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README.md

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+---
+annotations_creators:
+- machine-generated
+language_creators: []
+languages:
+- en-EN
+licenses:
+- wtfpl
+multilinguality:
+- monolingual
+pretty_name: test
+size_categories:
+- unknown
+source_datasets:
+- original
+task_categories:
+- other
+task_ids: []
+---
+
+# Dataset Card for frwiki_good_pages_el
+
+## Dataset Description
+
+- Repository: [enwiki_el](https://github.com/GaaH/enwiki_el)
+- Point of Contact: [Gaëtan Caillaut](mailto://[email protected])
+
+### Dataset Summary
+
+It is intended to be used to train Entity Linking (EL) systems. Links in Wikipedia articles are used to detect named entities.
+
+### Languages
+
+- English
+
+## Dataset Structure
+
+```
+{
+    "title": "Title of the page",
+    "qid": "QID of the corresponding Wikidata entity",
+    "words": ["tokens"],
+    "wikipedia": ["Wikipedia description of each entity"],
+    "labels": ["NER labels"],
+    "titles": ["Wikipedia title of each entity"],
+    "qids": ["QID of each entity"],
+}
+```
+
+The `words` field contains the article’s text splitted on white-spaces. The other fields are list with same length as `words` and contains data only when the respective token in `words` is the __start of an entity__. For instance, if the _i-th_ token in `words` is an entity, then the _i-th_ element of `wikipedia` contains a description, extracted from Wikipedia, of this entity. The same applies for the other fields. If the entity spans multiple words, then only the index of the first words contains data.
+
+The only exception is the `labels` field, which is used to delimit entities. It uses the IOB encoding: if the token is not part of an entity, the label is `"O"`; if it is the first word of a multi-word entity, the label is `"B"`; otherwise the label is `"I"`.

data.tar.gz

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f9a687a7120f6c5993a8f24471cae5e4f9c4f60d597d941fb7d6f699778b54e6
+size 7320271946

