id stringlengths 2 115 | lastModified stringlengths 24 24 | tags list | author stringlengths 2 42 ⌀ | description stringlengths 0 6.67k ⌀ | citation stringlengths 0 10.7k ⌀ | likes int64 0 3.66k | downloads int64 0 8.89M | created timestamp[us] | card stringlengths 11 977k | card_len int64 11 977k | embeddings list |
|---|---|---|---|---|---|---|---|---|---|---|---|
bigscience-data/roots_ar_ted_talks_iwslt | 2022-12-12T11:02:17.000Z | [
"language:ar",
"license:cc-by-nc-nd-4.0",
"region:us"
] | bigscience-data | null | null | 0 | 3 | 2022-05-18T09:07:28 | ---
language: ar
license: cc-by-nc-nd-4.0
extra_gated_prompt: 'By accessing this dataset, you agree to abide by the BigScience
Ethical Charter. The charter can be found at:
https://hf.co/spaces/bigscience/ethical-charter'
extra_gated_fields:
I have read and agree to abide by the BigScience Ethical Charter: checkbox
---
ROOTS Subset: roots_ar_ted_talks_iwslt
# WIT Ted Talks
- Dataset uid: `ted_talks_iwslt`
### Description
The Web Inventory Talk is a collection of the original Ted talks and their translated version. The translations are available in more than 109+ languages, though the distribution is not uniform.
### Homepage
https://github.com/huggingface/datasets/blob/master/datasets/ted_talks_iwslt/README.md
### Licensing
- open license
- cc-by-nc-4.0: Creative Commons Attribution Non Commercial 4.0 International
TED makes its collection of video recordings and transcripts of talks available under the Creative Commons BY-NC-ND license (look here). WIT3 acknowledges the authorship of TED talks (BY condition) and does not redistribute transcripts for commercial purposes (NC). As regards the integrity of the work (ND), WIT3 only changes the format of the container, while preserving the original contents. WIT3 aims to support research on human language processing as well as the diffusion of TED Talks!
### Speaker Locations
- Southern Europe
- Italy
### Sizes
- 0.0305 % of total
- 0.0736 % of ar
- 0.2002 % of pt
- 0.0128 % of zh
- 0.2236 % of vi
- 0.0330 % of fr
- 0.0545 % of es
- 0.0122 % of en
- 0.3704 % of id
- 0.0373 % of indic-hi
- 0.0330 % of indic-ta
- 0.1393 % of indic-mr
- 0.0305 % of ca
- 0.1179 % of indic-ur
- 0.0147 % of indic-bn
- 0.0240 % of indic-ml
- 0.0244 % of indic-te
- 0.0503 % of indic-gu
- 0.0211 % of indic-kn
- 0.0274 % of eu
- 0.0023 % of indic-as
- 0.0001 % of indic-pa
### BigScience processing steps
#### Filters applied to: ar
- dedup_document
- dedup_template_soft
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: pt
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: zh
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_1024
#### Filters applied to: vi
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: fr
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_1024
#### Filters applied to: es
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_1024
#### Filters applied to: en
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_1024
#### Filters applied to: id
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-hi
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-ta
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-mr
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: ca
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_1024
#### Filters applied to: indic-ur
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-bn
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-ml
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-te
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-gu
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-kn
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: eu
- dedup_document
- filter_remove_empty_docs
#### Filters applied to: indic-as
- dedup_document
- filter_remove_empty_docs
#### Filters applied to: indic-pa
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
| 4,095 | [
[
-0.0361328125,
-0.04669189453125,
0.026275634765625,
0.0027866363525390625,
-0.0186004638671875,
0.00807952880859375,
-0.01093292236328125,
-0.022552490234375,
0.04473876953125,
0.01708984375,
-0.0594482421875,
-0.053924560546875,
-0.0501708984375,
0.0180969... |
bigscience-data/roots_ar_sanad | 2022-12-12T11:02:27.000Z | [
"language:ar",
"license:cc-by-4.0",
"region:us"
] | bigscience-data | null | null | 0 | 3 | 2022-05-18T09:07:30 | ---
language: ar
license: cc-by-4.0
extra_gated_prompt: 'By accessing this dataset, you agree to abide by the BigScience
Ethical Charter. The charter can be found at:
https://hf.co/spaces/bigscience/ethical-charter'
extra_gated_fields:
I have read and agree to abide by the BigScience Ethical Charter: checkbox
---
ROOTS Subset: roots_ar_sanad
# sanad
- Dataset uid: `sanad`
### Description
### Homepage
### Licensing
### Speaker Locations
### Sizes
- 0.1312 % of total
- 1.2094 % of ar
### BigScience processing steps
#### Filters applied to: ar
- dedup_document
- dedup_template_soft
- filter_remove_empty_docs
- remove_html_spans_sanad
- filter_small_docs_bytes_300
| 696 | [
[
-0.04925537109375,
-0.03350830078125,
0.03277587890625,
0.01271820068359375,
-0.0243682861328125,
-0.01137542724609375,
0.00020420551300048828,
0.01458740234375,
0.0416259765625,
0.042327880859375,
-0.028717041015625,
-0.06536865234375,
-0.039398193359375,
0... |
bigscience-data/roots_en_wikivoyage | 2022-12-12T11:03:13.000Z | [
"language:en",
"license:cc-by-sa-3.0",
"region:us"
] | bigscience-data | null | null | 0 | 3 | 2022-05-18T09:08:46 | ---
language: en
license: cc-by-sa-3.0
extra_gated_prompt: 'By accessing this dataset, you agree to abide by the BigScience
Ethical Charter. The charter can be found at:
https://hf.co/spaces/bigscience/ethical-charter'
extra_gated_fields:
I have read and agree to abide by the BigScience Ethical Charter: checkbox
---
ROOTS Subset: roots_en_wikivoyage
# wikivoyage_filtered
- Dataset uid: `wikivoyage_filtered`
### Description
### Homepage
### Licensing
### Speaker Locations
### Sizes
- 0.0334 % of total
- 0.1097 % of en
- 0.0432 % of fr
- 0.0863 % of es
- 0.0084 % of zh
- 0.0892 % of vi
- 0.0464 % of indic-bn
- 0.0443 % of pt
- 0.0130 % of indic-hi
### BigScience processing steps
#### Filters applied to: en
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- filter_remove_empty_docs
- split_sentences_en
- dedup_template_soft
- replace_newline_with_space
- filter_small_docs_bytes_1024
#### Filters applied to: fr
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- filter_remove_empty_docs
- split_sentences_fr
- dedup_template_soft
- replace_newline_with_space
- filter_small_docs_bytes_1024
#### Filters applied to: es
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- filter_remove_empty_docs
- split_sentences_es
- dedup_template_soft
- replace_newline_with_space
- filter_small_docs_bytes_1024
#### Filters applied to: zh
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- filter_remove_empty_docs
- split_sentences_zhs
- dedup_template_soft
- replace_newline_with_space
- filter_small_docs_bytes_1024
#### Filters applied to: vi
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- filter_remove_empty_docs
- split_sentences_vi
- dedup_template_soft
- replace_newline_with_space
- filter_small_docs_bytes_300
#### Filters applied to: indic-bn
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- filter_remove_empty_docs
- split_sentences_indic-bn
- dedup_template_soft
- replace_newline_with_space
- filter_small_docs_bytes_300
#### Filters applied to: pt
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- filter_remove_empty_docs
- split_sentences_pt
- dedup_template_soft
- replace_newline_with_space
- filter_small_docs_bytes_300
#### Filters applied to: indic-hi
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- filter_remove_empty_docs
- split_sentences_indic-hi
- dedup_template_soft
- replace_newline_with_space
- filter_small_docs_bytes_300
| 2,580 | [
[
-0.043182373046875,
-0.036041259765625,
0.01788330078125,
0.0163116455078125,
-0.0095672607421875,
-0.005817413330078125,
-0.01320648193359375,
0.00164031982421875,
0.042083740234375,
0.031494140625,
-0.059234619140625,
-0.06402587890625,
-0.045135498046875,
... |
bigscience-data/roots_id_indosum | 2022-12-12T11:05:45.000Z | [
"language:id",
"license:apache-2.0",
"region:us"
] | bigscience-data | null | null | 1 | 3 | 2022-05-18T09:14:24 | ---
language: id
license: apache-2.0
extra_gated_prompt: 'By accessing this dataset, you agree to abide by the BigScience
Ethical Charter. The charter can be found at:
https://hf.co/spaces/bigscience/ethical-charter'
extra_gated_fields:
I have read and agree to abide by the BigScience Ethical Charter: checkbox
---
ROOTS Subset: roots_id_indosum
# Indosum
- Dataset uid: `indosum`
### Description
IndoSum: A New Benchmark Dataset for Indonesian Text Summarization
### Homepage
https://github.com/kata-ai/indosum
### Licensing
- apache-2.0: Apache License 2.0
Apache License, Version 2.0 Apache License Version 2.0, January 2004
http://www.apache.org/licenses/
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### Speaker Locations
- South-eastern Asia
- Indonesia
### Sizes
- 0.0035 % of total
- 1.3157 % of id
### BigScience processing steps
#### Filters applied to: id
- dedup_document
- dedup_template_soft
- filter_remove_empty_docs
- filter_small_docs_bytes_300
| 11,227 | [
[
-0.0164794921875,
-0.02838134765625,
0.039825439453125,
0.05072021484375,
-0.046142578125,
-0.0070953369140625,
-0.00994873046875,
-0.0513916015625,
0.032958984375,
0.05206298828125,
-0.033172607421875,
-0.041168212890625,
-0.05963134765625,
0.02578735351562... |
bigscience-data/roots_id_wikimedia | 2022-12-12T11:05:50.000Z | [
"language:id",
"license:cc-by-sa-3.0",
"region:us"
] | bigscience-data | null | null | 0 | 3 | 2022-05-18T09:14:39 | ---
language: id
license: cc-by-sa-3.0
extra_gated_prompt: 'By accessing this dataset, you agree to abide by the BigScience
Ethical Charter. The charter can be found at:
https://hf.co/spaces/bigscience/ethical-charter'
extra_gated_fields:
I have read and agree to abide by the BigScience Ethical Charter: checkbox
---
ROOTS Subset: roots_id_wikimedia
# wikimedia_filtered
- Dataset uid: `wikimedia_filtered`
### Description
### Homepage
### Licensing
### Speaker Locations
### Sizes
- 0.0005 % of total
- 0.0835 % of id
- 0.0126 % of ca
- 0.0054 % of pt
- 0.0005 % of indic-hi
### BigScience processing steps
#### Filters applied to: id
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- filter_remove_empty_docs
- split_sentences_id
- dedup_template_soft
- replace_newline_with_space
- filter_small_docs_bytes_300
#### Filters applied to: ca
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- filter_remove_empty_docs
- split_sentences_ca
- dedup_template_soft
- replace_newline_with_space
- filter_small_docs_bytes_1024
#### Filters applied to: pt
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- filter_remove_empty_docs
- split_sentences_pt
- dedup_template_soft
- replace_newline_with_space
- filter_small_docs_bytes_300
#### Filters applied to: indic-hi
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- filter_remove_empty_docs
- split_sentences_indic-hi
- dedup_template_soft
- replace_newline_with_space
- filter_small_docs_bytes_300
| 1,567 | [
[
-0.04620361328125,
-0.0277862548828125,
0.0295257568359375,
0.026580810546875,
-0.0220489501953125,
-0.01016998291015625,
-0.01441192626953125,
0.00933074951171875,
0.04833984375,
0.028839111328125,
-0.07000732421875,
-0.06640625,
-0.04559326171875,
0.032501... |
bigscience-data/roots_id_wikisource | 2022-12-12T11:05:55.000Z | [
"language:id",
"license:cc-by-sa-3.0",
"region:us"
] | bigscience-data | null | null | 0 | 3 | 2022-05-18T09:14:39 | ---
language: id
license: cc-by-sa-3.0
extra_gated_prompt: 'By accessing this dataset, you agree to abide by the BigScience
Ethical Charter. The charter can be found at:
https://hf.co/spaces/bigscience/ethical-charter'
extra_gated_fields:
I have read and agree to abide by the BigScience Ethical Charter: checkbox
---
ROOTS Subset: roots_id_wikisource
# wikisource_filtered
- Dataset uid: `wikisource_filtered`
### Description
### Homepage
### Licensing
### Speaker Locations
### Sizes
- 2.6306 % of total
- 12.7884 % of fr
- 19.8886 % of indic-bn
- 20.9966 % of indic-ta
- 2.3478 % of ar
- 4.7068 % of indic-hi
- 18.0998 % of indic-te
- 1.7155 % of es
- 19.4800 % of indic-kn
- 9.1737 % of indic-ml
- 17.1771 % of indic-mr
- 17.1870 % of indic-gu
- 70.3687 % of indic-as
- 1.0165 % of pt
- 7.8642 % of indic-pa
- 1.3501 % of vi
- 4.9411 % of indic-or
- 0.5307 % of ca
- 2.3593 % of id
- 1.5928 % of eu
### BigScience processing steps
#### Filters applied to: fr
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- dedup_template_soft
- filter_remove_empty_docs
- filter_small_docs_bytes_1024
#### Filters applied to: indic-bn
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- dedup_template_soft
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-ta
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- dedup_template_soft
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: ar
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- dedup_template_soft
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-hi
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- dedup_template_soft
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-te
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- dedup_template_soft
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: es
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- dedup_template_soft
- filter_remove_empty_docs
- filter_small_docs_bytes_1024
#### Filters applied to: indic-kn
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- dedup_template_soft
- filter_remove_empty_docs
- remove_wiki_mojibake
- filter_small_docs_bytes_300
#### Filters applied to: indic-ml
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- dedup_template_soft
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-mr
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- dedup_template_soft
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-gu
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- dedup_template_soft
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-as
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- dedup_template_soft
- filter_remove_empty_docs
#### Filters applied to: pt
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- dedup_template_soft
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-pa
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- dedup_template_soft
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: vi
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- dedup_template_soft
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-or
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- dedup_template_soft
- filter_remove_empty_docs
#### Filters applied to: ca
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- dedup_template_soft
- filter_remove_empty_docs
- filter_small_docs_bytes_1024
#### Filters applied to: id
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- dedup_template_soft
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: eu
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- dedup_template_soft
- filter_remove_empty_docs
| 4,382 | [
[
-0.049285888671875,
-0.0423583984375,
0.022705078125,
0.01117706298828125,
-0.007110595703125,
-0.0001322031021118164,
-0.01033782958984375,
-0.01323699951171875,
0.04278564453125,
0.0208587646484375,
-0.053436279296875,
-0.060943603515625,
-0.040771484375,
... |
bigscience-data/roots_id_ted_talks_iwslt | 2022-12-12T11:06:05.000Z | [
"language:id",
"license:cc-by-nc-nd-4.0",
"region:us"
] | bigscience-data | null | null | 0 | 3 | 2022-05-18T09:14:40 | ---
language: id
license: cc-by-nc-nd-4.0
extra_gated_prompt: 'By accessing this dataset, you agree to abide by the BigScience
Ethical Charter. The charter can be found at:
https://hf.co/spaces/bigscience/ethical-charter'
extra_gated_fields:
I have read and agree to abide by the BigScience Ethical Charter: checkbox
---
ROOTS Subset: roots_id_ted_talks_iwslt
# WIT Ted Talks
- Dataset uid: `ted_talks_iwslt`
### Description
The Web Inventory Talk is a collection of the original Ted talks and their translated version. The translations are available in more than 109+ languages, though the distribution is not uniform.
