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  1. README.md +4 -4
  2. coco.py +206 -0
README.md CHANGED
@@ -1,8 +1,4 @@
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  ---
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- TODO: Add YAML tags here. Copy-paste the tags obtained with the online tagging app: https://huggingface.co/spaces/huggingface/datasets-tagging
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- ---
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-
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- # Dataset Card for [COCO]
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  annotations_creators:
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  - expert-generated
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  language:
@@ -32,6 +28,10 @@ train-eval-index:
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  eval_split: test
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  task: token-classification
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  task_id: entity_extraction
 
 
 
 
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  ## Table of Contents
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  - [Table of Contents](#table-of-contents)
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  - [Dataset Description](#dataset-description)
 
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  ---
 
 
 
 
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  annotations_creators:
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  - expert-generated
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  language:
 
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  eval_split: test
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  task: token-classification
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  task_id: entity_extraction
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+ ---
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+
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+ # Dataset Card for [COCO]
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+
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  ## Table of Contents
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  - [Table of Contents](#table-of-contents)
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  - [Dataset Description](#dataset-description)
coco.py CHANGED
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+ # Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
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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
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+ # 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
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+ # limitations under the License.
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+ # TODO: Address all TODOs and remove all explanatory comments
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+ """TODO: Add a description here."""
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+
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+
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+ import csv
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+ import json
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+ import os
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+
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+ import datasets
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+
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+ _URL = "dataset_coco.json"
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+ _URLS = {
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+ "train": _URL
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+ }
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+
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+ # TODO: Add BibTeX citation
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+ # Find for instance the citation on arxiv or on the dataset repo/website
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+ _CITATION = """\
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+ @InProceedings{huggingface:dataset,
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+ title = {A great new dataset},
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+ author={huggingface, Inc.
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+ },
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+ year={2020}
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+ }
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+ """
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+
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+ # TODO: Add description of the dataset here
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+ # You can copy an official description
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+ _DESCRIPTION = """\
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+ This new dataset is designed to solve this great NLP task and is crafted with a lot of care.
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+ """
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+
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+ # TODO: Add a link to an official homepage for the dataset here
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+ _HOMEPAGE = ""
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+
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+ # TODO: Add the licence for the dataset here if you can find it
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+ _LICENSE = ""
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+
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+ # TODO: Add link to the official dataset URLs here
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+ # The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
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+ # This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
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+ _URLS = {
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+ "first_domain": "https://huggingface.co/great-new-dataset-first_domain.zip",
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+ "second_domain": "https://huggingface.co/great-new-dataset-second_domain.zip",
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+ }
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+
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+
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+ # TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
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+ class NewDataset(datasets.GeneratorBasedBuilder):
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+ """TODO: Short description of my dataset."""
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+
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+ VERSION = datasets.Version("1.1.0")
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+
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+ # This is an example of a dataset with multiple configurations.
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+ # If you don't want/need to define several sub-sets in your dataset,
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+ # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
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+
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+ # If you need to make complex sub-parts in the datasets with configurable options
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+ # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
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+ # BUILDER_CONFIG_CLASS = MyBuilderConfig
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+
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+ # You will be able to load one or the other configurations in the following list with
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+ # data = datasets.load_dataset('my_dataset', 'first_domain')
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+ # data = datasets.load_dataset('my_dataset', 'second_domain')
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+ BUILDER_CONFIGS = [
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+ datasets.BuilderConfig(name="first_domain", version=VERSION, description="This part of my dataset covers a first domain"),
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+ datasets.BuilderConfig(name="second_domain", version=VERSION, description="This part of my dataset covers a second domain"),
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+ ]
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+
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+ DEFAULT_CONFIG_NAME = "first_domain" # It's not mandatory to have a default configuration. Just use one if it make sense.
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+
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+ def _info(self):
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+ # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
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+ if self.config.name == "first_domain": # This is the name of the configuration selected in BUILDER_CONFIGS above
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+ features = datasets.Features(
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+ {
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+ "images": datasets.Sequence(
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+ {
