasets.Sequence(datasets.Value("string")),
-                "wikiDocumentIdx": datasets.Value("int64"),
-            }
-        )
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=features,
-            supervised_keys=None,
-            homepage=_HOMEPAGE,
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        """The linking part between Hinglish data and English data is inspired from the implementation in GLUECoS.
-        Refer here for the original script https://github.com/microsoft/GLUECoS/blob/7fdc51653e37a32aee17505c47b7d1da364fa77e/Data/Preprocess_Scripts/preprocess_mt_en_hi.py"""
-
-        eng_path = dl_manager.download_and_extract(_URL_ENGLISH)
-        data_dir_en = os.path.join(eng_path, "Conversations")
-
-        hi_en_path = dl_manager.download_and_extract(_URL_HINGLISH)
-        data_dir_hi_en = os.path.join(hi_en_path, "CMUHinglishDoG", "Conversations_Hinglish")
-
-        hi_en_dirs = {
-            "train": os.path.join(data_dir_hi_en, "train"),
-            "valid": os.path.join(data_dir_hi_en, "valid"),
-            "test": os.path.join(data_dir_hi_en, "test"),
-        }
-
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "hi_en_dir": hi_en_dirs["train"],
-                    "data_dir_en": data_dir_en,
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={
-                    "hi_en_dir": hi_en_dirs["test"],
-                    "data_dir_en": data_dir_en,
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                gen_kwargs={
-                    "hi_en_dir": hi_en_dirs["valid"],
-                    "data_dir_en": data_dir_en,
-                },
-            ),
-        ]
-
-    def _generate_examples(self, hi_en_dir, data_dir_en):
-        """Yields examples."""
-        english_files_train = os.listdir(os.path.join(data_dir_en, "train"))
-        english_files_val = os.listdir(os.path.join(data_dir_en, "valid"))
-        english_files_test = os.listdir(os.path.join(data_dir_en, "test"))
-
-        hinglish_files = os.listdir(hi_en_dir)
-        key = 0
-        for f in hinglish_files:
-            en_file_path = f.split(".json")[0] + ".json"
-            found = True
-            # Looks for the corresponding english file in all 3 splits
-            if en_file_path in english_files_train:
-                en = json.load(open(os.path.join(os.path.join(data_dir_en, "train"), en_file_path)))
-            elif en_file_path in english_files_val:
-                en = json.load(open(os.path.join(os.path.join(data_dir_en, "valid"), en_file_path)))
-            elif en_file_path in english_files_test:
-                en = json.load(open(os.path.join(os.path.join(data_dir_en, "test"), en_file_path)))
-            else:
-                found = False
-            if found:
-                hi_en = json.load(open(os.path.join(hi_en_dir, f)))
-
-                assert len(en["history"]) == len(hi_en["history"])
-
-                for x, y in zip(en["history"], hi_en["history"]):
-                    assert x["docIdx"] == y["docIdx"]
-                    assert x["uid"] == y["uid"]
-                    assert x["utcTimestamp"] == y["utcTimestamp"]
-
-                    x["text"] = re.sub("\t|\n", " ", x["text"])
-                    y["text"] = re.sub("\t|\n", " ", y["text"])
-                    line = {
-                        "date": hi_en["date"],
-                        "uid": x["uid"],
-                        "docIdx": x["docIdx"],
-                        "utcTimestamp": x["utcTimestamp"],
-                        "translation": {"hi_en": y["text"], "en": x["text"]},
-                        "rating": hi_en["rating"],
-                        "status": hi_en["status"],
-                        "uid1LogOutTime": hi_en.get("uid1LogOutTime"),
-                        "uid1LogInTime": hi_en["uid1LogInTime"],
-                        "uid1response": {
-                            "response": hi_en["uid1response"]["response"] if "uid1response" in hi_en else [],
-                            "type": hi_en["uid1response"]["type"] if "uid1response" in hi_en else None,
-                        },
-                        "uid2response": {
-                            "response": hi_en["uid2response"]["response"] if "uid2response" in hi_en else [],
-                            "type": hi_en["uid2response"]["type"] if "uid2response" in hi_en else None,
-                        },
-                        "user2_id": hi_en["user2_id"],
-                        "whoSawDoc": hi_en["whoSawDoc"],
-                        "wikiDocumentIdx": hi_en["wikiDocumentIdx"],
-                    }
-
-                    yield key, line
-                    key += 1

data/test-00000-of-00001.parquet

@@ -0,0 +1,3 @@
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+oid sha256:75aac173d55983b24852fc30f097a60e11e9055cfef485b719e80f77d1c7767e
+size 99568

data/train-00000-of-00001.parquet

@@ -0,0 +1,3 @@
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+oid sha256:ce36a4aa054be82e6fc6857afa796fd1145b6544566e9151655b799ef17bf22a
+size 843924

data/validation-00000-of-00001.parquet

@@ -0,0 +1,3 @@
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+oid sha256:b13707b2fde4203d77a10f7c0e6bfe6ea27ec8014dd7d7cd868ca67f1051cc35
+size 96336