### Homepage
https://github.com/huggingface/datasets/blob/master/datasets/ted_talks_iwslt/README.md
### Licensing
- open license
- cc-by-nc-4.0: Creative Commons Attribution Non Commercial 4.0 International
TED makes its collection of video recordings and transcripts of talks available under the Creative Commons BY-NC-ND license (look here). WIT3 acknowledges the authorship of TED talks (BY condition) and does not redistribute transcripts for commercial purposes (NC). As regards the integrity of the work (ND), WIT3 only changes the format of the container, while preserving the original contents. WIT3 aims to support research on human language processing as well as the diffusion of TED Talks!
### Speaker Locations
- Southern Europe
- Italy
### Sizes
- 0.0305 % of total
- 0.0736 % of ar
- 0.2002 % of pt
- 0.0128 % of zh
- 0.2236 % of vi
- 0.0330 % of fr
- 0.0545 % of es
- 0.0122 % of en
- 0.3704 % of id
- 0.0373 % of indic-hi
- 0.0330 % of indic-ta
- 0.1393 % of indic-mr
- 0.0305 % of ca
- 0.1179 % of indic-ur
- 0.0147 % of indic-bn
- 0.0240 % of indic-ml
- 0.0244 % of indic-te
- 0.0503 % of indic-gu
- 0.0211 % of indic-kn
- 0.0274 % of eu
- 0.0023 % of indic-as
- 0.0001 % of indic-pa
### BigScience processing steps
#### Filters applied to: ar
- dedup_document
- dedup_template_soft
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: pt
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: zh
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_1024
#### Filters applied to: vi
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: fr
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_1024
#### Filters applied to: es
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_1024
#### Filters applied to: en
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_1024
#### Filters applied to: id
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-hi
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-ta
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-mr
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: ca
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_1024
#### Filters applied to: indic-ur
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-bn
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-ml
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-te
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-gu
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-kn
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: eu
- dedup_document
- filter_remove_empty_docs
#### Filters applied to: indic-as
- dedup_document
- filter_remove_empty_docs
#### Filters applied to: indic-pa
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
| 4,095 | [
[
-0.03533935546875,
-0.046905517578125,
0.0265350341796875,
0.0030918121337890625,
-0.01861572265625,
0.007274627685546875,
-0.01145172119140625,
-0.0225677490234375,
0.0450439453125,
0.016510009765625,
-0.05950927734375,
-0.054595947265625,
-0.04931640625,
0... |
bigscience-data/roots_id_wikiquote | 2022-12-12T11:06:10.000Z | [
"language:id",
"license:cc-by-sa-3.0",
"region:us"
] | bigscience-data | null | null | 0 | 3 | 2022-05-18T09:14:41 | ---
language: id
license: cc-by-sa-3.0
extra_gated_prompt: 'By accessing this dataset, you agree to abide by the BigScience
Ethical Charter. The charter can be found at:
https://hf.co/spaces/bigscience/ethical-charter'
extra_gated_fields:
I have read and agree to abide by the BigScience Ethical Charter: checkbox
---
ROOTS Subset: roots_id_wikiquote
# wikiquote_filtered
- Dataset uid: `wikiquote_filtered`
### Description
### Homepage
### Licensing
### Speaker Locations
### Sizes
- 0.0462 % of total
- 0.1697 % of en
- 0.0326 % of fr
- 0.0216 % of ar
- 0.0066 % of zh
- 0.0833 % of pt
- 0.0357 % of es
- 0.0783 % of indic-ta
- 0.0361 % of indic-hi
- 0.0518 % of ca
- 0.0405 % of vi
- 0.0834 % of indic-ml
- 0.0542 % of indic-te
- 0.1172 % of indic-gu
- 0.0634 % of indic-kn
- 0.0539 % of id
- 0.0454 % of indic-ur
- 0.0337 % of indic-mr
- 0.0347 % of eu
### BigScience processing steps
#### Filters applied to: en
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- filter_remove_empty_docs
- split_sentences_en
- dedup_template_soft
- replace_newline_with_space
- filter_small_docs_bytes_1024
#### Filters applied to: fr
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- filter_remove_empty_docs
- split_sentences_fr
- dedup_template_soft
- replace_newline_with_space
- filter_small_docs_bytes_1024
#### Filters applied to: ar
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- filter_remove_empty_docs
- split_sentences_ar
- dedup_template_soft
- replace_newline_with_space
- filter_small_docs_bytes_300
#### Filters applied to: zh
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- filter_remove_empty_docs
- split_sentences_zhs
- dedup_template_soft
- replace_newline_with_space
- filter_small_docs_bytes_1024
#### Filters applied to: pt
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- filter_remove_empty_docs
- split_sentences_pt
- dedup_template_soft
- replace_newline_with_space
- filter_small_docs_bytes_300
#### Filters applied to: es
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- filter_remove_empty_docs
- split_sentences_es
- dedup_template_soft
- replace_newline_with_space
- filter_small_docs_bytes_1024
#### Filters applied to: indic-ta
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- filter_remove_empty_docs
- split_sentences_indic-ta
- dedup_template_soft
- replace_newline_with_space
- filter_small_docs_bytes_300
#### Filters applied to: indic-hi
- dedup_document
- filter_remove_empty_docs
- split_sentences_indic-hi
- dedup_template_soft
- filter_small_docs_bytes_300
#### Filters applied to: ca
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- filter_remove_empty_docs
- split_sentences_ca
- dedup_template_soft
- replace_newline_with_space
- filter_small_docs_bytes_1024
#### Filters applied to: vi
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- filter_remove_empty_docs
- split_sentences_vi
- dedup_template_soft
- replace_newline_with_space
- filter_small_docs_bytes_300
#### Filters applied to: indic-ml
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- filter_remove_empty_docs
- split_sentences_indic-ml
- dedup_template_soft
- replace_newline_with_space
- filter_small_docs_bytes_300
#### Filters applied to: indic-te
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- filter_remove_empty_docs
- split_sentences_indic-te
- dedup_template_soft
- replace_newline_with_space
- filter_small_docs_bytes_300
#### Filters applied to: indic-gu
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- filter_remove_empty_docs
- split_sentences_indic-gu
- dedup_template_soft
- replace_newline_with_space
- filter_small_docs_bytes_300
#### Filters applied to: indic-kn
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- filter_remove_empty_docs
- split_sentences_indic-kn
- dedup_template_soft
- replace_newline_with_space
- filter_small_docs_bytes_300
#### Filters applied to: id
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- filter_remove_empty_docs
- split_sentences_id
- dedup_template_soft
- replace_newline_with_space
- filter_small_docs_bytes_300
#### Filters applied to: indic-ur
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- dedup_template_soft
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-mr
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- filter_remove_empty_docs
- split_sentences_indic-mr
- dedup_template_soft
- replace_newline_with_space
- filter_small_docs_bytes_300
#### Filters applied to: eu
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- filter_remove_empty_docs
- split_sentences_eu
- dedup_template_soft
- replace_newline_with_space
| 4,987 | [
[
-0.04193115234375,
-0.051605224609375,
0.017364501953125,
0.01396942138671875,
-0.00315093994140625,
0.007221221923828125,
-0.01287078857421875,
-0.01541900634765625,
0.047515869140625,
0.021087646484375,
-0.048583984375,
-0.059661865234375,
-0.045562744140625,
... |
bigscience-data/roots_id_wikibooks | 2022-12-12T11:06:16.000Z | [
"language:id",
"license:cc-by-sa-3.0",
"region:us"
] | bigscience-data | null | null | 0 | 3 | 2022-05-18T09:14:41 | ---
language: id
license: cc-by-sa-3.0
extra_gated_prompt: 'By accessing this dataset, you agree to abide by the BigScience
Ethical Charter. The charter can be found at:
https://hf.co/spaces/bigscience/ethical-charter'
extra_gated_fields:
I have read and agree to abide by the BigScience Ethical Charter: checkbox
---
ROOTS Subset: roots_id_wikibooks
# wikibooks_filtered
- Dataset uid: `wikibooks_filtered`
### Description
### Homepage
### Licensing
### Speaker Locations
### Sizes
- 0.0897 % of total
- 0.2591 % of en
- 0.0965 % of fr
- 0.1691 % of es
- 0.2834 % of indic-hi
- 0.2172 % of pt
- 0.0149 % of zh
- 0.0279 % of ar
- 0.1374 % of vi
- 0.5025 % of id
- 0.3694 % of indic-ur
- 0.5744 % of eu
- 0.0769 % of ca
- 0.0519 % of indic-ta
- 0.1470 % of indic-mr
- 0.0751 % of indic-te
- 0.0156 % of indic-bn
- 0.0476 % of indic-ml
- 0.0087 % of indic-pa
### BigScience processing steps
#### Filters applied to: en
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- filter_remove_empty_docs
- split_sentences_en
- dedup_template_soft
- replace_newline_with_space
- filter_small_docs_bytes_1024
#### Filters applied to: fr
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- filter_remove_empty_docs
- split_sentences_fr
- dedup_template_soft
- replace_newline_with_space
- filter_small_docs_bytes_1024
#### Filters applied to: es
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- filter_remove_empty_docs
- split_sentences_es
- dedup_template_soft
- replace_newline_with_space
- filter_small_docs_bytes_1024
#### Filters applied to: indic-hi
- dedup_document
- filter_remove_empty_docs
- split_sentences_indic-hi
- dedup_template_soft
- filter_small_docs_bytes_300
#### Filters applied to: pt
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- filter_remove_empty_docs
- split_sentences_pt
- dedup_template_soft
- replace_newline_with_space
- filter_small_docs_bytes_300
#### Filters applied to: zh
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- filter_remove_empty_docs
- split_sentences_zhs
- dedup_template_soft
- replace_newline_with_space
- filter_small_docs_bytes_1024
#### Filters applied to: ar
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- filter_remove_empty_docs
- split_sentences_ar
- dedup_template_soft
- replace_newline_with_space
- filter_small_docs_bytes_300
#### Filters applied to: vi
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- filter_remove_empty_docs
- split_sentences_vi
- dedup_template_soft
- replace_newline_with_space
- filter_small_docs_bytes_300
#### Filters applied to: id
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- filter_remove_empty_docs
- split_sentences_id
- dedup_template_soft
- replace_newline_with_space
- filter_small_docs_bytes_300
#### Filters applied to: indic-ur
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- dedup_template_soft
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: eu
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- filter_remove_empty_docs
- split_sentences_eu
- dedup_template_soft
- replace_newline_with_space
#### Filters applied to: ca
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- filter_remove_empty_docs
- split_sentences_ca
- dedup_template_soft
- replace_newline_with_space
- filter_small_docs_bytes_1024
#### Filters applied to: indic-ta
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- filter_remove_empty_docs
- split_sentences_indic-ta
- dedup_template_soft
- replace_newline_with_space
- filter_small_docs_bytes_300
#### Filters applied to: indic-mr
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- filter_remove_empty_docs
- split_sentences_indic-mr
- dedup_template_soft
- replace_newline_with_space
- filter_small_docs_bytes_300
#### Filters applied to: indic-te
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- filter_remove_empty_docs
- split_sentences_indic-te
- dedup_template_soft
- replace_newline_with_space
- filter_small_docs_bytes_300
#### Filters applied to: indic-bn
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- filter_remove_empty_docs
- split_sentences_indic-bn
- dedup_template_soft
- replace_newline_with_space
- filter_small_docs_bytes_300
#### Filters applied to: indic-ml
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- filter_remove_empty_docs
- split_sentences_indic-ml
- dedup_template_soft
- replace_newline_with_space
- filter_small_docs_bytes_300
#### Filters applied to: indic-pa
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- filter_remove_empty_docs
- split_sentences_indic-pa
- dedup_template_soft
- replace_newline_with_space
- filter_small_docs_bytes_300
| 4,987 | [
[
-0.0447998046875,
-0.04803466796875,
0.015838623046875,
0.013427734375,
0.0005078315734863281,
0.0008835792541503906,
-0.0118560791015625,
-0.0159149169921875,
0.044647216796875,
0.019866943359375,
-0.045623779296875,
-0.057525634765625,
-0.042022705078125,
... |
bigscience-data/roots_vi_wikipedia | 2022-12-12T11:16:52.000Z | [
"language:vi",
"license:cc-by-sa-3.0",
"region:us"
] | bigscience-data | null | null | 0 | 3 | 2022-05-18T09:19:39 | ---
language: vi
license: cc-by-sa-3.0
extra_gated_prompt: 'By accessing this dataset, you agree to abide by the BigScience
Ethical Charter. The charter can be found at:
https://hf.co/spaces/bigscience/ethical-charter'
extra_gated_fields:
I have read and agree to abide by the BigScience Ethical Charter: checkbox
---
ROOTS Subset: roots_vi_wikipedia
# wikipedia
- Dataset uid: `wikipedia`
### Description
### Homepage
### Licensing
### Speaker Locations
### Sizes
- 3.2299 % of total
- 4.2071 % of en
- 5.6773 % of ar
- 3.3416 % of fr
- 5.2815 % of es
- 12.4852 % of ca
- 0.4288 % of zh
- 0.4286 % of zh
- 5.4743 % of indic-bn
- 8.9062 % of indic-ta
- 21.3313 % of indic-te
- 4.4845 % of pt
- 4.0493 % of indic-hi
- 11.3163 % of indic-ml
- 22.5300 % of indic-ur
- 4.4902 % of vi
- 16.9916 % of indic-kn
- 24.7820 % of eu
- 11.6241 % of indic-mr
- 9.8749 % of id
- 9.3489 % of indic-pa
- 9.4767 % of indic-gu
- 24.1132 % of indic-as
- 5.3309 % of indic-or
### BigScience processing steps
#### Filters applied to: en
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_1024
#### Filters applied to: ar
- filter_wiki_user_titles
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: fr
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_1024
#### Filters applied to: es
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_1024
#### Filters applied to: ca
- filter_wiki_user_titles
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_1024
#### Filters applied to: zh
#### Filters applied to: zh
#### Filters applied to: indic-bn
- filter_wiki_user_titles
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-ta
- filter_wiki_user_titles
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-te
- filter_wiki_user_titles
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: pt
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-hi
- filter_wiki_user_titles
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-ml
- filter_wiki_user_titles
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-ur
- filter_wiki_user_titles
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: vi
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-kn
- filter_wiki_user_titles
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: eu
- filter_wiki_user_titles
- dedup_document
- filter_remove_empty_docs
#### Filters applied to: indic-mr
- filter_wiki_user_titles
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: id
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-pa
- filter_wiki_user_titles
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-gu
- filter_wiki_user_titles
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-as
- filter_wiki_user_titles
- dedup_document
- filter_remove_empty_docs
#### Filters applied to: indic-or
- filter_wiki_user_titles
- dedup_document
- filter_remove_empty_docs
| 3,635 | [
[
-0.04437255859375,
-0.0382080078125,
0.022003173828125,
0.01157379150390625,
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-0.005748748779296875,
-0.01500701904296875,
-0.00930023193359375,
0.045166015625,
0.021209716796875,
-0.053253173828125,
-0.05877685546875,
-0.045501708984375,
... |
bigscience-data/roots_zh_wikinews | 2022-12-12T11:17:30.000Z | [
"language:zh",
"license:cc-by-sa-3.0",
"region:us"
] | bigscience-data | null | null | 2 | 3 | 2022-05-18T09:20:09 | ---
language: zh
license: cc-by-sa-3.0
extra_gated_prompt: 'By accessing this dataset, you agree to abide by the BigScience
Ethical Charter. The charter can be found at:
https://hf.co/spaces/bigscience/ethical-charter'
extra_gated_fields:
I have read and agree to abide by the BigScience Ethical Charter: checkbox
---
ROOTS Subset: roots_zh_wikinews
# wikinews_filtered
- Dataset uid: `wikinews_filtered`
### Description
### Homepage
### Licensing
### Speaker Locations
### Sizes
- 0.0307 % of total
- 0.0701 % of ar
- 0.3036 % of pt
- 0.0271 % of en
- 0.0405 % of fr
- 0.2119 % of indic-ta
- 0.0081 % of zh
- 0.0510 % of es
- 0.0725 % of ca
### BigScience processing steps
#### Filters applied to: ar
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- filter_remove_empty_docs
- split_sentences_ar
- dedup_template_soft
- replace_newline_with_space
- filter_small_docs_bytes_300
