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Datasets:
ICILS
/
isco_esco_occupations_taxonomy

Tasks:
Text Classification
Sub-tasks:
multi-class-classification
Source Datasets:
European Commission ESCO
Tags:
occupation coding
ESCO
ISCO-08
Dataset card Viewer Files Community
1
Daniel Duckworth commited on
Commit
b45a3ee
•
1 Parent(s): 23f6dc8

init

Browse files
Files changed (7) hide show
  1. README.md +816 -4
  2. data/all.jsonl +0 -0
  3. data/data.parquet +0 -3
  4. data/isco_occupations.jsonl +0 -0
  5. data/isco_taxonomy.jsonl +0 -0
  6. isco_esco_occupations_taxonomy.py +102 -85
  7. meta.md +677 -0
README.md CHANGED
@@ -1,12 +1,824 @@
1
  ---
 
 
 
 
 
 
 
2
  dataset_info:
 
3
  features:
4
  - name: text
5
  dtype: string
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
6
  splits:
7
  - name: train
8
- num_bytes: 1140361
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- num_examples: 40147
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- download_size: 1019906
11
- dataset_size: 1140361
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
12
  ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
  ---
2
+ # For reference on dataset card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/datasetcard.md?plain=1
3
+ # Doc / guide: https://huggingface.co/docs/hub/datasets-cards
4
+ task_categories:
5
+ - text-classification
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+ task_ids:
7
+ - multi-class-classification
8
+ pretty_name: ISCO-ESCO Occupations Taxonomy
9
  dataset_info:
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+ - config_name: isco_occupations
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  features:
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  - name: text
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  dtype: string
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+ - name: labels
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587
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627
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628
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633
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634
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635
+ '617': '9624'
636
+ '618': '9629'
637
  splits:
638
  - name: train
639
+ num_bytes: 248076
640
+ num_examples: 7018
641
+ - name: validation
642
+ num_bytes: 248076
643
+ num_examples: 7018
644
+ download_size: 458547
645
+ dataset_size: 496152
646
+ - config_name: isco_taxonomy
647
+ features:
648
+ - name: text
649
+ dtype: string
650
+ - name: labels
651
+ dtype:
652
+ class_label:
653
+ names:
654
+ '0': '0'
655
+ '1': '1'
656
+ '2': '2'
657
+ '3': '3'
658
+ '4': '4'
659
+ '5': '5'
660
+ '6': '6'
661
+ '7': '7'
662
+ '8': '8'
663
+ '9': '9'
664
+ splits:
665
+ - name: train
666
+ num_bytes: 1422420
667
+ num_examples: 3017
668
+ - name: validation
669
+ num_bytes: 1422420
670
+ num_examples: 3017
671
+ download_size: 8654054
672
+ dataset_size: 2844840
673
+ train-eval-index:
674
+ - config: isco_occupations
675
+ task: text-classification
676
+ task_id: multi-class-classification
677
+ splits:
678
+ train_split: train
679
+ eval_split: validation
680
+ col_mapping:
681
+ text: text
682
+ label: labels
683
+ metrics:
684
+ - type: accuracy
685
+ name: Accuracy
686
  ---
687
+
688
+ # Dataset Card for {{ pretty_name | default("Dataset Name", true) }}
689
+
690
+ <!-- Provide a quick summary of the dataset. -->
691
+
692
+ {{ dataset_summary | default("", true) }}
693
+
694
+ ## Dataset Details
695
+
696
+ ### Dataset Description
697
+
698
+ <!-- Provide a longer summary of what this dataset is. -->
699
+
700
+ {{ dataset_description | default("", true) }}
701
+
702
+ - **Curated by:** {{ curators | default("[More Information Needed]", true)}}
703
+ - **Funded by [optional]:** {{ funded_by | default("[More Information Needed]", true)}}
704
+ - **Shared by [optional]:** {{ shared_by | default("[More Information Needed]", true)}}
705
+ - **Language(s) (NLP):** {{ language | default("[More Information Needed]", true)}}
706
+ - **License:** {{ license | default("[More Information Needed]", true)}}
707
+
708
+ ### Dataset Sources [optional]
709
+
710
+ <!-- Provide the basic links for the dataset. -->
711
+
712
+ - **Repository:** {{ repo | default("[More Information Needed]", true)}}
713
+ - **Paper [optional]:** {{ paper | default("[More Information Needed]", true)}}
714
+ - **Demo [optional]:** {{ demo | default("[More Information Needed]", true)}}
715
+
716
+ ## Uses
717
+
718
+ <!-- Address questions around how the dataset is intended to be used. -->
719
+
720
+ ### Direct Use
721
+
722
+ <!-- This section describes suitable use cases for the dataset. -->
723
+
724
+ {{ direct_use | default("[More Information Needed]", true)}}
725
+
726
+ ### Out-of-Scope Use
727
+
728
+ <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
729
+
730
+ {{ out_of_scope_use | default("[More Information Needed]", true)}}
731
+
732
+ ## Dataset Structure
733
+
734
+ <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
735
+
736
+ {{ dataset_structure | default("[More Information Needed]", true)}}
737
+
738
+ ## Dataset Creation
739
+
740
+ ### Curation Rationale
741
+
742
+ <!-- Motivation for the creation of this dataset. -->
743
+
744
+ {{ curation_rationale_section | default("[More Information Needed]", true)}}
745
+
746
+ ### Source Data
747
+
748
+ <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
749
+
750
+ #### Data Collection and Processing
751
+
752
+ <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
753
+
754
+ {{ data_collection_and_processing_section | default("[More Information Needed]", true)}}
755
+
756
+ #### Who are the source data producers?
757
+
758
+ <!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
759
+
760
+ {{ source_data_producers_section | default("[More Information Needed]", true)}}
761
+
762
+ ### Annotations [optional]
763
+
764
+ <!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
765
+
766
+ #### Annotation process
767
+
768
+ <!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
769
+
770
+ {{ annotation_process_section | default("[More Information Needed]", true)}}
771
+
772
+ #### Who are the annotators?
773
+
774
+ <!-- This section describes the people or systems who created the annotations. -->
775
+
776
+ {{ who_are_annotators_section | default("[More Information Needed]", true)}}
777
+
778
+ #### Personal and Sensitive Information
779
+
780
+ <!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
781
+
782
+ {{ personal_and_sensitive_information | default("[More Information Needed]", true)}}
783
+
784
+ ## Bias, Risks, and Limitations
785
+
786
+ <!-- This section is meant to convey both technical and sociotechnical limitations. -->
787
+
788
+ {{ bias_risks_limitations | default("[More Information Needed]", true)}}
789
+
790
+ ### Recommendations
791
+
792
+ <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
793
+
794
+ {{ bias_recommendations | default("Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.", true)}}
795
+
796
+ ## Citation [optional]
797
+
798
+ <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
799
+
800
+ **BibTeX:**
801
+
802
+ {{ citation_bibtex | default("[More Information Needed]", true)}}
803
+
804
+ **APA:**
805
+
806
+ {{ citation_apa | default("[More Information Needed]", true)}}
807
+
808
+ ## Glossary [optional]
809
+
810
+ <!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
811
+
812
+ {{ glossary | default("[More Information Needed]", true)}}
813
+
814
+ ## More Information [optional]
815
+
816
+ {{ more_information | default("[More Information Needed]", true)}}
817
+
818
+ ## Dataset Card Authors [optional]
819
+
820
+ {{ dataset_card_authors | default("[More Information Needed]", true)}}
821
+
822
+ ## Dataset Card Contact
823
+
824
+ {{ dataset_card_contact | default("[More Information Needed]", true)}}
data/all.jsonl ADDED
The diff for this file is too large to render. See raw diff
 
