Datasets:
File size: 29,555 Bytes
9ddf5f1 b419176 b8f009e b419176 d329c13 0ad2c45 35ca6f3 d329c13 44d0be1 d329c13 44d0be1 d329c13 44d0be1 d329c13 44d0be1 d329c13 9078ecf d329c13 0d5ddef d329c13 0d5ddef d329c13 3b17622 d329c13 3ee22d3 d329c13 dd64621 d329c13 28e2471 0ad2c45 d329c13 44d0be1 0ad2c45 44d0be1 d329c13 28e2471 0ad2c45 d329c13 44d0be1 0ad2c45 44d0be1 0ad2c45 44d0be1 d329c13 28e2471 0ad2c45 d329c13 dacce38 d329c13 57cf7b1 d329c13 0d5ddef d329c13 b890947 d329c13 f5d9f50 b890947 f5d9f50 b890947 d329c13 b419176 c3f3d58 682c8bc c3f3d58 682c8bc c3f3d58 f5d9f50 b419176 9ddf5f1 b890947 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 |
import json
import os
from typing import List
from huggingface_hub import hf_hub_url
import datasets
num_plenary_shard = 28
num_of_committee_shards = 90
_KnessetCorpus_CITATION = "@misc{goldin2024knesset, title={The Knesset Corpus: An Annotated Corpus of Hebrew Parliamentary Proceedings}, author={Gili Goldin and Nick Howell and Noam Ordan and Ella Rabinovich and Shuly Wintner}, year={2024}, eprint={2405.18115}, archivePrefix={arXiv}, primaryClass={cs.CL} }"
_KnessetCorpus_DESCRIPTION = """
An annotated corpus of Hebrew parliamentary proceedings containing over 32 million sentences from all the (plenary and committee) protocols held in the Israeli parliament from 1998 to 2022.
Sentences are annotated with various levels of linguistic information, including part-of-speech tags, morphological features, dependency structures, and named entities.
They are also associated with detailed meta-information reflecting demographic and political properties of the speakers, based on a large database of parliament members and factions that we compiled.
"""
_AllFeaturesSentences_DESCRIPTION = """\
AllFeaturesSentences consists of samples of all the sentences in the corpus (plenary and committee) together with all the features available in the dataset:\
The features are consistent with the Sentence model features, the Protocol model features of the protocol the sentence belongs to,
the Person model features of the speaker and the Faction model features \
of the faction of the speaker at that time.
This is roughly equivalent to a join between all models in dataset (Protocol, Faction, Person, Sentence)
"""
_NoMorphAllFeaturesSentences_DESCRIPTION = """\
AllFeaturesSentences consists of samples of all the sentences in the corpus (plenary and committee) together with all the non-morphological features available in the dataset :\
The features are consistent with the Sentence model features, the Protocol model features of the protocol the sentence belongs to,
the Person model features of the speaker and the Faction model features \
of the faction of the speaker at that time.
This is roughly equivalent to a join between all models in dataset (Protocol, Faction, Person, Sentence)
Ideal if morpho-syntactic annotations aren't relevant to your work, providing a less disk space heavy option.
"""
_CommitteeAllFeaturesSentences_DESCRIPTION = """\
CommitteeAllFeaturesSentences consists of samples of all the sentences in the committee sessions in the corpus together with all the features available in the dataset:\
The features are consistent with the Sentence model features, the Protocol model features of the protocol the sentence belongs to,
the Person model features of the speaker and the Faction model features \
of the faction of the speaker at that time.
This is roughly equivalent to a join between all models in dataset (Protocol, Faction, Person, Sentence)
"""
_NoMorphCommitteeAllFeaturesSentences_DESCRIPTION = """\
AllFeaturesSentences consists of samples of all the sentences in the committee sessions in the corpus together with all the non-morphological features available in the dataset :\
The features are consistent with the Sentence model features, the Protocol model features of the protocol the sentence belongs to,
the Person model features of the speaker and the Faction model features \
of the faction of the speaker at that time.
This is roughly equivalent to a join between all models in dataset (Protocol, Faction, Person, Sentence)
Ideal if morpho-syntactic annotations aren't relevant to your work, providing a less disk space heavy option.
"""
_PlenaryAllFeaturesSentences_DESCRIPTION = """\
PlenaryAllFeaturesSentences consists of samples of all the sentences in the plenary sessions in the corpus together with all the features available in the dataset:\
The features are consistent with the Sentence model features, the Protocol model features of the protocol the sentence belongs to,
the Person model features of the speaker and the Faction model features \
of the faction of the speaker at that time.
