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README.md

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+---
+YAML tags:
+- copy-paste the tags obtained with the tagging app: https://github.com/huggingface/datasets-tagging
+---
+
+# Dataset Card for annotated_reference_strings
+
+## 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:** [https://www.github.com/kylase](https://www.github.com/kylase)
+- **Repository:** [https://www.github.com/kylase](https://www.github.com/kylase)
+- **Point of Contact:** [Yuan Chuan Kee](https://www.github.com/kylase) 
+
+### Dataset Summary
+
+The `annotated_reference_strings` dataset comprises millions of the annotated reference strings, i.e. each token of the strings have an associated label such as author, title, year, etc.
+
+These strings are synthesized using citation processor on millions of citations obtained from various sources, spanning different scientific domains.
+
+### Supported Tasks
+
+This dataset can be used for structure prediction.
+
+### Languages
+
+The dataset is composed of reference strings that are in English.
+
+## Dataset Structure
+
+### Data Instances
+
+```json
+{
+  "source": "pubmed",
+  "lang": "en",
+  "entry_type": "article",
+  "doi_prefix": "pubmed19n0001",
+  "csl_style": "annual-reviews",
+  "content": "<citation-number>8.</citation-number> <author>Mohr W.</author> <year>1977.</year> <title>[Morphology of bone tumors. 2. Morphology of benign bone tumors].</title> <container-title>Aktuelle Probleme in Chirurgie und Orthopadie.</container-title> <volume>5:</volume> <page>29–42</page>"
+}
+```
+
+**Important Note:** Each citation is synthesized to _at most_ **17** CSL styles. 
+Therefore, there will be near duplicates.
+
+All characters are enclosed by the tag. Only tokens that act as "conjunctions" are not enclosed in tags.
+
+Do note that, there will be instances where a token can be annotated to a hierarchical tag e.g. `accessed.year`.
+This depends on the author(s) of the CSL styles. 
+
+### Data Fields
+
+- `source`: Describe the source of the citation. `{pubmed, jstor, crossref}`
+- `lang`: Describe the language of the citation. `{en}`
+- `entry_type`: Describe the BibTeX entry type. `{article, book, inbook, misc, techreport, phdthesis, incollection, inproceedings}`
+- `doi_prefix`: For JSTOR and CrossRef, it is the prefix of the DOI. For PubMed, it is the directory (e.g. `pubmed19nXXXX` where `XXXX` is 4 digits) of which the citation is generated from.
+- `csl_style`: The CSL style which the citation is rendered as.
+- `content`: The rendered citation of a specific style with each segment enclosed by tags named after the CSL variables
+
+### Data Splits
+s
+Data splits are not available yet.
+
+## Dataset Creation
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+The citations that are used to generate these reference strings are obtained from 3 main sources:
+
+- [PubMed](https://www.nlm.nih.gov/databases/download/pubmed_medline.html) (2019 Baseline)
+- CrossRef via [Open Academic Graph v2](https://www.microsoft.com/en-us/research/project/open-academic-graph/)
+- JSTOR Sample Datasets (not available online as of publication date)
+
+If the citation is not in BibTeX format, [bibutils](https://sourceforge.net/p/bibutils/home/Bibutils/) is used to convert it to BibTeX.
+
+#### Who are the source language producers?
+
+The manner which the citations are rendered as reference strings are based on rules/specifications dictated by the publisher.
+[Citation Style Language](https://citationstyles.org/) (CSL) is an established standard which such specifications are prescribed. 
+Thousands of citation styles are available.
+
+### Annotations
+
+#### Annotation process
+
+The annotation process involves 2 main interventions:
+1. Modification of the styles' CSL specification to inject the CSL variable names as part of the render process
+2. Sanitization of the rendered strings using regular expressions to ensure all tokens and characters are enclosed in the tags
+
+#### Who are the annotators?
+
+The original CSL specification are available on [GitHub](https://github.com/citation-style-language/styles).
+
+The modification of the styles and the sanitization process are done by the author of this work.
+
+## Additional Information
+
+### Licensing Information
+
+This dataset is licensed under [Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/).
+
+### Citation Information
+
+This dataset is a product of a Master Project done in the National University of Singapore. 
+
+If you are using it, please cite the following: 
+
+```bibtex
+@techreport{kee2021,
+    author = {Yuan Chuan Kee},
+    title = {Synthesis of a large dataset of annotated reference strings for developing citation parsers},
+    institution = {National University of Singapore},
+    year = {2021}
+}
+```
+
+### Contributions
+
+Thanks to [@kylase](https://github.com/kylase) for adding this dataset.

annotated_reference_strings.py

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+"""\
+Annotated Reference Strings dataset synthesized using CSL processor on citations obtained from CrossRef, JSTOR and
+PubMed
+"""
+
+import gzip
+import json
+import os
+
+import datasets
+
+
+_CITATION = """\
+@techreport{kee2021,
+    author = {Yuan Chuan Kee},
+    title = {Synthesis of a large dataset of annotated reference strings for developing citation parsers},
+    institution = {National University of Singapore},
+    year = {2021}
+}
+"""
+
+# 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 dataset library don't host the datasets but only point to the original files
+# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
+_BASE_URL = "https://huggingface.co/datasets/yuanchuan/annotated_reference_strings"
+_URLs = {
+    "default": [f"{_BASE_URL}/resolve/main/data/jstor.jsonl.gz"]
+}
+
+
+class AnnotatedReferenceStringsDataset(datasets.GeneratorBasedBuilder):
+    """Annotated Reference Strings dataset"""
+
+    VERSION = datasets.Version("0.1.0")
+
+    # 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="default", version=VERSION,
+                               description="This dataset is the raw representation without tokenization."),
+    ]
+
+    DEFAULT_CONFIG_NAME = "default"
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "source": datasets.Value("string"),
+                "lang": datasets.Value("string"),
+                "entry_type": datasets.Value("string"),
+                "doi_prefix": datasets.Value("string"),
+                "csl_style": datasets.Value("string"),
+                "content": datasets.Value("string")
+            }
+        )
+
+        return datasets.DatasetInfo(
+            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,
+            # specify them here. They'll be used if as_supervised=True in
+            # builder.as_dataset.
+            supervised_keys=None,
+            # 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):
+        """Returns SplitGenerators."""
+        # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
+        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
+
+        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLs
+        # 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.
+        # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
+        data_urls = _URLs[self.config.name]
+        files = dl_manager.download(data_urls)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepaths": files,
+                    "split": "train",
+                },
+            )
+        ]
+
+    def _generate_examples(self, filepaths, split):
+        id_ = 0
+
+        for filepath in filepaths:
+            with gzip.open(open(filepath, "rb"), "rt", encoding="utf-8") as f:
+                for line in f:
+                    if line:
+                        example = json.loads(line)
+                        yield id_, example
+                        id_ += 1

data/jstor.jsonl.gz

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