Update files from the datasets library (from 1.2.0)

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

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README.md

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+---
+annotations_creators:
+- expert-generated
+language_creators:
+- expert-generated
+languages:
+- es
+licenses:
+- cc-by-nc-sa-4-0
+multilinguality:
+- monolingual
+size_categories:
+- 1K<n<10K
+source_datasets:
+- original
+task_categories:
+- structure-prediction
+task_ids:
+- named-entity-recognition
+- structure-prediction-other-relation-prediction
+---
+
+# Dataset Card for eHealth-KD
+
+## 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:** [eHealth-KD homepage](https://knowledge-learning.github.io/ehealthkd-2020/)
+- **Repository:** [eHealth-KD repository](https://github.com/knowledge-learning/ehealthkd-2020)
+- **Paper:** [eHealth-KD overview paper](http://ceur-ws.org/Vol-2664/eHealth-KD_overview.pdf)
+- **Leaderboard:** [eHealth-KD Challenge 2020 official results](https://knowledge-learning.github.io/ehealthkd-2020/results)
+- **Point of Contact:** [Yoan Gutiérrez Vázquez](mailto:[email protected]) (Organization Committee), [María Grandury](mailto:[email protected]) (Dataset Submitter)
+
+### Dataset Summary
+
+Dataset of the eHealth-KD Challenge at IberLEF 2020. It is designed for the identification of semantic 
+entities and relations in Spanish health documents.
+
+### Supported Tasks and Leaderboards
+
+The eHealth-KD challenge proposes two computational subtasks:
+
+- `named-entity-recognition`: Given a sentence of an eHealth document written in Spanish, the goal of this subtask is to 
+identify all the entities and their types.
+
+- `relation-prediction`: The purpose of this subtask is to recognise all relevant semantic relationships between the entities recognised.
+
+For an analysis of the most successful approaches of this challenge, read the [eHealth-KD overview paper](http://ceur-ws.org/Vol-2664/eHealth-KD_overview.pdf).
+
+### Languages
+
+The text in the dataset is in Spanish (BCP-47 code: `es`).
+
+## Dataset Structure
+
+### Data Instances
+
+The first example of the eHeatlh-KD Corpus train set looks as follows:
+```
+{
+'sentence': 'En la leucemia linfocítica crónica, hay demasiados linfocitos, un tipo de glóbulos blancos.',
+'entities': {
+    [
+        'ent_id: 'T1',
+        'ent_text': 'leucemia linfocítica crónica',
+        'ent_label': 0,
+        'start_character': 6,
+        'end_character': 34
+    ],
+    [
+        'ent_id: 'T2',
+        'ent_text': 'linfocitos',
+        'ent_label': 0,
+        'start_character': 51,
+        'end_character': 61
+    ],
+    [
+        'ent_id: 'T3',
+        'ent_text': 'glóbulos blancos',
+        'ent_label': 0,
+        'start_character': 74,
+        'end_character': 90
+    ]
+},
+relations: {
+    [
+        'rel_id: 'R0'
+        'rel_label': 0,
+        'arg1': T2
+        'arg2': T3
+    ],
+    [
+        'rel_id': 'R1'
+        'rel_label': 5,
+        'arg1': T1,
+        'arg2': T2
+    ]
+}
+}
+```
+
+### Data Fields
+
+- `sentence`: sentence of an eHealth document written in Spanish
+- `entities`: list of entities identified in the sentence
+    - `ent_id`: entity identifier (`T`+ a number)
+    - `ent_text`: entity, can consist of one or more complete words (i.e., not a prefix or a suffix of a word), and will
+     never include any surrounding punctuation symbols, parenthesis, etc.
+    - `ent_label`: type of entity (`Concept`, `Action`, `Predicate` or `Reference`)
+    - `start_character`: position of the first character of the entity
+    - `end_character`: position of the last character of the entity
+- `relations`: list of semantic relationships between the entities recognised
+    - `rel_id`: relation identifier (`R` + a number)
+    - `rel_label`: type of relation, can be a general relation (`is-a`, `same-as`, `has-property`, `part-of`, `causes`, `entails`),
+    a contextual relation (`in-time`, `in-place`, `in-context`) an action role (`subject`, `target`) or a predicate role (`domain`, `arg`). 
