Convert dataset to Parquet (#3)

- Convert dataset to Parquet (99d06ea2779a1f7f260133f38fa9cfee329f703e)
- Add 'lt' config data files (f885922d7574102b5b0b211182515f0a8f63671f)
- Add '1177' config data files (20192f0e24c3842ce43ae100244f9d88b5c7a212)
- Delete loading script (711bc70b277cd4fc9364abe5c8bc86656733e50f)

1177/train-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:af8d4cb45b64896e0fd4724e0a4b9766b879a577ce9b2d2a337f6d84e40b181b
+size 77472

README.md

@@ -22,7 +22,7 @@ pretty_name: SwedMedNER
 language_bcp47:
 - sv-SE
 dataset_info:
-- config_name: wiki
+- config_name: '1177'
   features:
   - name: sid
     dtype: string
@@ -45,10 +45,10 @@ dataset_info:
             '2': Body Structure
   splits:
   - name: train
-    num_bytes: 7044714
-    num_examples: 48720
-  download_size: 52272712
-  dataset_size: 7044714
+    num_bytes: 158979
+    num_examples: 927
+  download_size: 77472
+  dataset_size: 158979
 - config_name: lt
   features:
   - name: sid
@@ -72,11 +72,11 @@ dataset_info:
             '2': Body Structure
   splits:
   - name: train
-    num_bytes: 97955287
+    num_bytes: 97953187
     num_examples: 745753
-  download_size: 52272712
-  dataset_size: 97955287
-- config_name: '1177'
+  download_size: 52246351
+  dataset_size: 97953187
+- config_name: wiki
   features:
   - name: sid
     dtype: string
@@ -99,10 +99,23 @@ dataset_info:
             '2': Body Structure
   splits:
   - name: train
-    num_bytes: 159007
-    num_examples: 927
-  download_size: 52272712
-  dataset_size: 159007
+    num_bytes: 7044574
+    num_examples: 48720
+  download_size: 2571416
+  dataset_size: 7044574
+configs:
+- config_name: '1177'
+  data_files:
+  - split: train
+    path: 1177/train-*
+- config_name: lt
+  data_files:
+  - split: train
+    path: lt/train-*
+- config_name: wiki
+  data_files:
+  - split: train
+    path: wiki/train-*
 ---
 
 # Dataset Card for swedish_medical_ner

lt/train-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:390ccd01ff87fdf2011cbaffe7023deadc61f8e97daae99c7241cf77ee21779b
+size 52246351

