Update files from the datasets library (from 1.2.0)

Release notes: https://github.com/huggingface/datasets/releases/tag/1.2.0

.gitattributes

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+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bin.* filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
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+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
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+*tfevents* filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+annotations_creators:
+- crowdsourced
+language_creators:
+- crowdsourced
+languages:
+- en
+licenses:
+- other-nlm-license
+multilinguality:
+- monolingual
+size_categories:
+- n<1K
+source_datasets:
+- original
+task_categories:
+- conditional-text-generation
+- sequence-modeling
+- text-classification
+- text-scoring
+task_ids:
+- language-modeling
+- other-stuctured-to-text
+- text-scoring-other-citation-estimation
+- topic-classification
+---
+
+# Dataset Card for [Dataset Name]
+
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-instances)
+  - [Data Splits](#data-instances)
+- [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)
+
+## Dataset Description
+
+- **Homepage:** : [https://www.nlm.nih.gov/databases/download/pubmed_medline.html]()
+- **Documentation:** : [https://www.nlm.nih.gov/databases/download/pubmed_medline_documentation.html]()
+- **Repository:**
+- **Paper:**
+- **Leaderboard:**
+- **Point of Contact:**
+
+### Dataset Summary
+
+NLM produces a baseline set of MEDLINE/PubMed citation records in XML format for download on an annual basis. The annual baseline is released in December of each year. Each day, NLM produces update files that include new, revised and deleted citations. See our documentation page for more information.
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed]
+
+### Languages
+
+- English
+
+## Dataset Structure
+
+Bear in mind the data comes from XML that have various tags that are hard to reflect
+in a concise JSON format. Tags and list are kind of non "natural" to XML documents
+leading this library to make some choices regarding data. "Journal" info was dropped
+altogether as it would have led to many fields being empty all the time.
+
+The hierarchy is also a bit unnatural but the choice was made to keep as close as
+possible to the original data for future releases that may change schema from NLM's side.
+
+Author has been kept and contains either "ForeName", "LastName", "Initials", or "CollectiveName".
+(All the fields will be present all the time, but only some will be filled)
+
+### Data Instances
+
+```json
+{
+    "MedlineCitation": {
+        "PMID": 0,
+        "DateCompleted": {"Year": 0, "Month": 0, "Day": 0},
+        "NumberOfReferences": 0,
+        "DateRevised": {"Year": 0, "Month": 0, "Day": 0},
+        "Article": {
+            "Abstract": {"AbstractText": "Some abstract (can be missing)" },
+            "ArticleTitle": "Article title",
+            "AuthorList": {"Author": [
+                {"FirstName": "John", "ForeName": "Doe", "Initials": "JD", "CollectiveName": ""}
+                {"CollectiveName": "The Manhattan Project", "FirstName": "", "ForeName": "", "Initials": ""}
+            ]},
+            "Language": "en",
+            "GrantList": {
+                "Grant": [],
+            },
+            "PublicationTypeList": {"PublicationType": []},
+        },
+        "MedlineJournalInfo": {"Country": "France"},
+        "ChemicalList": {"Chemical": [{
+            "RegistryNumber": "XX",
+            "NameOfSubstance": "Methanol"
+        }]},
+        "CitationSubset": "AIM",
+        "MeshHeadingList": {
+            "MeshHeading": [],
+        },
+    },
+    "PubmedData": {
+        "ArticleIdList": {"ArticleId": "10.1002/bjs.1800650203"},
+        "PublicationStatus": "ppublish",
+        "History": {"PubMedPubDate": [{"Year": 0, "Month": 0, "Day": 0}]},
+        "ReferenceList": [{"Citation": "Somejournal", "CitationId": 01}],
+    },
+}
+```
+
+### Data Fields
+
+Main Fields will probably interest people are:
+
+- "MedlineCitation" > "Article" > "AuthorList" > "Author"
+- "MedlineCitation" > "Article" > "Abstract" > "AbstractText"
+- "MedlineCitation" > "Article" > "Article Title"
+- "MedlineCitation" > "ChemicalList" > "Chemical"
+- "MedlineCitation" > "NumberOfReferences"
+
+### Data Splits
+
+There are no splits in this dataset. It is given as is.
