Convert dataset to Parquet (#7)

- Convert dataset to Parquet (600f7a8087d9ba5267b035b3321fdd8d4ce408b8)
- Add 2.0.0 data files (b88a802b43dd71ecbc064e8103d2f8109b18d028)
- Add 3.0.0 data files (3adf6249f0cc8409a97a4d38471529ef5f7dc496)
- Delete loading script (898abee5394c5314a1d43f5c954745d0b891e8f1)
- Delete data folder (53e0d9e2320e7d11cc4b737b28db3af02bfb3bc3)

data/cnn_stories.tgz → 1.0.0/test-00000-of-00001.parquet

@@ -1,3 +1,3 @@
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-size 158577824
+oid sha256:c039e9a26d4a7f71ab2ae18ff31a8d31b04744c41ef47086f1b31e45512c342f
+size 29994057

data/dailymail_stories.tgz → 1.0.0/train-00000-of-00003.parquet

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1.0.0/train-00001-of-00003.parquet

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1.0.0/train-00002-of-00003.parquet

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1.0.0/validation-00000-of-00001.parquet

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2.0.0/validation-00000-of-00001.parquet

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3.0.0/test-00000-of-00001.parquet

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3.0.0/train-00001-of-00003.parquet

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3.0.0/validation-00000-of-00001.parquet

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README.md

@@ -19,17 +19,8 @@ task_ids:
 - news-articles-summarization
 paperswithcode_id: cnn-daily-mail-1
 pretty_name: CNN / Daily Mail
-train-eval-index:
-- config: 3.0.0
-  task: summarization
-  task_id: summarization
-  splits:
-    eval_split: test
-  col_mapping:
-    article: text
-    highlights: target
 dataset_info:
-- config_name: 3.0.0
+- config_name: 1.0.0
   features:
   - name: article
     dtype: string
@@ -39,17 +30,17 @@ dataset_info:
     dtype: string
   splits:
   - name: train
-    num_bytes: 1261704133
+    num_bytes: 1261703785
     num_examples: 287113
   - name: validation
-    num_bytes: 57732436
+    num_bytes: 57732412
     num_examples: 13368
   - name: test
-    num_bytes: 49925756
+    num_bytes: 49925732
     num_examples: 11490
-  download_size: 585439472
-  dataset_size: 1369362325
-- config_name: 1.0.0
+  download_size: 836927248
+  dataset_size: 1369361929
+- config_name: 2.0.0
   features:
   - name: article
     dtype: string
@@ -59,17 +50,17 @@ dataset_info:
     dtype: string
   splits:
   - name: train
-    num_bytes: 1261704133
+    num_bytes: 1261703785
     num_examples: 287113
   - name: validation
-    num_bytes: 57732436
+    num_bytes: 57732412
     num_examples: 13368
   - name: test
-    num_bytes: 49925756
+    num_bytes: 49925732
     num_examples: 11490
-  download_size: 585439472
-  dataset_size: 1369362325
-- config_name: 2.0.0
+  download_size: 837094602
+  dataset_size: 1369361929
+- config_name: 3.0.0
   features:
   - name: article
     dtype: string
@@ -79,16 +70,50 @@ dataset_info:
     dtype: string
   splits:
   - name: train
-    num_bytes: 1261704133
+    num_bytes: 1261703785
     num_examples: 287113
   - name: validation
-    num_bytes: 57732436
+    num_bytes: 57732412
     num_examples: 13368
   - name: test
-    num_bytes: 49925756
+    num_bytes: 49925732
     num_examples: 11490
-  download_size: 585439472
-  dataset_size: 1369362325
+  download_size: 837094602
+  dataset_size: 1369361929
+configs:
+- config_name: 1.0.0
+  data_files:
+  - split: train
+    path: 1.0.0/train-*
+  - split: validation
+    path: 1.0.0/validation-*
+  - split: test
+    path: 1.0.0/test-*
+- config_name: 2.0.0
+  data_files:
+  - split: train
+    path: 2.0.0/train-*
+  - split: validation
+    path: 2.0.0/validation-*
+  - split: test
+    path: 2.0.0/test-*
+- config_name: 3.0.0
+  data_files:
+  - split: train
+    path: 3.0.0/train-*
+  - split: validation
+    path: 3.0.0/validation-*
+  - split: test
+    path: 3.0.0/test-*
+train-eval-index:
+- config: 3.0.0
+  task: summarization
+  task_id: summarization
+  splits:
+    eval_split: test
+  col_mapping:
+    article: text
+    highlights: target
 ---
 # Dataset Card for CNN Dailymail Dataset
 

