ginco version1

ginco.py

@@ -0,0 +1,164 @@
+# 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.
+
+import csv
+import json
+import os
+
+import datasets
+
+_CITATION = """\
+ @misc{11356/1467,
+ title = {Slovene Web genre identification corpus {GINCO} 1.0},
+ author = {Kuzman, Taja and Brglez, Mojca and Rupnik, Peter and Ljube{\v s}i{\'c}, Nikola},
+ url = {http://hdl.handle.net/11356/1467},
+ note = {Slovenian language resource repository {CLARIN}.{SI}},
+ copyright = {Creative Commons - Attribution-{ShareAlike} 4.0 International ({CC} {BY}-{SA} 4.0)},
+ issn = {2820-4042},
+ year = {2021} }
+"""
+
+_DESCRIPTION = """\
+The Slovene Web genre identification corpus GINCO 1.0 contains web texts, manually annotated with genre,
+from two Slovene web corpora, the slWaC 2.0 corpus, crawled in 2014, and a web corpus, crawled in 2021 in the scope of the MaCoCu project.
+The corpus allows for automated genre identification and genre analyses as well as other web corpora research, and comprises two parts:
+- subcorpus of suitable texts, containing 1002 texts (478,969 words), manually annotated with 24 genre categories (News/Reporting, Announcement,
+Research Article, Instruction, Recipe, Call (such as a Call for Papers), Legal/Regulation, Information/Explanation, Opinionated News, Review,
+Opinion/Argumentation, Promotion of a Product, Promotion of Services, Invitation, Promotion, Interview, Forum, Correspondence, Script/Drama,
+Prose, Lyrical, FAQ (Frequently Asked Questions), List of Summaries/Excerpts, and Other)
+- subcorpus of unsuitable texts, containing 123 texts (173,778 words), discarded as not suitable for genre annotation due to reasons,
+encoded by the labels (Machine Translation, Generated Text, Not Slovene, Encoding Issues, HTML Source Code, Boilerplate, Too Short/Incoherent,
+Too Long (longer than 5,000 words), Non-Textual (no full sentences, e.g. tables, lists), and Multiple texts).
+The texts in the suitable subset are annotated with up to three genre categories, where the primary label is the most prevalent,
+and secondary and tertiary labels denote presence of additional genre(s). They are encoded in three levels of detail, allowing experiments
+with the full set (24 labels), set of 21 labels (labels with less than 5 instances are merged with label Other) and set of 12 labels (similar labels
+are merged). Additionally, the corpus contains some metadata about the text (e.g. url, domain, year) and its paragraphs (e.g. near-duplicates and their usefulness for the genre identification).
+"""
+
+_HOMEPAGE = "http://hdl.handle.net/11356/1467"
+
+_LICENSE = "Creative Commons - Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)"
+
+
+_URLS = {
+    "ginco": "https://www.clarin.si/repository/xmlui/bitstream/handle/11356/1467/GINCO-1.0-suitable.json.zip?sequence=5&isAllowed=y",
+}
+
+
+class Ginco(datasets.GeneratorBasedBuilder):
+    """Genre identification and genre analyses for Slovenian texts."""
+
+    VERSION = datasets.Version("1.1.0")
+
+    def _info(self):
+       
+        features = datasets.Features(
+            {
+                "id": datasets.Value("string"),
+                "url": datasets.Value("string"),
+                "crawled": datasets.Value("string"),
+                "hard": datasets.Value("bool"),
+                "paragraphs": [
+                    {
+                        "text": datasets.Value("string"),
+                        "duplicate": datasets.Value("bool"),
+                        "keep": datasets.Value("bool"),
+                    }
+                ],
+                "primary_level_1": datasets.Value("string"),
+                "primary_level_2": datasets.Value("string"),
+                "primary_level_3": datasets.Value("string"),
+                "secondary_level_1": datasets.Value("string"),
+                "secondary_level_2": datasets.Value("string"),
+                "secondary_level_3": datasets.Value("string"),
+                "tertiary_level_1": datasets.Value("string"),
+                "tertiary_level_2": datasets.Value("string"),
+                "tertiary_level_3": datasets.Value("string"),
+                "split": datasets.Value("string"),
+                "domain": datasets.Value("string"),                
+            }
+        )
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+    
+    
+
+    def _split_generators(self, dl_manager):
+        urls = _URLS["ginco"]
+        download_path = dl_manager.download_and_extract(urls)
+        download_path = os.path.join(download_path, "GINCO-1.0-suitable.json", "GINCO-1.0-suitable.json")
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": download_path,
+                    "split": "train",
+                },
+            ),
+
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "filepath": download_path,
+                    "split": "dev",
+                },
+            ),
+
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepath": download_path,
+                    "split": "test",
+                },
+            ), 
+        ]
+    
+    def _generate_examples(self, filepath, split):
+        
+        with open(filepath, "r", encoding='utf-8') as file_obj:
+            data = json.load(file_obj)
+            
+            if split == "train":
+                examples = [example for example in data if example["split"] == "train"]
+            elif split == "dev":
+                examples = [example for example in data if example["split"] == "dev"]
+            elif split == "test":
+                examples = [example for example in data if example["split"] == "test"]
+
+            for i, example in enumerate(examples):
+                yield i, {
+                    "id": example["id"],
+                    "url": example["url"],
+                    "crawled": example["crawled"],
+                    "hard": example["hard"],
+                    "paragraphs": example["paragraphs"],
+                    "primary_level_1": example["primary_level_1"],
+                    "primary_level_2": example["primary_level_2"],
+                    "primary_level_3": example["primary_level_3"],
+                    "secondary_level_1": example["secondary_level_1"],
+                    "secondary_level_2": example["secondary_level_2"],
+                    "secondary_level_3": example["secondary_level_3"],
+                    "tertiary_level_1": example["tertiary_level_1"],
+                    "tertiary_level_2": example["tertiary_level_2"],
+                    "tertiary_level_3": example["tertiary_level_3"],
+                    "split": example["split"],
+                    "domain": example["domain"],                    
+                }