Tailor-made identification of documents with a need-based and solution-oriented approach

Dataset
Country Climate Strategies
Other
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Republic of Rwanda
x=
11
5
6
1
1
3
0
0
0
3
3
y=
11
5
6
1
1
3
0
0
0
3
3
824
vision-strategy
Roma Capitale
x=
3
3
0
0
0
3
0
0
0
0
0
y=
3
3
0
0
0
3
0
0
0
0
0
823
digital-strategy
City of Kigali
x=
21
14
7
6
1
6
1
0
6
0
1
y=
21
14
7
6
1
6
1
0
6
0
1
822
general-strategy
Greater Manchester Local Enterprise Partnership
x=
22
4
18
0
3
1
0
6
3
3
6
y=
22
4
18
0
3
1
0
6
3
3
6
821
export-strategy
Ciudades Sostenibles
x=
1
1
0
0
1
0
0
0
0
0
0
y=
1
1
0
0
1
0
0
0
0
0
0
820
sustainability-strategy
C40 Cities
x=
26
16
10
6
1
6
3
3
1
0
6
y=
26
16
10
6
1
6
3
3
1
0
6
819
regions-and-cities-documents
Projeto Brasilia Inteligente
x=
32
14
18
6
1
6
1
6
3
3
6
y=
32
14
18
6
1
6
1
6
3
3
6
818
smart-strategy
Land Brandenburg
x=
23
13
10
6
1
6
0
6
1
0
3
y=
23
13
10
6
1
6
0
6
1
0
3
817
sustainability-strategy
Alcaldia Mayor de Bogota D.C.
x=
19
12
7
6
0
6
0
3
0
1
3
y=
19
12
7
6
0
6
0
3
0
1
3
816
smart-strategy
Ajuntament de Barcelona
x=
20
10
10
0
3
6
1
3
3
3
1
y=
20
10
10
0
3
6
1
3
3
3
1
815
digital-strategy
Roma Smart City
x=
28
7
21
1
3
3
0
6
6
3
6
y=
28
7
21
1
3
3
0
6
6
3
6
814
smart-strategy
Government of Quebec
x=
8
1
7
0
0
1
0
0
0
1
6
y=
8
1
7
0
0
1
0
0
0
1
6
813
digital-strategy
Mexico Government of the Republic
x=
16
4
12
0
0
3
1
3
0
6
3
y=
16
4
12
0
0
3
1
3
0
6
3
812
innovation-strategy
City of North Vancouver
x=
5
5
0
1
1
3
0
0
0
0
0
y=
5
5
0
1
1
3
0
0
0
0
0
811
mobility-strategy
New York City Food Policy
x=
14
10
4
6
1
3
0
0
3
0
1
y=
14
10
4
6
1
3
0
0
3
0
1
810
nutrition-strategy
Mexico City government mobility secretariat
x=
5
4
1
0
1
3
0
0
1
0
0
y=
5
4
1
0
1
3
0
0
1
0
0
809
mobility-strategy
Resilient Melbourne
x=
19
14
5
6
1
6
1
1
3
0
1
y=
19
14
5
6
1
6
1
1
3
0
1
808
resilience-strategy
Peru Debate 2021
x=
11
4
7
0
1
3
0
0
6
1
0
y=
11
4
7
0
1
3
0
0
6
1
0
807
mobility-strategy
Tecnológico de Monterrey
x=
15
3
12
0
0
3
0
0
0
6
6
y=
15
3
12
0
0
3
0
0
0
6
6
806
general-strategy
Metropolitan Institute of Bratislava
x=
23
15
8
6
3
6
0
0
6
1
1
y=
23
15
8
6
3
6
0
0
6
1
1
803
regions-and-cities-documents
wbscd
x=
23
5
18
0
1
1
3
6
6
0
6
y=
23
5
18
0
1
1
3
6
6
0
6
802
additional-strategic-documents
Małopolska Region
x=
28
13
15
1
3
6
3
6
0
6
3
0
0
0
0
0
y=
28
13
15
1
3
6