This is roughly equivalent to a join between all models in dataset (Protocol, Faction, Person, Sentence)
"""
_NoMorphPlenaryAllFeaturesSentences_DESCRIPTION = """\
AllFeaturesSentences consists of samples of all the sentences in the plenary sessions in the corpus together with all the non-morphological features available in the dataset :\
The features are consistent with the Sentence model features, the Protocol model features of the protocol the sentence belongs to,
the Person model features of the speaker and the Faction model features \
of the faction of the speaker at that time.
This is roughly equivalent to a join between all models in dataset (Protocol, Faction, Person, Sentence)
Ideal if morpho-syntactic annotations aren't relevant to your work, providing a less disk space heavy option.
"""
_KnessetMembers_DESCRIPTION = """\
KnessetMembers consists of samples of the knesset members in the dataset and their meta-data information such as name, gender and factions affiliations.
The features are consistent with the Person model features
"""
_Factions_DESCRIPTION = """\
Factions consists of samples of the factions in the dataset and their meta-data information such as name, political orientation and active periods.
The features are consistent with the Faction model features
"""
_Protocols_DESCRIPTION = """\
Protocols consists of samples of the protocols in the dataset and their meta-data information such as date, knesset number, session name and a list of its sentences.
The features are consistent with the Protocol model features and the features for each sentence are consistent with the Sentence model
"""
class KnessetCorpusConfig(datasets.BuilderConfig):
"""BuilderConfig for KnessetCorpus."""
def __init__(self, features, data_urls, citation, **kwargs):
"""BuilderConfig for KnessetCorpus.
Args:
features: *list[string]*, list of the features that will appear in the
feature dict. Should not include "label".
data_urls: *list[string]*, urls to download the files from.
citation: *string*, citation for the data set.
**kwargs: keyword arguments forwarded to super.
"""
super().__init__(version=datasets.Version("1.0.1"), **kwargs)
self.features = features
self.data_urls = data_urls
self.citation = citation
class KnessetCorpus(datasets.GeneratorBasedBuilder):
BUILDER_CONFIG_CLASS = KnessetCorpusConfig
BUILDER_CONFIGS = [
KnessetCorpusConfig(
name="knessetMembers",
description=_KnessetMembers_DESCRIPTION,
features=["person_id", "first_name", "last_name", "full_name", "is_knesset_member", "gender", "email", "is_current", "last_updated_date", "date_of_birth", "place_of_birth", "year_of_aliya", "date_of_death", "mother_tongue", "religion", "nationality", "religious_orientation", "residence", "factions_memberships", "languages","allSources", "wikiLink","notes" ],
data_urls=["https://huggingface.co/datasets/HaifaCLGroup/KnessetCorpus/raw/main/all_knesset_members_jsons.jsonl"],
citation=_KnessetCorpus_CITATION,
),
KnessetCorpusConfig(
name="factions",
description=_Factions_DESCRIPTION,
features=["faction_name", "faction_popular_initials", "faction_id", "active_periods", "knesset_numbers", "coalition_or_opposition_memberships", "political_orientation", "other_names", "notes", "wiki_link", ],
data_urls=["https://huggingface.co/datasets/HaifaCLGroup/KnessetCorpus/raw/main/factions_jsons.jsonl"],
citation=_KnessetCorpus_CITATION,
),
KnessetCorpusConfig(
name="protocols",
description=_Protocols_DESCRIPTION,
features=["protocol_name", "session_name", "parent_session_name", "knesset_number", "protocol_number", "protocol_date", "is_ocr_output", "protocol_type", "protocol_sentences"],
data_urls=[f"https://huggingface.co/datasets/HaifaCLGroup/KnessetCorpus/resolve/main/protocols_sentences/committee_protocols_jsons.zip", f"https://huggingface.co/datasets/HaifaCLGroup/KnessetCorpus/resolve/main/protocols_sentences/plenary_protocols_jsons.zip"],
citation=_KnessetCorpus_CITATION,
),
KnessetCorpusConfig(
name="all_features_sentences",
description=_AllFeaturesSentences_DESCRIPTION,
