Upload dataset + remove script

README.md

@@ -37,21 +37,6 @@ dataset_info:
     num_examples: 15840
   download_size: 0
   dataset_size: 626076394
-- config_name: unlabeled
-  features:
-  - name: image_url
-    dtype: string
-  - name: caption
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 584520156
-    num_examples: 3318333
-  - name: validation
-    num_bytes: 2698726
-    num_examples: 15840
-  download_size: 567211172
-  dataset_size: 587218882
 - config_name: labeled
   features:
   - name: image_url
@@ -66,10 +51,37 @@ dataset_info:
     sequence: float64
   splits:
   - name: train
-    num_bytes: 1199330856
+    num_bytes: 1199325228
     num_examples: 2007090
-  download_size: 1282463277
-  dataset_size: 1199330856
+  download_size: 532762865
+  dataset_size: 1199325228
+- config_name: unlabeled
+  features:
+  - name: image_url
+    dtype: string
+  - name: caption
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 584517500
+    num_examples: 3318333
+  - name: validation
+    num_bytes: 2698710
+    num_examples: 15840
+  download_size: 375258708
+  dataset_size: 587216210
+configs:
+- config_name: labeled
+  data_files:
+  - split: train
+    path: labeled/train-*
+- config_name: unlabeled
+  data_files:
+  - split: train
+    path: unlabeled/train-*
+  - split: validation
+    path: unlabeled/validation-*
+  default: true
 ---
 
 # Dataset Card for Conceptual Captions
@@ -153,7 +165,7 @@ def fetch_images(batch, num_threads, timeout=None, retries=0):
 