#### Filters applied to: pt
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- filter_remove_empty_docs
- split_sentences_pt
- dedup_template_soft
- replace_newline_with_space
- filter_small_docs_bytes_300
#### Filters applied to: en
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- filter_remove_empty_docs
- split_sentences_en
- dedup_template_soft
- replace_newline_with_space
- filter_small_docs_bytes_1024
#### Filters applied to: fr
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- dedup_template_soft
- filter_remove_empty_docs
- replace_newline_with_space
- filter_small_docs_bytes_1024
#### Filters applied to: indic-ta
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- filter_remove_empty_docs
- split_sentences_indic-ta
- dedup_template_soft
- replace_newline_with_space
- filter_small_docs_bytes_300
#### Filters applied to: zh
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- filter_remove_empty_docs
- split_sentences_zhs
- dedup_template_soft
- replace_newline_with_space
- filter_small_docs_bytes_1024
#### Filters applied to: es
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- filter_remove_empty_docs
- split_sentences_es
- dedup_template_soft
- replace_newline_with_space
- filter_small_docs_bytes_1024
#### Filters applied to: ca
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- filter_remove_empty_docs
- split_sentences_ca
- dedup_template_soft
- replace_newline_with_space
- filter_small_docs_bytes_1024
| 2,536 | [
[
-0.0391845703125,
-0.0400390625,
0.0242767333984375,
0.01511383056640625,
-0.0089569091796875,
-0.008056640625,
-0.01302337646484375,
0.0022563934326171875,
0.046295166015625,
0.0308990478515625,
-0.055877685546875,
-0.06658935546875,
-0.045684814453125,
0.0... |
spoiled/ecqa_classify_94 | 2022-05-18T13:53:37.000Z | [
"region:us"
] | spoiled | null | null | 0 | 3 | 2022-05-18T12:34:54 | Entry not found | 15 | [
[
-0.02142333984375,
-0.01495361328125,
0.05718994140625,
0.0288238525390625,
-0.035064697265625,
0.046539306640625,
0.052520751953125,
0.005062103271484375,
0.0513916015625,
0.016998291015625,
-0.052093505859375,
-0.014984130859375,
-0.060394287109375,
0.0379... |
Rexhaif/xnli-en-ru | 2022-05-28T20:53:05.000Z | [
"region:us"
] | Rexhaif | null | null | 0 | 3 | 2022-05-22T23:28:45 | Entry not found | 15 | [
[
-0.02142333984375,
-0.01495361328125,
0.05718994140625,
0.0288238525390625,
-0.035064697265625,
0.046539306640625,
0.052520751953125,
0.005062103271484375,
0.0513916015625,
0.016998291015625,
-0.052093505859375,
-0.014984130859375,
-0.060394287109375,
0.0379... |
Aniemore/REPV | 2022-07-01T16:41:13.000Z | [
"task_categories:audio-classification",
"task_ids:audio-emotion-recognition",
"annotations_creators:crowdsourced",
"language_creators:expert-generated",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:ru",
"license:... | Aniemore | null | null | 2 | 3 | 2022-05-26T22:15:17 | ---
annotations_creators:
- crowdsourced
language_creators:
- expert-generated
- crowdsourced
language:
- ru
license:
- mit
multilinguality:
- monolingual
pretty_name: Russian Emotional Phonetic Voices
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- audio-classification
task_ids:
- audio-emotion-recognition
---
# Citations
```
@misc{Aniemore,
author = {Артем Аментес, Илья Лубенец, Никита Давидчук},
title = {Открытая библиотека искусственного интеллекта для анализа и выявления эмоциональных оттенков речи человека},
year = {2022},
publisher = {Hugging Face},
journal = {Hugging Face Hub},
howpublished = {\url{https://huggingface.com/aniemore/Aniemore}},
email = {hello@socialcode.ru}
}
```
| 737 | [
[
-0.0200958251953125,
-0.044525146484375,
0.0352783203125,
0.02734375,
0.0022945404052734375,
0.008941650390625,
0.0218505859375,
-0.029998779296875,
0.06341552734375,
-0.0089874267578125,
-0.032073974609375,
-0.0433349609375,
-0.0435791015625,
0.031921386718... |
PoolC/1-fold-clone-detection-600k-5fold | 2022-06-01T06:49:02.000Z | [
"region:us"
] | PoolC | null | null | 2 | 3 | 2022-06-01T06:36:28 | Entry not found | 15 | [
[
-0.02142333984375,
-0.01495361328125,
0.05718994140625,
0.0288238525390625,
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0.046539306640625,
0.052520751953125,
0.005062103271484375,
0.0513916015625,
0.016998291015625,
-0.052093505859375,
-0.014984130859375,
-0.060394287109375,
0.0379... |
tomekkorbak/python-github-code | 2022-06-12T22:20:27.000Z | [
"region:us"
] | tomekkorbak | null | null | 1 | 3 | 2022-06-01T14:54:29 | Entry not found | 15 | [
[
-0.02142333984375,
-0.01495361328125,
0.05718994140625,
0.0288238525390625,
-0.035064697265625,
0.046539306640625,
0.052520751953125,
0.005062103271484375,
0.0513916015625,
0.016998291015625,
-0.052093505859375,
-0.014984130859375,
-0.060394287109375,
0.0379... |
BeIR/climate-fever-generated-queries | 2022-10-23T06:09:20.000Z | [
"task_categories:text-retrieval",
"task_ids:entity-linking-retrieval",
"task_ids:fact-checking-retrieval",
"multilinguality:monolingual",
"language:en",
"license:cc-by-sa-4.0",
"region:us"
] | BeIR | null | null | 0 | 3 | 2022-06-06T22:07:02 | ---
annotations_creators: []
language_creators: []
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
paperswithcode_id: beir
pretty_name: BEIR Benchmark
size_categories:
msmarco:
- 1M<n<10M
trec-covid:
- 100k<n<1M
nfcorpus:
- 1K<n<10K
nq:
- 1M<n<10M
hotpotqa:
- 1M<n<10M
fiqa:
- 10K<n<100K
arguana:
- 1K<n<10K
touche-2020:
- 100K<n<1M
cqadupstack:
- 100K<n<1M
quora:
- 100K<n<1M
dbpedia:
- 1M<n<10M
scidocs:
- 10K<n<100K
fever:
- 1M<n<10M
climate-fever:
- 1M<n<10M
scifact:
- 1K<n<10K
source_datasets: []
task_categories:
- text-retrieval
- zero-shot-retrieval
- information-retrieval
- zero-shot-information-retrieval
task_ids:
- passage-retrieval
- entity-linking-retrieval
- fact-checking-retrieval
- tweet-retrieval
- citation-prediction-retrieval
- duplication-question-retrieval
- argument-retrieval
- news-retrieval
- biomedical-information-retrieval
- question-answering-retrieval
---
# Dataset Card for BEIR Benchmark
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/UKPLab/beir
- **Repository:** https://github.com/UKPLab/beir
- **Paper:** https://openreview.net/forum?id=wCu6T5xFjeJ
- **Leaderboard:** https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns
- **Point of Contact:** nandan.thakur@uwaterloo.ca
### Dataset Summary
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
- Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact)
- Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/)
- Bio-Medical IR: [TREC-COVID](https://ir.nist.gov/covidSubmit/index.html), [BioASQ](http://bioasq.org), [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/)
- News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html)
- Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data)
- Duplicate Question Retrieval: [Quora](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs), [CqaDupstack](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/)
- Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs)
- Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html)
- Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/)
All these datasets have been preprocessed and can be used for your experiments.
```python
```
### Supported Tasks and Leaderboards
The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/).
### Languages
All tasks are in English (`en`).
## Dataset Structure
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
- `corpus` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with three fields `_id` with unique document identifier, `title` with document title (optional) and `text` with document paragraph or passage. For example: `{"_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}`
- `queries` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with two fields `_id` with unique query identifier and `text` with query text. For example: `{"_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}`
- `qrels` file: a `.tsv` file (tab-seperated) that contains three columns, i.e. the `query-id`, `corpus-id` and `score` in this order. Keep 1st row as header. For example: `q1 doc1 1`
### Data Instances
A high level example of any beir dataset:
```python
corpus = {
"doc1" : {
"title": "Albert Einstein",
"text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \
one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \
its influence on the philosophy of science. He is best known to the general public for his mass–energy \
equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \
Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \
of the photoelectric effect', a pivotal step in the development of quantum theory."
},
"doc2" : {
"title": "", # Keep title an empty string if not present
"text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \
malted barley. The two main varieties are German Weißbier and Belgian witbier; other types include Lambic (made\
with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)."
},
}
queries = {
"q1" : "Who developed the mass-energy equivalence formula?",
"q2" : "Which beer is brewed with a large proportion of wheat?"
}
qrels = {
"q1" : {"doc1": 1},
"q2" : {"doc2": 1},
}
```
### Data Fields
Examples from all configurations have the following features:
### Corpus
- `corpus`: a `dict` feature representing the document title and passage text, made up of:
- `_id`: a `string` feature representing the unique document id
- `title`: a `string` feature, denoting the title of the document.
- `text`: a `string` feature, denoting the text of the document.
### Queries
- `queries`: a `dict` feature representing the query, made up of:
- `_id`: a `string` feature representing the unique query id
- `text`: a `string` feature, denoting the text of the query.
### Qrels
- `qrels`: a `dict` feature representing the query document relevance judgements, made up of:
- `_id`: a `string` feature representing the query id
- `_id`: a `string` feature, denoting the document id.
- `score`: a `int32` feature, denoting the relevance judgement between query and document.
### Data Splits
| Dataset | Website| BEIR-Name | Type | Queries | Corpus | Rel D/Q | Down-load | md5 |
| -------- | -----| ---------| --------- | ----------- | ---------| ---------| :----------: | :------:|
| MSMARCO | [Homepage](https://microsoft.github.io/msmarco/)| ``msmarco`` | ``train``<br>``dev``<br>``test``| 6,980 | 8.84M | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/msmarco.zip) | ``444067daf65d982533ea17ebd59501e4`` |
| TREC-COVID | [Homepage](https://ir.nist.gov/covidSubmit/index.html)| ``trec-covid``| ``test``| 50| 171K| 493.5 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/trec-covid.zip) | ``ce62140cb23feb9becf6270d0d1fe6d1`` |
| NFCorpus | [Homepage](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) | ``nfcorpus`` | ``train``<br>``dev``<br>``test``| 323 | 3.6K | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip) | ``a89dba18a62ef92f7d323ec890a0d38d`` |
| BioASQ | [Homepage](http://bioasq.org) | ``bioasq``| ``train``<br>``test`` | 500 | 14.91M | 8.05 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#2-bioasq) |
| NQ | [Homepage](https://ai.google.com/research/NaturalQuestions) | ``nq``| ``train``<br>``test``| 3,452 | 2.68M | 1.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nq.zip) | ``d4d3d2e48787a744b6f6e691ff534307`` |
| HotpotQA | [Homepage](https://hotpotqa.github.io) | ``hotpotqa``| ``train``<br>``dev``<br>``test``| 7,405 | 5.23M | 2.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/hotpotqa.zip) | ``f412724f78b0d91183a0e86805e16114`` |
| FiQA-2018 | [Homepage](https://sites.google.com/view/fiqa/) | ``fiqa`` | ``train``<br>``dev``<br>``test``| 648 | 57K | 2.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fiqa.zip) | ``17918ed23cd04fb15047f73e6c3bd9d9`` |
| Signal-1M(RT) | [Homepage](https://research.signal-ai.com/datasets/signal1m-tweetir.html)| ``signal1m`` | ``test``| 97 | 2.86M | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#4-signal-1m) |
| TREC-NEWS | [Homepage](https://trec.nist.gov/data/news2019.html) | ``trec-news`` | ``test``| 57 | 595K | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#1-trec-news) |
| ArguAna | [Homepage](http://argumentation.bplaced.net/arguana/data) | ``arguana``| ``test`` | 1,406 | 8.67K | 1.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/arguana.zip) | ``8ad3e3c2a5867cdced806d6503f29b99`` |
| Touche-2020| [Homepage](https://webis.de/events/touche-20/shared-task-1.html) | ``webis-touche2020``| ``test``| 49 | 382K | 19.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/webis-touche2020.zip) | ``46f650ba5a527fc69e0a6521c5a23563`` |
| CQADupstack| [Homepage](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) | ``cqadupstack``| ``test``| 13,145 | 457K | 1.4 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/cqadupstack.zip) | ``4e41456d7df8ee7760a7f866133bda78`` |
| Quora| [Homepage](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) | ``quora``| ``dev``<br>``test``| 10,000 | 523K | 1.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/quora.zip) | ``18fb154900ba42a600f84b839c173167`` |
| DBPedia | [Homepage](https://github.com/iai-group/DBpedia-Entity/) | ``dbpedia-entity``| ``dev``<br>``test``| 400 | 4.63M | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/dbpedia-entity.zip) | ``c2a39eb420a3164af735795df012ac2c`` |
| SCIDOCS| [Homepage](https://allenai.org/data/scidocs) | ``scidocs``| ``test``| 1,000 | 25K | 4.9 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scidocs.zip) | ``38121350fc3a4d2f48850f6aff52e4a9`` |
| FEVER | [Homepage](http://fever.ai) | ``fever``| ``train``<br>``dev``<br>``test``| 6,666 | 5.42M | 1.2| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fever.zip) | ``5a818580227bfb4b35bb6fa46d9b6c03`` |
| Climate-FEVER| [Homepage](http://climatefever.ai) | ``climate-fever``|``test``| 1,535 | 5.42M | 3.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/climate-fever.zip) | ``8b66f0a9126c521bae2bde127b4dc99d`` |
| SciFact| [Homepage](https://github.com/allenai/scifact) | ``scifact``| ``train``<br>``test``| 300 | 5K | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scifact.zip) | ``5f7d1de60b170fc8027bb7898e2efca1`` |
| Robust04 | [Homepage](https://trec.nist.gov/data/robust/04.guidelines.html) | ``robust04``| ``test``| 249 | 528K | 69.9 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#3-robust04) |
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
Cite as:
```
@inproceedings{
thakur2021beir,
title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models},
author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych},
booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},
year={2021},
url={https://openreview.net/forum?id=wCu6T5xFjeJ}
}
```
### Contributions
Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset. | 13,988 | [
[
-0.0396728515625,
-0.03985595703125,
0.01094818115234375,
0.0036602020263671875,
0.00423431396484375,
0.00009590387344360352,
-0.0081939697265625,
-0.0188751220703125,
0.021697998046875,
0.00595855712890625,
-0.034332275390625,
-0.0545654296875,
-0.0263824462890... |
BeIR/dbpedia-entity-generated-queries | 2022-10-23T06:09:39.000Z | [
"task_categories:text-retrieval",
"task_ids:entity-linking-retrieval",
"task_ids:fact-checking-retrieval",
"multilinguality:monolingual",
"language:en",
"license:cc-by-sa-4.0",
"region:us"
] | BeIR | null | null | 1 | 3 | 2022-06-06T22:21:33 | ---
annotations_creators: []
language_creators: []
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
paperswithcode_id: beir
pretty_name: BEIR Benchmark
size_categories:
msmarco:
- 1M<n<10M
trec-covid:
- 100k<n<1M
nfcorpus:
- 1K<n<10K
nq:
- 1M<n<10M
hotpotqa:
- 1M<n<10M
fiqa:
- 10K<n<100K
arguana:
- 1K<n<10K
touche-2020:
- 100K<n<1M
cqadupstack:
- 100K<n<1M
quora:
- 100K<n<1M
dbpedia:
- 1M<n<10M
scidocs:
- 10K<n<100K
fever:
- 1M<n<10M
climate-fever:
- 1M<n<10M
scifact:
- 1K<n<10K
source_datasets: []
task_categories:
- text-retrieval
- zero-shot-retrieval
- information-retrieval
- zero-shot-information-retrieval
task_ids:
- passage-retrieval
- entity-linking-retrieval
- fact-checking-retrieval
- tweet-retrieval
- citation-prediction-retrieval
- duplication-question-retrieval
- argument-retrieval
- news-retrieval
- biomedical-information-retrieval
- question-answering-retrieval
---
# Dataset Card for BEIR Benchmark
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/UKPLab/beir
- **Repository:** https://github.com/UKPLab/beir
- **Paper:** https://openreview.net/forum?id=wCu6T5xFjeJ
- **Leaderboard:** https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns
- **Point of Contact:** nandan.thakur@uwaterloo.ca
### Dataset Summary
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
- Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact)
- Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/)
- Bio-Medical IR: [TREC-COVID](https://ir.nist.gov/covidSubmit/index.html), [BioASQ](http://bioasq.org), [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/)
- News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html)
- Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data)
- Duplicate Question Retrieval: [Quora](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs), [CqaDupstack](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/)
- Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs)
- Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html)
- Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/)
All these datasets have been preprocessed and can be used for your experiments.