data/data.parquet DELETED
@@ -1,3 +0,0 @@
1
- version https://git-lfs.github.com/spec/v1
2
- oid sha256:3cb1c1d1e34fb263a3a65b186ac82aaf84c356855bd08bc29c536de9e0afb8ce
3
- size 995287
 
 
 
 
data/isco_occupations.jsonl ADDED
The diff for this file is too large to render. See raw diff
 
data/isco_taxonomy.jsonl ADDED
The diff for this file is too large to render. See raw diff
 
isco_esco_occupations_taxonomy.py CHANGED
@@ -1,12 +1,15 @@
1
  from typing import List
2
- from datasets.tasks import QuestionAnsweringExtractive, LanguageModeling, MaskedLM, MultipleChoice, TextClassification, TextToTextGeneration
3
  import pandas as pd
4
- import datasets
 
5
  import os
6
 
 
 
7
  logger = datasets.logging.get_logger(__name__)
8
 
9
- CITATION = """TBA"""
10
 
11
  _DESCRIPTION = """\
12
  ISCO ESCO Occupations Taxonomy Dataset (IEOTD) is a hierarhical \
@@ -15,116 +18,130 @@ occupations from ESCO and definitions.
15
  """
16
 
17
  # TODO: Update license based on ILO and ESCO
18
- LICENSE = """\
19
  By accessing ISCO ESCO Occupations Taxonomy Dataset, you indicate that you agree to the terms and conditions associated with their use. Please read the IEA Disclaimer and License Agreement for full details. [Disclaimer_and_License_Agreement.pdf (iea.nl)](https://www.iea.nl/sites/default/files/data-repository/Disclaimer_and_License_Agreement.pdf)
20
  """
21
 