swedish_medical_ner.py

@@ -1,202 +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.
-"""SwedMedNER: A Named Entity Recognition Dataset on medical texts in Swedish"""
-
-
-import re
-
-import datasets
-
-
-_CITATION = """\
-@inproceedings{almgrenpavlovmogren2016bioner,
-  title={Named Entity Recognition in Swedish Medical Journals with Deep Bidirectional Character-Based LSTMs},
-  author={Simon Almgren, Sean Pavlov, Olof Mogren},
-  booktitle={Proceedings of the Fifth Workshop on Building and Evaluating Resources for Biomedical Text Mining (BioTxtM 2016)},
-  pages={1},
-  year={2016}
-}
-"""
-
-
-_DESCRIPTION = """\
-SwedMedNER is a dataset for training and evaluating Named Entity Recognition systems on medical texts in Swedish.
-It is derived from medical articles on the Swedish Wikipedia, Läkartidningen, and 1177 Vårdguiden.
-"""
-
-
-_LICENSE = """\
-Creative Commons Attribution-ShareAlike 4.0 International Public License (CC BY-SA 4.0)
-See http://creativecommons.org/licenses/by-sa/4.0/ for the summary of the license.
-"""
-
-
-_URL = "https://github.com/olofmogren/biomedical-ner-data-swedish"
-
-
-_DATA_URL = "https://raw.githubusercontent.com/olofmogren/biomedical-ner-data-swedish/master/"
-
-
-class SwedishMedicalNerConfig(datasets.BuilderConfig):
-    """BuilderConfig for SwedMedNER"""
-
-    def __init__(self, **kwargs):
-        """
-        Args:
-          **kwargs: keyword arguments forwarded to super.
-        """
-        super(SwedishMedicalNerConfig, self).__init__(**kwargs)
-
-
-class SwedishMedicalNer(datasets.GeneratorBasedBuilder):
-    """SwedMedNER: A Named Entity Recognition Dataset on medical texts in Swedish"""
-
-    VERSION = datasets.Version("1.0.0")
-
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(name="wiki", version=VERSION, description="The Swedish Wikipedia part of the dataset"),
-        datasets.BuilderConfig(name="lt", version=VERSION, description="The Läkartidningen part of the dataset"),
-        datasets.BuilderConfig(name="1177", version=VERSION, description="The 1177 Vårdguiden part of the dataset"),
-    ]
-
-    def _info(self):
-        if self.config.name == "wiki":
-            features = datasets.Features(
-                {
-                    "sid": datasets.Value("string"),
-                    "sentence": datasets.Value("string"),
-                    "entities": datasets.Sequence(
-                        {
-                            "start": datasets.Value("int32"),
-                            "end": datasets.Value("int32"),
-                            "text": datasets.Value("string"),
-                            "type": datasets.ClassLabel(
-                                names=["Disorder and Finding", "Pharmaceutical Drug", "Body Structure"]
-                            ),
-                        }
-                    ),
-                }
-            )
-        elif self.config.name == "lt":
-            features = datasets.Features(
-                {
-                    "sid": datasets.Value("string"),
-                    "sentence": datasets.Value("string"),
-                    "entities": datasets.Sequence(
-                        {
-                            "start": datasets.Value("int32"),
-                            "end": datasets.Value("int32"),
-                            "text": datasets.Value("string"),
-                            "type": datasets.ClassLabel(
-                                names=["Disorder and Finding", "Pharmaceutical Drug", "Body Structure"]
-                            ),
-                        }
-                    ),
-                }
-            )
-        elif self.config.name == "1177":
-            features = datasets.Features(
-                {
-                    "sid": datasets.Value("string"),
-                    "sentence": datasets.Value("string"),
-                    "entities": datasets.Sequence(
-                        {
-                            "start": datasets.Value("int32"),
-                            "end": datasets.Value("int32"),
-                            "text": datasets.Value("string"),
-                            "type": datasets.ClassLabel(
-                                names=["Disorder and Finding", "Pharmaceutical Drug", "Body Structure"]
-                            ),
-                        }
-                    ),
-                }
-            )
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=features,
-            supervised_keys=None,
-            homepage=_URL,
-            license=_LICENSE,
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-        urls_to_download = {
-            "wiki": _DATA_URL + "Wiki_annotated_60.txt",
-            "lt": _DATA_URL + "LT_annotated_60.txt",
-            "1177": _DATA_URL + "1177_annotated_sentences.txt",
-        }
-        downloaded_files = dl_manager.download_and_extract(urls_to_download)
-
-        if self.config.name == "wiki":
-            return [
-                datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["wiki"]})
-            ]
-        elif self.config.name == "lt":
-            return [
-                datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["lt"]})
-            ]
-        elif self.config.name == "1177":
-            return [
-                datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["1177"]})
-            ]
-
-    def _generate_examples(self, filepath):
-        """Yields examples as (key, example) tuples."""
-
-        def find_type(s, e):
-            if (s == "(") and (e == ")"):
-                return "Disorder and Finding"
-            elif (s == "[") and (e == "]"):
-                return "Pharmaceutical Drug"
-            elif (s == "{") and (e == "}"):
-                return "Body Structure"
-
-        pattern = r"\[([^\[\]()]+)\]|\(([^\[\]()]+)\)|\{([^\[\]()]+)\}"
-        with open(filepath, encoding="utf-8") as f:
-            for id_, row in enumerate(f):
-                sentence = row.replace("\n", "")
-
-                if self.config.name == "1177":
-                    targets = [
-                        {
-                            "start": m.start(0),
-                            "end": m.end(0),
-                            "text": sentence[m.start(0) + 2 : m.end(0) - 2],
-                            "type": find_type(sentence[m.start(0)], sentence[m.end(0) - 1]),
-                        }
-                        for m in re.finditer(pattern, sentence)
-                    ]
-                    yield id_, {
-                        "sid": self.config.name + "_" + str(id_),
-                        "sentence": sentence,
-                        "entities": targets if targets else [],
-                    }
-                else:
-                    targets = [
-                        {
-                            "start": m.start(0),
-                            "end": m.end(0),
-                            "text": sentence[m.start(0) + 1 : m.end(0) - 1],
-                            "type": find_type(sentence[m.start(0)], sentence[m.end(0) - 1]),
-                        }
-                        for m in re.finditer(pattern, sentence)
-                    ]
-                    yield id_, {
-                        "sid": self.config.name + "_" + str(id_),
-                        "sentence": sentence,
-                        "entities": targets if targets else [],
-                    }

wiki/train-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c651896521ab3226834c37aa1ecc68c06d2c237ce8502acd9122737224dedc85
+size 2571416