Convert dataset to Parquet (#4)

- Convert dataset to Parquet (660cb7ffe80f230d9c26cbe6fdb2cea0337bb5e2)
- Delete loading script (41d9710294cfd6f09fdeb22b534f9cb319b710dc)

README.md

@@ -21,6 +21,7 @@ pretty_name: eHealth-KD
 tags:
 - relation-prediction
 dataset_info:
+  config_name: ehealth_kd
   features:
   - name: sentence
     dtype: string
@@ -67,19 +68,28 @@ dataset_info:
       dtype: string
     - name: arg2
       dtype: string
-  config_name: ehealth_kd
   splits:
   - name: train
-    num_bytes: 425713
+    num_bytes: 425681
     num_examples: 800
   - name: validation
-    num_bytes: 108154
+    num_bytes: 108122
     num_examples: 199
   - name: test
-    num_bytes: 47314
+    num_bytes: 47282
     num_examples: 100
-  download_size: 565900
-  dataset_size: 581181
+  download_size: 349989
+  dataset_size: 581085
+configs:
+- config_name: ehealth_kd
+  data_files:
+  - split: train
+    path: ehealth_kd/train-*
+  - split: validation
+    path: ehealth_kd/validation-*
+  - split: test
+    path: ehealth_kd/test-*
+  default: true
 ---
 
 # Dataset Card for eHealth-KD

ehealth_kd.py

@@ -1,185 +0,0 @@
-# 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."""
-
-
-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"

ehealth_kd/test-00000-of-00001.parquet

@@ -0,0 +1,3 @@
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+oid sha256:7ceec65dacc5e13f07c08ec5ecbe8a349a9a59cfadb0c275af003f7dc9c6da06
+size 35298

ehealth_kd/train-00000-of-00001.parquet

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a67e4e45fbb06e05d986580f1276459397732674d9ed212cd943bc337860c1f2
+size 245584

ehealth_kd/validation-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:743183f1be4c074dab31050516c8ac070cb5d112abcc2a9ddccb678e0b722d61
+size 69107