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Update task categories, add link to paper (#2)

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- Update task categories, add link to paper (f062c7cd6cc5820845f0f9c0191af43bde74bbca)


Co-authored-by: Niels Rogge <[email protected]>

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  1. README.md +239 -242
README.md CHANGED
@@ -1,254 +1,249 @@
1
-
2
  ---
3
  annotations_creators:
4
- - found
5
  language_creators:
6
- - expert-generated
7
  language:
8
- - ace
9
- - acm
10
- - acq
11
- - aeb
12
- - af
13
- - ajp
14
- - ak
15
- - als
16
- - am
17
- - apc
18
- - ar
19
- - ars
20
- - ary
21
- - arz
22
- - as
23
- - ast
24
- - awa
25
- - ayr
26
- - azb
27
- - azj
28
- - ba
29
- - bm
30
- - ban
31
- - be
32
- - bem
33
- - bn
34
- - bho
35
- - bjn
36
- - bo
37
- - bs
38
- - bug
39
- - bg
40
- - ca
41
- - ceb
42
- - cs
43
- - cjk
44
- - ckb
45
- - crh
46
- - cy
47
- - da
48
- - de
49
- - dik
50
- - dyu
51
- - dz
52
- - el
53
- - en
54
- - eo
55
- - et
56
- - eu
57
- - ee
58
- - fo
59
- - fj
60
- - fi
61
- - fon
62
- - fr
63
- - fur
64
- - fuv
65
- - gaz
66
- - gd
67
- - ga
68
- - gl
69
- - gn
70
- - gu
71
- - ht
72
- - ha
73
- - he
74
- - hi
75
- - hne
76
- - hr
77
- - hu
78
- - hy
79
- - ig
80
- - ilo
81
- - id
82
- - is
83
- - it
84
- - jv
85
- - ja
86
- - kab
87
- - kac
88
- - kam
89
- - kn
90
- - ks
91
- - ka
92
- - kk
93
- - kbp
94
- - kea
95
- - khk
96
- - km
97
- - ki
98
- - rw
99
- - ky
100
- - kmb
101
- - kmr
102
- - knc
103
- - kg
104
- - ko
105
- - lo
106
- - lij
107
- - li
108
- - ln
109
- - lt
110
- - lmo
111
- - ltg
112
- - lb
113
- - lua
114
- - lg
115
- - luo
116
- - lus
117
- - lvs
118
- - mag
119
- - mai
120
- - ml
121
- - mar
122
- - min
123
- - mk
124
- - mt
125
- - mni
126
- - mos
127
- - mi
128
- - my
129
- - nl
130
- - nn
131
- - nb
132
- - npi
133
- - nqo
134
- - nso
135
- - nus
136
- - ny
137
- - oc
138
- - ory
139
- - pag
140
- - pa
141
- - pap
142
- - pbt
143
- - pes
144
- - plt
145
- - pl
146
- - pt
147
- - prs
148
- - quy
149
- - ro
150
- - rn
151
- - ru
152
- - sg
153
- - sa
154
- - sat
155
- - scn
156
- - shn
157
- - si
158
- - sk
159
- - sl
160
- - sm
161
- - sn
162
- - sd
163
- - so
164
- - st
165
- - es
166
- - sc
167
- - sr
168
- - ss
169
- - su
170
- - sv
171
- - swh
172
- - szl
173
- - ta
174
- - taq
175
- - tt
176
- - te
177
- - tg
178
- - tl
179
- - th
180
- - ti
181
- - tpi
182
- - tn
183
- - ts
184
- - tk
185
- - tum
186
- - tr
187
- - tw
188
- - tzm
189
- - ug
190
- - uk
191
- - umb
192
- - ur
193
- - uzn