features=[ "_id", "knesset_number", "protocol_name", "protocol_number", "protocol_type", "session_name", "parent_session_name", "@timestamp", "protocol_date", "is_ocr_output", "sentence_id", "speaker_id", "speaker_name", "is_valid_speaker", "is_chairman", "turn_num_in_protocol", "sent_num_in_turn", "sentence_text", "morphological_fields", "factuality_fields", "speaker_first_name", "speaker_last_name", "speaker_is_knesset_member", "speaker_gender", "speaker_email", "speaker_last_updated_date", "speaker_date_of_birth", "speaker_place_of_birth","speaker_year_of_aliya", "speaker_date_of_death", "speaker_mother_tongue", "speaker_religion", "speaker_nationality", "speaker_religious_orientation", "speaker_residence", "speaker_factions_memberships", "speaker_languages", "speaker_sources", "speaker_notes","faction_id", "faction_general_name", "knesset_faction_id", "current_faction_name", "faction_popular_initials", "faction_active_periods", "faction_knesset_numbers","faction_coalition_or_opposition_memberships", "faction_political_orientation", "faction_other_names", "faction_notes", "faction_wiki_link"],
data_urls=[f"https://huggingface.co/datasets/HaifaCLGroup/KnessetCorpus/resolve/main/protocols_sentences/plenary_full_sentences_shards_bzip2_files/plenary_full_sentences_shard_{str(i).zfill(2)}.jsonl.bz2" for i in range(num_plenary_shard)] + [f"https://huggingface.co/datasets/HaifaCLGroup/KnessetCorpus/resolve/main/protocols_sentences/committee_full_sentences_shards_bzip2_files/committee_full_sentences_shard_{str(i).zfill(2)}.jsonl.bz2" for i in range(num_of_committee_shards)],
citation=_KnessetCorpus_CITATION,
),
KnessetCorpusConfig(
name="no_morph_all_features_sentences",
description=_NoMorphAllFeaturesSentences_DESCRIPTION,
features=["_id", "knesset_number", "protocol_name", "protocol_number", "protocol_type", "session_name",
"parent_session_name", "@timestamp", "protocol_date", "is_ocr_output", "sentence_id",
"speaker_id", "speaker_name", "is_valid_speaker", "is_chairman", "turn_num_in_protocol",
"sent_num_in_turn", "sentence_text", "factuality_fields",
"speaker_first_name", "speaker_last_name", "speaker_is_knesset_member", "speaker_gender",
"speaker_email", "speaker_last_updated_date", "speaker_date_of_birth", "speaker_place_of_birth",
"speaker_year_of_aliya", "speaker_date_of_death", "speaker_mother_tongue", "speaker_religion",
"speaker_nationality", "speaker_religious_orientation", "speaker_residence",
"speaker_factions_memberships", "speaker_languages", "speaker_sources", "speaker_notes",
"faction_id", "faction_general_name", "knesset_faction_id", "current_faction_name",
"faction_popular_initials", "faction_active_periods", "faction_knesset_numbers",
"faction_coalition_or_opposition_memberships", "faction_political_orientation",
"faction_other_names", "faction_notes", "faction_wiki_link"],
data_urls=[
f"https://huggingface.co/datasets/HaifaCLGroup/KnessetCorpus/resolve/main/protocols_sentences/plenary_no_morph_sentences_shards_bzip2_files/no_morph_plenary_full_sentences_shard_{str(i).zfill(2)}.jsonl.bz2" for i in range(num_plenary_shard)] + [f"https://huggingface.co/datasets/HaifaCLGroup/KnessetCorpus/resolve/main/protocols_sentences/committee_no_morph_sentences_shards_bzip2_files/no_morph_committee_full_sentences_shard_{str(i).zfill(2)}.jsonl.bz2" for i in range(num_plenary_shard)],#TODO update num of shards
citation=_KnessetCorpus_CITATION,
),
KnessetCorpusConfig(
name="committees_all_features_sentences",
description=_AllFeaturesSentences_DESCRIPTION,
features=[ "_id", "knesset_number", "protocol_name", "protocol_number", "protocol_type", "session_name", "parent_session_name", "@timestamp", "protocol_date", "is_ocr_output", "sentence_id", "speaker_id", "speaker_name", "is_valid_speaker", "is_chairman", "turn_num_in_protocol", "sent_num_in_turn", "sentence_text", "morphological_fields", "factuality_fields", "speaker_first_name", "speaker_last_name", "speaker_is_knesset_member", "speaker_gender", "speaker_email", "speaker_last_updated_date", "speaker_date_of_birth", "speaker_place_of_birth","speaker_year_of_aliya", "speaker_date_of_death", "speaker_mother_tongue", "speaker_religion", "speaker_nationality", "speaker_religious_orientation", "speaker_residence", "speaker_factions_memberships", "speaker_languages", "speaker_sources", "speaker_notes","faction_id", "faction_general_name", "knesset_faction_id", "current_faction_name", "faction_popular_initials", "faction_active_periods", "faction_knesset_numbers","faction_coalition_or_opposition_memberships", "faction_political_orientation", "faction_other_names", "faction_notes", "faction_wiki_link"],
