fernando-peres
commited on
Commit
·
9db4098
1
Parent(s):
f3007b2
resolving card bugs
Browse files- .gitignore +5 -1
- config.py +0 -2
- features_specs.py +0 -34
- obligations.py +0 -200
- py_legislation_metadata.py +0 -69
.gitignore
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env
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.vscode
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/.vscode
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env
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/env
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/.vscode
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features_specs.py
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obligations.py
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py_legislation_metadata.py
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config.py
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config.py
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LANGUAGE = "ES"
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features_specs.py
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import datasets
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from obligations import affected_entity, cost_type, aa_categories, aa_categories_unique, io_categories
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BASIC_FEATURES_SPEC = {
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"source_id": datasets.Value(dtype="int64"),
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"doc_source_id": datasets.Value(dtype="int64"),
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"document": datasets.Value(dtype="string"),
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"text": datasets.Value(dtype="string"),
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}
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RAW_FEATURES_SPEC = {
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"source_id": datasets.Value(dtype="int64"),
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"doc_source_id": datasets.Value(dtype="int64"),
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"document": datasets.Value(dtype="string"),
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"text": datasets.Value(dtype="string"),
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}
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SENTENCES_UNLABELED_FEATURES_SPEC = {
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"source_id": datasets.Value(dtype="int64"),
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"doc_source_id": datasets.Value(dtype="int64"),
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"document": datasets.Value(dtype="string"),
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"text": datasets.Value(dtype="string"),
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#
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# Categories
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"cost_type": datasets.ClassLabel(names=cost_type,),
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"affected_entity": datasets.ClassLabel(names=affected_entity,),
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"io_categories": datasets.Sequence(datasets.ClassLabel(names=io_categories,)),
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"aa_categories": datasets.Sequence(datasets.ClassLabel(names=aa_categories,)),
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"aa_categories_unique": datasets.Sequence(datasets.ClassLabel(names=aa_categories_unique,)),
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}
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obligations.py
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from config import LANGUAGE
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cost_type = ["no_cost", "adm_cost", "direct_cost", "other_cost"]
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cost_type_ES = ["sin_costo", "costo_adm", "costo_directo", "otro_costo"]
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cost_type_PT = ["sem_custo", "custo_adm", "custo_direto", "outro_custo"]
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affected_entity = ["no_affected_ent", "companies", "citizens", "public_adm"]
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affected_entity_ES = ["ent_no_afectada",
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"empresas", "ciudadanos", "adm_publica"]
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affected_entity_PT = ["ent_nao_afetada", "empresas", "cidadaos", "adm_publica"]
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# [i] -
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# [i] IO Categories -
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# [i] -
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io_categories_PT = [
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"prestacao_info_empresarial_e_fiscal",
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"pedidos_de_licencas_e_outros",
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"registos_e_notificacoes",
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"candidatura_a_subsidios_e_outros",
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"disponibilizacao_de_manuais_e_outros",
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"cooperacao_com_auditorias_e_outros",
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"prestacao_info_a_consumidores",
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"outras_ois"
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],
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io_categories_ES = [
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"prestacion_de_informacion_empresarial_y_fiscal"
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"solicitudes_de_licencias_y_otras"
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"registros_y_notificaciones"
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"solicitud_de_subsidios_y_otras"
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"disponibilidad_de_manuales_y_otras"
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"cooperacion_con_auditorías_y_otras"
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"prestacion_de_informacion_a_consumidores"
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"otras_OIS"
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]
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# [i] -
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# [i] IO Categories Unique -
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# [i] -
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aa_categories_unique_PT = [
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"familiarizacao_com_oi",
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"recolha_e_organizacao_de_info",
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"processamento_de_info",
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"tempos_de_espera",
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"deslocacoes",
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"submissao_de_info",
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"preservacao_de_info"
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]
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aa_categories_unique_ES = [
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"familiarizacion_con_OI"
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"recoleccion_y_organizacion_de_informacion"
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"procesamiento_de_informacion"
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"tiempos_de_espera"
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"desplazamientos"
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"envio_de_informacion"