194
- - vec
195
- - vi
196
- - war
197
- - wo
198
- - xh
199
- - ydd
200
- - yo
201
- - yue
202
- - zh
203
- - zsm
204
- - zu
205
  license:
206
- - cc-by-sa-4.0
207
  multilinguality:
208
- - multilingual
209
- pretty_name: MVL-SIB
210
- language_details: >-
211
- ace_Arab, ace_Latn, acm_Arab, acq_Arab, aeb_Arab, afr_Latn, ajp_Arab,
212
- aka_Latn, amh_Ethi, apc_Arab, arb_Arab, ars_Arab, ary_Arab, arz_Arab,
213
- asm_Beng, ast_Latn, awa_Deva, ayr_Latn, azb_Arab, azj_Latn, bak_Cyrl,
214
- bam_Latn, ban_Latn,bel_Cyrl, bem_Latn, ben_Beng, bho_Deva, bjn_Arab, bjn_Latn,
215
- bod_Tibt, bos_Latn, bug_Latn, bul_Cyrl, cat_Latn, ceb_Latn, ces_Latn,
216
- cjk_Latn, ckb_Arab, crh_Latn, cym_Latn, dan_Latn, deu_Latn, dik_Latn,
217
- dyu_Latn, dzo_Tibt, ell_Grek, eng_Latn, epo_Latn, est_Latn, eus_Latn,
218
- ewe_Latn, fao_Latn, pes_Arab, fij_Latn, fin_Latn, fon_Latn, fra_Latn,
219
- fur_Latn, fuv_Latn, gla_Latn, gle_Latn, glg_Latn, grn_Latn, guj_Gujr,
220
- hat_Latn, hau_Latn, heb_Hebr, hin_Deva, hne_Deva, hrv_Latn, hun_Latn,
221
- hye_Armn, ibo_Latn, ilo_Latn, ind_Latn, isl_Latn, ita_Latn, jav_Latn,
222
- jpn_Jpan, kab_Latn, kac_Latn, kam_Latn, kan_Knda, kas_Arab, kas_Deva,
223
- kat_Geor, knc_Arab, knc_Latn, kaz_Cyrl, kbp_Latn, kea_Latn, khm_Khmr,
224
- kik_Latn, kin_Latn, kir_Cyrl, kmb_Latn, kon_Latn, kor_Hang, kmr_Latn,
225
- lao_Laoo, lvs_Latn, lij_Latn, lim_Latn, lin_Latn, lit_Latn, lmo_Latn,
226
- ltg_Latn, ltz_Latn, lua_Latn, lug_Latn, luo_Latn, lus_Latn, mag_Deva,
227
- mai_Deva, mal_Mlym, mar_Deva, min_Latn, mkd_Cyrl, plt_Latn, mlt_Latn,
228
- mni_Beng, khk_Cyrl, mos_Latn, mri_Latn, zsm_Latn, mya_Mymr, nld_Latn,
229
- nno_Latn, nob_Latn, npi_Deva, nqo_Nkoo, nso_Latn, nus_Latn, nya_Latn, oci_Latn,
230
- gaz_Latn, ory_Orya, pag_Latn, pan_Guru, pap_Latn, pol_Latn, por_Latn,
231
- prs_Arab, pbt_Arab, quy_Latn, ron_Latn, run_Latn, rus_Cyrl, sag_Latn,
232
- san_Deva, sat_Beng, scn_Latn, shn_Mymr, sin_Sinh, slk_Latn, slv_Latn,
233
- smo_Latn, sna_Latn, snd_Arab, som_Latn, sot_Latn, spa_Latn, als_Latn,
234
- srd_Latn, srp_Cyrl, ssw_Latn, sun_Latn, swe_Latn, swh_Latn, szl_Latn,
235
- tam_Taml, tat_Cyrl, tel_Telu, tgk_Cyrl, tgl_Latn, tha_Thai, tir_Ethi,
236
- taq_Latn, taq_Tfng, tpi_Latn, tsn_Latn, tso_Latn, tuk_Latn, tum_Latn,
237
- tur_Latn, twi_Latn, tzm_Tfng, uig_Arab, ukr_Cyrl, umb_Latn, urd_Arab,
238
- uzn_Latn, vec_Latn, vie_Latn, war_Latn, wol_Latn, xho_Latn, ydd_Hebr,
239
- yor_Latn, yue_Hant, zho_Hans, zho_Hant, zul_Latn
240
  size_categories:
241
- - 1K<n<10K
242
  source_datasets:
243
- - original
244