+    - `arg1`: ID of the first entity of the relation
+    - `arg2`: ID of the second entity of the relation
+
+For more information about the types of entities and relations, click [here](https://knowledge-learning.github.io/ehealthkd-2020/tasks).
+
+### Data Splits
+
+The data is split into a training, validation and test set. The split sizes are as follow:
+
+|                 | Train  | Val   | Test |
+| -----           | ------ | ----- | ---- |
+| eHealth-KD 2020 | 800    | 199   | 100  |
+
+In the challenge there are 4 different scenarios for testing. The test data of this dataset corresponds to the third scenario.
+More information about the testing data [here](https://github.com/knowledge-learning/ehealthkd-2020/tree/master/data/testing).
+
+## Dataset Creation
+
+### Curation Rationale
+
+The vast amount of clinical text available online has motivated the development of automatic
+knowledge discovery systems that can analyse this data and discover relevant facts.
+
+The eHealth Knowledge Discovery (eHealth-KD) challenge, in its third edition, leverages
+a semantic model of human language that encodes the most common expressions of factual
+knowledge, via a set of four general-purpose entity types and thirteen semantic relations among
+them. The challenge proposes the design of systems that can automatically annotate entities and
+relations in clinical text in the Spanish language.
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+As in the previous edition, the corpus for eHealth-KD 2020 has been extracted from MedlinePlus sources. This platform 
+freely provides large health textual data from which we have made a selection for constituting the eHealth-KD corpus. 
+The selection has been made by sampling specific XML files from the collection available in the [Medline website](https://medlineplus.gov/xml.html).
+
+```
+“MedlinePlus is the National Institutes of Health’s Website for patients and their families and
+friends. Produced by the National Library of Medicine, the world’s largest medical library, it 
+brings you information about diseases, conditions, and wellness issues in language you can 
+understand. MedlinePlus offers reliable, up-to-date health information, anytime, anywhere, for free.”
+```
+
+These files contain several entries related to health and medicine topics and have been processed to remove all 
+XML markup to extract the textual content. Only Spanish language items were considered. Once cleaned, each individual 
+item was converted to a plain text document, and some further post-processing is applied to remove unwanted sentences, 
+such as headers, footers and similar elements, and to flatten HTML lists into plain sentences. 
+
+#### Who are the source language producers?
+
+As in the previous edition, the corpus for eHealth-KD 2020 was extracted from [MedlinePlus](https://medlineplus.gov/xml.html) sources.
+
+### Annotations
+
+#### Annotation process
+
+Once the MedlinePlus files were cleaned, they were manually tagged using [BRAT](http://brat.nlplab.org/) by a group of 
+annotators. After tagging, a post-processing was applied to BRAT’s output files (ANN format) to obtain the output files 
+in the formats needed for the challenge.
+
+#### Who are the annotators?
+
+The data was manually tagged.
+
+### Personal and Sensitive Information
+
+[More Information Needed]
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+"The eHealth-KD 2020 proposes –as the previous editions– modeling the human language in a scenario in which Spanish 
+electronic health documents could be machine-readable from a semantic point of view. 
+ 
+ With this task, we expect to encourage the development of software technologies to automatically extract a large variety 
+ of knowledge from eHealth documents written in the Spanish Language."