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"preservacion_de_informacion"
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]
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# [i] -
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# [i] AA Categories -
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# [i] -
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aa_categories_PT = [
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"aa_1_familiarizacao_com_oi",
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"aa_1_recolha_e_organizacao_de_info",
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"aa_1_processamento_de_info",
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"aa_1_tempos_de_espera",
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"aa_1_deslocacoes",
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"aa_1_submissao_de_info",
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"aa_1_preservacao_de_info",
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"aa_2_familiarizacao_com_oi",
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"aa_2_recolha_e_organizacao_de_info",
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"aa_2_processamento_de_info",
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"aa_2_tempos_de_espera",
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"aa_2_deslocacoes",
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"aa_2_submissao_de_info",
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"aa_2_preservacao_de_info",
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"aa_3_familiarizacao_com_oi",
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"aa_3_recolha_e_organizacao_de_info",
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"aa_3_processamento_de_info",
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"aa_3_tempos_de_espera",
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"aa_3_deslocacoes",
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"aa_3_submissao_de_info",
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"aa_3_preservacao_de_info",
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"aa_4_familiarizacao_com_oi",
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"aa_4_recolha_e_organizacao_de_info",
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"aa_4_processamento_de_info",
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"aa_4_tempos_de_espera",
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"aa_4_deslocacoes",
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"aa_4_submissao_de_info",
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"aa_4_preservacao_de_info",
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"aa_5_familiarizacao_com_oi",
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"aa_5_recolha_e_organizacao_de_info",
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"aa_5_processamento_de_info",
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"aa_5_tempos_de_espera",
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"aa_5_deslocacoes",
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"aa_5_submissao_de_info",
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"aa_5_preservacao_de_info",
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"aa_6_familiarizacao_com_oi",
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"aa_6_recolha_e_organizacao_de_info",
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"aa_6_processamento_de_info",
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"aa_6_tempos_de_espera",
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"aa_6_deslocacoes",
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"aa_6_submissao_de_info",
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"aa_6_preservacao_de_info",
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"aa_7_familiarizacao_com_oi",
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"aa_7_recolha_e_organizacao_de_info",
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"aa_7_processamento_de_info",
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"aa_7_tempos_de_espera",
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"aa_7_deslocacoes",
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"aa_7_submissao_de_info",
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"aa_7_preservacao_de_info"
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]
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aa_categories_ES = [
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"aa_1_familiarizacion_con_OI"
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"aa_1_recoleccion_y_organizacion_de_informacion"
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"aa_1_procesamiento_de_informacion"
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"aa_1_tiempos_de_espera"
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"aa_1_desplazamientos"
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"aa_1_envio_de_informacion"
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"aa_1_preservacion_de_informacion"
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"aa_2_familiarizacion_con_OI"
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"aa_2_recoleccion_y_organizacion_de_informacion"
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"aa_2_procesamiento_de_informacion"
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"aa_2_tiempos_de_espera"
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"aa_2_desplazamientos"
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"aa_2_envio_de_informacion"
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"aa_2_preservacion_de_informacion"
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"aa_3_familiarizacion_con_OI"
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"aa_3_recoleccion_y_organizacion_de_informacion"
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"aa_3_procesamiento_de_informacion"
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"aa_3_tiempos_de_espera"
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"aa_3_desplazamientos"
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"aa_3_envio_de_informacion"
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"aa_3_preservacion_de_informacion"
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"aa_4_familiarizacion_con_OI"
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"aa_4_recoleccion_y_organizacion_de_informacion"
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"aa_4_procesamiento_de_informacion"
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"aa_4_tiempos_de_espera"
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"aa_4_desplazamientos"
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"aa_4_envio_de_informacion"
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"aa_4_preservacion_de_informacion"
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"aa_5_familiarizacion_con_OI"
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"aa_5_recoleccion_y_organizacion_de_informacion"
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"aa_5_procesamiento_de_informacion"
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"aa_5_tiempos_de_espera"
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"aa_5_desplazamientos"
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"aa_5_envio_de_informacion"
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"aa_5_preservacion_de_informacion"
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"aa_6_familiarizacion_con_OI"
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"aa_6_recoleccion_y_organizacion_de_informacion"
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"aa_6_procesamiento_de_informacion"
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"aa_6_tiempos_de_espera"