```python
```
### Supported Tasks and Leaderboards
The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/).
### Languages
All tasks are in English (`en`).
## Dataset Structure
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
- `corpus` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with three fields `_id` with unique document identifier, `title` with document title (optional) and `text` with document paragraph or passage. For example: `{"_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}`
- `queries` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with two fields `_id` with unique query identifier and `text` with query text. For example: `{"_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}`
- `qrels` file: a `.tsv` file (tab-seperated) that contains three columns, i.e. the `query-id`, `corpus-id` and `score` in this order. Keep 1st row as header. For example: `q1 doc1 1`
### Data Instances
A high level example of any beir dataset:
```python
corpus = {
"doc1" : {
"title": "Albert Einstein",
"text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \
one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \
its influence on the philosophy of science. He is best known to the general public for his mass–energy \
equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \
Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \
of the photoelectric effect', a pivotal step in the development of quantum theory."
},
"doc2" : {
"title": "", # Keep title an empty string if not present
"text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \
malted barley. The two main varieties are German Weißbier and Belgian witbier; other types include Lambic (made\
with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)."
},
}
queries = {
"q1" : "Who developed the mass-energy equivalence formula?",
"q2" : "Which beer is brewed with a large proportion of wheat?"
}
qrels = {
"q1" : {"doc1": 1},
"q2" : {"doc2": 1},
}
```
### Data Fields
Examples from all configurations have the following features:
### Corpus
- `corpus`: a `dict` feature representing the document title and passage text, made up of:
- `_id`: a `string` feature representing the unique document id
- `title`: a `string` feature, denoting the title of the document.
- `text`: a `string` feature, denoting the text of the document.
### Queries
- `queries`: a `dict` feature representing the query, made up of:
- `_id`: a `string` feature representing the unique query id
- `text`: a `string` feature, denoting the text of the query.
### Qrels
- `qrels`: a `dict` feature representing the query document relevance judgements, made up of:
- `_id`: a `string` feature representing the query id
- `_id`: a `string` feature, denoting the document id.
- `score`: a `int32` feature, denoting the relevance judgement between query and document.
### Data Splits
| Dataset | Website| BEIR-Name | Type | Queries | Corpus | Rel D/Q | Down-load | md5 |
| -------- | -----| ---------| --------- | ----------- | ---------| ---------| :----------: | :------:|
| MSMARCO | [Homepage](https://microsoft.github.io/msmarco/)| ``msmarco`` | ``train``<br>``dev``<br>``test``| 6,980 | 8.84M | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/msmarco.zip) | ``444067daf65d982533ea17ebd59501e4`` |
| TREC-COVID | [Homepage](https://ir.nist.gov/covidSubmit/index.html)| ``trec-covid``| ``test``| 50| 171K| 493.5 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/trec-covid.zip) | ``ce62140cb23feb9becf6270d0d1fe6d1`` |
| NFCorpus | [Homepage](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) | ``nfcorpus`` | ``train``<br>``dev``<br>``test``| 323 | 3.6K | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip) | ``a89dba18a62ef92f7d323ec890a0d38d`` |
| BioASQ | [Homepage](http://bioasq.org) | ``bioasq``| ``train``<br>``test`` | 500 | 14.91M | 8.05 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#2-bioasq) |
| NQ | [Homepage](https://ai.google.com/research/NaturalQuestions) | ``nq``| ``train``<br>``test``| 3,452 | 2.68M | 1.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nq.zip) | ``d4d3d2e48787a744b6f6e691ff534307`` |
| HotpotQA | [Homepage](https://hotpotqa.github.io) | ``hotpotqa``| ``train``<br>``dev``<br>``test``| 7,405 | 5.23M | 2.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/hotpotqa.zip) | ``f412724f78b0d91183a0e86805e16114`` |
| FiQA-2018 | [Homepage](https://sites.google.com/view/fiqa/) | ``fiqa`` | ``train``<br>``dev``<br>``test``| 648 | 57K | 2.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fiqa.zip) | ``17918ed23cd04fb15047f73e6c3bd9d9`` |
| Signal-1M(RT) | [Homepage](https://research.signal-ai.com/datasets/signal1m-tweetir.html)| ``signal1m`` | ``test``| 97 | 2.86M | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#4-signal-1m) |
| TREC-NEWS | [Homepage](https://trec.nist.gov/data/news2019.html) | ``trec-news`` | ``test``| 57 | 595K | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#1-trec-news) |
| ArguAna | [Homepage](http://argumentation.bplaced.net/arguana/data) | ``arguana``| ``test`` | 1,406 | 8.67K | 1.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/arguana.zip) | ``8ad3e3c2a5867cdced806d6503f29b99`` |
| Touche-2020| [Homepage](https://webis.de/events/touche-20/shared-task-1.html) | ``webis-touche2020``| ``test``| 49 | 382K | 19.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/webis-touche2020.zip) | ``46f650ba5a527fc69e0a6521c5a23563`` |
| CQADupstack| [Homepage](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) | ``cqadupstack``| ``test``| 13,145 | 457K | 1.4 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/cqadupstack.zip) | ``4e41456d7df8ee7760a7f866133bda78`` |
| Quora| [Homepage](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) | ``quora``| ``dev``<br>``test``| 10,000 | 523K | 1.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/quora.zip) | ``18fb154900ba42a600f84b839c173167`` |
| DBPedia | [Homepage](https://github.com/iai-group/DBpedia-Entity/) | ``dbpedia-entity``| ``dev``<br>``test``| 400 | 4.63M | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/dbpedia-entity.zip) | ``c2a39eb420a3164af735795df012ac2c`` |
| SCIDOCS| [Homepage](https://allenai.org/data/scidocs) | ``scidocs``| ``test``| 1,000 | 25K | 4.9 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scidocs.zip) | ``38121350fc3a4d2f48850f6aff52e4a9`` |
| FEVER | [Homepage](http://fever.ai) | ``fever``| ``train``<br>``dev``<br>``test``| 6,666 | 5.42M | 1.2| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fever.zip) | ``5a818580227bfb4b35bb6fa46d9b6c03`` |
| Climate-FEVER| [Homepage](http://climatefever.ai) | ``climate-fever``|``test``| 1,535 | 5.42M | 3.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/climate-fever.zip) | ``8b66f0a9126c521bae2bde127b4dc99d`` |
| SciFact| [Homepage](https://github.com/allenai/scifact) | ``scifact``| ``train``<br>``test``| 300 | 5K | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scifact.zip) | ``5f7d1de60b170fc8027bb7898e2efca1`` |
| Robust04 | [Homepage](https://trec.nist.gov/data/robust/04.guidelines.html) | ``robust04``| ``test``| 249 | 528K | 69.9 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#3-robust04) |
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
Cite as:
```
@inproceedings{
thakur2021beir,
title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models},
author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych},
booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},
year={2021},
url={https://openreview.net/forum?id=wCu6T5xFjeJ}
}
```
### Contributions
Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset. | 13,988 | [
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0.01094818115234375,
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-0.0081939697265625,
-0.0188751220703125,
0.021697998046875,
0.00595855712890625,
-0.034332275390625,
-0.0545654296875,
-0.0263824462890... |
Unso/ted2020_pairs_en_x | 2022-06-10T07:33:19.000Z | [
"region:us"
] | Unso | null | null | 0 | 3 | 2022-06-09T07:29:42 | Entry not found | 15 | [
[
-0.02142333984375,
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0.0288238525390625,
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0.052520751953125,
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0.016998291015625,
-0.052093505859375,
-0.014984130859375,
-0.060394287109375,
0.0379... |
florentgbelidji/autotrain-data-quality-customer-reviews | 2022-10-25T10:29:24.000Z | [
"task_categories:text-classification",
"language:en",
"region:us"
] | florentgbelidji | null | null | 0 | 3 | 2022-06-09T09:35:36 | ---
language:
- en
task_categories:
- text-classification
---
# AutoTrain Dataset for project: quality-customer-reviews
## Dataset Descritpion
This dataset has been automatically processed by AutoTrain for project quality-customer-reviews.
### Languages
The BCP-47 code for the dataset's language is en.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"text": " Love this truck, I think it is light years better than the competition. I have driven or owned all [...]",
"target": 1
},
{
"text": " I purchased this to haul our 4 horse trailer since the standard iterations of the domestic vehicles[...]",
"target": 0
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"text": "Value(dtype='string', id=None)",
"target": "ClassLabel(num_classes=5, names=['good', 'great', 'ok', 'poor', 'terrible'], id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 9166 |
| valid | 2295 |
| 1,185 | [
[
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0.02099609375,
-0.03363037109375,
-0.047821044921875,
-0.032043457031... |
AlekseyKorshuk/fantasy-books | 2022-06-10T04:36:42.000Z | [
"region:us"
] | AlekseyKorshuk | null | null | 2 | 3 | 2022-06-10T04:36:14 | Entry not found | 15 | [
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0.0379... |
AlekseyKorshuk/drama-books | 2022-06-11T13:26:37.000Z | [
"region:us"
] | AlekseyKorshuk | null | null | 1 | 3 | 2022-06-11T13:26:35 | Entry not found | 15 | [
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0.037... |
angie-chen55/javascript-github-code | 2022-06-15T08:08:09.000Z | [
"region:us"
] | angie-chen55 | null | null | 0 | 3 | 2022-06-15T07:36:45 | Entry not found | 15 | [
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-0.0604248046875,
0.037... |
omarxadel/MaWPS-ar | 2022-07-12T15:31:07.000Z | [
"task_categories:text2text-generation",
"task_ids:explanation-generation",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:1K<n<10K",
"language:en",
"language:ar",
"license:mit",
"region:us"
] | omarxadel | null | null | 0 | 3 | 2022-06-15T20:39:14 | ---
annotations_creators:
- crowdsourced
language:
- en
- ar
language_creators:
- found
license:
- mit
multilinguality:
- multilingual
pretty_name: MAWPS_ar
size_categories:
- 1K<n<10K
source_datasets: []
task_categories:
- text2text-generation
task_ids:
- explanation-generation
---
# Dataset Card for MAWPS_ar
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
### Dataset Summary
MAWPS: A Math Word Problem Repository
### Supported Tasks
Math Word Problem Solving
### Languages
Supports Arabic and English
## Dataset Structure
### Data Fields
- `text_en`: a `string` feature.
- `text_ar`: a `string` feature.
- `eqn`: a `string` feature.
### Data Splits
|train|validation|test|
|----:|---------:|---:|
| 3636| 1040| 520|
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[Rik Koncel-Kedziorski**, Subhro Roy**, Aida Amini, Nate Kushman and Hannaneh Hajishirzi.](https://aclanthology.org/N16-1136.pdf)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Contributions
Special thanks to Associate Professor Marwan Torki and all my colleagues in CC491N (NLP) class for helping me translate this dataset. | 4,012 | [
[
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0.040191650390625,
-0.06365966796875,
-0.06658935546875,
-0.059326171875,
0.00612... |
BeIR/hotpotqa-generated-queries | 2022-10-23T06:15:30.000Z | [
"task_categories:text-retrieval",
"task_ids:entity-linking-retrieval",
"task_ids:fact-checking-retrieval",
"multilinguality:monolingual",
"language:en",
"license:cc-by-sa-4.0",
"region:us"
] | BeIR | null | null | 0 | 3 | 2022-06-17T13:20:35 | ---
annotations_creators: []
language_creators: []
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
paperswithcode_id: beir
pretty_name: BEIR Benchmark
size_categories:
msmarco:
- 1M<n<10M
trec-covid:
- 100k<n<1M
nfcorpus:
- 1K<n<10K
nq:
- 1M<n<10M
hotpotqa:
- 1M<n<10M
fiqa:
- 10K<n<100K
arguana:
- 1K<n<10K
touche-2020:
- 100K<n<1M
cqadupstack:
- 100K<n<1M
quora:
- 100K<n<1M
dbpedia:
- 1M<n<10M
scidocs:
- 10K<n<100K
fever:
- 1M<n<10M
climate-fever:
- 1M<n<10M
scifact:
- 1K<n<10K
source_datasets: []
task_categories:
- text-retrieval
- zero-shot-retrieval
- information-retrieval
- zero-shot-information-retrieval
task_ids:
- passage-retrieval
- entity-linking-retrieval
- fact-checking-retrieval
- tweet-retrieval
- citation-prediction-retrieval
- duplication-question-retrieval
- argument-retrieval
- news-retrieval
- biomedical-information-retrieval
- question-answering-retrieval
---
# Dataset Card for BEIR Benchmark
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/UKPLab/beir
- **Repository:** https://github.com/UKPLab/beir
- **Paper:** https://openreview.net/forum?id=wCu6T5xFjeJ
- **Leaderboard:** https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns
- **Point of Contact:** nandan.thakur@uwaterloo.ca
### Dataset Summary
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
- Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact)
- Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/)
- Bio-Medical IR: [TREC-COVID](https://ir.nist.gov/covidSubmit/index.html), [BioASQ](http://bioasq.org), [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/)
- News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html)
- Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data)
- Duplicate Question Retrieval: [Quora](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs), [CqaDupstack](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/)
- Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs)
- Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html)
- Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/)
All these datasets have been preprocessed and can be used for your experiments.
```python
```
### Supported Tasks and Leaderboards
The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/).
### Languages
All tasks are in English (`en`).
## Dataset Structure
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
- `corpus` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with three fields `_id` with unique document identifier, `title` with document title (optional) and `text` with document paragraph or passage. For example: `{"_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}`
- `queries` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with two fields `_id` with unique query identifier and `text` with query text. For example: `{"_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}`
- `qrels` file: a `.tsv` file (tab-seperated) that contains three columns, i.e. the `query-id`, `corpus-id` and `score` in this order. Keep 1st row as header. For example: `q1 doc1 1`
### Data Instances
A high level example of any beir dataset:
```python
corpus = {
"doc1" : {
"title": "Albert Einstein",
"text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \
one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \
its influence on the philosophy of science. He is best known to the general public for his mass–energy \
equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \
Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \
of the photoelectric effect', a pivotal step in the development of quantum theory."
},
"doc2" : {
"title": "", # Keep title an empty string if not present
"text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \
malted barley. The two main varieties are German Weißbier and Belgian witbier; other types include Lambic (made\
with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)."