+
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed]
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+[https://www.nlm.nih.gov/databases/download/pubmed_medline_faq.html]()
+
+#### 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
+
+[https://www.nlm.nih.gov/databases/download/terms_and_conditions.html]()
+
+### Citation Information
+
+[More Information Needed]

dummy/2021/1.0.0/dummy_data.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e0ef6fe2745e3d8469ea99bdb1eecf97d87d75c93055c051c80912c6ab51885e
+size 1920

pubmed.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."""
+
+from __future__ import absolute_import, division, print_function
+
+import copy
+import logging
+import xml.etree.ElementTree as etree
+
+import datasets
+
+
+logger = logging.getLogger(__name__)
+
+
+# Find for instance the citation on arxiv or on the dataset repo/website
+_CITATION = """\
+"""
+
+# You can copy an official description
+_DESCRIPTION = """\
+NLM produces a baseline set of MEDLINE/PubMed citation records in XML format for download on an annual basis. The annual baseline is released in December of each year. Each day, NLM produces update files that include new, revised and deleted citations. See our documentation page for more information.
+"""
+
+_HOMEPAGE = "https://www.nlm.nih.gov/databases/download/pubmed_medline.html"
+
+_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)
+_URLs = [f"ftp://ftp.ncbi.nlm.nih.gov/pubmed/baseline/pubmed21n{i:04d}.xml.gz" for i in range(1, 1063)]
+
+
+# Copyright Ferry Boender, released under the MIT license.
+# Modified by @Narsil to handle more oddities
+def deepupdate(target, src):
+    """Deep update target dict with src
+    For each k,v in src: if k doesn't exist in target, it is deep copied from
+    src to target. Otherwise, if v is a list, target[k] is extended with
+    src[k]. If v is a set, target[k] is updated with v, If v is a dict,
+    recursively deep-update it.
+
+    Examples:
+    >>> t = {'name': 'Ferry', 'hobbies': ['programming', 'sci-fi']}
+    >>> deepupdate(t, {'hobbies': ['gaming']})
+    >>> print(t)
+    {'name': 'Ferry', 'hobbies': ['programming', 'sci-fi', 'gaming']}
+    """
+    for k, v in src.items():
+        if k in target and isinstance(target[k], int) and isinstance(v, str):
+            try:
+                v = int(v)
+            except Exception:
+                pass
+        if k in target and type(target[k]) != type(v):
+            logger.warning(f"Ignoring field {k} it's a {type(v)} and we expect a {type(target[k])}")
+            continue
+
+        if type(v) == list:
+            if k not in target:
+                target[k] = copy.deepcopy(v)
+            elif isinstance(target[k], list):
+                target[k].extend(v)
+            elif isinstance(target[k], str):
+                # Very special case to handle `AbstractText` which sometimes end up
+                # being a list.
+                new_v = " ".join(el for el in v if isinstance(el, str))
+                target[k] = new_v
+            else:
+                logger.warning(f"Ignoring field {k} it's a {type(v)} and we expect a {type(target[k])}")
+        elif type(v) == dict:
+            if k not in target:
+                target[k] = copy.deepcopy(v)
+            elif isinstance(target[k], dict):
+                deepupdate(target[k], v)
+            else:
+                logger.warning(f"Ignoring field {k} it's a {type(v)} and we expect a {type(target[k])}")
+        elif type(v) == set:
+            if k not in target:
+                target[k] = v.copy()
+            elif isinstance(target[k], set):
+                target[k].update(v.copy())
+            else:
+                logger.warning(f"Ignoring field {k} it's a {type(v)} and we expect a {type(target[k])}")
+        else:
+            if isinstance(target[k], (list, tuple, dict)):
+                logger.warning(f"Ignoring field {k} it's a {type(v)} and we expect a {type(target[k])}")
+                continue
+
+            target[k] = copy.copy(v)
+
+
+def default_date():
+    return {"Year": 0, "Month": 0, "Day": 0}
+
+
+def default_inline_article():
+    return {
+        # 'Journal': Journal,
+        "Abstract": {"AbstractText": ""},
+        "ArticleTitle": "",
+        # 'Pagination': {'MedlinePgn': datasets.Value('string')},
+        "AuthorList": {"Author": []},
+        "Language": "",
+        "GrantList": {
+            "Grant": [],
+        },
+        "PublicationTypeList": {"PublicationType": []},
+    }
+
+
+def default_article():
+    return {
+        "MedlineCitation": {
+            "PMID": 0,
+            "DateCompleted": default_date(),
+            "NumberOfReferences": 0,
+            "DateRevised": default_date(),
+            "Article": default_inline_article(),
+            "MedlineJournalInfo": {"Country": ""},