data_urls=[f"https://huggingface.co/datasets/HaifaCLGroup/KnessetCorpus/resolve/main/protocols_sentences/committee_full_sentences_shards_bzip2_files/committee_full_sentences_shard_{str(i).zfill(2)}.jsonl.bz2" for i in range(num_of_committee_shards)],
citation=_KnessetCorpus_CITATION,
),
KnessetCorpusConfig(
name="no_morph_committee_all_features_sentences",
description=_NoMorphCommitteeAllFeaturesSentences_DESCRIPTION,
features=["_id", "knesset_number", "protocol_name", "protocol_number", "protocol_type", "session_name",
"parent_session_name", "@timestamp", "protocol_date", "is_ocr_output", "sentence_id",
"speaker_id", "speaker_name", "is_valid_speaker", "is_chairman", "turn_num_in_protocol",
"sent_num_in_turn", "sentence_text", "factuality_fields",
"speaker_first_name", "speaker_last_name", "speaker_is_knesset_member", "speaker_gender",
"speaker_email", "speaker_last_updated_date", "speaker_date_of_birth", "speaker_place_of_birth",
"speaker_year_of_aliya", "speaker_date_of_death", "speaker_mother_tongue", "speaker_religion",
"speaker_nationality", "speaker_religious_orientation", "speaker_residence",
"speaker_factions_memberships", "speaker_languages", "speaker_sources", "speaker_notes",
"faction_id", "faction_general_name", "knesset_faction_id", "current_faction_name",
"faction_popular_initials", "faction_active_periods", "faction_knesset_numbers",
"faction_coalition_or_opposition_memberships", "faction_political_orientation",
"faction_other_names", "faction_notes", "faction_wiki_link"],
data_urls=[ f"https://huggingface.co/datasets/HaifaCLGroup/KnessetCorpus/resolve/main/protocols_sentences/committee_no_morph_sentences_shards_bzip2_files/no_morph_committee_full_sentences_shard_{str(i).zfill(2)}.jsonl.bz2" for i in range(num_of_committee_shards)],
citation=_KnessetCorpus_CITATION,
),
KnessetCorpusConfig(
name="no_morph_plenary_all_features_sentences",
description=_NoMorphPlenaryAllFeaturesSentences_DESCRIPTION,
features=["_id", "knesset_number", "protocol_name", "protocol_number", "protocol_type", "session_name",
"parent_session_name", "@timestamp", "protocol_date", "is_ocr_output", "sentence_id",
"speaker_id", "speaker_name", "is_valid_speaker", "is_chairman", "turn_num_in_protocol",
"sent_num_in_turn", "sentence_text", "factuality_fields",
"speaker_first_name", "speaker_last_name", "speaker_is_knesset_member", "speaker_gender",
"speaker_email", "speaker_last_updated_date", "speaker_date_of_birth", "speaker_place_of_birth",
"speaker_year_of_aliya", "speaker_date_of_death", "speaker_mother_tongue", "speaker_religion",
"speaker_nationality", "speaker_religious_orientation", "speaker_residence",
"speaker_factions_memberships", "speaker_languages", "speaker_sources", "speaker_notes",
"faction_id", "faction_general_name", "knesset_faction_id", "current_faction_name",
"faction_popular_initials", "faction_active_periods", "faction_knesset_numbers",
"faction_coalition_or_opposition_memberships", "faction_political_orientation",
"faction_other_names", "faction_notes", "faction_wiki_link"],
data_urls=[
f"https://huggingface.co/datasets/HaifaCLGroup/KnessetCorpus/resolve/main/protocols_sentences/plenary_no_morph_sentences_shards_bzip2_files/no_morph_plenary_full_sentences_shard_{str(i).zfill(2)}.jsonl.bz2" for i in range(num_plenary_shard)],
citation=_KnessetCorpus_CITATION,
),
KnessetCorpusConfig(
name="plenary_all_features_sentences",
description=_AllFeaturesSentences_DESCRIPTION,