 
 num_threads = 20
-dset = load_dataset("conceptual_captions")
+dset = load_dataset("google-research-datasets/conceptual_captions")
 dset = dset.map(fetch_images, batched=True, batch_size=100, fn_kwargs={"num_threads": num_threads})
 ```
 

conceptual_captions.py

@@ -1,159 +0,0 @@
-# coding=utf-8
-# Copyright 2020 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
-"""Conceptual Captions dataset."""
-
-import csv
-import textwrap
-
-import datasets
-
-
-_DESCRIPTION = """\
-Google's Conceptual Captions dataset has more than 3 million images, paired with natural-language captions.
-In contrast with the curated style of the MS-COCO images, Conceptual Captions images and their raw descriptions are harvested from the web,
-and therefore represent a wider variety of styles. The raw descriptions are harvested from the Alt-text HTML attribute associated with web images.
-The authors developed an automatic pipeline that extracts, filters, and transforms candidate image/caption pairs, with the goal of achieving a balance of cleanliness,
-informativeness, fluency, and learnability of the resulting captions.
-"""
-
-_HOMEPAGE = "http://data.statmt.org/cc-100/"
-
-_LICENSE = """\
-The dataset may be freely used for any purpose, although acknowledgement of
-Google LLC ("Google") as the data source would be appreciated. The dataset is
-provided "AS IS" without any warranty, express or implied. Google disclaims all
-liability for any damages, direct or indirect, resulting from the use of the
-dataset.
-"""
-
-_CITATION = """\
-@inproceedings{sharma2018conceptual,
-  title = {Conceptual Captions: A Cleaned, Hypernymed, Image Alt-text Dataset For Automatic Image Captioning},
-  author = {Sharma, Piyush and Ding, Nan and Goodman, Sebastian and Soricut, Radu},
-  booktitle = {Proceedings of ACL},
-  year = {2018},
-}
-"""
-
-_URLS = {
-    "unlabeled": {
-        "train": "https://storage.googleapis.com/gcc-data/Train/GCC-training.tsv?_ga=2.191230122.-1896153081.1529438250",
-        "validation": "https://storage.googleapis.com/gcc-data/Validation/GCC-1.1.0-Validation.tsv?_ga=2.141047602.-1896153081.1529438250",
-    },
-    "labeled": {
-        "train": "https://storage.googleapis.com/conceptual-captions-v1-1-labels/Image_Labels_Subset_Train_GCC-Labels-training.tsv?_ga=2.234395421.-20118413.1607637118",
-    },
-}
-
-_DESCRIPTIONS = {
-    "unlabeled": textwrap.dedent(
-        """\
-        The basic version of the dataset split into Training, Validation, and Test splits.
-        The Training split consists of 3,318,333 image-URL/caption pairs, with a total number of 51,201 total token types in the captions (i.e., total vocabulary).
-        The average number of tokens per captions is 10.3 (standard deviation of 4.5), while the median is 9.0 tokens per caption.
-        The Validation split consists of 15,840 image-URL/caption pairs, with similar statistics.
-        """
-    ),
-    "labeled": textwrap.dedent(
-        """\
-        A subset of 2,007,090 image-URL/caption pairs from the training set with machine-generated image labels.
-        The image labels are obtained using the Google Cloud Vision API.
-        Each image label has a machine-generated identifier (MID) corresponding to the label's Google Knowledge Graph entry and a confidence score for its presence in the image.
-
-        Note: 2,007,528 is the number of image-URL/caption pairs specified by the authors, but some rows are missing labels, so they are not included.
-        """
-    ),
-}
-
-
-class ConceptualCaptions(datasets.GeneratorBasedBuilder):
-    """Builder for Conceptual Captions dataset."""
-
-    VERSION = datasets.Version("1.0.0")
-
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig("unlabeled", version=VERSION, description=_DESCRIPTIONS["unlabeled"]),
-        datasets.BuilderConfig("labeled", version=VERSION, description=_DESCRIPTIONS["labeled"]),
-    ]
-
-    DEFAULT_CONFIG_NAME = "unlabeled"
-
-    def _info(self):
-        features = datasets.Features(
-            {
-                "image_url": datasets.Value("string"),
-                "caption": datasets.Value("string"),
-            },
-        )
-        if self.config.name == "labeled":
-            features.update(
-                {
-                    "labels": datasets.Sequence(datasets.Value("string")),
-                    "MIDs": datasets.Sequence(datasets.Value("string")),
-                    "confidence_scores": datasets.Sequence(datasets.Value("float64")),
-                }
-            )
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=features,
-            supervised_keys=None,
-            homepage=_HOMEPAGE,
-            license=_LICENSE,
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        downloaded_data = dl_manager.download(_URLS[self.config.name])
-        splits = [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={"annotations_file": downloaded_data["train"]},
-            ),
-        ]
-        if self.config.name == "unlabeled":
-            splits += [
-                datasets.SplitGenerator(
-                    name=datasets.Split.VALIDATION,
-                    gen_kwargs={"annotations_file": downloaded_data["validation"]},
-                ),
-            ]
-        return splits
-
-    def _generate_examples(self, annotations_file):
-        if self.config.name == "unlabeled":
-            with open(annotations_file, encoding="utf-8") as f:
-                for i, row in enumerate(csv.reader(f, delimiter="\t")):
-                    # Sanity check
-                    assert len(row) == 2
-                    caption, image_url = row
-                    yield i, {
-                        "image_url": image_url,
-                        "caption": caption,
-                    },
-        else:
-            with open(annotations_file, encoding="utf-8") as f:
-                for i, row in enumerate(csv.reader(f, delimiter="\t")):
-                    caption, image_url, labels, MIDs, confidence_scores = row
-                    if not labels:
-                        continue
-                    yield i, {
-                        "image_url": image_url,
-                        "caption": caption,
-                        "labels": labels.split(","),
-                        "MIDs": MIDs.split(","),
-                        "confidence_scores": [float(x) for x in confidence_scores.split(",")],
-                    },

labeled/train-00000-of-00003.parquet

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labeled/train-00001-of-00003.parquet

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labeled/train-00002-of-00003.parquet

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unlabeled/train-00000-of-00002.parquet

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unlabeled/validation-00000-of-00001.parquet

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