+
+### Discussion of Biases
+
+[More Information Needed]
+
+### Other Known Limitations
+
+[More Information Needed]
+
+## Additional Information
+
+### Dataset Curators
+
+#### Organization Committee
+
+|                   Name                  |         Email         |          Institution          |
+|:---------------------------------------:|:---------------------:|:-----------------------------:|
+| Yoan Gutiérrez Vázquez (contact person) | [email protected] | University of Alicante, Spain |
+| Suilan Estévez Velarde                  | [email protected] | University of Havana, Cuba    |
+| Alejandro Piad Morffis                  | [email protected]    | University of Havana, Cuba    |
+| Yudivián Almeida Cruz                   | [email protected]     | University of Havana, Cuba    |
+| Andrés Montoyo Guijarro                 | [email protected]    | University of Alicante, Spain |
+| Rafael Muñoz Guillena                   | [email protected]     | University of Alicante, Spain |
+
+#### Funding
+
+This research has been supported by a Carolina Foundation grant in agreement with University of Alicante and University 
+of Havana. Moreover, it has also been partially funded by both aforementioned universities, IUII, Generalitat Valenciana,
+Spanish Government, Ministerio de Educación, Cultura y Deporte through the projects SIIA (PROMETEU/2018/089) and 
+LIVINGLANG (RTI2018-094653-B-C22).
+
+### Licensing Information
+
+This dataset is under the Attribution-NonCommercial-ShareAlike 4.0 International 
+[(CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/).
+
+To accept the distribution terms, please fill in the following [form](https://forms.gle/pUJutSDq2FYLwNWQA).
+
+### Citation Information
+
+In the following link you can find the 
+[preliminar bibtexts of the systems’ working-notes](https://knowledge-learning.github.io/ehealthkd-2020/shared/eHealth-KD_2020_bibtexts.zip).
+In addition, to cite the eHealth-KD challenge you can use the following preliminar bibtext:
+
+```
+@inproceedings{overview_ehealthkd2020,
+  author    = {Piad{-}Morffis, Alejandro and
+               Guti{\'{e}}rrez, Yoan and
+               Ca{\~{n}}izares-Diaz, Hian and
+               Estevez{-}Velarde, Suilan and 
+               Almeida{-}Cruz, Yudivi{\'{a}}n and
+               Mu{\~{n}}oz, Rafael and
+               Montoyo, Andr{\'{e}}s},
+  title     = {Overview of the eHealth Knowledge Discovery Challenge at IberLEF 2020},
+  booktitle = ,
+  year      = {2020},
+}
+```

dataset_infos.json

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+{"ehealth_kd": {"description": "Dataset of the eHealth Knowledge Discovery Challenge at IberLEF 2020. It is designed for\nthe identification of semantic entities and relations in Spanish health documents.\n", "citation": "@inproceedings{overview_ehealthkd2020,\n  author    = {Piad{-}Morffis, Alejandro and\n               Guti{'{e}}rrez, Yoan and\n               Ca\u00f1izares-Diaz, Hian and\n               Estevez{-}Velarde, Suilan and\n               Almeida{-}Cruz, Yudivi{'{a}}n and\n               Mu\u00f1oz, Rafael and\n               Montoyo, Andr{'{e}}s},\n  title     = {Overview of the eHealth Knowledge Discovery Challenge at IberLEF 2020},\n  booktitle = ,\n  year      = {2020},\n}\n", "homepage": "https://knowledge-learning.github.io/ehealthkd-2020/", "license": "https://creativecommons.org/licenses/by-nc-sa/4.0/", "features": {"sentence": {"dtype": "string", "id": null, "_type": "Value"}, "entities": [{"ent_id": {"dtype": "string", "id": null, "_type": "Value"}, "ent_text": {"dtype": "string", "id": null, "_type": "Value"}, "ent_label": {"num_classes": 4, "names": ["Concept", "Action", "Predicate", "Reference"], "names_file": null, "id": null, "_type": "ClassLabel"}, "start_character": {"dtype": "int32", "id": null, "_type": "Value"}, "end_character": {"dtype": "int32", "id": null, "_type": "Value"}}], "relations": [{"rel_id": {"dtype": "string", "id": null, "_type": "Value"}, "rel_label": {"num_classes": 