22
- HOMEPAGE_URL = "https://iea.nl"
23
 
24
- _URL = "/"
25
  _URLS = {
26
- "full": _URL + "data/data.parquet",
 
27
  }
28
 
29
 
30
- class IscoEscoTaxonomyConfig(datasets.BuilderConfig):
31
  """BuilderConfig for ISCO ESCO Taxonomy."""
32
-
33
  def __init__(self, **kwargs):
34
  """BuilderConfig for SQUAD.
35
  Args:
36
  **kwargs: keyword arguments forwarded to super.
37
  """
38
- super(IscoEscoTaxonomyConfig, self).__init__(**kwargs)
39
 
40
- class IscoEscoTaxonomy(datasets.GeneratorBasedBuilder):
 
41
  """The ISCO ESCO Occupations Taxonomy Dataset v1.0.0"""
42
-
43
  BUILDER_CONFIGS = [
44
  datasets.BuilderConfig(
45
- name="isco_1-2",
46
  version=datasets.Version("1.0.0", ""),
47
- description="ISCO hierarchy levels 1-2",
 
 
 
 
 
48
  ),
49
  ]
50
 
51
- BUILDER_CONFIG_CLASS = IscoEscoTaxonomyConfig
52
- DEFAULT_CONFIG_NAME = "default"
53
-
54
  def _info(self):
55
- return datasets.DatasetInfo(
56
- description=_DESCRIPTION,
57
- features=datasets.Features(
 
58
  {
59
- "id": datasets.Value("string"),
60
- "entailment": datasets.features.Sequence(
61
- {
62
- "ISCO_DEFINITION_1": datasets.Value("string"),
63
- "ISCO_CODE_1": datasets.ClassLabel(names_file="labels/isco_code_1.txt"),
64
- "ISCO_LABEL_1": datasets.Value("string"),
65
- "ESCO_DESCRIPTION": datasets.Value("string"),
66
- }
67
  ),
68
  }
69
- ),
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
70
  )
71
 
72
- def _info(self):
73
- cwd=os.getcwd()
74
- script_dir = os.path.join(cwd)
75
- return datasets.DatasetInfo(
76
- description="The ISCO ESCO Occupations Taxonomy Dataset",
77
- citation=CITATION,
78
- homepage=HOMEPAGE_URL,
79
- license=LICENSE,
80
- builder_name="isco_esco_occupations",
81
- supervised_keys=None,
82
- task_templates=[
83
- TextClassification(task="text-classification", text_column="ESCO_DESCRIPTION", label_column="ESCO_OCCUPATION"),
84
- # TaskTemplate("text-to-text", text_column="ESCO_DESCRIPTION", summary_column="ESCO_OCCUPATION"),
85
- ],
86
- features=Features({
87
- "ISCO_CODE_1": ClassLabel(names_file=os.path.join(cwd, "../", "isco_esco_occupations_taxonomy", "labels", "isco_code_1.txt")),
88
- # "ISCO_CODE_1": ClassLabel(names_file=os.path.join(script_dir, "../", "isco_esco_occupations_taxonomy", "labels", "isco_code_1.txt")),
89
- "ISCO_LABEL_1": ClassLabel(names_file="labels/isco_label_1.txt"),
90
- "ISCO_DEFINITION_1": Value("string"),
91
- "ISCO_CODE_2": ClassLabel(names_file="labels/isco_code_2.txt"),
92
- "ISCO_LABEL_2": ClassLabel(names_file="labels/isco_label_2.txt"),
93
- "ISCO_DEFINITION_2": Value("string"),
94
- "ISCO_CODE_3": ClassLabel(names_file="labels/isco_code_3.txt"),
95
- "ISCO_LABEL_3": ClassLabel(names_file="labels/isco_label_3.txt"),
96
- "ISCO_DEFINITION_3": Value("string"),
97
- "ISCO_CODE_4": ClassLabel(names_file="labels/isco_code_4.txt"),
98
- "ISCO_LABEL_4": ClassLabel(names_file="labels/isco_label_4.txt"),
99
- "ISCO_DEFINITION_4": Value("string"),
100
- "ISCO_CODES": ClassLabel(names_file="labels/isco_codes.txt"),
101
- "ISCO_LABELS": ClassLabel(names_file="labels/isco_labels.txt"),
102
- "ESCO_CODE": ClassLabel(names_file="labels/esco_code.txt"),
103
- "ESCO_LABELS": ClassLabel(names_file="labels/esco_labels.txt"),
104
- "ESCO_OCCUPATION": ClassLabel(names_file="labels/esco_occupation.txt"),
105
- "ESCO_DESCRIPTION": Value("string"),
106
- "LANGUAGE": ClassLabel(names_file="labels/language.txt"),
107
- 'isco1': Sequence(
108
- feature={
109
- 'ISCO_DEFINITION_1': Value(dtype='large_string'),
110
- 'ISCO_CODE_1': ClassLabel(names_file="labels/isco_code_1.txt"),
111
- 'ISCO_LABEL_1': ClassLabel(names_file="labels/isco_label_1.txt")
112
- }
113
- )
114
- }))
115
-
116
-
117
-
118
- def _split_generators(self, dl_manager: DownloadManager) -> List[SplitGenerator]:
119
- isco_esco_all = dl_manager.download_and_extract(DOWNLOAD_URL)
120
 