},
}
queries = {
"q1" : "Who developed the mass-energy equivalence formula?",
"q2" : "Which beer is brewed with a large proportion of wheat?"
}
qrels = {
"q1" : {"doc1": 1},
"q2" : {"doc2": 1},
}
```
### Data Fields
Examples from all configurations have the following features:
### Corpus
- `corpus`: a `dict` feature representing the document title and passage text, made up of:
- `_id`: a `string` feature representing the unique document id
- `title`: a `string` feature, denoting the title of the document.
- `text`: a `string` feature, denoting the text of the document.
### Queries
- `queries`: a `dict` feature representing the query, made up of:
- `_id`: a `string` feature representing the unique query id
- `text`: a `string` feature, denoting the text of the query.
### Qrels
- `qrels`: a `dict` feature representing the query document relevance judgements, made up of:
- `_id`: a `string` feature representing the query id
- `_id`: a `string` feature, denoting the document id.
- `score`: a `int32` feature, denoting the relevance judgement between query and document.
### Data Splits
| Dataset | Website| BEIR-Name | Type | Queries | Corpus | Rel D/Q | Down-load | md5 |
| -------- | -----| ---------| --------- | ----------- | ---------| ---------| :----------: | :------:|
| MSMARCO | [Homepage](https://microsoft.github.io/msmarco/)| ``msmarco`` | ``train``<br>``dev``<br>``test``| 6,980 | 8.84M | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/msmarco.zip) | ``444067daf65d982533ea17ebd59501e4`` |
| TREC-COVID | [Homepage](https://ir.nist.gov/covidSubmit/index.html)| ``trec-covid``| ``test``| 50| 171K| 493.5 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/trec-covid.zip) | ``ce62140cb23feb9becf6270d0d1fe6d1`` |
| NFCorpus | [Homepage](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) | ``nfcorpus`` | ``train``<br>``dev``<br>``test``| 323 | 3.6K | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip) | ``a89dba18a62ef92f7d323ec890a0d38d`` |
| BioASQ | [Homepage](http://bioasq.org) | ``bioasq``| ``train``<br>``test`` | 500 | 14.91M | 8.05 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#2-bioasq) |
| NQ | [Homepage](https://ai.google.com/research/NaturalQuestions) | ``nq``| ``train``<br>``test``| 3,452 | 2.68M | 1.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nq.zip) | ``d4d3d2e48787a744b6f6e691ff534307`` |
| HotpotQA | [Homepage](https://hotpotqa.github.io) | ``hotpotqa``| ``train``<br>``dev``<br>``test``| 7,405 | 5.23M | 2.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/hotpotqa.zip) | ``f412724f78b0d91183a0e86805e16114`` |
| FiQA-2018 | [Homepage](https://sites.google.com/view/fiqa/) | ``fiqa`` | ``train``<br>``dev``<br>``test``| 648 | 57K | 2.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fiqa.zip) | ``17918ed23cd04fb15047f73e6c3bd9d9`` |
| Signal-1M(RT) | [Homepage](https://research.signal-ai.com/datasets/signal1m-tweetir.html)| ``signal1m`` | ``test``| 97 | 2.86M | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#4-signal-1m) |
| TREC-NEWS | [Homepage](https://trec.nist.gov/data/news2019.html) | ``trec-news`` | ``test``| 57 | 595K | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#1-trec-news) |
| ArguAna | [Homepage](http://argumentation.bplaced.net/arguana/data) | ``arguana``| ``test`` | 1,406 | 8.67K | 1.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/arguana.zip) | ``8ad3e3c2a5867cdced806d6503f29b99`` |
| Touche-2020| [Homepage](https://webis.de/events/touche-20/shared-task-1.html) | ``webis-touche2020``| ``test``| 49 | 382K | 19.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/webis-touche2020.zip) | ``46f650ba5a527fc69e0a6521c5a23563`` |
| CQADupstack| [Homepage](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) | ``cqadupstack``| ``test``| 13,145 | 457K | 1.4 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/cqadupstack.zip) | ``4e41456d7df8ee7760a7f866133bda78`` |
| Quora| [Homepage](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) | ``quora``| ``dev``<br>``test``| 10,000 | 523K | 1.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/quora.zip) | ``18fb154900ba42a600f84b839c173167`` |
| DBPedia | [Homepage](https://github.com/iai-group/DBpedia-Entity/) | ``dbpedia-entity``| ``dev``<br>``test``| 400 | 4.63M | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/dbpedia-entity.zip) | ``c2a39eb420a3164af735795df012ac2c`` |
| SCIDOCS| [Homepage](https://allenai.org/data/scidocs) | ``scidocs``| ``test``| 1,000 | 25K | 4.9 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scidocs.zip) | ``38121350fc3a4d2f48850f6aff52e4a9`` |
| FEVER | [Homepage](http://fever.ai) | ``fever``| ``train``<br>``dev``<br>``test``| 6,666 | 5.42M | 1.2| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fever.zip) | ``5a818580227bfb4b35bb6fa46d9b6c03`` |
| Climate-FEVER| [Homepage](http://climatefever.ai) | ``climate-fever``|``test``| 1,535 | 5.42M | 3.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/climate-fever.zip) | ``8b66f0a9126c521bae2bde127b4dc99d`` |
| SciFact| [Homepage](https://github.com/allenai/scifact) | ``scifact``| ``train``<br>``test``| 300 | 5K | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scifact.zip) | ``5f7d1de60b170fc8027bb7898e2efca1`` |
| Robust04 | [Homepage](https://trec.nist.gov/data/robust/04.guidelines.html) | ``robust04``| ``test``| 249 | 528K | 69.9 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#3-robust04) |
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
Cite as:
```
@inproceedings{
thakur2021beir,
title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models},
author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych},
booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},
year={2021},
url={https://openreview.net/forum?id=wCu6T5xFjeJ}
}
```
### Contributions
Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset. | 13,988 | [
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valurank/Adult-content-dataset | 2023-01-19T02:40:10.000Z | [
"task_categories:text-classification",
"multilinguality:monolingual",
"language:en",
"license:other",
"region:us"
] | valurank | null | null | 3 | 3 | 2022-06-17T21:54:46 | ---
license:
- other
language:
- en
multilinguality:
- monolingual
task_categories:
- text-classification
task_ids: []
---
# Dataset Card for Adult_Content_Detection
## Table of Contents
- [Dataset Description](#dataset-description)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Source Data](#source-data)
## Dataset Description
850 Articles descriptions classified into two different categories namely: Adult, and Non_Adult
## Languages
The text in the dataset is in English
## Dataset Structure
The dataset consists of two columns namely Description and Category.
The Description column consists of the overview of the article and the Category column consists of the class each article belongs to
## Source Data
The dataset is scrapped across different platforms
| 803 | [
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huggingartists/headie-one | 2022-10-25T10:32:29.000Z | [
"language:en",
"huggingartists",
"lyrics",
"region:us"
] | huggingartists | This dataset is designed to generate lyrics with HuggingArtists. | @InProceedings{huggingartists:dataset,
title = {Lyrics dataset},
author={Aleksey Korshuk
},
year={2021}
} | 1 | 3 | 2022-06-20T15:09:53 | ---
language:
- en
tags:
- huggingartists
- lyrics
---
# Dataset Card for "huggingartists/headie-one"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [How to use](#how-to-use)
- [Dataset Structure](#dataset-structure)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [About](#about)
## Dataset Description
- **Homepage:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
- **Repository:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of the generated dataset:** 0.679898 MB
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/f803e312226f5034989742ff1fb4b583.1000x1000x1.jpg')">
</div>
</div>
<a href="https://huggingface.co/huggingartists/headie-one">
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
</a>
<div style="text-align: center; font-size: 16px; font-weight: 800">Headie One</div>
<a href="https://genius.com/artists/headie-one">
<div style="text-align: center; font-size: 14px;">@headie-one</div>
</a>
</div>
### Dataset Summary
The Lyrics dataset parsed from Genius. This dataset is designed to generate lyrics with HuggingArtists.
Model is available [here](https://huggingface.co/huggingartists/headie-one).
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
en
## How to use
How to load this dataset directly with the datasets library:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/headie-one")
```
## Dataset Structure
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"text": "Look, I was gonna go easy on you\nNot to hurt your feelings\nBut I'm only going to get this one chance\nSomething's wrong, I can feel it..."
}
```
### Data Fields
The data fields are the same among all splits.
- `text`: a `string` feature.
### Data Splits
| train |validation|test|
|------:|---------:|---:|
|224| -| -|
'Train' can be easily divided into 'train' & 'validation' & 'test' with few lines of code:
```python
from datasets import load_dataset, Dataset, DatasetDict
import numpy as np
datasets = load_dataset("huggingartists/headie-one")
train_percentage = 0.9
validation_percentage = 0.07
test_percentage = 0.03
train, validation, test = np.split(datasets['train']['text'], [int(len(datasets['train']['text'])*train_percentage), int(len(datasets['train']['text'])*(train_percentage + validation_percentage))])
datasets = DatasetDict(
{
'train': Dataset.from_dict({'text': list(train)}),
'validation': Dataset.from_dict({'text': list(validation)}),
'test': Dataset.from_dict({'text': list(test)})
}
)
```
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@InProceedings{huggingartists,
author={Aleksey Korshuk}
year=2022
}
```
## About
*Built by Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
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Nexdata/Indian_English_Speech_Data_by_Mobile_Phone | 2023-08-28T08:39:25.000Z | [
"region:us"
] | Nexdata | null | null | 0 | 3 | 2022-06-22T07:47:10 | ---
YAML tags:
- copy-paste the tags obtained with the tagging app: https://github.com/huggingface/datasets-tagging
---
# Dataset Card for Nexdata/Indian_English_Speech_Data_by_Mobile_Phone
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://www.nexdata.ai/datasets/940?source=Huggingface
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
Indian English audio data captured by mobile phones, 1,012 hours in total, recorded by 2,100 Indian native speakers. The recorded text is designed by linguistic experts, covering generic, interactive, on-board, home and other categories. The text has been proofread manually with high accuracy; this data set can be used for automatic speech recognition, machine translation, and voiceprint recognition.
For more details, please refer to the link: https://www.nexdata.ai/datasets/940?source=Huggingface
### Supported Tasks and Leaderboards
automatic-speech-recognition, audio-speaker-identification: The dataset can be used to train a model for Automatic Speech Recognition (ASR).
### Languages
Indian English
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
Commerical License: https://drive.google.com/file/d/1saDCPm74D4UWfBL17VbkTsZLGfpOQj1J/view?usp=sharing
### Citation Information
[More Information Needed]
### Contributions
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polinaeterna/audiofolder_example | 2022-09-22T17:15:18.000Z | [
"region:us"
] | polinaeterna | null | null | 0 | 3 | 2022-06-22T12:07:33 | Entry not found | 15 | [
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0.0379028... |
eugenetanjc/speech_accent_1000 | 2022-06-23T13:58:26.000Z | [
"region:us"
] | eugenetanjc | null | null | 0 | 3 | 2022-06-23T13:57:02 | Entry not found | 15 | [
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PedroDKE/LibriS2S | 2023-03-23T13:28:39.000Z | [
"task_categories:text-to-speech",
"task_categories:automatic-speech-recognition",
"task_categories:translation",
"multilinguality:multilingual",
"size_categories:10K<n<100K",
"language:en",
"language:de",
"license:cc-by-nc-sa-4.0",
"LibriS2S",
"LibrivoxDeEn",
"Speech-to-Speech translation",
"L... | PedroDKE | null | null | 1 | 3 | 2022-06-23T14:39:57 | ---
annotations_creators: []
language:
- en
- de
language_creators: []
license:
- cc-by-nc-sa-4.0
multilinguality:
- multilingual
pretty_name: LibriS2S German-English Speech and Text pairs
size_categories:
- 10K<n<100K
source_datasets: []
tags:
- LibriS2S
- LibrivoxDeEn
- Speech-to-Speech translation
- LREC2022
task_categories:
- text-to-speech
- automatic-speech-recognition
- translation
task_ids: []
---
# LibriS2S
This repo contains scripts and alignment data to create a dataset build further upon [librivoxDeEn](https://www.cl.uni-heidelberg.de/statnlpgroup/librivoxdeen/) such that it contains (German audio, German transcription, English audio, English transcription) quadruplets and can be used for Speech-to-Speech translation research. Because of this, the alignments are released under the same [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License](https://creativecommons.org/licenses/by-nc-sa/4.0/) <div>
These alignments were collected by downloading the English audiobooks and using [aeneas](https://github.com/readbeyond/aeneas) to align the book chapters to the transcripts. For more information read the original [paper](https://arxiv.org/abs/2204.10593) (Presented at LREC 2022)
### The data
The English/German audio are available in the folder EN/DE respectively and can be downloaded from [this onedrive](https://onedrive.live.com/embed?cid=DCE49ACC2BDA7D8C&resid=DCE49ACC2BDA7D8C%2115663&authkey=ANmUz8gRUoyxmjk). In case there are any problems with the download, feel free to open an issue here or on [GitHub](https://github.com/PedroDKE/LibriS2S). <br/>
The repo structure is as follow:
- Alignments : Contains all the alignments for each book and chapter
- DE : Contains the German audio for each chapter per book.
- EN : Contains the English audio for each chapter per book.
- Example : contains example files on for the scraping and aligning explanations that were used to build this dataset.
- LibrivoxDeEn_alignments : Contains the base alignments from the LibrivoxDeEn dataset. <br/>
In case you feel a part of the data is missing, feel free to open an issue!
The full zipfile is about 52 GB of size.
### Scraping a book from Librivox
To download all chapters from a librivox url the following command can be used:
```
python scrape_audio_from_librivox.py \
--url https://librivox.org/undine-by-friedrich-de-la-motte-fouque/ \
--save_dir ./examples
```
### Allign a book from Librivox with the text from LibrivoxDeEn
To allign the previously downloaded book with the txt files and tsv tables provided by LibrivoxDeEn the following command, based on the example provided with this repo, can be used:
```
python align_text_and_audio.py \
--text_dir ./example/en_text/ \
--audio_path ./example/audio_chapters/ \
--aeneas_path ./example/aeneas/ \
--en_audio_export_path ./example/sentence_level_audio/ \
--total_alignment_path ./example/bi-lingual-alignment/ \
--librivoxdeen_alignment ./example/undine_data.tsv \
--aeneas_head_max 120 \
--aeneas_tail_min 5 \
```
**note:** the example folder in this repo already contains the first two chapters from [Undine](https://librivox.org/undine-by-friedrich-de-la-motte-fouque/) scraped from librivox and their transcripts and (modified to only contain the first 2 chapters) tsv table retrieved from LibrivoxDeEn.
Additional data to align can be scraped by using the same file shown previously and combined with the provided data from LibriVoxDeEn
Additionally with this repo the full alignment for the 8 following books with following LibrivoxDeEn id's are also given:
[9](https://librivox.org/the-picture-of-dorian-gray-1891-version-by-oscar-wilde/), [10](https://librivox.org/pandoras-box-by-frank-wedekind/), [13](https://librivox.org/survivors-of-the-chancellor-by-jules-verne/), [18](https://librivox.org/undine-by-friedrich-de-la-motte-fouque/), [23](https://librivox.org/around-the-world-in-80-days-by-jules-verne/), [108](https://librivox.org/elective-affinities-by-johann-wolfgang-von-goethe/), [110](https://librivox.org/candide-by-voltaire-3/), [120](https://librivox.org/the-metamorphosis-by-franz-kafka/).