13, "names": ["is-a", "same-as", "has-property", "part-of", "causes", "entails", "in-time", "in-place", "in-context", "subject", "target", "domain", "arg"], "names_file": null, "id": null, "_type": "ClassLabel"}, "arg1": {"dtype": "string", "id": null, "_type": "Value"}, "arg2": {"dtype": "string", "id": null, "_type": "Value"}}]}, "post_processed": null, "supervised_keys": null, "builder_name": "ehealth_kd", "config_name": "ehealth_kd", "version": {"version_str": "1.1.0", "description": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 425713, "num_examples": 800, "dataset_name": "ehealth_kd"}, "validation": {"name": "validation", "num_bytes": 108154, "num_examples": 199, "dataset_name": "ehealth_kd"}, "test": {"name": "test", "num_bytes": 47314, "num_examples": 100, "dataset_name": "ehealth_kd"}}, "download_checksums": {"https://raw.githubusercontent.com/knowledge-learning/ehealthkd-2020/master/data/training/scenario.txt": {"num_bytes": 72905, "checksum": "247d41d7c5152d5afb3670e55ccf632d7665f772f42fbd95331b8e65efadaa4e"}, "https://raw.githubusercontent.com/knowledge-learning/ehealthkd-2020/master/data/training/scenario.ann": {"num_bytes": 343367, "checksum": "b4e26cd473cf54bc7e4ad2d5b98896dbeb9b7f4bb5adc426ee2014ce4fce0b88"}, "https://raw.githubusercontent.com/knowledge-learning/ehealthkd-2020/master/data/development/main/scenario.txt": {"num_bytes": 19060, "checksum": "184b5e9a9e69512d5332c81f22d8765ae1e26632e0f5dc089af6e101c9b04149"}, "https://raw.githubusercontent.com/knowledge-learning/ehealthkd-2020/master/data/development/main/scenario.ann": {"num_bytes": 85446, "checksum": "9a47927d13260a10e067d82ebca59d2a43982c7338babb01004c02329611dfb3"}, "https://raw.githubusercontent.com/knowledge-learning/ehealthkd-2020/master/data/testing/scenario3-taskB/scenario.txt": {"num_bytes": 8685, "checksum": "63b6e7ff05445b1fde9c8d9b3bb346a1d9e037858550b4d509fb10d702f682e6"}, "https://raw.githubusercontent.com/knowledge-learning/ehealthkd-2020/master/data/testing/scenario3-taskB/scenario.ann": {"num_bytes": 36437, "checksum": "37102084c1bde2b5eaebc55361b4df7fd0f012495b56f664aa0ad52292a38f00"}}, "download_size": 565900, "post_processing_size": null, "dataset_size": 581181, "size_in_bytes": 1147081}}

dummy/ehealth_kd/1.1.0/dummy_data.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e4e23493872c14d002394955eedcf99832b8ae56258a851287da6b1193b94811
+size 1079

ehealth_kd.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.
+"""The eHealth-KD 2020 Corpus."""
+
+from __future__ import absolute_import, division, print_function
+
+import datasets
+
+
+_CITATION = """\
+@inproceedings{overview_ehealthkd2020,
+  author    = {Piad{-}Morffis, Alejandro and
+               Guti{\'{e}}rrez, Yoan and
+               Cañizares-Diaz, Hian and
+               Estevez{-}Velarde, Suilan and
+               Almeida{-}Cruz, Yudivi{\'{a}}n and
+               Muñoz, Rafael and
+               Montoyo, Andr{\'{e}}s},
+  title     = {Overview of the eHealth Knowledge Discovery Challenge at IberLEF 2020},
+  booktitle = ,
+  year      = {2020},
+}
+"""
+
+_DESCRIPTION = """\
+Dataset of the eHealth Knowledge Discovery Challenge at IberLEF 2020. It is designed for
+the identification of semantic entities and relations in Spanish health documents.
+"""
+
+_HOMEPAGE = "https://knowledge-learning.github.io/ehealthkd-2020/"
+
+_LICENSE = "https://creativecommons.org/licenses/by-nc-sa/4.0/"
+
+_URL = "https://raw.githubusercontent.com/knowledge-learning/ehealthkd-2020/master/data/"
+_TRAIN_DIR = "training/"
+_DEV_DIR = "development/main/"
+_TEST_DIR = "testing/scenario3-taskB/"
+_TEXT_FILE = "scenario.txt"
+_ANNOTATIONS_FILE = "scenario.ann"
+
+
+class EhealthKD(datasets.GeneratorBasedBuilder):
+    """The eHealth-KD 2020 Corpus."""