 
 
 
 
 
121
  return [
122
- SplitGenerator(name=datasets.Split.ALL, gen_kwargs={"filepaths": isco_esco_all}),
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
123
  ]
124
-
125
- def _generate_examples(self, filepath):
126
- """This function returns the examples in the raw (text) form."""
127
- logger.info("generating examples from = %s", filepath)
128
- df = pd.read_parquet(filepath)
129
- for i, row in df.iterrows():
130
- yield i, row.to_dict()
 
 
 
 
 
 
 
 
 
 
 
 
 
1
  from typing import List
2
+ from datasets.tasks import LanguageModeling, TextClassification, TaskTemplate
3
  import pandas as pd
4
+ import csv
5
+ import json
6
  import os
7
 
8
+ import datasets
9
+
10
  logger = datasets.logging.get_logger(__name__)
11
 
12
+ _CITATION = """TBA"""
13
 
14
  _DESCRIPTION = """\
15
  ISCO ESCO Occupations Taxonomy Dataset (IEOTD) is a hierarhical \
 
18
  """
19
 
20
  # TODO: Update license based on ILO and ESCO
21
+ _LICENSE = """\
22
  By accessing ISCO ESCO Occupations Taxonomy Dataset, you indicate that you agree to the terms and conditions associated with their use. Please read the IEA Disclaimer and License Agreement for full details. [Disclaimer_and_License_Agreement.pdf (iea.nl)](https://www.iea.nl/sites/default/files/data-repository/Disclaimer_and_License_Agreement.pdf)
23
  """
24
 
25
+ _HOMEPAGE = "https://iea.nl"
26
 
27
+ _URL = "./data"
28
  _URLS = {
29
+ "isco_taxonomy": _URL + "/isco_taxonomy.jsonl",
30
+ "isco_occupations": _URL + "/isco_occupations.jsonl",
31
  }
32
 
33
 
34
+ class IscoTaxonomyConfig(datasets.BuilderConfig):
35
  """BuilderConfig for ISCO ESCO Taxonomy."""
36
+
37
  def __init__(self, **kwargs):
38
  """BuilderConfig for SQUAD.
39
  Args:
40
  **kwargs: keyword arguments forwarded to super.
41
  """
42
+ super(IscoTaxonomyConfig, self).__init__(**kwargs)
43
 
44
+
45
+ class IscoTaxonomy(datasets.GeneratorBasedBuilder):
46
  """The ISCO ESCO Occupations Taxonomy Dataset v1.0.0"""
47
+
48
  BUILDER_CONFIGS = [
49
  datasets.BuilderConfig(
50
+ name="isco_taxonomy",
51
  version=datasets.Version("1.0.0", ""),
52
+ description="ISCO groups and definitions, and ESCO occupations and definitions.",
53
+ ),
54
+ datasets.BuilderConfig(
55
+ name="isco_occupations",
56
+ version=datasets.Version("1.0.0", ""),
57
+ description="ISCO occupations index.",
58
  ),
59
  ]
60
 
61
+ BUILDER_CONFIG_CLASS = IscoTaxonomyConfig
62
+ DEFAULT_CONFIG_NAME = "isco_taxonomy"
63
+
64
  def _info(self):
65
+ if (
66
+ self.config.name == "isco_taxonomy"
67
+ ): # This is the name of the configuration selected in BUILDER_CONFIGS above
68
+ features = datasets.Features(
69
  {
70
+ "text": datasets.features.Value("string"),
71
+ "labels": datasets.features.ClassLabel(
72
+ names_file="labels/isco_code_1.txt"
 
 
 
 
 