Other books such as [11](https://librivox.org/the-castle-of-otranto-by-horace-walpole/), [36](https://librivox.org/the-rider-on-the-white-horse-by-theodor-storm/), [67](https://librivox.org/frankenstein-or-the-modern-prometheus-1818-by-mary-wollstonecraft-shelley/) and [54](https://librivox.org/white-nights-other-stories-by-fyodor-dostoyevsky/) are also inside of the librivoxDeEn dataset but the chapters do not correspond in a 1:1 mannner(for example: the German version of book 67 has 27 chapters but the English version has 29 and thus need to be re-aligned before the allignment script in this repo will work). Therefore these alignments are given but might have be different if you scrape them yourselves as the re-alignments might be different for you.
### Metrics on the alignment given in this repo.
Using the alignments given in this repo some metrics were collected and quickly displayed here, for this table and the next figure the books which were manually alligned, although provided in the zip, were not accounted for, but the full table can be found in the original paper.
| | German | English |
| :---: | :-: | :-: |
|number of files | 18868 | 18868 |
|total time (hh:mm:ss) | 39:11:08 | 40:52:31 |
|Speakers | 41 |22 |
note: the speakers were counted for each book seperatly so some speakers might be counter more than once.
the number of hours for each book aligned in this repo:<br>
<img src="https://user-images.githubusercontent.com/43861296/122250648-1f5f7f80-ceca-11eb-84fd-344a2261bf47.png" width="500">
when using this work, please cite the original paper and the LibrivoxDeEn authors
```
@inproceedings{jeuris-niehues-2022-libris2s,
title = "{L}ibri{S}2{S}: A {G}erman-{E}nglish Speech-to-Speech Translation Corpus",
author = "Jeuris, Pedro and
Niehues, Jan",
booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference",
month = jun,
year = "2022",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2022.lrec-1.98",
pages = "928--935",
abstract = "Recently, we have seen an increasing interest in the area of speech-to-text translation. This has led to astonishing improvements in this area. In contrast, the activities in the area of speech-to-speech translation is still limited, although it is essential to overcome the language barrier. We believe that one of the limiting factors is the availability of appropriate training data. We address this issue by creating LibriS2S, to our knowledge the first publicly available speech-to-speech training corpus between German and English. For this corpus, we used independently created audio for German and English leading to an unbiased pronunciation of the text in both languages. This allows the creation of a new text-to-speech and speech-to-speech translation model that directly learns to generate the speech signal based on the pronunciation of the source language. Using this created corpus, we propose Text-to-Speech models based on the example of the recently proposed FastSpeech 2 model that integrates source language information. We do this by adapting the model to take information such as the pitch, energy or transcript from the source speech as additional input.",
}
```
```
@article{beilharz19,
title = {LibriVoxDeEn: A Corpus for German-to-English Speech Translation and Speech Recognition},
author = {Beilharz, Benjamin and Sun, Xin and Karimova, Sariya and Riezler, Stefan},
journal = {Proceedings of the Language Resources and Evaluation Conference},
journal-abbrev = {LREC},
year = {2020},
city = {Marseille, France},
url = {https://arxiv.org/pdf/1910.07924.pdf}
}
```
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rkstgr/mtg-jamendo | 2022-07-22T12:56:25.000Z | [
"size_categories:10K<n<100K",
"source_datasets:original",
"license:apache-2.0",
"region:us"
] | rkstgr | Repackaging of the MTG Jamendo dataset.
We present the MTG-Jamendo Dataset, a new open dataset for music auto-tagging.
It is built using music available at Jamendo under Creative Commons licenses and tags provided by content creators.
The dataset contains over 55,000 full audio tracks with 195 tags from genre, instrument, and mood/theme categories. | @conference {bogdanov2019mtg,
author = "Bogdanov, Dmitry and Won, Minz and Tovstogan, Philip and Porter, Alastair and Serra, Xavier",
title = "The MTG-Jamendo Dataset for Automatic Music Tagging",
booktitle = "Machine Learning for Music Discovery Workshop, International Conference on Machine Learning (ICML 2019)",
year = "2019",
address = "Long Beach, CA, United States",
url = "http://hdl.handle.net/10230/42015"
} | 2 | 3 | 2022-06-24T13:51:38 | ---
license:
- apache-2.0
size_categories:
- 10K<n<100K
source_datasets:
- original
pretty_name: MTG Jamendo
---
# Dataset Card for MTG Jamendo Dataset
## Dataset Description
- **Repository:** [MTG Jamendo dataset repository](https://github.com/MTG/mtg-jamendo-dataset)
### Dataset Summary
MTG-Jamendo Dataset, a new open dataset for music auto-tagging. It is built using music available at Jamendo under Creative Commons licenses and tags provided by content uploaders. The dataset contains over 55,000 full audio tracks with 195 tags from genre, instrument, and mood/theme categories. We provide elaborated data splits for researchers and report the performance of a simple baseline approach on five different sets of tags: genre, instrument, mood/theme, top-50, and overall.
## Dataset structure
### Data Fields
- `id`: an integer containing the id of the track
- `artist_id`: an integer containing the id of the artist
- `album_id`: an integer containing the id of the album
- `duration_in_sec`: duration of the track as a float
- `genres`: list of strings, describing genres the track is assigned to
- `instruments`: list of strings for the main instruments of the track
- `moods`: list of strings, describing the moods the track is assigned to
- `audio`: audio of the track
### Data Splits
This dataset has 2 balanced splits: _train_ (90%) and _validation_ (10%)
### Licensing Information
This dataset version 1.0.0 is released under the [Apache-2.0 License](http://www.apache.org/licenses/LICENSE-2.0).
### Citation Information
```
@conference {bogdanov2019mtg,
author = "Bogdanov, Dmitry and Won, Minz and Tovstogan, Philip and Porter, Alastair and Serra, Xavier",
title = "The MTG-Jamendo Dataset for Automatic Music Tagging",
booktitle = "Machine Learning for Music Discovery Workshop, International Conference on Machine Learning (ICML 2019)",
year = "2019",
address = "Long Beach, CA, United States",
url = "http://hdl.handle.net/10230/42015"
}
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rjac/kaggle-entity-annotated-corpus-ner-dataset-oversampled | 2022-06-26T01:48:24.000Z | [
"region:us"
] | rjac | null | null | 0 | 3 | 2022-06-24T20:32:51 | this dataset is the same as [rjac/kaggle-entity-annotated-corpus-ner-dataset](https://huggingface.co/datasets/rjac/kaggle-entity-annotated-corpus-ner-dataset)
with oversampled instances of 'ART', 'EVE'and 'NAT' entities (25K of all three classes).
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autoevaluate/xsum-sample | 2022-08-22T13:02:13.000Z | [
"region:us"
] | autoevaluate | null | null | 0 | 3 | 2022-06-25T08:58:40 | Entry not found | 15 | [
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sophieb/dynamically_generated_hate_speech_dataset | 2022-06-25T18:02:18.000Z | [
"region:us"
] | sophieb | null | null | 0 | 3 | 2022-06-25T17:48:05 | # Dataset card for dynamically generated dataset hate speech detection
## Dataset summary
This dataset that was dynamically generated for training and improving hate speech detection models. A group of trained annotators generated and labeled challenging examples so that hate speech models could be tricked and consequently improved. This dataset contains about 40,000 examples of which 54% are labeled as hate speech. It also provides the target of hate speech, including vulnerable, marginalized, and discriminated groups. Overall, this is a balanced dataset which makes it different from the already available hate speech datasets you can find on the web.
This dataset was presented in the article [Learning from the Worst: Dynamically Generated Datasets to Improve Online Hate Detection published](https://aclanthology.org/2021.acl-long.132.pdf) in 2021. The article describes the process for generating and annotating the data. Also, it describes how they used the generated data for training and improving hate speech detection models. The full author list is the following: Bertie Vidgen (The Alan Turing Institute), Tristan Thrush (Facebook), Zeerak Waseem (University of Sheffield), and Douwe Kiela (Facebook).
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... |
ConvLab/metalwoz | 2022-11-25T09:11:36.000Z | [
"task_categories:conversational",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"language:en",
"region:us"
] | ConvLab | null | null | 1 | 3 | 2022-06-28T01:51:55 | ---
language:
- en
license: []
multilinguality:
- monolingual
pretty_name: MetaLWOZ
size_categories:
- 10K<n<100K
task_categories:
- conversational
---
# Dataset Card for MetaLWOZ
- **Repository:** https://www.microsoft.com/en-us/research/project/metalwoz/
- **Paper:** https://www.microsoft.com/en-us/research/publication/results-of-the-multi-domain-task-completion-dialog-challenge/
- **Leaderboard:** None
- **Who transforms the dataset:** Qi Zhu(zhuq96 at gmail dot com)
To use this dataset, you need to install [ConvLab-3](https://github.com/ConvLab/ConvLab-3) platform first. Then you can load the dataset via:
```
from convlab.util import load_dataset, load_ontology, load_database
dataset = load_dataset('metalwoz')
ontology = load_ontology('metalwoz')
database = load_database('metalwoz')
```
For more usage please refer to [here](https://github.com/ConvLab/ConvLab-3/tree/master/data/unified_datasets).
### Dataset Summary
This large dataset was created by crowdsourcing 37,884 goal-oriented dialogs, covering 227 tasks in 47 domains. Domains include bus schedules, apartment search, alarm setting, banking, and event reservation. Each dialog was grounded in a scenario with roles, pairing a person acting as the bot and a person acting as the user. (This is the Wizard of Oz reference—using people behind the curtain who act as the machine). Each pair were given a domain and a task, and instructed to converse for 10 turns to satisfy the user’s queries. For example, if a user asked if a bus stop was operational, the bot would respond that the bus stop had been moved two blocks north, which starts a conversation that addresses the user’s actual need.
- **How to get the transformed data from original data:**
- Download [metalwoz-v1.zip](https://www.microsoft.com/en-us/download/58389) and [metalwoz-test-v1.zip](https://www.microsoft.com/en-us/download/100639).
- Run `python preprocess.py` in the current directory.
- **Main changes of the transformation:**
- `CITI_INFO`, `HOME_BOT`, `NAME_SUGGESTER`, and `TIME_ZONE` are randomly selected as the valiation domains.
- Remove the first utterance by the system since it is "Hello how may I help you?" in most case.
- Add goal description according to the original task description: user_role+user_prompt+system_role+system_prompt.
- **Annotations:**
- domain, goal
### Supported Tasks and Leaderboards
RG, User simulator
### Languages
English
### Data Splits
| split | dialogues | utterances | avg_utt | avg_tokens | avg_domains | cat slot match(state) | cat slot match(goal) | cat slot match(dialogue act) | non-cat slot span(dialogue act) |
|------------|-------------|--------------|-----------|--------------|---------------|-------------------------|------------------------|--------------------------------|-----------------------------------|
| train | 34261 | 357092 | 10.42 | 7.48 | 1 | - | - | - | - |
| validation | 3623 | 37060 | 10.23 | 6.59 | 1 | - | - | - | - |
| test | 2319 | 23882 | 10.3 | 7.96 | 1 | - | - | - | - |
| all | 40203 | 418034 | 10.4 | 7.43 | 1 | - | - | - | - |
51 domains: ['AGREEMENT_BOT', 'ALARM_SET', 'APARTMENT_FINDER', 'APPOINTMENT_REMINDER', 'AUTO_SORT', 'BANK_BOT', 'BUS_SCHEDULE_BOT', 'CATALOGUE_BOT', 'CHECK_STATUS', 'CITY_INFO', 'CONTACT_MANAGER', 'DECIDER_BOT', 'EDIT_PLAYLIST', 'EVENT_RESERVE', 'GAME_RULES', 'GEOGRAPHY', 'GUINESS_CHECK', 'HOME_BOT', 'HOW_TO_BASIC', 'INSURANCE', 'LIBRARY_REQUEST', 'LOOK_UP_INFO', 'MAKE_RESTAURANT_RESERVATIONS', 'MOVIE_LISTINGS', 'MUSIC_SUGGESTER', 'NAME_SUGGESTER', 'ORDER_PIZZA', 'PET_ADVICE', 'PHONE_PLAN_BOT', 'PHONE_SETTINGS', 'PLAY_TIMES', 'POLICY_BOT', 'PRESENT_IDEAS', 'PROMPT_GENERATOR', 'QUOTE_OF_THE_DAY_BOT', 'RESTAURANT_PICKER', 'SCAM_LOOKUP', 'SHOPPING', 'SKI_BOT', 'SPORTS_INFO', 'STORE_DETAILS', 'TIME_ZONE', 'UPDATE_CALENDAR', 'UPDATE_CONTACT', 'WEATHER_CHECK', 'WEDDING_PLANNER', 'WHAT_IS_IT', 'BOOKING_FLIGHT', 'HOTEL_RESERVE', 'TOURISM', 'VACATION_IDEAS']
- **cat slot match**: how many values of categorical slots are in the possible values of ontology in percentage.
- **non-cat slot span**: how many values of non-categorical slots have span annotation in percentage.
### Citation
```
@inproceedings{li2020results,
author = {Li, Jinchao and Peng, Baolin and Lee, Sungjin and Gao, Jianfeng and Takanobu, Ryuichi and Zhu, Qi and Minlie Huang and Schulz, Hannes and Atkinson, Adam and Adada, Mahmoud},
title = {Results of the Multi-Domain Task-Completion Dialog Challenge},
booktitle = {Proceedings of the 34th AAAI Conference on Artificial Intelligence, Eighth Dialog System Technology Challenge Workshop},
year = {2020},
month = {February},
url = {https://www.microsoft.com/en-us/research/publication/results-of-the-multi-domain-task-completion-dialog-challenge/},
}
```
### Licensing Information
[Microsoft Research Data License Agreement](https://msropendata-web-api.azurewebsites.net/licenses/2f933be3-284d-500b-7ea3-2aa2fd0f1bb2/view)
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ConvLab/dailydialog | 2022-11-25T09:06:49.000Z | [
"task_categories:conversational",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"language:en",
"license:cc-by-nc-sa-4.0",
"arxiv:1710.03957",
"region:us"
] | ConvLab | null | null | 0 | 3 | 2022-06-28T02:07:17 | ---
language:
- en
license:
- cc-by-nc-sa-4.0
multilinguality:
- monolingual
pretty_name: DailyDialog
size_categories:
- 10K<n<100K
task_categories:
- conversational
---
# Dataset Card for DailyDialog
- **Repository:** http://yanran.li/dailydialog
- **Paper:** https://arxiv.org/pdf/1710.03957.pdf
- **Leaderboard:** None
- **Who transforms the dataset:** Qi Zhu(zhuq96 at gmail dot com)
To use this dataset, you need to install [ConvLab-3](https://github.com/ConvLab/ConvLab-3) platform first. Then you can load the dataset via:
```
from convlab.util import load_dataset, load_ontology, load_database
dataset = load_dataset('dailydialog')
ontology = load_ontology('dailydialog')
database = load_database('dailydialog')
```
For more usage please refer to [here](https://github.com/ConvLab/ConvLab-3/tree/master/data/unified_datasets).
### Dataset Summary
DailyDialog is a high-quality multi-turn dialog dataset. It is intriguing in several aspects. The language is human-written and less noisy. The dialogues in the dataset reflect our daily communication way and cover various topics about our daily life. We also manually label the developed dataset with communication intention and emotion information.
- **How to get the transformed data from original data:**
- Download [ijcnlp_dailydialog.zip](http://yanran.li/files/ijcnlp_dailydialog.zip).
- Run `python preprocess.py` in the current directory.
- **Main changes of the transformation:**
- Use `topic` annotation as `domain`. If duplicated dialogs are annotated with different topics, use the most frequent one.
- Use `intent` annotation as `binary` dialogue act.
- Retain emotion annotation in the `emotion` field of each turn.
- Use nltk to remove space before punctuation: `utt = ' '.join([detokenizer.detokenize(word_tokenize(s)) for s in sent_tokenize(utt)])`.
- Replace `" ’ "` with `"'"`: `utt = utt.replace(' ’ ', "'")`.
