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1 Parent(s): b5b1745

Upload nusaparagraph_rhetoric.py with huggingface_hub

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  1. nusaparagraph_rhetoric.py +19 -19
nusaparagraph_rhetoric.py CHANGED
@@ -2,14 +2,14 @@ from pathlib import Path
2
  from typing import Dict, List, Tuple
3
  import datasets
4
  import pandas as pd
5
- from nusacrowd.utils import schemas
6
- from nusacrowd.utils.configs import NusantaraConfig
7
- from nusacrowd.utils.constants import (DEFAULT_NUSANTARA_VIEW_NAME,
8
  DEFAULT_SOURCE_VIEW_NAME, Tasks)
9
  _LOCAL = False
10
  _DATASETNAME = "nusaparagraph_rhetoric"
11
  _SOURCE_VIEW_NAME = DEFAULT_SOURCE_VIEW_NAME
12
- _UNIFIED_VIEW_NAME = DEFAULT_NUSANTARA_VIEW_NAME
13
  _LANGUAGES = [
14
  "btk", "bew", "bug", "jav", "mad", "mak", "min", "mui", "rej", "sun"
15
  ] # We follow ISO639-3 language code (https://iso639-3.sil.org/code_tables/639/data)
@@ -31,7 +31,7 @@ _HOMEPAGE = "https://github.com/IndoNLP/nusa-writes"
31
  _LICENSE = "Creative Commons Attribution Share-Alike 4.0 International"
32
  _SUPPORTED_TASKS = [Tasks.RHETORIC_MODE_CLASSIFICATION]
33
  _SOURCE_VERSION = "1.0.0"
34
- _NUSANTARA_VERSION = "1.0.0"
35
  _URLS = {
36
  "train":
37
  "https://raw.githubusercontent.com/IndoNLP/nusa-writes/main/data/nusa_alinea-paragraph-{lang}-train.csv",
@@ -40,12 +40,12 @@ _URLS = {
40
  "test":
41
  "https://raw.githubusercontent.com/IndoNLP/nusa-writes/main/data/nusa_alinea-paragraph-{lang}-test.csv",
42
  }
43
- def nusantara_config_constructor(lang, schema, version):
44
- """Construct NusantaraConfig with nusaparagraph_rhetoric_{lang}_{schema} as the name format"""
45
- if schema != "source" and schema != "nusantara_text":
46
  raise ValueError(f"Invalid schema: {schema}")
47
  if lang == "":
48
- return NusantaraConfig(
49
  name="nusaparagraph_rhetoric_{schema}".format(schema=schema),
50
  version=datasets.Version(version),
51
  description=
@@ -55,7 +55,7 @@ def nusantara_config_constructor(lang, schema, version):
55
  subset_id="nusaparagraph_rhetoric",
56
  )
57
  else:
58
- return NusantaraConfig(
59
  name="nusaparagraph_rhetoric_{lang}_{schema}".format(lang=lang,
60
  schema=schema),
61
  version=datasets.Version(version),
@@ -80,15 +80,15 @@ LANGUAGES_MAP = {
80
  class NusaParagraphRhetoric(datasets.GeneratorBasedBuilder):
81
  """NusaParagraph-Rhetoric is a 50labels (narrative, persuasive, argumentative, descriptive, and expository) rhetoric mode classification dataset for 10 Indonesian local languages."""
82
  BUILDER_CONFIGS = ([
83
- nusantara_config_constructor(lang, "source", _SOURCE_VERSION)
84
  for lang in LANGUAGES_MAP
85
  ] + [
86
- nusantara_config_constructor(lang, "nusantara_text",
87
- _NUSANTARA_VERSION)
88
  for lang in LANGUAGES_MAP
89
  ] + [
90
- nusantara_config_constructor("", "source", _SOURCE_VERSION),
91
- nusantara_config_constructor("", "nusantara_text", _NUSANTARA_VERSION)
92
  ])
93
  DEFAULT_CONFIG_NAME = "nusaparagraph_rhetoric_ind_source"
94
  def _info(self) -> datasets.DatasetInfo:
@@ -98,7 +98,7 @@ class NusaParagraphRhetoric(datasets.GeneratorBasedBuilder):
98
  "text": datasets.Value("string"),
99
  "label": datasets.Value("string"),
100
  })
101
- elif self.config.schema == "nusantara_text":
102
  features = schemas.text_features([
103
  "narrative", "persuasive", "argumentative", "descriptive", "expository"
104
  ])
@@ -113,7 +113,7 @@ class NusaParagraphRhetoric(datasets.GeneratorBasedBuilder):
113
  self, dl_manager: datasets.DownloadManager
114
  ) -> List[datasets.SplitGenerator]:
115
  """Returns SplitGenerators."""
116
- if self.config.name == "nusaparagraph_rhetoric_source" or self.config.name == "nusaparagraph_rhetoric_nusantara_text":
117
  # Load all 12 languages
118
  train_csv_path = dl_manager.download_and_extract([
119
  _URLS["train"].format(lang=lang)
@@ -153,9 +153,9 @@ class NusaParagraphRhetoric(datasets.GeneratorBasedBuilder):
153
  ),
154
  ]
155
  def _generate_examples(self, filepath: Path) -> Tuple[int, Dict]:
156
- if self.config.schema != "source" and self.config.schema != "nusantara_text":
157
  raise ValueError(f"Invalid config: {self.config.name}")
158
- if self.config.name == "nusaparagraph_rhetoric_source" or self.config.name == "nusaparagraph_rhetoric_nusantara_text":
159
  ldf = []
160
  for fp in filepath:
161
  ldf.append(pd.read_csv(fp))
 