cnn_dailymail.py

@@ -1,250 +0,0 @@
-# coding=utf-8
-# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
-#
-# 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.
-
-# Lint as: python3
-"""CNN/DailyMail Summarization dataset, non-anonymized version."""
-
-import hashlib
-import os
-
-import datasets
-
-
-logger = datasets.logging.get_logger(__name__)
-
-
-_HOMEPAGE = "https://github.com/abisee/cnn-dailymail"
-
-_DESCRIPTION = """\
-CNN/DailyMail non-anonymized summarization dataset.
-
-There are two features:
-  - article: text of news article, used as the document to be summarized
-  - highlights: joined text of highlights with <s> and </s> around each
-    highlight, which is the target summary
-"""
-
-# The second citation introduces the source data, while the first
-# introduces the specific form (non-anonymized) we use here.
-_CITATION = """\
-@article{DBLP:journals/corr/SeeLM17,
-  author    = {Abigail See and
-               Peter J. Liu and
-               Christopher D. Manning},
-  title     = {Get To The Point: Summarization with Pointer-Generator Networks},
-  journal   = {CoRR},
-  volume    = {abs/1704.04368},
-  year      = {2017},
-  url       = {http://arxiv.org/abs/1704.04368},
-  archivePrefix = {arXiv},
-  eprint    = {1704.04368},
-  timestamp = {Mon, 13 Aug 2018 16:46:08 +0200},
-  biburl    = {https://dblp.org/rec/bib/journals/corr/SeeLM17},
-  bibsource = {dblp computer science bibliography, https://dblp.org}
-}
-
-@inproceedings{hermann2015teaching,
-  title={Teaching machines to read and comprehend},
-  author={Hermann, Karl Moritz and Kocisky, Tomas and Grefenstette, Edward and Espeholt, Lasse and Kay, Will and Suleyman, Mustafa and Blunsom, Phil},
-  booktitle={Advances in neural information processing systems},
-  pages={1693--1701},
-  year={2015}
-}
-"""
-
-_DL_URLS = {
-    "cnn_stories": "https://huggingface.co/datasets/cnn_dailymail/resolve/11343c3752184397d56efc19a8a7cceb68089318/data/cnn_stories.tgz",
-    "dm_stories": "https://huggingface.co/datasets/cnn_dailymail/resolve/11343c3752184397d56efc19a8a7cceb68089318/data/dailymail_stories.tgz",
-    "train": "https://raw.githubusercontent.com/abisee/cnn-dailymail/master/url_lists/all_train.txt",
-    "validation": "https://raw.githubusercontent.com/abisee/cnn-dailymail/master/url_lists/all_val.txt",
-    "test": "https://raw.githubusercontent.com/abisee/cnn-dailymail/master/url_lists/all_test.txt",
-}
-
-_HIGHLIGHTS = "highlights"
-_ARTICLE = "article"
-
-_SUPPORTED_VERSIONS = [
-    # Using cased version.
-    datasets.Version("3.0.0", "Using cased version."),
-    # Same data as 0.0.2
-    datasets.Version("1.0.0", ""),
-    # Having the model predict newline separators makes it easier to evaluate
-    # using summary-level ROUGE.
-    datasets.Version("2.0.0", "Separate target sentences with newline."),
-]
-
-
-_DEFAULT_VERSION = datasets.Version("3.0.0", "Using cased version.")
-
-
-class CnnDailymailConfig(datasets.BuilderConfig):
-    """BuilderConfig for CnnDailymail."""
-
-    def __init__(self, **kwargs):
-        """BuilderConfig for CnnDailymail.
-
-        Args:
-
-          **kwargs: keyword arguments forwarded to super.
-        """
-        super(CnnDailymailConfig, self).__init__(**kwargs)
-
-
-def _get_url_hashes(path):
-    """Get hashes of urls in file."""
-    urls = _read_text_file_path(path)
-
-    def url_hash(u):
-        h = hashlib.sha1()
-        try:
-            u = u.encode("utf-8")
-        except UnicodeDecodeError:
-            logger.error("Cannot hash url: %s", u)
-        h.update(u)
-        return h.hexdigest()
-
-    return {url_hash(u) for u in urls}
-
-
-def _get_hash_from_path(p):
-    """Extract hash from path."""
-    return os.path.splitext(os.path.basename(p))[0]
-
-
-DM_SINGLE_CLOSE_QUOTE = "\u2019"  # unicode
-DM_DOUBLE_CLOSE_QUOTE = "\u201d"
-# acceptable ways to end a sentence
-END_TOKENS = [".", "!", "?", "...", "'", "`", '"', DM_SINGLE_CLOSE_QUOTE, DM_DOUBLE_CLOSE_QUOTE, ")"]