73
  ),
74
  }
75
+ )
76
+ elif self.config.name == "isco_occupations": # This is an example to show how to have different features for "first_domain" and "second_domain"
77
+ features = datasets.Features(
78
+ {
79
+ "text": datasets.features.Value("string"),
80
+ "labels": datasets.features.ClassLabel(
81
+ names_file="labels/isco_codes.txt"
82
+ ),
83
+ # These are the features of your dataset like images, labels ...
84
+ }
85
+ )
86
+ return datasets.DatasetInfo(
87
+ # This is the description that will appear on the datasets page.
88
+ description=_DESCRIPTION,
89
+ # This defines the different columns of the dataset and their types
90
+ features=features, # Here we define them above because they are different between the two configurations
91
+ # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
92
+ # specify them. They'll be used if as_supervised=True in builder.as_dataset.
93
+ supervised_keys=("text", "labels"),
94
+ # Homepage of the dataset for documentation
95
+ homepage=_HOMEPAGE,
96
+ # License for the dataset if available
97
+ license=_LICENSE,
98
+ # Citation for the dataset
99
+ citation=_CITATION,
100
  )
101
 
102
+ def _split_generators(self, dl_manager):
103
+ # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
104
+ # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
105
 
106
+ # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
107
+ # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
108
+ # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
109
+ urls = _URLS[self.config.name]
110
+ data_dir = dl_manager.download_and_extract(urls)
111
  return [
112
+ datasets.SplitGenerator(
113
+ name=datasets.Split.TRAIN,
114
+ # These kwargs will be passed to _generate_examples
115
+ gen_kwargs={
116
+ "filepath": os.path.join(data_dir),
117
+ "split": "isco_taxonomy",
118
+ },
119
+ ),
120
+ datasets.SplitGenerator(
121
+ name=datasets.Split.VALIDATION,
122
+ # These kwargs will be passed to _generate_examples
123
+ gen_kwargs={
124
+ "filepath": os.path.join(data_dir),
125
+ "split": "isco_occupations",
126
+ },
127
+ ),
128
  ]
129
+
130
+ # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
131
+ def _generate_examples(self, filepath, split):
132
+ # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
133
+ # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
134
+ with open(filepath, encoding="utf-8") as f:
135
+ for key, row in enumerate(f):
136
+ data = json.loads(row)
137
+ if self.config.name == "isco_taxonomy":
138
+ # Yields examples as (key, example) tuples
139
+ yield key, {
140
+ "text": data["ISCO_DEFINITION_1"],
141
+ "labels": "" if split == "test" else data["ISCO_CODE_1"],
142
+ }
143
+ elif self.config.name == "isco_occupations":
144
+ yield key, {
145
+ "text": data["ISCO_OCCUPATION"],
146
+ "labels": "" if split == "test" else data["ISCO_CODE"],
147
+ }
meta.md ADDED
@@ -0,0 +1,677 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ pretty_name: ISCO-ESCO Occupations Taxonomy
3
+ dataset_info:
4
+ - config_name: default
5
+ features:
6
+ - name: text
7
+ dtype: string
8
+ splits:
9
+ - name: train
10
+ num_bytes: 1140361
11
+ num_examples: 40147
12
+ download_size: 1019906
13
+ dataset_size: 1140361
14
+ - config_name: isco_occupations
15
+ features:
16
+ - name: text
17
+ dtype: string
18
+ - name: labels
19
+ dtype:
20
+ class_label:
21
+ names:
22
+ '0': '0'
23
+ '1': '01'
24
+ '2': '011'
25
+ '3': '0110'
26
+ '4': '02'
27
+ '5': '021'
28
+ '6': '0210'
29
+ '7': '03'
30
+ '8': '031'
31
+ '9': '0310'
32
+ '10': '1'
33
+ '11': '11'
34
+ '12': '111'
35
+ '13': '1111'
36
+ '14': '1112'
37
+ '15': '1113'
38
+ '16': '1114'
39
+ '17': '112'
40
+ '18': '1120'
41
+ '19': '12'
42
+ '20': '121'
43
+ '21': '1211'
44
+ '22': '1212'
45
+ '23': '1213'
46
+ '24': '1219'
47
+ '25': '122'
48
+ '26': '1221'
49
+ '27': '1222'
50
+ '28': '1223'