- tags:
245
- - sib-200
246
- - sib200
247
  task_categories:
248
- - text-classification
249
- - visual-question-answering
250
  task_ids:
251
- - topic-classification
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
252
  ---
253
 
254
  # MVL-SIB: Massively Multilingual Visual-Language SIB
@@ -257,6 +252,8 @@ task_ids:
257
 
258
  MVL-SIB is a multilingual dataset that provides image-sentence pairs spanning 205 languages and 7 topical categories (`entertainment`, `geography`, `health`, `politics`, `science`, `sports`, `travel`). It was constructed by extending the [SIB-200](https://huggingface.co/datasets/Davlan/sib200) benchmark. For each topic, a set of [10 permissively licensed images](https://huggingface.co/datasets/WueNLP/mvl-sib200/tree/main/data/images/sib200) was manually collected to distinctly represent each category. The dataset creates three instances per original sentence, pairing it with multiple positive and negative image-sentence combinations to challenge both multimodal reasoning and language understanding. MVL-SIB supports detailed evaluations across text-only and cross-modal tasks.
259
 
 
 
260
  ## Usage Example
261
 
262
  Below is an example of how to load and use the MVL-SIB dataset with the Hugging Face `datasets` library in Python:
@@ -359,4 +356,4 @@ Should you be using MVL-SIB or refer to the findings of our paper, please cite u
359
  primaryClass={cs.CL},
360
  url={https://arxiv.org/abs/2502.12852},
361
  }
362
- ```
 
 
1
  ---
2
  annotations_creators:
3
+ - found
4
  language_creators:
5
+ - expert-generated
6
  language:
7
+ - ace
8
+ - acm
9
+ - acq
10
+ - aeb
11
+ - af
12
+ - ajp
13
+ - ak
14
+ - als
15
+ - am
16
+ - apc
17
+ - ar
18
+ - ars
19
+ - ary
20
+ - arz
21
+ - as
22
+ - ast
23
+ - awa
24
+ - ayr
25
+ - azb
26
+ - azj
27
+ - ba
28
+ - bm
29
+ - ban
30
+ - be
31
+ - bem
32
+ - bn
33
+ - bho
34
+ - bjn
35
+ - bo
36
+ - bs
37
+ - bug
38
+ - bg
39
+ - ca
40
+ - ceb
41
+ - cs
42
+ - cjk
43
+ - ckb
44
+ - crh
45
+ - cy
46
+ - da
47
+ - de
48
+ - dik
49
+ - dyu
50
+ - dz
51
+ - el
52
+ - en
53
+ - eo
54
+ - et
55
+ - eu
56
+ - ee
57
+ - fo
58
+ - fj
59
+ - fi
60
+ - fon
61
+ - fr
62
+ - fur
63
+ - fuv
64
+ - gaz
65
+ - gd
66
+ - ga
67
+ - gl
68
+ - gn
69
+ - gu
70
+ - ht
71
+ - ha
72
+ - he
73
+ - hi
74
+ - hne
75
+ - hr
76
+ - hu
77
+ - hy
78
+ - ig
79
+ - ilo
80
+ - id
81
+ - is
82
+ - it
83
+ - jv
84
+ - ja
85
+ - kab
86
+ - kac
87
+ - kam
88
+ - kn
89
+ - ks
90
+ - ka
91
+ - kk
92
+ - kbp
93
+ - kea
94
+ - khk
95
+ - km
96
+ - ki
97
+ - rw
98
+ - ky
99