-
-
-def _read_text_file_path(path):
-    with open(path, "r", encoding="utf-8") as f:
-        lines = [line.strip() for line in f]
-    return lines
-
-
-def _read_text_file(file):
-    return [line.decode("utf-8").strip() for line in file]
-
-
-def _get_art_abs(story_file, tfds_version):
-    """Get abstract (highlights) and article from a story file path."""
-    # Based on https://github.com/abisee/cnn-dailymail/blob/master/
-    #     make_datafiles.py
-
-    lines = _read_text_file(story_file)
-
-    # The github code lowercase the text and we removed it in 3.0.0.
-
-    # Put periods on the ends of lines that are missing them
-    # (this is a problem in the dataset because many image captions don't end in
-    # periods; consequently they end up in the body of the article as run-on
-    # sentences)
-    def fix_missing_period(line):
-        """Adds a period to a line that is missing a period."""
-        if "@highlight" in line:
-            return line
-        if not line:
-            return line
-        if line[-1] in END_TOKENS:
-            return line
-        return line + " ."
-
-    lines = [fix_missing_period(line) for line in lines]
-
-    # Separate out article and abstract sentences
-    article_lines = []
-    highlights = []
-    next_is_highlight = False
-    for line in lines:
-        if not line:
-            continue  # empty line
-        elif line.startswith("@highlight"):
-            next_is_highlight = True
-        elif next_is_highlight:
-            highlights.append(line)
-        else:
-            article_lines.append(line)
-
-    # Make article into a single string
-    article = " ".join(article_lines)
-
-    if tfds_version >= "2.0.0":
-        abstract = "\n".join(highlights)
-    else:
-        abstract = " ".join(highlights)
-
-    return article, abstract
-
-
-class CnnDailymail(datasets.GeneratorBasedBuilder):
-    """CNN/DailyMail non-anonymized summarization dataset."""
-
-    BUILDER_CONFIGS = [
-        CnnDailymailConfig(name=str(version), description="Plain text", version=version)
-        for version in _SUPPORTED_VERSIONS
-    ]
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    _ARTICLE: datasets.Value("string"),
-                    _HIGHLIGHTS: datasets.Value("string"),
-                    "id": datasets.Value("string"),
-                }
-            ),
-            supervised_keys=None,
-            homepage=_HOMEPAGE,
-            citation=_CITATION,
-        )
-
-    def _vocab_text_gen(self, paths):
-        for _, ex in self._generate_examples(paths):
-            yield " ".join([ex[_ARTICLE], ex[_HIGHLIGHTS]])
-
-    def _split_generators(self, dl_manager):
-        dl_paths = dl_manager.download(_DL_URLS)
-        return [
-            datasets.SplitGenerator(
-                name=split,
-                gen_kwargs={
-                    "urls_file": dl_paths[split],
-                    "files_per_archive": [
-                        dl_manager.iter_archive(dl_paths["cnn_stories"]),
-                        dl_manager.iter_archive(dl_paths["dm_stories"]),
-                    ],
-                },
-            )
-            for split in [datasets.Split.TRAIN, datasets.Split.VALIDATION, datasets.Split.TEST]
-        ]
-
-    def _generate_examples(self, urls_file, files_per_archive):
-        urls = _get_url_hashes(urls_file)
-        idx = 0
-        for files in files_per_archive:
-            for path, file in files:
-                hash_from_path = _get_hash_from_path(path)
-                if hash_from_path in urls:
-                    article, highlights = _get_art_abs(file, self.config.version)
-                    if not article or not highlights:
-                        continue
-                    yield idx, {
-                        _ARTICLE: article,
-                        _HIGHLIGHTS: highlights,
-                        "id": hash_from_path,
-                    }
-                    idx += 1