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+ "filepath": datasets.Value("string"),
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+ "sentids": datasets.Sequence(datasets.Value("int64")),
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+ "filename": datasets.Value("string"),
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+ "imgid": datasets.Value("int64"),
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+ "split":datasets.Value("string"),
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+ "sentences": datasets.Sequence(
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+ {
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+ "tokens": datasets.Sequence(datasets.Value("string")),
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+ "raw": datasets.Value("string"),
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+ "imgid": datasets.Value("int64"),
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+ "sentid": datasets.Value("int64")
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+ }
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+ ),
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+ "cocoid": datasets.Value("int64")
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+ }
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+ ),
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+ "datasets": datasets.Value("string")
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+ }
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+ )
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+ else: # This is an example to show how to have different features for "first_domain" and "second_domain"
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+ features = datasets.Features(
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+ {
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+ "images": datasets.Sequence(
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+ {
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+ "filepath": datasets.Value("string"),
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+ "sentids": datasets.Sequence(datasets.Value("int64")),
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+ "filename": datasets.Value("string"),
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+ "imgid": datasets.Value("int64"),
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+ "split":datasets.Value("string"),
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+ "sentences": datasets.Sequence(
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+ {
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+ "tokens": datasets.Sequence(datasets.Value("string")),
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+ "raw": datasets.Value("string"),
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+ "imgid": datasets.Value("int64"),
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+ "sentid": datasets.Value("int64")
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+ }
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+ ),
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+ "cocoid": datasets.Value("int64")
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+ }
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+ ),
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+ "datasets": datasets.Value("string")
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+ }
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+ )
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+ return datasets.DatasetInfo(
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+ # This is the description that will appear on the datasets page.
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+ description=_DESCRIPTION,
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+ # This defines the different columns of the dataset and their types
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+ features=features, # Here we define them above because they are different between the two configurations
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+ # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
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+ # specify them. They'll be used if as_supervised=True in builder.as_dataset.
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+ # supervised_keys=("sentence", "label"),
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+ # Homepage of the dataset for documentation
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+ homepage=_HOMEPAGE,
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+ # License for the dataset if available
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+ license=_LICENSE,
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+ # Citation for the dataset
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+ citation=_CITATION,
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+ )
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+
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+ def _split_generators(self, dl_manager):
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+ # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
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+ # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
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+
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+ # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
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+ # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
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+ # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
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+ urls = _URLS[self.config.name]
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+ data_dir = dl_manager.download_and_extract(urls)
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+ return [
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+ datasets.SplitGenerator(
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+ name=datasets.Split.TRAIN,
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+ # These kwargs will be passed to _generate_examples
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+ gen_kwargs={
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+ "filepath": os.path.join(data_dir, "train.jsonl"),
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+ "split": "train",
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+ },
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+ ),
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+ datasets.SplitGenerator(
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+ name=datasets.Split.VALIDATION,
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+ # These kwargs will be passed to _generate_examples
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+ gen_kwargs={
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+ "filepath": os.path.join(data_dir, "dev.jsonl"),
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+ "split": "dev",
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+ },
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+ ),
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+ datasets.SplitGenerator(
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+ name=datasets.Split.TEST,
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+ # These kwargs will be passed to _generate_examples
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+ gen_kwargs={
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+ "filepath": os.path.join(data_dir, "test.jsonl"),
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+ "split": "test"
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+ },
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+ ),
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+ ]
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+
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+ # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
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+ def _generate_examples(self, filepath, split):
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+ # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
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+ # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
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+ with open(filepath, encoding="utf-8") as f:
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+ for key, row in enumerate(f):
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+ data = json.loads(row)
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+ if self.config.name == "first_domain":
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+ # Yields examples as (key, example) tuples
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+ yield key, {
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+ "sentence": data["sentence"],
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+ "option1": data["option1"],
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+ "answer": "" if split == "test" else data["answer"],
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+ }
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+ else:
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+ yield key, {
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+ "sentence": data["sentence"],
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+ "option2": data["option2"],
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+ "second_domain_answer": "" if split == "test" else data["second_domain_answer"],
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+ }