51
+ '29': '13'
52
+ '30': '131'
53
+ '31': '1311'
54
+ '32': '1312'
55
+ '33': '132'
56
+ '34': '1321'
57
+ '35': '1322'
58
+ '36': '1323'
59
+ '37': '1324'
60
+ '38': '133'
61
+ '39': '1330'
62
+ '40': '134'
63
+ '41': '1341'
64
+ '42': '1342'
65
+ '43': '1343'
66
+ '44': '1344'
67
+ '45': '1345'
68
+ '46': '1346'
69
+ '47': '1349'
70
+ '48': '14'
71
+ '49': '141'
72
+ '50': '1411'
73
+ '51': '1412'
74
+ '52': '142'
75
+ '53': '1420'
76
+ '54': '143'
77
+ '55': '1431'
78
+ '56': '1439'
79
+ '57': '2'
80
+ '58': '21'
81
+ '59': '211'
82
+ '60': '2111'
83
+ '61': '2112'
84
+ '62': '2113'
85
+ '63': '2114'
86
+ '64': '212'
87
+ '65': '2120'
88
+ '66': '213'
89
+ '67': '2131'
90
+ '68': '2132'
91
+ '69': '2133'
92
+ '70': '214'
93
+ '71': '2141'
94
+ '72': '2142'
95
+ '73': '2143'
96
+ '74': '2144'
97
+ '75': '2145'
98
+ '76': '2146'
99
+ '77': '2149'
100
+ '78': '215'
101
+ '79': '2151'
102
+ '80': '2152'
103
+ '81': '2153'
104
+ '82': '216'
105
+ '83': '2161'
106
+ '84': '2162'
107
+ '85': '2163'
108
+ '86': '2164'
109
+ '87': '2165'
110
+ '88': '2166'
111
+ '89': '22'
112
+ '90': '221'
113
+ '91': '2211'
114
+ '92': '2212'
115
+ '93': '222'
116
+ '94': '2221'
117
+ '95': '2222'
118
+ '96': '223'
119
+ '97': '2230'
120
+ '98': '224'
121
+ '99': '2240'
122
+ '100': '225'
123
+ '101': '2250'
124
+ '102': '226'
125
+ '103': '2261'
126
+ '104': '2262'
127
+ '105': '2263'
128
+ '106': '2264'
129
+ '107': '2265'
130
+ '108': '2266'
131
+ '109': '2267'
132
+ '110': '2269'
133
+ '111': '23'
134
+ '112': '231'
135
+ '113': '2310'
136
+ '114': '232'
137
+ '115': '2320'
138
+ '116': '233'
139
+ '117': '2330'
140
+ '118': '234'
141
+ '119': '2341'
142
+ '120': '2342'
143
+ '121': '235'
144
+ '122': '2351'
145
+ '123': '2352'
146
+ '124': '2353'
147
+ '125': '2354'
148
+ '126': '2355'
149
+ '127': '2356'
150
+ '128': '2359'
151
+ '129': '24'
152
+ '130': '241'
153
+ '131': '2411'
154
+ '132': '2412'
155
+ '133': '2413'
156
+ '134': '242'
157
+ '135': '2421'
158
+ '136': '2422'
159
+ '137': '2423'
160
+ '138': '2424'
161
+ '139': '243'
162
+ '140': '2431'
163
+ '141': '2432'
164
+ '142': '2433'
165
+ '143': '2434'
166
+ '144': '25'
167
+ '145': '251'
168
+ '146': '2511'
169
+ '147': '2512'
170
+ '148': '2513'
171
+ '149': '2514'
172
+ '150': '2519'
173
+ '151': '252'
174
+ '152': '2521'
175
+ '153': '2522'
176
+ '154': '2523'
177
+ '155': '2529'
178
+ '156': '26'
179
+ '157': '261'
180
+ '158': '2611'
181
+ '159': '2612'
182
+ '160': '2619'
183
+ '161': '262'
184
+ '162': '2621'
185
+ '163': '2622'
186
+ '164': '263'
187
+ '165': '2631'
188
+ '166': '2632'
189
+ '167': '2633'
190
+ '168': '2634'
191
+ '169': '2635'
192
+ '170': '2636'
193
+ '171': '264'
194
+ '172': '2641'
195
+ '173': '2642'
196
+ '174': '2643'
197
+ '175': '265'
198
+ '176': '2651'
199
+ '177': '2652'
200
+ '178': '2653'
201
+ '179': '2654'
202
+ '180': '2655'
203
+ '181': '2656'
204
+ '182': '2659'
205
+ '183': '3'
206
+ '184': '31'
207
+ '185': '311'
208
+ '186': '3111'
209
+ '187': '3112'
210
+ '188': '3113'
211
+ '189': '3114'
212
+ '190': '3115'
213
+ '191': '3116'
214
+ '192': '3117'
215
+ '193': '3118'
216
+ '194': '3119'
217
+ '195': '312'
218
+ '196': '3121'
219
+ '197': '3122'
220
+ '198': '3123'
221
+ '199': '313'
222
+ '200': '3131'
223
+ '201': '3132'
224
+ '202': '3133'
225
+ '203': '3134'
226
+ '204': '3135'
227
+ '205': '3139'
228
+ '206': '314'
229
+ '207': '3141'
230
+ '208': '3142'
231
+ '209': '3143'
232
+ '210': '315'
233
+ '211': '3151'
234
+ '212': '3152'
235
+ '213': '3153'
236
+ '214': '3154'
237
+ '215': '3155'
238
+ '216': '32'
239
+ '217': '321'
240
+ '218': '3211'
241
+ '219': '3212'
242
+ '220': '3213'
243
+ '221': '3214'
244
+ '222': '322'
245
+ '223': '3221'
246
+ '224': '3222'
247
+ '225': '323'
248
+ '226': '3230'
249
+ '227': '324'
250
+ '228': '3240'
251
+ '229': '325'
252
+ '230': '3251'
253