+            "ChemicalList": {"Chemical": []},
+            "CitationSubset": "",
+            "MeshHeadingList": {"MeshHeading": []},
+        },
+        "PubmedData": {
+            "ArticleIdList": [{"ArticleId": []}],
+            "PublicationStatus": "",
+            "History": {"PubMedPubDate": []},
+            "ReferenceList": [],
+        },
+    }
+
+
+class Pubmed(datasets.GeneratorBasedBuilder):
+    """Pubmed citations records"""
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name="2021", description="The 2021 annual record", version=datasets.Version("1.0.0")),
+    ]
+
+    # FILLED automatically from features
+    SIMPLE_KEYS = {"PubmedArticleSet"}
+    LIST_KEYS = {"PubmedArticle"}
+    IGNORE_KEYS = set()
+
+    def fill_keys_from_features(self, features):
+        if isinstance(features, dict):
+            for key, value in features.items():
+                if isinstance(value, datasets.Sequence):
+                    self.LIST_KEYS.add(key)
+                    self.fill_keys_from_features(value.feature)
+                else:
+                    self.SIMPLE_KEYS.add(key)
+                    self.fill_keys_from_features(value)
+
+    def xml_to_dictionnary(self, parentElement):
+        data = {}
+        if parentElement.tag in {"AbstractText", "ArticleTitle"}:
+            # XXX
+            # Very special case, it will contain html leading to having very odd structure
+            tag = parentElement.tag
+            string = etree.tostring(parentElement).decode("utf-8").strip()
+            inner_string = string[len(f"<{tag}>") : -len(f"</{tag}>")]
+            return {parentElement.tag: inner_string}
+
+        for child in list(parentElement):
+            child.text = child.text if (child.text is not None) else " "
+            key = child.tag
+            if len(child) == 0:
+                value = child.text.strip()
+            else:
+                value = self.xml_to_dictionnary(child)
+                if isinstance(value, dict) and set(value.keys()) == {key}:
+                    value = value[key]
+
+            if key in data:
+                old_value = data[key]
+                if isinstance(old_value, dict):
+                    data[key] = [old_value, value]
+                elif isinstance(old_value, list):
+                    data[key].append(value)
+            elif key in self.LIST_KEYS:
+                data[key] = [value]
+            elif key in self.SIMPLE_KEYS:
+                data[key] = value
+            elif key in self.IGNORE_KEYS:
+                continue
+            else:
+                logger.info(f"Ignoring key {key} from {parentElement.tag}")
+                self.IGNORE_KEYS.add(key)
+
+        # Filling defaults
+        if parentElement.tag == "MeshHeading" and "QualifierName" not in data:
+            data["QualifierName"] = ""
+        elif parentElement.tag == "Author":
+            if "ForeName" not in data:
+                data["ForeName"] = ""
+            if "Initials" not in data:
+                data["Initials"] = ""
+            if "LastName" not in data:
+                data["LastName"] = ""
+            if "CollectiveName" not in data:
+                data["CollectiveName"] = ""
+        elif parentElement.tag == "JournalIssue":
+            if "Volume" not in data:
+                data["Volume"] = ""
+            if "Issue" not in data:
+                data["Issue"] = ""
+        elif parentElement.tag == "Grant" and "GrantID" not in data:
+            data["GrantID"] = ""
+
+        return {parentElement.tag: data}
+
+    def _info(self):
+        Date = {
+            "Year": datasets.Value("int32"),
+            "Month": datasets.Value("int32"),
+            "Day": datasets.Value("int32"),
+        }
+
+        MeshHeading = {"DescriptorName": datasets.Value("string"), "QualifierName": datasets.Value("string")}
+
+        MedlineJournalInfo = {
+            "Country": datasets.Value("string"),
+            # Too inconsistent
+            # 'MedlineTA': datasets.Value('string'),
+            # 'NlmUniqueID': datasets.Value('string'),
+            # 'ISSNLinking': datasets.Value('string'),
+        }
+        Chemical = {
+            "RegistryNumber": datasets.Value("string"),
+            "NameOfSubstance": datasets.Value("string"),
+        }
+        # Too inconsistent in the data to be used
+        # Journal = {
+        #         'ISSN': datasets.Value('string'),
+        #         'JournalIssue': {
+        #             'Volume': datasets.Value('string'),
+        #             'Issue': datasets.Value('string'),
+        #         },
+        #         # 'PubDate': Date,
+        #         'Title': datasets.Value('string'),
+        #         'ISOAbbreviation': datasets.Value('string')
+        #         }
+        Author = {
+            "LastName": datasets.Value("string"),
