Datasets:
Upload 4 files
Browse files- .gitattributes +3 -0
- recognasumm.py +93 -0
- test.jsonl +3 -0
- train.jsonl +3 -0
- validation.jsonl +3 -0
.gitattributes
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@@ -59,3 +59,6 @@ validation.xlsx filter=lfs diff=lfs merge=lfs -text
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test.json filter=lfs diff=lfs merge=lfs -text
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train.json filter=lfs diff=lfs merge=lfs -text
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validation.json filter=lfs diff=lfs merge=lfs -text
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test.json filter=lfs diff=lfs merge=lfs -text
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train.json filter=lfs diff=lfs merge=lfs -text
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validation.json filter=lfs diff=lfs merge=lfs -text
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test.jsonl filter=lfs diff=lfs merge=lfs -text
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train.jsonl filter=lfs diff=lfs merge=lfs -text
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validation.jsonl filter=lfs diff=lfs merge=lfs -text
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recognasumm.py
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import csv
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import json
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import os
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import datasets
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_CITATION = """\
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Coming soon
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}
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"""
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_DESCRIPTION = """\
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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.
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"""
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_HOMEPAGE = ""
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_LICENSE = "mit"
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class RecognaSumm(datasets.GeneratorBasedBuilder):
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VERSION = datasets.Version("1.0.0")
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BUILDER_CONFIGS = [
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datasets.BuilderConfig(name="default", version=VERSION, description="Default setup of dataset"),
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]
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DEFAULT_CONFIG_NAME = "default"
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def _info(self):
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features = datasets.Features(
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{
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"index": datasets.Value("int"),
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"Titulo": datasets.Value("string"),
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"Subtitulo": datasets.Value("string"),
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"Noticia": datasets.Value("string"),
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"Categoria": datasets.Value("string"),
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"Autor": datasets.Value("string"),
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"Data": datasets.Value("string"),
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"URL": datasets.Value("string"),
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"Autor_corrigido": datasets.Value("string"),
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"Sumario": datasets.Value("string"),
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}
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)
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return datasets.DatasetInfo(
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description=_DESCRIPTION,
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features=features,
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homepage=_HOMEPAGE,
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license=_LICENSE,
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citation=_CITATION,
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)
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def _split_generators(self, dl_manager):
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return [
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datasets.SplitGenerator(
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name=datasets.Split.TRAIN,
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# These kwargs will be passed to _generate_examples
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gen_kwargs={
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"filepath": "train.jsonl",
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"split": "train",
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},
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),
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datasets.SplitGenerator(
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name=datasets.Split.VALIDATION,
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# These kwargs will be passed to _generate_examples
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gen_kwargs={
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"filepath": "validation.jsonl",
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"split": "validation",
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},
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),
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datasets.SplitGenerator(
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name=datasets.Split.TEST,
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# These kwargs will be passed to _generate_examples
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gen_kwargs={
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"filepath": "test.jsonl",
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"split": "test"
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},
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),
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]
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# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
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def _generate_examples(self, filepath, split):
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with open(filepath, encoding="utf-8") as f:
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for key, row in enumerate(f):
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data = json.loads(row)
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yield key, {
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"index": data["index"],
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"Noticia": data["Noticia"],
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"Sumario": data["Sumario"]
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}
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test.jsonl
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version https://git-lfs.github.com/spec/v1
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oid sha256:7c3b747956f856da20925156b7aa8a1d54b7091d3c24382076464fd4b957e4c1
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size 95999004
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train.jsonl
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version https://git-lfs.github.com/spec/v1
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oid sha256:925f2ab858ccbe99bfa4e5d7e7ea7adafb4d75a5b0498d6f90ac4cffd5233bc9
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size 288582909
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validation.jsonl
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version https://git-lfs.github.com/spec/v1
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oid sha256:24baaf963b74205ea9633e74f2efd015a906365e3de2e7c3d531cc64e54ef8e7
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size 95284358
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