- Add space after full-stop
- **Annotations:**
- intent, emotion
### Supported Tasks and Leaderboards
NLU, NLG
### Languages
English
### Data Splits
| split | dialogues | utterances | avg_utt | avg_tokens | avg_domains | cat slot match(state) | cat slot match(goal) | cat slot match(dialogue act) | non-cat slot span(dialogue act) |
|------------|-------------|--------------|-----------|--------------|---------------|-------------------------|------------------------|--------------------------------|-----------------------------------|
| train | 11118 | 87170 | 7.84 | 11.22 | 1 | - | - | - | - |
| validation | 1000 | 8069 | 8.07 | 11.16 | 1 | - | - | - | - |
| test | 1000 | 7740 | 7.74 | 11.36 | 1 | - | - | - | - |
| all | 13118 | 102979 | 7.85 | 11.22 | 1 | - | - | - | - |
10 domains: ['Ordinary Life', 'School Life', 'Culture & Education', 'Attitude & Emotion', 'Relationship', 'Tourism', 'Health', 'Work', 'Politics', 'Finance']
- **cat slot match**: how many values of categorical slots are in the possible values of ontology in percentage.
- **non-cat slot span**: how many values of non-categorical slots have span annotation in percentage.
### Citation
```
@InProceedings{li2017dailydialog,
author = {Li, Yanran and Su, Hui and Shen, Xiaoyu and Li, Wenjie and Cao, Ziqiang and Niu, Shuzi},
title = {DailyDialog: A Manually Labelled Multi-turn Dialogue Dataset},
booktitle = {Proceedings of The 8th International Joint Conference on Natural Language Processing (IJCNLP 2017)},
year = {2017}
}
```
### Licensing Information
[**CC BY-NC-SA 4.0**](https://creativecommons.org/licenses/by-nc-sa/4.0/) | 4,159 | [
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ctu-aic/enfever_nli | 2022-06-29T13:05:10.000Z | [
"region:us"
] | ctu-aic | EnfeverNLI is a NLI version of the fever dataset | todo | 1 | 3 | 2022-06-29T12:55:00 | Entry not found | 15 | [
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launch/ampere | 2022-11-09T01:57:52.000Z | [
"task_categories:text-classification",
"annotations_creators:expert-generated",
"multilinguality:monolingual",
"language:en",
"license:cc-by-4.0",
"region:us"
] | launch | null | null | 0 | 3 | 2022-07-01T02:29:23 | ---
annotations_creators:
- expert-generated
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
task_categories:
- text-classification
task_ids: []
pretty_name: AMPERE
---
# Dataset Card for AMPERE
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Structure](#dataset-structure)
- [Dataset Creation](#dataset-creation)
## Dataset Description
This dataset is released together with our NAACL 2019 Paper "[`Argument Mining for Understanding Peer Reviews`](https://aclanthology.org/N19-1219/)". If you find our work useful, please cite:
```
@inproceedings{hua-etal-2019-argument,
title = "Argument Mining for Understanding Peer Reviews",
author = "Hua, Xinyu and
Nikolov, Mitko and
Badugu, Nikhil and
Wang, Lu",
booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N19-1219",
doi = "10.18653/v1/N19-1219",
pages = "2131--2137",
}
```
This dataset includes 400 scientific peer reviews collected from ICLR 2018 hosted at the Openreview platform. Each review is segmented into multiple propositions. We include the original untokenized text for each proposition. Each proposition is labeled as one of the following types:
- **evaluation**: a proposition that is not objectively verifiable and does not require any action to be performed, such as qualitative judgement and interpretation of the paper, e.g. "The paper shows nice results on a number of small tasks."
- **request**: a proposition that is not objectively verifiable and suggests a course of action to be taken, such as recommendation and suggestion for new experiments, e.g. "I would really like to see how the method performs without this hack."
- **fact**: a proposition that is verifiable with objective evidence, such as mathematical conclusion and common knowledge of the field, e.g. "This work proposes a dynamic weight update scheme."
- **quote**: a quote from the paper or another source, e.g. "The author wrote 'where r is lower bound of feature norm'."
- **reference**: a proposition that refers to an objective evidence, such as URL link and citation, e.g. "see MuseGAN (Dong et al), MidiNet (Yang et al), etc."
- **non-arg**: a non-argumentative discourse unit that does not contribute to the overall agenda of the review, such as greetings, metadata, and clarification questions, e.g. "Aha, now I understand."
## Dataset Structure
The dataset is partitioned into train/val/test sets. Each set is uploaded as a jsonl format. Each line contains the following elements:
- `doc_id` (str): a unique id for review document
- `text` (list[str]): a list of segmented propositions
- `labels` (list[str]): a list of labels corresponding to the propositions
An example looks as follows.
```
{
"doc_id": "H1WORsdlG",
"text": [
"This paper addresses the important problem of understanding mathematically how GANs work.",
"The approach taken here is to look at GAN through the lense of the scattering transform.",
"Unfortunately the manuscrit submitted is very poorly written.",
"Introduction and flow of thoughts is really hard to follow.",
"In method sections, the text jumps from one concept to the next without proper definitions.",
"Sorry I stopped reading on page 3.",
"I suggest to rewrite this work before sending it to review.",
"Among many things: - For citations use citep and not citet to have () at the right places.",
"- Why does it seems -> Why does it seem etc.",
],
"labels": [
'fact',
'fact',
'evaluation',
'evaluation',
'evaluation',
'evaluation',
'request',
'request',
'request',
]
}
```
## Dataset Creation
For human annotators, they will be asked to first read the above definitions and controversial cases carefully. The dataset to be annotated consists of 400 reviews partitioned in 20 batches. Each annotator will follow the following steps for annotation:
- Step 1: Open a review file with a text editor. The unannotated review file contains only one line, please separate it into multiple lines with each line corresponding to one single proposition. Repeat the above actions on all 400 reviews.
- Step 2: Based on the segmented units, label the type for each proposition. Start labeling at the end of each file with the marker "## Labels:". Indicate the line number of the proposition first, then annotate the type, e.g. "1. evaluation" for the first proposition. Repeat the above actions on all 400 reviews.
A third annotator then resolves the disagreements between the two annotators on both segmentation and proposition type.
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Tritkoman/autotrain-data-Rusynpannonianpure | 2022-10-25T10:39:40.000Z | [
"task_categories:translation",
"language:en",
"language:es",
"region:us"
] | Tritkoman | null | null | 0 | 3 | 2022-07-01T05:20:02 | ---
language:
- en
- es
task_categories:
- translation
---
# AutoTrain Dataset for project: Rusynpannonianpure
## Dataset Descritpion
This dataset has been automatically processed by AutoTrain for project Rusynpannonianpure.
### Languages
The BCP-47 code for the dataset's language is en2es.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"source": "\"I came to the region to meet with the leaders of the parties and discuss the progress in normalizin[...]",
"target": "\"\u042f \u043f\u0440\u0438\u0448\u043e\u043b \u0434\u043e \u0440\u0435\u0491\u0438\u043e\u043d\u0443 \u043f\u0440\u0438\u0440\u0438\u0445\u0442\u0430\u0446 \u0448\u043b\u0457\u0434\u0443\u044e\u0446\u0438 \u0441\u0445\u043e\u0434 \u043b\u0438\u0434\u0435\u0440\u043e\u0445 \u0438 \u0431\u0435\u0448\u0435\u0434\u043e\u0432\u0430\u0446 \u043e \u043d\u0430\u043f\u0440\u0435\u0434\u043e\u0432\u0430\u043d\u044e \u0443 \u043d\u043e\u0440\u043c\u0430\u043b\u0438\u0437\u0430\u0446\u0438\u0457 \u043e\u0434\u043d\u043e\u0448\u0435[...]"
},
{
"source": "\"We had a very good discussion yesterday evening about the situation and it is normal to look for a [...]",
"target": "\"\u041c\u0430\u043b\u0438 \u0437\u043c\u0435 \u0454\u0434\u043d\u0443 \u043e\u0437\u0431\u0438\u043b\u044c\u043d\u0443 \u0440\u043e\u0437\u0433\u0432\u0430\u0440\u043a\u0443 \u0432\u0447\u0435\u0440\u0430 \u0432\u0435\u0447\u0430\u0440 \u043e \u0441\u0438\u0442\u0443\u0430\u0446\u0438\u0457 \u0438 \u043d\u043e\u0440\u043c\u0430\u043b\u043d\u043e \u0436\u0435 \u043f\u043e\u0442\u0440\u0435\u0431\u043d\u0435 \u0433\u043b\u0454\u0434\u0430\u0446 \u0440\u0438\u0448\u0435\u043d\u0454 \u043f\u0440\u0435\u0437 \u0434[...]"
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"source": "Value(dtype='string', id=None)",
"target": "Value(dtype='string', id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 3 |
| valid | 1 |
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joelniklaus/covid19_emergency_event | 2022-09-22T13:44:15.000Z | [
"task_categories:text-classification",
"task_ids:multi-label-classification",
"annotations_creators:found",
"annotations_creators:other",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"language:fr",
"language:hu"... | joelniklaus | null | null | 0 | 3 | 2022-07-01T11:26:15 | ---
annotations_creators:
- found
- other
language_creators:
- found
language:
- en
- fr
- hu
- it
- nb
- nl
- pl
license:
- cc0-1.0
multilinguality:
- multilingual
pretty_name: EXCEPTIUS Corpus
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-label-classification
---
# Dataset Card for EXCEPTIUS Corpus
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://exceptius.com/
- **Repository:** https://github.com/tommasoc80/COVID19_emergency_event
- **Paper:** Tziafas, G., de Saint-Phalle, E., de Vries, W., Egger, C., & Caselli, T. (2021). A Multilingual Approach to Identify and Classify Exceptional Measures against {COVID}-19. Proceedings of the Natural Legal Language Processing Workshop 2021, 46–62. https://doi.org/10.18653/v1/2021.nllp-1.5
- **Leaderboard:**
- **Point of Contact:** [Joel Niklaus](mailto:joel.niklaus.2@bfh.ch)
### Dataset Summary
This dataset presents a new corpus of legislative documents from 8 European countries (Beglium, France, Hunary, Italy, Netherlands, Norway, Poland, UK) in 7 languages (Dutch, English, French, Hungarian, Italian, Norwegian Bokmål, Polish) manually annotated for exceptional measures against COVID-19. The annotation was done on the sentence level.
### Supported Tasks and Leaderboards
The dataset can be used for multi-label text classification tasks.
### Languages
Dutch, English, French, Hungarian, Italian, Norwegian Bokmål, Polish
## Dataset Structure
### Data Instances
The file format is jsonl and three data splits are present (train, validation and test).
### Data Fields
The jsonl files have the following basic columns:
- `language`: The language of the sentence (set based on the country)
- `country`: The country of the sentence
- `text`: Sentence that has been annotated
The documents have been annotated with 8 labels, each label representing a specific measurement against COVID-19. Each label is represented by one boolean field in the jsonl file. The labels, i.e. the specific measure classes, are:
- `event1`: State of Emergency
- `event2`: Restrictions of fundamental rights and civil liberties
- `event3`: Restrictions of daily liberties
- `event4`: Closures / lockdown
- `event5`: Suspension of international cooperation and commitments
- `event6`: Police mobilization
- `event7`: Army mobilization
- `event8`: Government oversight
- `all_events`: an aggregate column containing all applicable events combined
### Data Splits
All annotated sentences combined have the following split:
- train: 3312 (80%)
- dev: 418 (10%)
- test: 418 (10%)
The splits have been performed on each country and have later been merged. Therefore, each split contains sentences from each country.
The following label distribution shows the number of occurrences per label per split. `total occurrences` sums up the previous rows (total number of events per split). `split size` is the number of sentences per split.
| Event | train | validation | test |
|:----------------------|----------:|-----------:|----------:|
| event1 | 383 | 54 | 47 |
| event2 | 253 | 39 | 42 |
| event3 | 412 | 70 | 62 |
| event4 | 617 | 75 | 93 |
| event5 | 52 | 4 | 6 |
| event6 | 15 | 2 | 1 |
| event7 | 45 | 4 | 5 |
| event8 | 146 | 21 | 19 |
| **total occurrences** | **1923** | **269** | **275** |
| **split size** | **3312** | **418** | **418** |
## Dataset Creation
### Curation Rationale
*"Investigate the potential of multilingual pretrained language models in order to
facilitate the analysis, exploration, and comparison of legal texts on COVID-19 exceptional measures"* (Tziafas et al., 2021)
### Source Data
#### Initial Data Collection and Normalization
*“The corpus collection process has been overseen by four political science experts working in partnership with national legal experts. All documents were retrieved from official governmental websites that publish legal acts. The identification of the relevant documents has been done by means of 4 keywords (i.e., “COVID”, “COVID-19”, “Coronavirus” and “Health emergency”). For each language, the corresponding language specific keywords were used. In this initial phase, we focus on a sample of 19 EEA countries on measures adopted at the national level. To do so, we identify publicly available links to relevant documents 2 plus UK and Switzerland. We could not find corresponding documents for two countries of the EEA (i.e., Bulgaria and Greece). All documents have been collected either by manually downloading them or by automatic scraping. For countries with more than one official language (e.g., Switzerland), legal acts were collected in all available languages.”*(Tziafas et al., 2021)
#### Who are the source language producers?
Politicians and legal experts have been involved in producing the language material.
### Annotations
#### Annotation process
*"A subset of 281 documents in eight languages has been selected for manual annotation. The annotation of the exceptional measures applies at sentence-level. The sample is based on the French, Polish, Dutch, English, Hungarian, Belgian, Italian, and Norwegian sub-corpora. Annotators were allowed to assign as many subclasses as they consider relevant to each sentence, but with a total of eight main classes of exceptional measures. Sentences can potentially entail multiple exceptional classes, making this a multi-label annotation task. The annotation process results in eight binary annotations per sentence, with 0 if the specific class is not identified within the sentence and 1 if it is. The annotation has been conducted by three experts in political science working under the supervision of the project’s Scientific Board. Since the annotators are not fluent in all languages and due to the impossibility of recruiting expert native speakers, some documents need to be translated into English to be manually annotated. No inter-annotator agreement study has been conducted in this initial phase. We intend to remedy this limitation in the project’s next development cycle. However, during the annotation phase, annotators met on a weekly basis to discuss ambiguous cases and the guidelines. Annotators are encouraged to propose new classes or subclasses. For a new (sub)class to be accepted, the measure should have been independently identified by the majority of the annotators. In this phase, no new classes were proposed."* (Tziafas et al., 2021)
#### Who are the annotators?
*"The annotation has been conducted by three experts in political science working under the supervision of the project’s Scientific Board."* (Tziafas et al., 2021)
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
Note that the information given in this dataset card refer to the dataset version as provided by Joel Niklaus and Veton Matoshi. The dataset at hand is intended to be part of a bigger benchmark dataset. Creating a benchmark dataset consisting of several other datasets from different sources requires postprocessing. Therefore, the structure of the dataset at hand, including the folder structure, may differ considerably from the original dataset. In addition to that, differences with regard to dataset statistics as give in the respective papers can be expected. The reader is advised to have a look at the conversion script ```convert_to_hf_dataset.py``` in order to retrace the steps for converting the original dataset into the present jsonl-format. For further information on the original dataset structure, we refer to the bibliographical references and the original Github repositories and/or web pages provided in this dataset card.
## Additional Information
### Dataset Curators
The names of the original dataset curators and creators can be found in references given below, in the section *Citation Information*.
Additional changes were made by Joel Niklaus ([Email](mailto:joel.niklaus.2@bfh.ch); [Github](https://github.com/joelniklaus)) and Veton Matoshi ([Email](mailto:veton.matoshi@bfh.ch); [Github](https://github.com/kapllan)).
