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dataset_coco.json → Luka-Wang--COCO/json-train.parquet

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README.md

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----
-annotations_creators:
-- expert-generated
-language:
-- en
-language_creators:
-- found
-license:
-- mit
-multilinguality:
-- monolingual
-paperswithcode_id: acronym-identification
-pretty_name: Acronym Identification Dataset
-size_categories:
-- 10K<n<100K
-source_datasets:
-- original
-task_categories:
-- token-classification
-task_ids:
-- token-classification-other-acronym-identification
-train-eval-index:
-- col_mapping:
-    labels: tags
-    tokens: tokens
-  config: default
-  splits:
-    eval_split: test
-  task: token-classification
-  task_id: entity_extraction
----
-
-# Dataset Card for [COCO]
-
-## Table of Contents
-- [Table of Contents](#table-of-contents)
-- [Dataset Description](#dataset-description)
-  - [Dataset Summary](#dataset-summary)
-  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
-  - [Languages](#languages)
-- [Dataset Structure](#dataset-structure)
-  - [Data Instances](#data-instances)
-  - [Data Fields](#data-fields)
-  - [Data Splits](#data-splits)
-- [Dataset Creation](#dataset-creation)
-  - [Curation Rationale](#curation-rationale)
-  - [Source Data](#source-data)
-  - [Annotations](#annotations)
-  - [Personal and Sensitive Information](#personal-and-sensitive-information)
-- [Considerations for Using the Data](#considerations-for-using-the-data)
-  - [Social Impact of Dataset](#social-impact-of-dataset)
-  - [Discussion of Biases](#discussion-of-biases)
-  - [Other Known Limitations](#other-known-limitations)
-- [Additional Information](#additional-information)
-  - [Dataset Curators](#dataset-curators)
-  - [Licensing Information](#licensing-information)
-  - [Citation Information](#citation-information)
-  - [Contributions](#contributions)
-
-## Dataset Description
-
-- **Homepage:**
-- **Repository:**
-- **Paper:**
-- **Leaderboard:**
-- **Point of Contact:**
-
-### Dataset Summary
-
-[More Information Needed]
-
-### Supported Tasks and Leaderboards
-
-[More Information Needed]
-
-### Languages
-
-[More Information Needed]
-
-## Dataset Structure
-
-### Data Instances
-
-[More Information Needed]
-
-### Data Fields
-
-[More Information Needed]
-
-### Data Splits
-
-[More Information Needed]
-
-## Dataset Creation
-
-### Curation Rationale
-
-[More Information Needed]
-
-### Source Data
-
-#### Initial Data Collection and Normalization
-
-[More Information Needed]
-
-#### Who are the source language producers?
-
-[More Information Needed]
-
-### Annotations
-
-#### Annotation process
-
-[More Information Needed]
-
-#### Who are the annotators?
-
-[More Information Needed]
-
-### Personal and Sensitive Information
-
-[More Information Needed]
-
-## Considerations for Using the Data
-
-### Social Impact of Dataset
-
-[More Information Needed]
-
-### Discussion of Biases
-
-[More Information Needed]
-
-### Other Known Limitations
-
-[More Information Needed]
-
-## Additional Information
-
-### Dataset Curators
-
-[More Information Needed]
-
-### Licensing Information
-
-[More Information Needed]
-
-### Citation Information
-
-[More Information Needed]
-
-### Contributions
-
-Thanks to [@github-scuwyh2000](https://github.com/scuwyh2000) for adding this dataset.

coco.py

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-# 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.
-# TODO: Address all TODOs and remove all explanatory comments
-"""TODO: Add a description here."""
-
-
-import csv
-import json
-import os
-
-import datasets
-
-
-# TODO: Add BibTeX citation
-# Find for instance the citation on arxiv or on the dataset repo/website
-_CITATION = """\
-@InProceedings{huggingface:dataset,
-title = {A great new dataset},
-author={huggingface, Inc.
-},
-year={2020}
-}
-"""
-
-# TODO: Add description of the dataset here
-# You can copy an official description
-_DESCRIPTION = """\
-This new dataset is designed to solve this great NLP task and is crafted with a lot of care.
-"""
-
-# TODO: Add a link to an official homepage for the dataset here
-_HOMEPAGE = ""
-
-# TODO: Add the licence for the dataset here if you can find it
-_LICENSE = ""
-
-# TODO: Add link to the official dataset URLs here
-# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
-# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
-_URL = "dataset_coco.json"
-_URLS = {
-    "train": _URL
-}
-
-
-# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
-class NewDataset(datasets.GeneratorBasedBuilder):
-    """TODO: Short description of my dataset."""
-
-    VERSION = datasets.Version("1.1.0")
-
-    # This is an example of a dataset with multiple configurations.
-    # If you don't want/need to define several sub-sets in your dataset,
-    # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
-
-    # If you need to make complex sub-parts in the datasets with configurable options
-    # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
-    # BUILDER_CONFIG_CLASS = MyBuilderConfig
-
-    # You will be able to load one or the other configurations in the following list with
-    # data = datasets.load_dataset('my_dataset', 'first_domain')
-    # data = datasets.load_dataset('my_dataset', 'second_domain')
-    #BUILDER_CONFIGS = [
-    #    datasets.BuilderConfig(name="images", version=VERSION, description="This part of my dataset covers a first domain"),
-    #]
-
-    #DEFAULT_CONFIG_NAME = "train"  # It's not mandatory to have a default configuration. Just use one if it make sense.
-
-    def _info(self):
-        # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
-        features=datasets.Features(
-                {
-                    "filepath": datasets.Value("string"),
-                    "filename": datasets.Value("string")
-                    #"raw": datasets.Value("string")
-                }
-            ),
-        return datasets.DatasetInfo(
-            # This is the description that will appear on the datasets page.
-            description=_DESCRIPTION,
-            # This defines the different columns of the dataset and their types
-            features=features,  # Here we define them above because they are different between the two configurations
-            # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
-            # specify them. They'll be used if as_supervised=True in builder.as_dataset.
-            # supervised_keys=("sentence", "label"),
-            # Homepage of the dataset for documentation
-            homepage=_HOMEPAGE,
-            # License for the dataset if available
-            license=_LICENSE,
-            # Citation for the dataset
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        urls_to_download = _URLS
-        downloaded_files = dl_manager.download_and_extract(urls_to_download)
-        print(download_files['train'])
-        return [
-            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
-            # datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]}),
-        ]
-
-    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
-    def _generate_examples(self, filepath):
-        """This function returns the examples in the raw (text) form."""
-        logger.info("generating examples from = %s", filepath)
-        print(download_files['train'])
-        with open(filepath, encoding="utf-8") as f:
-            data = json.loads(f)
-            for image in data["images"]:
-                file_path = image["filepath"]
-                file_name = image["filename"]
-                yield id_, {
-                        "filepath": file_path,
-                        "filename": file_name
-                    }
-                #for sentence in image["sentences"]:
-                #    raw = sentence["raw"]
-                #    yield id_, {
-                #        "filepath": filepath,
-                #        "filename": filename,
-                #        "raw": raw
-                #    }