+ '231': '3252'
254
+ '232': '3253'
255
+ '233': '3254'
256
+ '234': '3255'
257
+ '235': '3256'
258
+ '236': '3257'
259
+ '237': '3258'
260
+ '238': '3259'
261
+ '239': '33'
262
+ '240': '331'
263
+ '241': '3311'
264
+ '242': '3312'
265
+ '243': '3313'
266
+ '244': '3314'
267
+ '245': '3315'
268
+ '246': '332'
269
+ '247': '3321'
270
+ '248': '3322'
271
+ '249': '3323'
272
+ '250': '3324'
273
+ '251': '333'
274
+ '252': '3331'
275
+ '253': '3332'
276
+ '254': '3333'
277
+ '255': '3334'
278
+ '256': '3339'
279
+ '257': '334'
280
+ '258': '3341'
281
+ '259': '3342'
282
+ '260': '3343'
283
+ '261': '3344'
284
+ '262': '335'
285
+ '263': '3351'
286
+ '264': '3352'
287
+ '265': '3353'
288
+ '266': '3354'
289
+ '267': '3355'
290
+ '268': '3359'
291
+ '269': '34'
292
+ '270': '341'
293
+ '271': '3411'
294
+ '272': '3412'
295
+ '273': '3413'
296
+ '274': '342'
297
+ '275': '3421'
298
+ '276': '3422'
299
+ '277': '3423'
300
+ '278': '343'
301
+ '279': '3431'
302
+ '280': '3432'
303
+ '281': '3433'
304
+ '282': '3434'
305
+ '283': '3435'
306
+ '284': '35'
307
+ '285': '351'
308
+ '286': '3511'
309
+ '287': '3512'
310
+ '288': '3513'
311
+ '289': '3514'
312
+ '290': '352'
313
+ '291': '3521'
314
+ '292': '3522'
315
+ '293': '4'
316
+ '294': '41'
317
+ '295': '411'
318
+ '296': '4110'
319
+ '297': '412'
320
+ '298': '4120'
321
+ '299': '413'
322
+ '300': '4131'
323
+ '301': '4132'
324
+ '302': '42'
325
+ '303': '421'
326
+ '304': '4211'
327
+ '305': '4212'
328
+ '306': '4213'
329
+ '307': '4214'
330
+ '308': '422'
331
+ '309': '4221'
332
+ '310': '4222'
333
+ '311': '4223'
334
+ '312': '4224'
335
+ '313': '4225'
336
+ '314': '4226'
337
+ '315': '4227'
338
+ '316': '4229'
339
+ '317': '43'
340
+ '318': '431'
341
+ '319': '4311'
342
+ '320': '4312'
343
+ '321': '4313'
344
+ '322': '432'
345
+ '323': '4321'
346
+ '324': '4322'
347
+ '325': '4323'
348
+ '326': '44'
349
+ '327': '441'
350
+ '328': '4411'
351
+ '329': '4412'
352
+ '330': '4413'
353
+ '331': '4414'
354
+ '332': '4415'
355
+ '333': '4416'
356
+ '334': '4419'
357
+ '335': '5'
358
+ '336': '51'
359
+ '337': '511'
360
+ '338': '5111'
361
+ '339': '5112'
362
+ '340': '5113'
363
+ '341': '512'
364
+ '342': '5120'
365
+ '343': '513'
366
+ '344': '5131'
367
+ '345': '5132'
368
+ '346': '514'
369
+ '347': '5141'
370
+ '348': '5142'
371
+ '349': '515'
372
+ '350': '5151'
373
+ '351': '5152'
374
+ '352': '5153'
375
+ '353': '516'
376
+ '354': '5161'
377
+ '355': '5162'
378
+ '356': '5163'
379
+ '357': '5164'
380
+ '358': '5165'
381
+ '359': '5169'
382
+ '360': '52'
383
+ '361': '521'
384
+ '362': '5211'
385
+ '363': '5212'
386
+ '364': '522'
387
+ '365': '5221'
388
+ '366': '5222'
389
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390
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391
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392
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393
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394
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395
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396
+ '374': '5244'
397
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398
+ '376': '5246'
399
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400
+ '378': '53'
401
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402
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403
+ '381': '5312'
404
+ '382': '532'
405
+ '383': '5321'
406
+ '384': '5322'
407
+ '385': '5329'
408
+ '386': '54'
409
+ '387': '541'
410
+ '388': '5411'
411
+ '389': '5412'
412
+ '390': '5413'
413
+ '391': '5414'
414
+ '392': '5419'
415
+ '393': '6'
416
+ '394': '61'
417
+ '395': '611'
418
+ '396': '6111'
419
+ '397': '6112'
420
+ '398': '6113'
421
+ '399': '6114'
422
+ '400': '612'
423
+ '401': '6121'
424
+ '402': '6122'
425
+ '403': '6123'
426
+ '404': '6129'
427
+ '405': '613'
428
+ '406': '6130'
