Delete recognasumm.py

recognasumm.py

@@ -1,93 +0,0 @@
-import csv
-import json
-import os
-
-import datasets
-
-_CITATION = """\
-Coming soon
-}
-"""
-
-_DESCRIPTION = """\
-RecognaSumm is a novel and comprehensive database specifically designed for the task of automatic text summarization in Portuguese. RecognaSumm stands out due to its diverse origin, composed of news collected from a variety of information sources, including agencies and online news portals. The database was constructed using web scraping techniques and careful curation, re sulting in a rich and representative collection of documents covering various topics and journalis tic styles. The creation of RecognaSumm aims to fill a significant void in Portuguese language summarization research, providing a training and evaluation foundation that can be used for the development and enhancement of automated summarization models.
-"""
-
-_HOMEPAGE = ""
-
-_LICENSE = "mit"
-
-class RecognaSumm(datasets.GeneratorBasedBuilder):
-
-    VERSION = datasets.Version("1.0.0")
-
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(name="default", version=VERSION, description="Default setup of dataset"),
-    ]
-
-    DEFAULT_CONFIG_NAME = "default" 
-
-    def _info(self):
-      
-        features = datasets.Features(
-             {
-                 "index": datasets.Value("int"),
-                 "Titulo": datasets.Value("string"),
-                 "Subtitulo": datasets.Value("string"),
-                 "Noticia": datasets.Value("string"),
-                 "Categoria": datasets.Value("string"),
-                 "Autor": datasets.Value("string"),
-                 "Data": datasets.Value("string"),
-                 "URL": datasets.Value("string"),
-                 "Autor_corrigido": datasets.Value("string"),
-                 "Sumario": datasets.Value("string"),
-             }
-        )
-
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=features,  
-            homepage=_HOMEPAGE,
-            license=_LICENSE,
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "filepath": "train.jsonl",
-                    "split": "train",
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "filepath": "validation.jsonl",
-                    "split": "validation",
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "filepath": "test.jsonl",
-                    "split": "test"
-                },
-            ),
-        ]
-
-    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
-    def _generate_examples(self, filepath, split):
-        with open(filepath, encoding="utf-8") as f:
-            for key, row in enumerate(f):
-                data = json.loads(row)
-                yield key, {
-                    "index": data["index"],
-                    "Noticia": data["Noticia"],
-                    "Sumario": data["Sumario"]
-                }
-