3
6
0
6
3
0
0
0
0
0
rgn-best-Ma-opolska-Region
highest-rated-strategy-document-from-each-city-region
Kraków
x=
16
10
6
1
3
6
0
6
0
0
0
0
0
0
0
0
y=
16
10
6
1
3
6
0
6
0
0
0
0
0
0
0
0
rgn-best-Krak-w
highest-rated-strategy-document-from-each-city-region
Klagenfurt am Wörthersee
x=
12
8
4
1
1
6
0
1
3
0
0
0
0
0
0
0
y=
12
8
4
1
1
6
0
1
3
0
0
0
0
0
0
0
rgn-best-Klagenfurt-am-W-rthersee
highest-rated-strategy-document-from-each-city-region
Małopolska Region
x=
28
13
15
1
3
6
3
6
0
6
3
0
0
0
0
0
y=
28
13
15
1
3
6
3
6
0
6
3
0
0
0
0
0
region-Ma-opolska-Region
all-city-region-climate-strategies-average
Kraków
x=
16
10
6
1
3
6
0
6
0
0
0
0
0
0
0
0
y=
16
10
6
1
3
6
0
6
0
0
0
0
0
0
0
0
region-Krak-w
all-city-region-climate-strategies-average
Klagenfurt am Wörthersee
x=
12
8
4
1
1
6
0
1
3
0
0
0
0
0
0
0
y=
12
8
4
1
1
6
0
1
3
0
0
0
0
0
0
0
region-Klagenfurt-am-W-rthersee
all-city-region-climate-strategies-average
Bratislava Innovation Team
x=
19
11
8
1
3
6
1
6
0
1
1
y=
19
11
8
1
3
6
1
6
0
1
1
826
innovation-strategy
Vienna
x=
28
15
13
6
3
6
0
6
6
1
0
0
0
0
0
0
y=
28
15
13
6
3
6
0
6
6
1
0
0
0
0
0
0
rgn-best-Vienna
highest-rated-strategy-document-from-each-city-region
Warsaw
x=
16
7
9
1
3
3
0
6
0
3
0
0
0
0
0
0
y=
16
7
9
1
3
3
0
6
0
3
0
0
0
0
0
0
rgn-best-Warsaw
highest-rated-strategy-document-from-each-city-region
Taipei
x=
10
4
6
0
1
3
0
0
0
0
6
0
0
0
0
0
y=
10
4
6
0
1
3
0
0
0
0
6
0
0
0
0
0
rgn-best-Taipei
highest-rated-strategy-document-from-each-city-region
Sao Paulo
x=
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
y=
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
rgn-best-Sao-Paulo
highest-rated-strategy-document-from-each-city-region
Wellington
x=
9
8
1
1
1
6
0
0
1
0
0
0
0
0
0
0
y=
9
8
1
1
1
6
0
0
1
0
0
0
0
0
0
0
rgn-best-Wellington
highest-rated-strategy-document-from-each-city-region
Vancouver
x=
22
13
9
6
1
6
0
3
6
0
0
0
0
0
0
0
y=
22
13
9
6
1
6
0
3
6
0
0
0
0
0
0
0
rgn-best-Vancouver
highest-rated-strategy-document-from-each-city-region
Toronto
x=
26
13
13
1
3
6
3
3
6
1
3
0
0
0
0
0
y=
26
13
13
1
3
6
3
3
6
1
3
0
0
0
0
0
rgn-best-Toronto
highest-rated-strategy-document-from-each-city-region
Tokyo
x=
17
10
7
0
3
6
1
6
0
0
1
0
0
0
0
0
y=
17
10
7
0
3
6
1
6
0
0
1
0
0
0
0
0
rgn-best-Tokyo
highest-rated-strategy-document-from-each-city-region
n/a
None selected
Y
X
None selected
n/a
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