albertvillanova HF staff commited on
Commit
e44149a
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verified ·
1 Parent(s): b4b3e8b

Update loading script

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Files changed (1) hide show
  1. senti_lex.py +3 -17
senti_lex.py CHANGED
@@ -41,7 +41,8 @@ _HOMEPAGE = "https://sites.google.com/site/datascienceslab/projects/multilingual
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  _LICENSE = "GNU General Public License v3"
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- _URLs = "https://www.kaggle.com/rtatman/sentiment-lexicons-for-81-languages"
 
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  LANGS = [
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  "af",
@@ -135,17 +136,6 @@ class SentiLex(datasets.GeneratorBasedBuilder):
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  VERSION = datasets.Version("1.1.0")
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- @property
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- def manual_download_instructions(self):
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- return """\
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- You should download the dataset from https://www.kaggle.com/rtatman/sentiment-lexicons-for-81-languages
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- The webpage requires registration.
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- After downloading, please put the files in a dir of your choice,
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- which will be used as a manual_dir, e.g. `~/.manual_dirs/senti_lex`
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- SentiLex can then be loaded via:
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- `datasets.load_dataset("newsroom", data_dir="~/.manual_dirs/senti_lex")`.
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- """
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-
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  BUILDER_CONFIGS = [
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  datasets.BuilderConfig(
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  name=i,
@@ -180,11 +170,7 @@ class SentiLex(datasets.GeneratorBasedBuilder):
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  def _split_generators(self, dl_manager):
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  """Returns SplitGenerators."""
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- data_dir = os.path.abspath(os.path.expanduser(dl_manager.manual_dir))
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- if not os.path.exists(data_dir):
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- raise FileNotFoundError(
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- f"{data_dir} does not exist. Make sure you insert a manual dir via `datasets.load_dataset('newsroom', data_dir=...)` that includes files unzipped from the reclor zip. Manual download instructions: {self.manual_download_instructions}"
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- )
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  return [
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  datasets.SplitGenerator(
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  name=datasets.Split.TRAIN,
 
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  _LICENSE = "GNU General Public License v3"
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+ # Origin data from: "https://www.kaggle.com/rtatman/sentiment-lexicons-for-81-languages"
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+ _URL = "data.zip"
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  LANGS = [
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  "af",
 
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  VERSION = datasets.Version("1.1.0")
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  BUILDER_CONFIGS = [
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  datasets.BuilderConfig(
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  name=i,
 
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  def _split_generators(self, dl_manager):
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  """Returns SplitGenerators."""
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+ data_dir = dl_manager.download_and_extract(_URL)
 
 
 
 
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  return [
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  datasets.SplitGenerator(
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  name=datasets.Split.TRAIN,