429
+ '407': '62'
430
+ '408': '621'
431
+ '409': '6210'
432
+ '410': '622'
433
+ '411': '6221'
434
+ '412': '6222'
435
+ '413': '6223'
436
+ '414': '6224'
437
+ '415': '63'
438
+ '416': '631'
439
+ '417': '6310'
440
+ '418': '632'
441
+ '419': '6320'
442
+ '420': '633'
443
+ '421': '6330'
444
+ '422': '634'
445
+ '423': '6340'
446
+ '424': '7'
447
+ '425': '71'
448
+ '426': '711'
449
+ '427': '7111'
450
+ '428': '7112'
451
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452
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453
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454
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455
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456
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457
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458
+ '436': '7123'
459
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460
+ '438': '7125'
461
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462
+ '440': '7127'
463
+ '441': '713'
464
+ '442': '7131'
465
+ '443': '7132'
466
+ '444': '7133'
467
+ '445': '72'
468
+ '446': '721'
469
+ '447': '7211'
470
+ '448': '7212'
471
+ '449': '7213'
472
+ '450': '7214'
473
+ '451': '7215'
474
+ '452': '722'
475
+ '453': '7221'
476
+ '454': '7222'
477
+ '455': '7223'
478
+ '456': '7224'
479
+ '457': '723'
480
+ '458': '7231'
481
+ '459': '7232'
482
+ '460': '7233'
483
+ '461': '7234'
484
+ '462': '73'
485
+ '463': '731'
486
+ '464': '7311'
487
+ '465': '7312'
488
+ '466': '7313'
489
+ '467': '7314'
490
+ '468': '7315'
491
+ '469': '7316'
492
+ '470': '7317'
493
+ '471': '7318'
494
+ '472': '7319'
495
+ '473': '732'
496
+ '474': '7321'
497
+ '475': '7322'
498
+ '476': '7323'
499
+ '477': '74'
500
+ '478': '741'
501
+ '479': '7411'
502
+ '480': '7412'
503
+ '481': '7413'
504
+ '482': '742'
505
+ '483': '7421'
506
+ '484': '7422'
507
+ '485': '75'
508
+ '486': '751'
509
+ '487': '7511'
510
+ '488': '7512'
511
+ '489': '7513'
512
+ '490': '7514'
513
+ '491': '7515'
514
+ '492': '7516'
515
+ '493': '752'
516
+ '494': '7521'
517
+ '495': '7522'
518
+ '496': '7523'
519
+ '497': '753'
520
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521
+ '499': '7532'
522
+ '500': '7533'
523
+ '501': '7534'
524
+ '502': '7535'
525
+ '503': '7536'
526
+ '504': '754'
527
+ '505': '7541'
528
+ '506': '7542'
529
+ '507': '7543'
530
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531
+ '509': '7549'
532
+ '510': '8'
533
+ '511': '81'
534
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535
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536
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537
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538
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539
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540
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541
+ '519': '8122'
542
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543
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544
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545
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546
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547
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548
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549
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550
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551
+ '529': '8152'
552
+ '530': '8153'
553
+ '531': '8154'
554
+ '532': '8155'
555
+ '533': '8156'
556
+ '534': '8157'
557
+ '535': '8159'
558
+ '536': '816'
559
+ '537': '8160'
560
+ '538': '817'
561
+ '539': '8171'
562
+ '540': '8172'
563
+ '541': '818'
564
+ '542': '8181'
565
+ '543': '8182'
566
+ '544': '8183'
567
+ '545': '8189'
568
+ '546': '82'
569
+ '547': '821'
570
+ '548': '8211'
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+ ---