enwiki_el_dataset.py

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+# coding=utf-8
+# 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: Add a description here."""
+
+
+import pandas as pd
+import re
+import gzip
+import json
+import datasets
+from pathlib import Path
+
+
+def get_open_method(path):
+    path = Path(path)
+    ext = path.suffix
+
+    if ext == ".gz":
+        import gzip
+        open_func = gzip.open
+    elif ext == ".bz2":
+        import bz2
+        open_func = bz2.open
+    else:
+        open_func = open
+    return open_func
+
+
+def read_file(path):
+    open_func = get_open_method(path)
+    with open_func(path, "rt", encoding="UTF-8") as f:
+        return f.read()
+
+
+# TODO: Add BibTeX citation
+# Find for instance the citation on arxiv or on the dataset repo/website
+_CITATION = ""
+
+_DESCRIPTION = """\
+English Wikipedia dataset for Entity Linking
+"""
+
+_HOMEPAGE = "https://github.com/GaaH/enwiki_el"
+
+_LICENSE = ""
+
+_URLs = {
+    "enwiki": "data.tar.gz",
+    "entities": "data.tar.gz",
+}
+
+_NER_CLASS_LABELS = [
+    "B",
+    "I",
+    "O",
+]
+
+
+def text_to_el_features(doc_qid, doc_title, text, title2qid, title2wikipedia):
+    res = {
+        "title": doc_title.replace("_", " "),
+        "qid": doc_qid,
+    }
+    text_dict = {
+        "words": [],
+        "labels": [],
+        "qids": [],
+        "titles": [],
+        "wikipedia": [],
+    }
+    entity_pattern = r"\[E=(.+?)\](.+?)\[/E\]"
+
+    # start index of the previous text
+    i = 0
+    for m in re.finditer(entity_pattern, text):
+        mention_title = m.group(1)
+        mention = m.group(2)
+
+        mention_qid = title2qid.get(mention_title, "").replace("_", " ")
+        mention_wikipedia = title2wikipedia.get(mention_title, "")
+
+        # Removes entity tags in descriptions
+        mention_wikipedia = re.sub(entity_pattern, r"\2", mention_wikipedia)
+
+        # mention_qid = title2qid.get(mention_title, "YARIEN")
+        # mention_wikipedia = title2wikipedia.get(mention_title, "YARIEN")
+        # mention_wikidata = title2wikidata.get(mention_title, "YARIEN")
+
+        mention_words = mention.split()
+
+        j = m.start(0)
+        prev_text = text[i:j].split()
+        len_prev_text = len(prev_text)
+        text_dict["words"].extend(prev_text)
+        text_dict["labels"].extend(["O"] * len_prev_text)
+        text_dict["qids"].extend([None] * len_prev_text)
+        text_dict["titles"].extend([None] * len_prev_text)
+        text_dict["wikipedia"].extend([None] * len_prev_text)
+
+        text_dict["words"].extend(mention_words)
+
+        # If there is no description, learning can’t be done so we treat the mention as not en entity
+        if mention_wikipedia == "":
+            len_mention = len(mention_words)
+            text_dict["labels"].extend(["O"] * len_mention)
+            text_dict["qids"].extend([None] * len_mention)
+            text_dict["titles"].extend([None] * len_mention)
+            text_dict["wikipedia"].extend([None] * len_mention)
+        else:
+            len_mention_tail = len(mention_words) - 1
+            # wikipedia_words = mention_wikipedia.split()
+            # wikidata_words = mention_wikidata.split()
+            # title_words = mention_title.replace("_", " ").split()
+
+            text_dict["labels"].extend(["B"] + ["I"] * len_mention_tail)
+            text_dict["qids"].extend([mention_qid] + [None] * len_mention_tail)
+            text_dict["titles"].extend(
+                [mention_title] + [None] * len_mention_tail)
+            text_dict["wikipedia"].extend(
+                [mention_wikipedia] + [None] * len_mention_tail)
+
+        i = m.end(0)
+
+    tail = text[i:].split()
+    len_tail = len(tail)
+    text_dict["words"].extend(tail)
+    text_dict["labels"].extend(["O"] * len_tail)
+    text_dict["qids"].extend([None] * len_tail)
+    text_dict["titles"].extend([None] * len_tail)
+    text_dict["wikipedia"].extend([None] * len_tail)
+    res.update(text_dict)
+    return res
+
+
+class EnWikiELDataset(datasets.GeneratorBasedBuilder):
+    """
+    """
+
+    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="enwiki", version=VERSION,
+                               description="The enwiki dataset for Entity Linking"),
+        datasets.BuilderConfig(name="entities", version=VERSION,
+                               description="Entities and their descriptions"),
+    ]
+
+    # It's not mandatory to have a default configuration. Just use one if it make sense.
+    DEFAULT_CONFIG_NAME = "enwiki"
+
+    def _info(self):
+        if self.config.name == "enwiki":
+            features = datasets.Features({
+                "title": datasets.Value("string"),
+                "qid": datasets.Value("string"),
+                "words": [datasets.Value("string")],
+                "wikipedia": [datasets.Value("string")],
+                "labels": [datasets.ClassLabel(names=_NER_CLASS_LABELS)],
+                "titles": [datasets.Value("string")],
+                "qids": [datasets.Value("string")],
+            })
+        elif self.config.name == "entities":
+            features = datasets.Features({
+                "qid": datasets.Value("string"),
+                "title": datasets.Value("string"),
+                "wikipedia": 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
+            # Here we define them above because they are different between the two configurations
+            features=features,
+            # 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
+        my_urls = _URLs[self.config.name]
+        data_dir = dl_manager.download_and_extract(my_urls)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "data_dir": Path(data_dir, "data"),
+                    "split": "train"
+                }
+            )
+        ]
+
+    def _generate_examples(
+        # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+        self, data_dir, split
+    ):
+        """ Yields examples as (key, example) tuples. """
+        # This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
+        # The `key` is here for legacy reason (tfds) and is not important in itself.
+
+        entities_path = Path(data_dir, "entities.jsonl.gz")
+        corpus_path = Path(data_dir, "corpus.jsonl.gz")
+
+        def _identiy(x):
+            return x
+
+        if self.config.name == "enwiki":
+            title2wikipedia = {}
+            title2qid = {}
+            with gzip.open(entities_path, "rt", encoding="UTF-8") as ent_file:
+                for line in ent_file:
+                    item = json.loads(
+                        line, parse_int=_identiy, parse_float=_identiy, parse_constant=_identiy)
+                    title = item["title"]
+                    title2wikipedia[title] = item["wikipedia_description"]
+                    title2qid[title] = item["qid"]
+
+            with gzip.open(corpus_path, "rt", encoding="UTF-8") as crps_file:
+                for id, line in enumerate(crps_file):
+                    item = json.loads(line, parse_int=lambda x: x,
+                                      parse_float=lambda x: x, parse_constant=lambda x: x)
+                    qid = item["qid"]
+                    title = item["title"]
+                    text = item["text"]
+
+                    features = text_to_el_features(
+                        qid, title, text, title2qid, title2wikipedia)
+                    yield id, features
+        elif self.config.name == "entities":
+            entity_pattern = r"\[E=(.+?)\](.+?)\[/E\]"
+            with gzip.open(entities_path, "rt", encoding="UTF-8") as ent_file:
+                for id, line in enumerate(ent_file):
+                    item = json.loads(
+                        line, parse_int=_identiy, parse_float=_identiy, parse_constant=_identiy)
+                    try:
+                        qid = item["qid"]
+                        item["wikipedia"] = re.sub(
+                            entity_pattern,
+                            r"\2",
+                            item.pop("wikipedia_description")
+                        )
+                        if qid is None or qid == "":
+                            item["qid"] = ""
+                        yield id, item
+                    except:
+                        import sys
+                        print(item, file=sys.stderr)
+                        return