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"aa_6_desplazamientos"
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"aa_6_envio_de_informacion"
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"aa_6_preservacion_de_informacion"
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"aa_7_familiarizacion_con_OI"
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"aa_7_recoleccion_y_organizacion_de_informacion"
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"aa_7_procesamiento_de_informacion"
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"aa_7_tiempos_de_espera"
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"aa_7_desplazamientos"
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"aa_7_envio_de_informacion"
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"aa_7_preservacion_de_informacion"
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]
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io_categories = [],
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aa_categories_unique = []
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aa_categories = []
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if LANGUAGE == "ES":
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io_categories = io_categories_ES,
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aa_categories_unique = aa_categories_unique_ES
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aa_categories = aa_categories_ES
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cost_type = cost_type_ES
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affected_entity = affected_entity_ES
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elif LANGUAGE == "PT":
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io_categories = io_categories_PT,
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aa_categories_unique = aa_categories_unique_PT
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aa_categories = aa_categories_PT
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cost_type = cost_type_PT
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affected_entity = affected_entity_PT
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urls = {
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"raw": "./data/raw.parquet",
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"sentences_unlabeled": "./data/unlabeled.parquet",
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"sentences_labeled_train": "./data/labeled_train.parquet",
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"sentences_labeled_test": "./data/labeled_test.parquet",
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},
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py_legislation_metadata.py
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import textwrap
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import datasets
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# [i] -
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# [i] GENERAL DESCRIPTIONS -
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PY_LEGISLATION_METADATA = {
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"citation" : """\
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@InProceedings{
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huggingface:dataset,
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title = {Paraguay Legislation Dataset},
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author={Peres, Fernando; Costa, Victor},
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year={2023}
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}
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""",
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"description" :textwrap.dedent("""\
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Dataset for researching - NLP techniques on PARAGUAY legislation.
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"""),
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"homepage" : "https://www.leyes.com.py/",
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"license" : "apache-2.0",
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"urls" : {
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"raw": "./data/0_raw/raw.parquet",
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"sentences_unlabeled": "./data/1_sentences_unlabeled/unlabeled.parquet",
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"sentences_labeled": "./data/2_sentences_labeled/labeled.parquet",
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},
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"raw-description" : textwrap.dedent("""
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Data extracted from the sources files (URls, PDFs and Word files) without any transformation or sentence splitter. It can be helpful because you can access the raw data extracted from the seeds (PDFs and Word files) and apply other preprocessing tasks from this point to prepare the data without returning to extract texts from source files.
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"""),
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"sentences-unlabeled-description" : textwrap.dedent("""
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Unlabeled corpora of Paraguay legislation. This data is prepared to be labeled by the experts. Each instance of the dataset represents a specific text passage, split by its original formatting extracted from raw text (from original documents)
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Each observation of the dataset represents a specific text passage.
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"""),
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"sentences-labeled-description" : textwrap.dedent("""
|
48 |
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The labeled data is the ground truth data used to train the models. This data is annotated by legal experts indicating the existence of administrative costs (and other types) in the legislation.
|
49 |
-
|
50 |
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Each observation of the dataset represents a specific text passage.
|
51 |
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"""),
|
52 |
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}
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53 |
-
|
54 |
-
|
55 |
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x = {
|
56 |
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"config_names" : {
|
57 |
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"raw": {
|
58 |
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"description" : "",
|
59 |
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"features" : {
|
60 |
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"source_id": datasets.Value(dtype="int64"),
|
61 |
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"doc_source_id": datasets.Value(dtype="int64"),
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62 |
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"document": datasets.Value(dtype="string"),
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63 |
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"text": datasets.Value(dtype="string"),
|
64 |
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}
|
65 |
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}
|
66 |
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}
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67 |
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}
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68 |
-
|
69 |
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# x["config_names"]["raw"]["description"]
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