+
+    VERSION = datasets.Version("1.1.0")
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name="ehealth_kd", version=VERSION, description="eHealth-KD Corpus"),
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "sentence": datasets.Value("string"),
+                    "entities": [
+                        {
+                            "ent_id": datasets.Value("string"),
+                            "ent_text": datasets.Value("string"),
+                            "ent_label": datasets.ClassLabel(names=["Concept", "Action", "Predicate", "Reference"]),
+                            "start_character": datasets.Value("int32"),
+                            "end_character": datasets.Value("int32"),
+                        }
+                    ],
+                    "relations": [
+                        {
+                            "rel_id": datasets.Value("string"),
+                            "rel_label": datasets.ClassLabel(
+                                names=[
+                                    "is-a",
+                                    "same-as",
+                                    "has-property",
+                                    "part-of",
+                                    "causes",
+                                    "entails",
+                                    "in-time",
+                                    "in-place",
+                                    "in-context",
+                                    "subject",
+                                    "target",
+                                    "domain",
+                                    "arg",
+                                ]
+                            ),
+                            "arg1": datasets.Value("string"),
+                            "arg2": datasets.Value("string"),
+                        }
+                    ],
+                }
+            ),
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        urls_to_download = {
+            k: [f"{_URL}{v}{_TEXT_FILE}", f"{_URL}{v}{_ANNOTATIONS_FILE}"]
+            for k, v in zip(["train", "dev", "test"], [_TRAIN_DIR, _DEV_DIR, _TEST_DIR])
+        }
+
+        downloaded_files = dl_manager.download_and_extract(urls_to_download)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"txt_path": downloaded_files["train"][0], "ann_path": downloaded_files["train"][1]},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={"txt_path": downloaded_files["dev"][0], "ann_path": downloaded_files["dev"][1]},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={"txt_path": downloaded_files["test"][0], "ann_path": downloaded_files["test"][1]},
+            ),
+        ]
+
+    def _generate_examples(self, txt_path, ann_path):
+        """ Yields examples. """
+        with open(txt_path, encoding="utf-8") as txt_file, open(ann_path, encoding="utf-8") as ann_file:
+            _id = 0
+            entities = []
+            relations = []
+
+            annotations = ann_file.readlines()
+            last = annotations[-1]
+
+            # Create a variable to keep track of the last annotation (entity or relation) to know when a sentence is fully annotated
+            # In the annotations file, the entities are before the relations
+            last_annotation = ""
+
+            for annotation in annotations:
+                if annotation == last:
+                    sentence = txt_file.readline().strip()
+                    yield _id, {"sentence": sentence, "entities": entities, "relations": relations}
+
+                if annotation.startswith("T"):
+                    if last_annotation == "relation":
+                        sentence = txt_file.readline().strip()
+                        yield _id, {"sentence": sentence, "entities": entities, "relations": relations}
+                        _id += 1
+                        entities = []
+                        relations = []
+
+                    ent_id, mid, ent_text = annotation.strip().split("\t")
+                    ent_label, spans = mid.split(" ", 1)
+                    start_character = spans.split(" ")[0]
+                    end_character = spans.split(" ")[-1]
+
+                    entities.append(
+                        {
+                            "ent_id": ent_id,
+                            "ent_text": ent_text,
+                            "ent_label": ent_label,
+                            "start_character": start_character,
+                            "end_character": end_character,
+                        }
+                    )
+
+                    last_annotation = "entity"
+
+                else:
+                    rel_id, rel_label, arg1, arg2 = annotation.strip().split()
+                    if annotation.startswith("R"):
+                        arg1 = arg1.split(":")[1]
+                        arg2 = arg2.split(":")[1]
+
+                    relations.append({"rel_id": rel_id, "rel_label": rel_label, "arg1": arg1, "arg2": arg2})
+
+                    last_annotation = "relation"