albertvillanova HF staff lhoestq HF staff commited on
Commit
0bb028f
1 Parent(s): ce994f5

Upload dataset + remove script (#3)

Browse files

- Upload dataset (29f23282b10ae67524312674a28f65892329aa0c)
- Upload dataset (ec82bd00b5fe48276aca95c0e23a08ce2301770a)
- Delete conceptual_captions.py (5ecc0c80db882861e8e161c0a9504500826a2a0d)
- nit (76e94d5db028aa350f09083be754f95609a08958)


Co-authored-by: Quentin Lhoest <lhoestq@users.noreply.huggingface.co>

README.md CHANGED
@@ -37,21 +37,6 @@ dataset_info:
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  num_examples: 15840
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  download_size: 0
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  dataset_size: 626076394
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- - config_name: unlabeled
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- features:
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- - name: image_url
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- dtype: string
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- - name: caption
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- dtype: string
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- splits:
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- - name: train
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- num_bytes: 584520156
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- num_examples: 3318333
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- - name: validation
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- num_bytes: 2698726
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- num_examples: 15840
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- download_size: 567211172
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- dataset_size: 587218882
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  - config_name: labeled
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  features:
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  - name: image_url
@@ -66,10 +51,37 @@ dataset_info:
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  sequence: float64
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  splits:
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  - name: train
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- num_bytes: 1199330856
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  num_examples: 2007090
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- download_size: 1282463277
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- dataset_size: 1199330856
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
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  # Dataset Card for Conceptual Captions
@@ -153,7 +165,7 @@ def fetch_images(batch, num_threads, timeout=None, retries=0):
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  num_threads = 20
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- dset = load_dataset("conceptual_captions")
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  dset = dset.map(fetch_images, batched=True, batch_size=100, fn_kwargs={"num_threads": num_threads})
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  ```
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  num_examples: 15840
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  download_size: 0
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  dataset_size: 626076394
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  - config_name: labeled
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  features:
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  - name: image_url
 
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  sequence: float64
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  splits:
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  - name: train
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+ num_bytes: 1199325228
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  num_examples: 2007090
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+ download_size: 532762865
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+ dataset_size: 1199325228
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+ - config_name: unlabeled
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+ features:
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+ - name: image_url
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+ dtype: string
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+ - name: caption
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+ dtype: string
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+ splits:
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+ - name: train
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+ num_bytes: 584517500
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+ num_examples: 3318333
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+ - name: validation
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+ num_bytes: 2698710
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+ num_examples: 15840
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+ download_size: 375258708
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+ dataset_size: 587216210
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+ configs:
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+ - config_name: labeled
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+ data_files:
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+ - split: train
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+ path: labeled/train-*
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+ - config_name: unlabeled
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+ data_files:
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+ - split: train
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+ path: unlabeled/train-*
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+ - split: validation
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+ path: unlabeled/validation-*
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+ default: true
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  ---
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  # Dataset Card for Conceptual Captions
 