features=[ "_id", "knesset_number", "protocol_name", "protocol_number", "protocol_type", "session_name", "parent_session_name", "@timestamp", "protocol_date", "is_ocr_output", "sentence_id", "speaker_id", "speaker_name", "is_valid_speaker", "is_chairman", "turn_num_in_protocol", "sent_num_in_turn", "sentence_text", "morphological_fields", "factuality_fields", "speaker_first_name", "speaker_last_name", "speaker_is_knesset_member", "speaker_gender", "speaker_email", "speaker_last_updated_date", "speaker_date_of_birth", "speaker_place_of_birth","speaker_year_of_aliya", "speaker_date_of_death", "speaker_mother_tongue", "speaker_religion", "speaker_nationality", "speaker_religious_orientation", "speaker_residence", "speaker_factions_memberships", "speaker_languages", "speaker_sources", "speaker_notes","faction_id", "faction_general_name", "knesset_faction_id", "current_faction_name", "faction_popular_initials", "faction_active_periods", "faction_knesset_numbers","faction_coalition_or_opposition_memberships", "faction_political_orientation", "faction_other_names", "faction_notes", "faction_wiki_link"],
data_urls=[f"https://huggingface.co/datasets/HaifaCLGroup/KnessetCorpus/resolve/main/protocols_sentences/plenary_full_sentences_shards_bzip2_files/plenary_full_sentences_shard_{str(i).zfill(2)}.jsonl.bz2" for i in range(num_plenary_shard)],
citation=_KnessetCorpus_CITATION,
)]
DEFAULT_CONFIG_NAME = "all_features_sentences"
def _info(self):
features = {feature: datasets.Value("string") for feature in self.config.features}
if "all_features_sentences" in self.config.name:
if not "no_morph" in self.config.name:
features.update({"morphological_fields": datasets.Sequence({
"id": datasets.Value("string"),
"form": datasets.Value("string"),
"lemma": datasets.Value("string"),
"upos": datasets.Value("string"),
"xpos": datasets.Value("string"),
"feats": datasets.Value("string"),
"head": datasets.Value("int32"),
"deprel": datasets.Value("string"),
"deps": datasets.Value("string"),
"misc": datasets.Value("string")})})
features.update({
"faction_active_periods": datasets.Sequence({
"end_date": datasets.Value("string"), # Date converted to string
"start_date": datasets.Value("string"), # Date converted to string
}),
"faction_coalition_or_opposition_memberships": datasets.Sequence({
"end_date": datasets.Value("string"), # Date converted to string
"knesset_faction_name": datasets.Value("string"),
"knesset_num": datasets.Value("string"),
"member_of_coalition": datasets.Value("bool"),
"notes": datasets.Value("string"),
"start_date": datasets.Value("string") # Date converted to string
}),
"faction_other_names": datasets.Sequence({
"name": datasets.Value("string"),
"name_end_date": datasets.Value("string"), # Date converted to string
"name_start_date": datasets.Value("string") # Date converted to string
}),
"is_chairman": datasets.Value("bool"),
"is_ocr_output": datasets.Value("bool"),
"is_valid_speaker": datasets.Value("bool"),
"sent_num_in_turn": datasets.Value("int64"),
"turn_num_in_protocol": datasets.Value("int64"),
"speaker_factions_memberships": datasets.Sequence({
"end_date": datasets.Value("string"),
"faction_id": datasets.Value("string"),
"faction_name": datasets.Value("string"),
"knesset_faction_id": datasets.Value("string"),
"knesset_number": datasets.Value("string"),
"start_date": datasets.Value("string")}),
"speaker_is_knesset_member": datasets.Value("bool"),
"speaker_notes": datasets.Sequence(datasets.Value("string")),
"speaker_languages": datasets.Sequence(datasets.Value("string"))
})
if self.config.name == "protocols":
features.update({
"is_ocr_output": datasets.Value("bool"),
"protocol_sentences": datasets.Sequence({
"sentence_id": datasets.Value("string"),
"protocol_name": datasets.Value("string"),
"speaker_id": datasets.Value("string"),
"speaker_name": datasets.Value("string"),
"is_valid_speaker": datasets.Value("bool"),
"turn_num_in_protocol": datasets.Value("int64"),
"sent_num_in_turn": datasets.Value("int64"),
"sentence_text": datasets.Value("string"),
"is_chairman": datasets.Value("bool"),
"morphological_fields": datasets.Sequence({
"id": datasets.Value("string"),
"form": datasets.Value("string"),
"lemma": datasets.Value("string"),
"upos": datasets.Value("string"),
"xpos": datasets.Value("string"),
"feats": datasets.Value("string"),