+ - kmb
100
+ - kmr
101
+ - knc
102
+ - kg
103
+ - ko
104
+ - lo
105
+ - lij
106
+ - li
107
+ - ln
108
+ - lt
109
+ - lmo
110
+ - ltg
111
+ - lb
112
+ - lua
113
+ - lg
114
+ - luo
115
+ - lus
116
+ - lvs
117
+ - mag
118
+ - mai
119
+ - ml
120
+ - mar
121
+ - min
122
+ - mk
123
+ - mt
124
+ - mni
125
+ - mos
126
+ - mi
127
+ - my
128
+ - nl
129
+ - nn
130
+ - nb
131
+ - npi
132
+ - nqo
133
+ - nso
134
+ - nus
135
+ - ny
136
+ - oc
137
+ - ory
138
+ - pag
139
+ - pa
140
+ - pap
141
+ - pbt
142
+ - pes
143
+ - plt
144
+ - pl
145
+ - pt
146
+ - prs
147
+ - quy
148
+ - ro
149
+ - rn
150
+ - ru
151
+ - sg
152
+ - sa
153
+ - sat
154
+ - scn
155
+ - shn
156
+ - si
157
+ - sk
158
+ - sl
159
+ - sm
160
+ - sn
161
+ - sd
162
+ - so
163
+ - st
164
+ - es
165
+ - sc
166
+ - sr
167
+ - ss
168
+ - su
169
+ - sv
170
+ - swh
171
+ - szl
172
+ - ta
173
+ - taq
174
+ - tt
175
+ - te
176
+ - tg
177
+ - tl
178
+ - th
179
+ - ti
180
+ - tpi
181
+ - tn
182
+ - ts
183
+ - tk
184
+ - tum
185
+ - tr
186
+ - tw
187
+ - tzm
188
+ - ug
189
+ - uk
190
+ - umb
191
+ - ur
192
+ - uzn
193
+ - vec
194
+ - vi
195
+ - war
196
+ - wo
197
+ - xh
198
+ - ydd
199
+ - yo
200
+ - yue
201
+ - zh
202
+ - zsm
203
+ - zu
204
  license:
205
+ - cc-by-sa-4.0
206
  multilinguality:
207
+ - multilingual
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
208
  size_categories:
209
+ - 1K<n<10K
210
  source_datasets:
211
+ - original
 
 
 
212
  task_categories:
213
+ - image-text-to-text
214
+ - cross-modal-retrieval
215
  task_ids:
216
+ - topic-classification
217
+ pretty_name: MVL-SIB
218
+ language_details: ace_Arab, ace_Latn, acm_Arab, acq_Arab, aeb_Arab, afr_Latn, ajp_Arab,
219
+ aka_Latn, amh_Ethi, apc_Arab, arb_Arab, ars_Arab, ary_Arab, arz_Arab, asm_Beng,
220
+ ast_Latn, awa_Deva, ayr_Latn, azb_Arab, azj_Latn, bak_Cyrl, bam_Latn, ban_Latn,bel_Cyrl,
221
+ bem_Latn, ben_Beng, bho_Deva, bjn_Arab, bjn_Latn, bod_Tibt, bos_Latn, bug_Latn,
222
+ bul_Cyrl, cat_Latn, ceb_Latn, ces_Latn, cjk_Latn, ckb_Arab, crh_Latn, cym_Latn,
223
+ dan_Latn, deu_Latn, dik_Latn, dyu_Latn, dzo_Tibt, ell_Grek, eng_Latn, epo_Latn,
224
+ est_Latn, eus_Latn, ewe_Latn, fao_Latn, pes_Arab, fij_Latn, fin_Latn, fon_Latn,
225
+ fra_Latn, fur_Latn, fuv_Latn, gla_Latn, gle_Latn, glg_Latn, grn_Latn, guj_Gujr,
226
+ hat_Latn, hau_Latn, heb_Hebr, hin_Deva, hne_Deva, hrv_Latn, hun_Latn, hye_Armn,
227
+ ibo_Latn, ilo_Latn, ind_Latn, isl_Latn, ita_Latn, jav_Latn, jpn_Jpan, kab_Latn,
228
+ kac_Latn, kam_Latn, kan_Knda, kas_Arab, kas_Deva, kat_Geor, knc_Arab, knc_Latn,
229