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  num_threads = 20
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+ dset = load_dataset("google-research-datasets/conceptual_captions")
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  dset = dset.map(fetch_images, batched=True, batch_size=100, fn_kwargs={"num_threads": num_threads})
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  ```
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conceptual_captions.py DELETED
@@ -1,159 +0,0 @@
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- # coding=utf-8
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- # Copyright 2020 HuggingFace Datasets Authors.
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- #
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- # Licensed under the Apache License, Version 2.0 (the "License");
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- # you may not use this file except in compliance with the License.
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- # You may obtain a copy of the License at
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- #
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- # http://www.apache.org/licenses/LICENSE-2.0
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- #
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- # Unless required by applicable law or agreed to in writing, software
11
- # distributed under the License is distributed on an "AS IS" BASIS,
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- # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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- # See the License for the specific language governing permissions and
14
- # limitations under the License.
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-
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- # Lint as: python3
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- """Conceptual Captions dataset."""
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-
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- import csv
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- import textwrap
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-
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- import datasets
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-
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-
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- _DESCRIPTION = """\
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- Google's Conceptual Captions dataset has more than 3 million images, paired with natural-language captions.
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- In contrast with the curated style of the MS-COCO images, Conceptual Captions images and their raw descriptions are harvested from the web,
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- and therefore represent a wider variety of styles. The raw descriptions are harvested from the Alt-text HTML attribute associated with web images.
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- The authors developed an automatic pipeline that extracts, filters, and transforms candidate image/caption pairs, with the goal of achieving a balance of cleanliness,
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- informativeness, fluency, and learnability of the resulting captions.
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- """
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-
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- _HOMEPAGE = "http://data.statmt.org/cc-100/"
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-
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- _LICENSE = """\
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- The dataset may be freely used for any purpose, although acknowledgement of
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- Google LLC ("Google") as the data source would be appreciated. The dataset is
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- provided "AS IS" without any warranty, express or implied. Google disclaims all
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- liability for any damages, direct or indirect, resulting from the use of the
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- dataset.
41
- """
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-
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- _CITATION = """\
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- @inproceedings{sharma2018conceptual,
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- title = {Conceptual Captions: A Cleaned, Hypernymed, Image Alt-text Dataset For Automatic Image Captioning},
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- author = {Sharma, Piyush and Ding, Nan and Goodman, Sebastian and Soricut, Radu},
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- booktitle = {Proceedings of ACL},
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- year = {2018},
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- }
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- """
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-
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- _URLS = {
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- "unlabeled": {
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- "train": "https://storage.googleapis.com/gcc-data/Train/GCC-training.tsv?_ga=2.191230122.-1896153081.1529438250",
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- "validation": "https://storage.googleapis.com/gcc-data/Validation/GCC-1.1.0-Validation.tsv?_ga=2.141047602.-1896153081.1529438250",
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- },
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- "labeled": {
58
- "train": "https://storage.googleapis.com/conceptual-captions-v1-1-labels/Image_Labels_Subset_Train_GCC-Labels-training.tsv?_ga=2.234395421.-20118413.1607637118",
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- },
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- }
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-
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- _DESCRIPTIONS = {
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- "unlabeled": textwrap.dedent(
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- """\
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- The basic version of the dataset split into Training, Validation, and Test splits.
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- 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).
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- The average number of tokens per captions is 10.3 (standard deviation of 4.5), while the median is 9.0 tokens per caption.
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- The Validation split consists of 15,840 image-URL/caption pairs, with similar statistics.
69
- """
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- ),
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- "labeled": textwrap.dedent(
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- """\
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- A subset of 2,007,090 image-URL/caption pairs from the training set with machine-generated image labels.
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- The image labels are obtained using the Google Cloud Vision API.
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- 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.
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-
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- 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.
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- """
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- ),
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- }
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-
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-
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- class ConceptualCaptions(datasets.GeneratorBasedBuilder):
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- """Builder for Conceptual Captions dataset."""
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-
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- VERSION = datasets.Version("1.0.0")
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-
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- BUILDER_CONFIGS = [
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- datasets.BuilderConfig("unlabeled", version=VERSION, description=_DESCRIPTIONS["unlabeled"]),
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- datasets.BuilderConfig("labeled", version=VERSION, description=_DESCRIPTIONS["labeled"]),
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- ]
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-
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- DEFAULT_CONFIG_NAME = "unlabeled"
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-
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- def _info(self):
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- features = datasets.Features(
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- {
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- "image_url": datasets.Value("string"),
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- "caption": datasets.Value("string"),
100
- },
101
- )
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- if self.config.name == "labeled":
103
- features.update(
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- {
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- "labels": datasets.Sequence(datasets.Value("string")),
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- "MIDs": datasets.Sequence(datasets.Value("string")),
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- "confidence_scores": datasets.Sequence(datasets.Value("float64")),
108
- }
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- )
110
- return datasets.DatasetInfo(
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- description=_DESCRIPTION,
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- features=features,
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- supervised_keys=None,
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- homepage=_HOMEPAGE,
115
- license=_LICENSE,
116
- citation=_CITATION,
117
- )
118
-
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- def _split_generators(self, dl_manager):
120
- downloaded_data = dl_manager.download(_URLS[self.config.name])
121
- splits = [
122
- datasets.SplitGenerator(
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- name=datasets.Split.TRAIN,
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- gen_kwargs={"annotations_file": downloaded_data["train"]},
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- ),
126
- ]
127
- if self.config.name == "unlabeled":
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- splits += [
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- datasets.SplitGenerator(
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- name=datasets.Split.VALIDATION,
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- gen_kwargs={"annotations_file": downloaded_data["validation"]},
132
- ),
133
- ]
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- return splits
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-
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- def _generate_examples(self, annotations_file):
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- if self.config.name == "unlabeled":
138
- with open(annotations_file, encoding="utf-8") as f:
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- for i, row in enumerate(csv.reader(f, delimiter="\t")):
140
- # Sanity check
141
- assert len(row) == 2
142
- caption, image_url = row
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- yield i, {
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- "image_url": image_url,
145
- "caption": caption,
146
- },
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- else:
148
- with open(annotations_file, encoding="utf-8") as f:
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- for i, row in enumerate(csv.reader(f, delimiter="\t")):
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- caption, image_url, labels, MIDs, confidence_scores = row
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- if not labels:
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- continue
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- yield i, {
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- "image_url": image_url,
155
- "caption": caption,
156
- "labels": labels.split(","),
157
- "MIDs": MIDs.split(","),
158
- "confidence_scores": [float(x) for x in confidence_scores.split(",")],
159
- },
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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