+            "ForeName": datasets.Value("string"),
+            "Initials": datasets.Value("string"),
+            "CollectiveName": datasets.Value("string"),
+        }
+        Reference = {
+            "Citation": datasets.Value("string"),
+            "CitationId": datasets.Value("int32"),
+        }
+        Grant = {
+            "GrantID": datasets.Value("string"),
+            "Agency": datasets.Value("string"),
+            "Country": datasets.Value("string"),
+        }
+        Article = {
+            # 'Journal': Journal,
+            "Abstract": {"AbstractText": datasets.Value("string")},
+            "ArticleTitle": datasets.Value("string"),
+            # Too inconistent
+            # 'Pagination': {'MedlinePgn': datasets.Value('string')},
+            "AuthorList": {"Author": datasets.Sequence(Author)},
+            "Language": datasets.Value("string"),
+            "GrantList": {
+                "Grant": datasets.Sequence(Grant),
+            },
+            "PublicationTypeList": {"PublicationType": datasets.Sequence(datasets.Value("string"))},
+        }
+        features = datasets.Features(
+            {
+                "MedlineCitation": {
+                    "PMID": datasets.Value("int32"),
+                    "DateCompleted": Date,
+                    "NumberOfReferences": datasets.Value("int32"),
+                    "DateRevised": Date,
+                    "Article": Article,
+                    "MedlineJournalInfo": MedlineJournalInfo,
+                    "ChemicalList": {"Chemical": datasets.Sequence(Chemical)},
+                    "CitationSubset": datasets.Value("string"),
+                    "MeshHeadingList": {
+                        "MeshHeading": datasets.Sequence(MeshHeading),
+                    },
+                },
+                "PubmedData": {
+                    "ArticleIdList": datasets.Sequence({"ArticleId": datasets.Sequence(datasets.Value("string"))}),
+                    "PublicationStatus": datasets.Value("string"),
+                    "History": {"PubMedPubDate": datasets.Sequence(Date)},
+                    "ReferenceList": datasets.Sequence(Reference),
+                },
+            }
+        )
+        self.fill_keys_from_features(features)
+        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,
+            # 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."""
+        dl_dir = dl_manager.download_and_extract(_URLs)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={"filenames": dl_dir},
+            ),
+        ]
+
+    def update_citation(self, article):
+        """
+        ArticleId and ArticleIdList are already used field name so we rewrite and
+        flatten those as {Citation, CitationId}.
+        """
+        citations = []
+        try:
+            list_ = article["PubmedData"]["ReferenceList"]
+        except Exception:
+            return
+
+        for ref in list_:
+            if "Reference" not in ref:
+                continue
+            for re in ref["Reference"]:
+                if "Citation" not in re:
+                    continue
+                citation = re["Citation"]
+                if "ArticleIdList" not in re:
+                    continue
+                for r in re["ArticleIdList"]:
+                    if "ArticleId" not in r:
+                        continue
+                    for rr in r["ArticleId"]:
+                        try:
+                            citation = {"Citation": citation, "CitationId": int(rr)}
+                        except Exception:
+                            continue
+                        citations.append(citation)
+        article["PubmedData"]["ReferenceList"] = citations
+
+    def _generate_examples(self, filenames):
+        """ Yields examples. """
+        id_ = 0
+        for filename in filenames:
+            try:
+                tree = etree.parse(filename)
+                root = tree.getroot()
+                xmldict = self.xml_to_dictionnary(root)
+            except etree.ParseError:
+                logger.warning(f"Ignoring file {filename}, it is malformed")
+                continue
+
+            for article in xmldict["PubmedArticleSet"]["PubmedArticle"]:
+                self.update_citation(article)
+                new_article = default_article()
+
+                try:
+                    deepupdate(new_article, article)
+                except Exception:
+                    logger.warning(f"Ignoring article {article}, it is malformed")
+                    continue
+
+                try:
+                    _ = self.info.features.encode_example(new_article)
+                except Exception as e:
+                    logger.warning(f"Ignore example because {e}")
+                    continue
+                yield id_, new_article
+                id_ += 1