2
  from typing import Dict, List, Tuple
3
  import datasets
4
  import pandas as pd
5
+ from seacrowd.utils import schemas
6
+ from seacrowd.utils.configs import SEACrowdConfig
7
+ from seacrowd.utils.constants import (DEFAULT_SEACROWD_VIEW_NAME,
8
  DEFAULT_SOURCE_VIEW_NAME, Tasks)
9
  _LOCAL = False
10
  _DATASETNAME = "nusaparagraph_rhetoric"
11
  _SOURCE_VIEW_NAME = DEFAULT_SOURCE_VIEW_NAME
12
+ _UNIFIED_VIEW_NAME = DEFAULT_SEACROWD_VIEW_NAME
13
  _LANGUAGES = [
14
  "btk", "bew", "bug", "jav", "mad", "mak", "min", "mui", "rej", "sun"
15
  ] # We follow ISO639-3 language code (https://iso639-3.sil.org/code_tables/639/data)
 
31
  _LICENSE = "Creative Commons Attribution Share-Alike 4.0 International"
32
  _SUPPORTED_TASKS = [Tasks.RHETORIC_MODE_CLASSIFICATION]
33
  _SOURCE_VERSION = "1.0.0"
34
+ _SEACROWD_VERSION = "2024.06.20"
35
  _URLS = {
36
  "train":
37
  "https://raw.githubusercontent.com/IndoNLP/nusa-writes/main/data/nusa_alinea-paragraph-{lang}-train.csv",
 
40
  "test":
41
  "https://raw.githubusercontent.com/IndoNLP/nusa-writes/main/data/nusa_alinea-paragraph-{lang}-test.csv",
42
  }
43
+ def seacrowd_config_constructor(lang, schema, version):
44
+ """Construct SEACrowdConfig with nusaparagraph_rhetoric_{lang}_{schema} as the name format"""
45
+ if schema != "source" and schema != "seacrowd_text":
46
  raise ValueError(f"Invalid schema: {schema}")
47
  if lang == "":
48
+ return SEACrowdConfig(
49
  name="nusaparagraph_rhetoric_{schema}".format(schema=schema),
50
  version=datasets.Version(version),
51
  description=
 
55
  subset_id="nusaparagraph_rhetoric",
56
  )
57
  else:
58
+ return SEACrowdConfig(
59
  name="nusaparagraph_rhetoric_{lang}_{schema}".format(lang=lang,
60
  schema=schema),
61
  version=datasets.Version(version),
 
80
  class NusaParagraphRhetoric(datasets.GeneratorBasedBuilder):
81
  """NusaParagraph-Rhetoric is a 50labels (narrative, persuasive, argumentative, descriptive, and expository) rhetoric mode classification dataset for 10 Indonesian local languages."""
82
  BUILDER_CONFIGS = ([
83
+ seacrowd_config_constructor(lang, "source", _SOURCE_VERSION)
84
  for lang in LANGUAGES_MAP
85
  ] + [
86
+ seacrowd_config_constructor(lang, "seacrowd_text",
87
+ _SEACROWD_VERSION)
88
  for lang in LANGUAGES_MAP
89
  ] + [
90
+ seacrowd_config_constructor("", "source", _SOURCE_VERSION),
91
+ seacrowd_config_constructor("", "seacrowd_text", _SEACROWD_VERSION)
92
  ])
93
  DEFAULT_CONFIG_NAME = "nusaparagraph_rhetoric_ind_source"
94
  def _info(self) -> datasets.DatasetInfo:
 
98
  "text": datasets.Value("string"),
99
  "label": datasets.Value("string"),
100
  })
101
+ elif self.config.schema == "seacrowd_text":
102
  features = schemas.text_features([
103
  "narrative", "persuasive", "argumentative", "descriptive", "expository"
104
  ])
 
113
  self, dl_manager: datasets.DownloadManager
114
  ) -> List[datasets.SplitGenerator]:
115
  """Returns SplitGenerators."""
116
+ if self.config.name == "nusaparagraph_rhetoric_source" or self.config.name == "nusaparagraph_rhetoric_seacrowd_text":
117
  # Load all 12 languages
118
  train_csv_path = dl_manager.download_and_extract([
119
  _URLS["train"].format(lang=lang)
 
153
  ),
154
  ]
155
  def _generate_examples(self, filepath: Path) -> Tuple[int, Dict]:
156
+ if self.config.schema != "source" and self.config.schema != "seacrowd_text":
157
  raise ValueError(f"Invalid config: {self.config.name}")
158
+ if self.config.name == "nusaparagraph_rhetoric_source" or self.config.name == "nusaparagraph_rhetoric_seacrowd_text":
159
  ldf = []
160
  for fp in filepath:
161
  ldf.append(pd.read_csv(fp))