"head": datasets.Value("int32"),
"deprel": datasets.Value("string"),
"deps": datasets.Value("string"),
"misc": datasets.Value("string"),
}),
"factuality_fields": datasets.Value("string")
})
})
if self.config.name == "factions":
features.update({
"active_periods": datasets.Sequence({
"end_date": datasets.Value("string"), # Date converted to string
"start_date": datasets.Value("string"), # Date converted to string
}),
"coalition_or_opposition_memberships": datasets.Sequence({
"end_date": datasets.Value("string"), # Date converted to string
"knesset_faction_name": datasets.Value("string"),
"knesset_num": datasets.Value("string"),
"member_of_coalition": datasets.Value("bool"),
"notes": datasets.Value("string"),
"start_date": datasets.Value("string"), # Date converted to string
}),
"other_names": datasets.Sequence({
"name": datasets.Value("string"),
"name_end_date": datasets.Value("string"), # Date converted to string
"name_start_date": datasets.Value("string"), # Date converted to string
})
})
if self.config.name == "KnessetMembers":
features.update({
"is_knesset_member": datasets.Value("bool"),
"factions_memberships": datasets.Sequence({
"end_date": datasets.Value("string"),
"faction_id": datasets.Value("string"),
"faction_name": datasets.Value("string"),
"knesset_faction_id": datasets.Value("string"),
"knesset_number": datasets.Value("string"),
"start_date": datasets.Value("string")}),
"notes": datasets.Sequence(datasets.Value("string")),
"languages": datasets.Sequence(datasets.Value("string"))
})
return datasets.DatasetInfo(
description=_KnessetCorpus_DESCRIPTION + self.config.description,
features=datasets.Features(features),
citation=self.config.citation
)
def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
urls = self.config.data_urls
downloaded_files = dl_manager.download_and_extract(urls)
# Handle single file case
if not isinstance(downloaded_files, list):
downloaded_files = [downloaded_files]
# Only process if we detect zip files
if any('zip://' in str(file) for file in downloaded_files):
extracted_files = []
for file_path in downloaded_files:
# Remove the zip:// prefix and download the actual zip content
actual_url = str(file_path).replace('zip://::', '')
extracted = dl_manager.download_and_extract(actual_url)
# If it's a directory, get all files inside
if os.path.isdir(extracted):
for root, _, files in os.walk(extracted):
for filename in files:
full_path = os.path.join(root, filename)
extracted_files.append(full_path)
else:
extracted_files.append(extracted)
data_files = extracted_files
else:
data_files = downloaded_files
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"data_files": data_files,
},
),
]
def _generate_examples(self, data_files):
if "all_features_sentences" in self.config.name:
id_field_name = "sentence_id"
elif self.config.name == "protocols":
id_field_name = "protocol_name"
elif self.config.name == "factions":
id_field_name = "faction_id"
elif self.config.name == "knessetMembers":
id_field_name = "person_id"
for i, filepath in enumerate(data_files):
try:
with open(filepath, encoding="utf-8") as f:
for line in f:
sample = {}
try:
row = json.loads(line)
except Exception as e:
print(f'couldnt load sample. error was: {e}. Continuing to next sample')
continue
id_ = row.get(id_field_name, None)
if id_ is None:
print(f"Key '{id_field_name}' not found in row. Skipping this row. row is: {row}")
continue
for feature in self._info().features:
if feature == "protocol_sentences":
protocol_sentences = row.get("protocol_sentences", [])
# Ensure protocol_sentences is a list of dictionaries
if isinstance(protocol_sentences, dict):
protocol_sentences = list(protocol_sentences.values())
elif not isinstance(protocol_sentences, list):
print(f"'protocol_sentences' is not a list or dict. Skipping this row. Row: {row}")
continue
sample[feature] = protocol_sentences
else:
sample[feature] = row.get(feature, None)
try:
yield id_, sample
except Exception as e:
print(f'couldnt yield sample. error: {e}. sample is: {sample}.', flush=True)
except Exception as e:
print(f"Error opening file '{filepath}': {e}. Skipping.")
|