holylovenia commited on
Commit
2b7c2b1
1 Parent(s): 6a5ab6a

Upload kopi_nllb.py with huggingface_hub

Browse files
Files changed (1) hide show
  1. kopi_nllb.py +12 -12
kopi_nllb.py CHANGED
@@ -18,9 +18,9 @@ import json
18
  import datasets
19
  import zstandard as zstd
20
 
21
- from nusacrowd.utils import schemas
22
- from nusacrowd.utils.configs import NusantaraConfig
23
- from nusacrowd.utils.constants import (DEFAULT_NUSANTARA_VIEW_NAME,
24
  DEFAULT_SOURCE_VIEW_NAME, Tasks)
25
 
26
  logger = datasets.logging.get_logger(__name__)
@@ -62,7 +62,7 @@ _SUPPORTED_TASKS = [Tasks.SELF_SUPERVISED_PRETRAINING]
62
 
63
  _LANGUAGES = ["ind", "jav", "ace", "ban", "bjn", "min", "sun"]
64
 
65
- _NUSANTARA_VERSION = "1.0.0"
66
 
67
  _SOURCE_VERSION = "2022.09.13"
68
 
@@ -70,20 +70,20 @@ _LOCAL = False
70
 
71
  _SOURCE_VIEW_NAME = DEFAULT_SOURCE_VIEW_NAME
72
 
73
- _UNIFIED_VIEW_NAME = DEFAULT_NUSANTARA_VIEW_NAME
74
 
75
  _URL = "https://huggingface.co/datasets/allenai/nllb"
76
 
77
 
78
- def nusantara_config_constructor(lang, schema, version):
79
- """Construct NusantaraConfig"""
80
- if schema != "source" and schema != "nusantara_ssp":
81
  raise ValueError(f"Invalid schema: {schema}")
82
 
83
  if lang == "":
84
  raise ValueError(f"Snapshot is required. Choose one of these Snapshot: {_ALL_CONFIG}.")
85
  elif lang in _ALL_CONFIG:
86
- return NusantaraConfig(
87
  name=f"{_DATASETNAME}_{lang}_{schema}",
88
  version=datasets.Version(version),
89
  description=f"KoPI-NLLB with {schema} schema for {lang}",
@@ -108,7 +108,7 @@ class KoPINLLBConfig(datasets.BuilderConfig):
108
  class KoPINLLB(datasets.GeneratorBasedBuilder):
109
  """KoPI NLLB corpus."""
110
 
111
- BUILDER_CONFIGS = [nusantara_config_constructor(sn, "source", _SOURCE_VERSION) for sn in _ALL_CONFIG] + [nusantara_config_constructor(sn, "nusantara_ssp", _NUSANTARA_VERSION) for sn in _ALL_CONFIG]
112
 
113
  def _info(self):
114
 
@@ -121,7 +121,7 @@ class KoPINLLB(datasets.GeneratorBasedBuilder):
121
  "source": datasets.Value("string"),
122
  }
123
  )
124
- elif self.config.schema == "nusantara_ssp":
125
  features = schemas.self_supervised_pretraining.features
126
  return datasets.DatasetInfo(
127
  description=_DESCRIPTION,
@@ -151,7 +151,7 @@ class KoPINLLB(datasets.GeneratorBasedBuilder):
151
  for line in f:
152
  if line:
153
  example = json.loads(line)
154
- if self.config.schema == "nusantara_ssp":
155
  yield id_, {"id": str(id_), "text": example["text"]}
156
  id_ += 1
157
  else:
 
18
  import datasets
19
  import zstandard as zstd
20
 
21
+ from seacrowd.utils import schemas
22
+ from seacrowd.utils.configs import SEACrowdConfig
23
+ from seacrowd.utils.constants import (DEFAULT_SEACROWD_VIEW_NAME,
24
  DEFAULT_SOURCE_VIEW_NAME, Tasks)
25
 
26
  logger = datasets.logging.get_logger(__name__)
 
62
 
63
  _LANGUAGES = ["ind", "jav", "ace", "ban", "bjn", "min", "sun"]
64
 
65
+ _SEACROWD_VERSION = "2024.06.20"
66
 
67
  _SOURCE_VERSION = "2022.09.13"
68
 
 
70
 
71
  _SOURCE_VIEW_NAME = DEFAULT_SOURCE_VIEW_NAME
72
 
73
+ _UNIFIED_VIEW_NAME = DEFAULT_SEACROWD_VIEW_NAME
74
 
75
  _URL = "https://huggingface.co/datasets/allenai/nllb"
76
 
77
 
78
+ def seacrowd_config_constructor(lang, schema, version):
79
+ """Construct SEACrowdConfig"""
80
+ if schema != "source" and schema != "seacrowd_ssp":
81
  raise ValueError(f"Invalid schema: {schema}")
82
 
83
  if lang == "":
84
  raise ValueError(f"Snapshot is required. Choose one of these Snapshot: {_ALL_CONFIG}.")
85
  elif lang in _ALL_CONFIG:
86
+ return SEACrowdConfig(
87
  name=f"{_DATASETNAME}_{lang}_{schema}",
88
  version=datasets.Version(version),
89
  description=f"KoPI-NLLB with {schema} schema for {lang}",
 
108
  class KoPINLLB(datasets.GeneratorBasedBuilder):
109
  """KoPI NLLB corpus."""
110
 
111
+ BUILDER_CONFIGS = [seacrowd_config_constructor(sn, "source", _SOURCE_VERSION) for sn in _ALL_CONFIG] + [seacrowd_config_constructor(sn, "seacrowd_ssp", _SEACROWD_VERSION) for sn in _ALL_CONFIG]
112
 
113
  def _info(self):
114
 
 
121
  "source": datasets.Value("string"),
122
  }
123
  )
124
+ elif self.config.schema == "seacrowd_ssp":
125
  features = schemas.self_supervised_pretraining.features
126
  return datasets.DatasetInfo(
127
  description=_DESCRIPTION,
 
151
  for line in f:
152
  if line:
153
  example = json.loads(line)
154
+ if self.config.schema == "seacrowd_ssp":
155
  yield id_, {"id": str(id_), "text": example["text"]}
156
  id_ += 1
157
  else: