Updated the dataset with numbered parts

.gitattributes

@@ -25,4 +25,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zstandard filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
-data/jstor.jsonl.gz filter=lfs diff=lfs merge=lfs -text
+*.jsonl.gz filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,6 +1,23 @@
 ---
-YAML tags:
-- copy-paste the tags obtained with the tagging app: https://github.com/huggingface/datasets-tagging
+annotations_creators:
+- other
+language_creators:
+- found
+languages:
+- en-US
+licenses:
+- cc-by-4.0
+multilinguality:
+- monolingual
+pretty_name: Annotated Reference Strings
+size_categories:
+- 10M<n<100M
+source_datasets:
+- original
+task_categories:
+- structure-prediction
+task_ids:
+- parsing
 ---
 
 # Dataset Card for annotated_reference_strings
@@ -65,13 +82,12 @@ The dataset is composed of reference strings that are in English.
 }
 ```
 
-**Important Note:** Each citation is synthesized to _at most_ **17** CSL styles. 
-Therefore, there will be near duplicates.
+#### Important Note 
 
-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. 
+1. Each citation is rendered to _at most_ **17** CSL styles. Therefore, there will be near duplicates.
+2. All characters (including punctuations) of a segment (**a segment consists of 1 or more token**) are enclosed by tag(s). 
+   1. Only tokens that act as "conjunctions" are not enclosed in tags. These tokens will be labelled as `other`.
+3. There will be instances which a segment can be enclosed by more than one tag e.g. `<issued><year>2021</year></issued>`. This depends on how the styles' author(s).
 
 ### Data Fields
 
@@ -83,7 +99,7 @@ This depends on the author(s) of the CSL styles.
 - `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

annotated_reference_strings.py

@@ -19,43 +19,38 @@ _CITATION = """\
 }
 """
 
-# 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.
+A repository of reference strings annotated using CSL processor using citations obtained from various sources.
 """
 
 # TODO: Add a link to an official homepage for the dataset here
-_HOMEPAGE = ""
+_HOMEPAGE = "https://www.github.com/kylase"
 
-# TODO: Add the licence for the dataset here if you can find it
-_LICENSE = ""
+_LICENSE = "cc-by-4.0"
+
+_BASE_URL = "https://huggingface.co/datasets/yuanchuan/annotated_reference_strings/resolve/main/data"
+_URL_FORMAT = "{base_url}/{source}-part-{part:05}.jsonl.gz"
+
+_SOURCES_PARTS = {
+    "crossref": 16,
+    "pubmed": 32,
+    "jstor": 1
+}
 
-# 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"]
+    "default": [
+        _URL_FORMAT.format(base_url=_BASE_URL, source=source, part=i)
+        for source, total_parts in _SOURCES_PARTS.items()
+        for i in range(1, total_parts + 1)
+    ]
 }
 
 
 class AnnotatedReferenceStringsDataset(datasets.GeneratorBasedBuilder):
     """Annotated Reference Strings dataset"""
 
-    VERSION = datasets.Version("0.1.0")
+    VERSION = datasets.Version("0.2.0")
 
-    # 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."),
@@ -77,28 +72,15 @@ class AnnotatedReferenceStringsDataset(datasets.GeneratorBasedBuilder):
 
         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.
+            features=features,
             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 [

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