+ kaz_Cyrl, kbp_Latn, kea_Latn, khm_Khmr, kik_Latn, kin_Latn, kir_Cyrl, kmb_Latn,
230
+ kon_Latn, kor_Hang, kmr_Latn, lao_Laoo, lvs_Latn, lij_Latn, lim_Latn, lin_Latn,
231
+ lit_Latn, lmo_Latn, ltg_Latn, ltz_Latn, lua_Latn, lug_Latn, luo_Latn, lus_Latn,
232
+ mag_Deva, mai_Deva, mal_Mlym, mar_Deva, min_Latn, mkd_Cyrl, plt_Latn, mlt_Latn,
233
+ mni_Beng, khk_Cyrl, mos_Latn, mri_Latn, zsm_Latn, mya_Mymr, nld_Latn, nno_Latn,
234
+ nob_Latn, npi_Deva, nqo_Nkoo, nso_Latn, nus_Latn, nya_Latn, oci_Latn, gaz_Latn,
235
+ ory_Orya, pag_Latn, pan_Guru, pap_Latn, pol_Latn, por_Latn, prs_Arab, pbt_Arab,
236
+ quy_Latn, ron_Latn, run_Latn, rus_Cyrl, sag_Latn, san_Deva, sat_Beng, scn_Latn,
237
+ shn_Mymr, sin_Sinh, slk_Latn, slv_Latn, smo_Latn, sna_Latn, snd_Arab, som_Latn,
238
+ sot_Latn, spa_Latn, als_Latn, srd_Latn, srp_Cyrl, ssw_Latn, sun_Latn, swe_Latn,
239
+ swh_Latn, szl_Latn, tam_Taml, tat_Cyrl, tel_Telu, tgk_Cyrl, tgl_Latn, tha_Thai,
240
+ tir_Ethi, taq_Latn, taq_Tfng, tpi_Latn, tsn_Latn, tso_Latn, tuk_Latn, tum_Latn,
241
+ tur_Latn, twi_Latn, tzm_Tfng, uig_Arab, ukr_Cyrl, umb_Latn, urd_Arab, uzn_Latn,
242
+ vec_Latn, vie_Latn, war_Latn, wol_Latn, xho_Latn, ydd_Hebr, yor_Latn, yue_Hant,
243
+ zho_Hans, zho_Hant, zul_Latn
244
+ tags:
245
+ - sib-200
246
+ - sib200
247
  ---
248
 
249
  # MVL-SIB: Massively Multilingual Visual-Language SIB
 
252
 
253
  MVL-SIB is a multilingual dataset that provides image-sentence pairs spanning 205 languages and 7 topical categories (`entertainment`, `geography`, `health`, `politics`, `science`, `sports`, `travel`). It was constructed by extending the [SIB-200](https://huggingface.co/datasets/Davlan/sib200) benchmark. For each topic, a set of [10 permissively licensed images](https://huggingface.co/datasets/WueNLP/mvl-sib200/tree/main/data/images/sib200) was manually collected to distinctly represent each category. The dataset creates three instances per original sentence, pairing it with multiple positive and negative image-sentence combinations to challenge both multimodal reasoning and language understanding. MVL-SIB supports detailed evaluations across text-only and cross-modal tasks.
254
 
255
+ Paper: [MVL-SIB: A Massively Multilingual Vision-Language Benchmark for Cross-Modal Topical Matching](https://huggingface.co/papers/2502.12852)
256
+
257
  ## Usage Example
258
 
259
  Below is an example of how to load and use the MVL-SIB dataset with the Hugging Face `datasets` library in Python:
 
356
  primaryClass={cs.CL},
357
  url={https://arxiv.org/abs/2502.12852},
358
  }
359
+ ```