### Licensing Information
Creative Commons Zero v1.0 Universal
### Citation Information
```
@inproceedings{tziafas-etal-2021-multilingual,
title = "A Multilingual Approach to Identify and Classify Exceptional Measures against {COVID}-19",
author = "Tziafas, Georgios and
de Saint-Phalle, Eugenie and
de Vries, Wietse and
Egger, Clara and
Caselli, Tommaso",
booktitle = "Proceedings of the Natural Legal Language Processing Workshop 2021",
month = nov,
year = "2021",
address = "Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.nllp-1.5",
pages = "46--62",
}
```
### Contributions
Thanks to [@JoelNiklaus](https://github.com/joelniklaus) and [@kapllan](https://github.com/kapllan) for adding this dataset.
| 10,501 | [
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0.03466796875,
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0.0266... |
FIdo-AI/ua-squad | 2022-07-09T20:55:51.000Z | [
"region:us"
] | FIdo-AI | null | null | 3 | 3 | 2022-07-03T15:28:24 | annotations_creators:
- crowdsourced
language:
- uk
language_creators:
- crowdsourced
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
paperswithcode_id: squad
pretty_name: ''
size_categories:
- 100K<n<1M
source_datasets:
- extended|squad_v2
task_categories:
- question-answering
task_ids:
- open-domain-qa
- extractive-qa
train-eval-index:
- col_mapping:
answers:
answer_start: answer_start
text: text
context: context
question: question
config: squad_v2
metrics:
- name: SQuAD v2
type: squad_v2
splits:
eval_split: validation
train_split: train
task: question-answering
task_id: extractive_question_answering
# Dataset Card for ua-squad
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** https://github.com/fido-ai/ua-datasets
- **Repository:** https://huggingface.co/datasets/FIdo-AI/ua-squad
- **Paper:** [Needs More Information]
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Needs More Information]
### Dataset Summary
Ukrainian translation of the Stanford Question Answering Dataset (SQuAD) 2.0
### Supported Tasks and Leaderboards
question-answering
### Languages
Ukrainian
## Dataset Structure
### Data Instances
[Needs More Information]
### Data Fields
[Needs More Information]
### Data Splits
[Needs More Information]
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
[Needs More Information] | 3,117 | [
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0.036376953125,
0.037750244140625,
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0.0... |
lyakaap/laion-mini-ja | 2022-07-05T02:30:45.000Z | [
"region:us"
] | lyakaap | null | null | 1 | 3 | 2022-07-04T23:18:55 | #samples=5007831
```
dataset = load_dataset('lyakaap/laion2B-japanese-subset', split='train')
dataset = dataset.remove_columns(['LANGUAGE', 'NSFW', 'LICENSE', 'SAMPLE_ID'])
dataset = dataset.filter(lambda x: x['HEIGHT'] <= 384 and x['WIDTH'] <= 384)
dataset = dataset.filter(lambda x: x['HEIGHT'] >= 128 and x['WIDTH'] >= 128)
dataset = dataset.filter(lambda x: x['similarity'] >= 0.31)
dataset.push_to_hub('lyakaap/laion-mini-ja', token='XXX')
``` | 449 | [
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Paul/hatecheck-polish | 2022-07-05T10:26:41.000Z | [
"task_categories:text-classification",
"task_ids:hate-speech-detection",
"annotations_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:pl",
"license:cc-by-4.0",
"arxiv:2206.09917",
"regi... | Paul | null | null | 1 | 3 | 2022-07-05T10:24:24 | ---
annotations_creators:
- crowdsourced
language_creators:
- expert-generated
language:
- pl
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: Polish HateCheck
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- hate-speech-detection
---
# Dataset Card for Multilingual HateCheck
## Dataset Description
Multilingual HateCheck (MHC) is a suite of functional tests for hate speech detection models in 10 different languages: Arabic, Dutch, French, German, Hindi, Italian, Mandarin, Polish, Portuguese and Spanish.
For each language, there are 25+ functional tests that correspond to distinct types of hate and challenging non-hate.
This allows for targeted diagnostic insights into model performance.
For more details, please refer to our paper about MHC, published at the 2022 Workshop on Online Abuse and Harms (WOAH) at NAACL 2022. If you are using MHC, please cite our work!
- **Paper:** Röttger et al. (2022) - Multilingual HateCheck: Functional Tests for Multilingual Hate Speech Detection Models. https://arxiv.org/abs/2206.09917
- **Repository:** https://github.com/rewire-online/multilingual-hatecheck
- **Point of Contact:** paul@rewire.online
## Dataset Structure
The csv format mostly matches the original HateCheck data, with some adjustments for specific languages.
**mhc_case_id**
The test case ID that is unique to each test case across languages (e.g., "mandarin-1305")
**functionality**
The shorthand for the functionality tested by the test case (e.g, "target_obj_nh"). The same functionalities are tested in all languages, except for Mandarin and Arabic, where non-Latin script required adapting the tests for spelling variations.
**test_case**
The test case text.
**label_gold**
The gold standard label ("hateful" or "non-hateful") of the test case. All test cases within a given functionality have the same gold standard label.
**target_ident**
Where applicable, the protected group that is targeted or referenced in the test case. All HateChecks cover seven target groups, but their composition varies across languages.
**ref_case_id**
For hateful cases, where applicable, the ID of the hateful case which was perturbed to generate this test case. For non-hateful cases, where applicable, the ID of the hateful case which is contrasted by this test case.
**ref_templ_id**
The equivalent to ref_case_id, but for template IDs.
**templ_id**
The ID of the template from which the test case was generated.
**case_templ**
The template from which the test case was generated (where applicable).
**gender_male** and **gender_female**
For gender-inflected languages (French, Spanish, Portuguese, Hindi, Arabic, Italian, Polish, German), only for cases where gender inflection is relevant, separate entries for gender_male and gender_female replace case_templ.
**label_annotated**
A list of labels given by the three annotators who reviewed the test case (e.g., "['hateful', 'hateful', 'hateful']").
**label_annotated_maj**
The majority vote of the three annotators (e.g., "hateful"). In some cases this differs from the gold label given by our language experts.
**disagreement_in_case**
True if label_annotated_maj does not match label_gold for the entry.
**disagreement_in_template**
True if the test case is generated from an IDENT template and there is at least one case with disagreement_in_case generated from the same template. This can be used to exclude entire templates from MHC. | 3,489 | [
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Paul/hatecheck-mandarin | 2022-07-05T10:32:33.000Z | [
"task_categories:text-classification",
"task_ids:hate-speech-detection",
"annotations_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:zh",
"license:cc-by-4.0",
"arxiv:2206.09917",
"regi... | Paul | null | null | 1 | 3 | 2022-07-05T10:31:28 | ---
annotations_creators:
- crowdsourced
language_creators:
- expert-generated
language:
- zh
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: Mandarin HateCheck
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- hate-speech-detection
---
# Dataset Card for Multilingual HateCheck
## Dataset Description
Multilingual HateCheck (MHC) is a suite of functional tests for hate speech detection models in 10 different languages: Arabic, Dutch, French, German, Hindi, Italian, Mandarin, Polish, Portuguese and Spanish.
For each language, there are 25+ functional tests that correspond to distinct types of hate and challenging non-hate.
This allows for targeted diagnostic insights into model performance.
For more details, please refer to our paper about MHC, published at the 2022 Workshop on Online Abuse and Harms (WOAH) at NAACL 2022. If you are using MHC, please cite our work!
- **Paper:** Röttger et al. (2022) - Multilingual HateCheck: Functional Tests for Multilingual Hate Speech Detection Models. https://arxiv.org/abs/2206.09917
- **Repository:** https://github.com/rewire-online/multilingual-hatecheck
- **Point of Contact:** paul@rewire.online
## Dataset Structure
The csv format mostly matches the original HateCheck data, with some adjustments for specific languages.
**mhc_case_id**
The test case ID that is unique to each test case across languages (e.g., "mandarin-1305")
**functionality**
The shorthand for the functionality tested by the test case (e.g, "target_obj_nh"). The same functionalities are tested in all languages, except for Mandarin and Arabic, where non-Latin script required adapting the tests for spelling variations.
**test_case**
The test case text.
**label_gold**
The gold standard label ("hateful" or "non-hateful") of the test case. All test cases within a given functionality have the same gold standard label.
**target_ident**
Where applicable, the protected group that is targeted or referenced in the test case. All HateChecks cover seven target groups, but their composition varies across languages.
**ref_case_id**
For hateful cases, where applicable, the ID of the hateful case which was perturbed to generate this test case. For non-hateful cases, where applicable, the ID of the hateful case which is contrasted by this test case.
**ref_templ_id**
The equivalent to ref_case_id, but for template IDs.
**templ_id**
The ID of the template from which the test case was generated.
**case_templ**
The template from which the test case was generated (where applicable).
**gender_male** and **gender_female**
For gender-inflected languages (French, Spanish, Portuguese, Hindi, Arabic, Italian, Polish, German), only for cases where gender inflection is relevant, separate entries for gender_male and gender_female replace case_templ.
**label_annotated**
A list of labels given by the three annotators who reviewed the test case (e.g., "['hateful', 'hateful', 'hateful']").
**label_annotated_maj**
The majority vote of the three annotators (e.g., "hateful"). In some cases this differs from the gold label given by our language experts.
**disagreement_in_case**
True if label_annotated_maj does not match label_gold for the entry.
**disagreement_in_template**
True if the test case is generated from an IDENT template and there is at least one case with disagreement_in_case generated from the same template. This can be used to exclude entire templates from MHC. | 3,491 | [
[
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-0.05206298828125,
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0.006702423095703125,
-0.008392333984375,
0.00782012939453125,
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Paul/hatecheck-italian | 2022-07-05T10:35:17.000Z | [
"task_categories:text-classification",
"task_ids:hate-speech-detection",
"annotations_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:it",
"license:cc-by-4.0",
"arxiv:2206.09917",
"regi... | Paul | null | null | 1 | 3 | 2022-07-05T10:33:01 | ---
annotations_creators:
- crowdsourced
language_creators:
- expert-generated
language:
- it
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: Italian HateCheck
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- hate-speech-detection
---
# Dataset Card for Multilingual HateCheck
## Dataset Description
Multilingual HateCheck (MHC) is a suite of functional tests for hate speech detection models in 10 different languages: Arabic, Dutch, French, German, Hindi, Italian, Mandarin, Polish, Portuguese and Spanish.
For each language, there are 25+ functional tests that correspond to distinct types of hate and challenging non-hate.
This allows for targeted diagnostic insights into model performance.
For more details, please refer to our paper about MHC, published at the 2022 Workshop on Online Abuse and Harms (WOAH) at NAACL 2022. If you are using MHC, please cite our work!
- **Paper:** Röttger et al. (2022) - Multilingual HateCheck: Functional Tests for Multilingual Hate Speech Detection Models. https://arxiv.org/abs/2206.09917
- **Repository:** https://github.com/rewire-online/multilingual-hatecheck
- **Point of Contact:** paul@rewire.online
## Dataset Structure
The csv format mostly matches the original HateCheck data, with some adjustments for specific languages.
**mhc_case_id**
The test case ID that is unique to each test case across languages (e.g., "mandarin-1305")
**functionality**
The shorthand for the functionality tested by the test case (e.g, "target_obj_nh"). The same functionalities are tested in all languages, except for Mandarin and Arabic, where non-Latin script required adapting the tests for spelling variations.
**test_case**
The test case text.
**label_gold**
The gold standard label ("hateful" or "non-hateful") of the test case. All test cases within a given functionality have the same gold standard label.
**target_ident**
Where applicable, the protected group that is targeted or referenced in the test case. All HateChecks cover seven target groups, but their composition varies across languages.
**ref_case_id**
For hateful cases, where applicable, the ID of the hateful case which was perturbed to generate this test case. For non-hateful cases, where applicable, the ID of the hateful case which is contrasted by this test case.
**ref_templ_id**
The equivalent to ref_case_id, but for template IDs.
**templ_id**
The ID of the template from which the test case was generated.
**case_templ**
The template from which the test case was generated (where applicable).
**gender_male** and **gender_female**
For gender-inflected languages (French, Spanish, Portuguese, Hindi, Arabic, Italian, Polish, German), only for cases where gender inflection is relevant, separate entries for gender_male and gender_female replace case_templ.
**label_annotated**
A list of labels given by the three annotators who reviewed the test case (e.g., "['hateful', 'hateful', 'hateful']").
**label_annotated_maj**
The majority vote of the three annotators (e.g., "hateful"). In some cases this differs from the gold label given by our language experts.
**disagreement_in_case**
True if label_annotated_maj does not match label_gold for the entry.
**disagreement_in_template**
True if the test case is generated from an IDENT template and there is at least one case with disagreement_in_case generated from the same template. This can be used to exclude entire templates from MHC. | 3,490 | [
[
-0.046661376953125,
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0.006702423095703125,
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0.00782012939453125,
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... |
Paul/hatecheck-hindi | 2022-07-05T10:36:37.000Z | [
"task_categories:text-classification",
"task_ids:hate-speech-detection",
"annotations_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:hi",
"license:cc-by-4.0",
"arxiv:2206.09917",
"regi... | Paul | null | null | 0 | 3 | 2022-07-05T10:35:40 | ---
annotations_creators:
- crowdsourced
language_creators:
- expert-generated
language:
- hi
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: Hindi HateCheck
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- hate-speech-detection
---
# Dataset Card for Multilingual HateCheck
## Dataset Description
Multilingual HateCheck (MHC) is a suite of functional tests for hate speech detection models in 10 different languages: Arabic, Dutch, French, German, Hindi, Italian, Mandarin, Polish, Portuguese and Spanish.
For each language, there are 25+ functional tests that correspond to distinct types of hate and challenging non-hate.
This allows for targeted diagnostic insights into model performance.
For more details, please refer to our paper about MHC, published at the 2022 Workshop on Online Abuse and Harms (WOAH) at NAACL 2022. If you are using MHC, please cite our work!
- **Paper:** Röttger et al. (2022) - Multilingual HateCheck: Functional Tests for Multilingual Hate Speech Detection Models. https://arxiv.org/abs/2206.09917
- **Repository:** https://github.com/rewire-online/multilingual-hatecheck
- **Point of Contact:** paul@rewire.online
## Dataset Structure
The csv format mostly matches the original HateCheck data, with some adjustments for specific languages.
**mhc_case_id**
The test case ID that is unique to each test case across languages (e.g., "mandarin-1305")
**functionality**
The shorthand for the functionality tested by the test case (e.g, "target_obj_nh"). The same functionalities are tested in all languages, except for Mandarin and Arabic, where non-Latin script required adapting the tests for spelling variations.
**test_case**
The test case text.
**label_gold**
The gold standard label ("hateful" or "non-hateful") of the test case. All test cases within a given functionality have the same gold standard label.
**target_ident**
Where applicable, the protected group that is targeted or referenced in the test case. All HateChecks cover seven target groups, but their composition varies across languages.
**ref_case_id**
For hateful cases, where applicable, the ID of the hateful case which was perturbed to generate this test case. For non-hateful cases, where applicable, the ID of the hateful case which is contrasted by this test case.
**ref_templ_id**
The equivalent to ref_case_id, but for template IDs.
**templ_id**
The ID of the template from which the test case was generated.
**case_templ**
The template from which the test case was generated (where applicable).
**gender_male** and **gender_female**
For gender-inflected languages (French, Spanish, Portuguese, Hindi, Arabic, Italian, Polish, German), only for cases where gender inflection is relevant, separate entries for gender_male and gender_female replace case_templ.
**label_annotated**
A list of labels given by the three annotators who reviewed the test case (e.g., "['hateful', 'hateful', 'hateful']").
**label_annotated_maj**
The majority vote of the three annotators (e.g., "hateful"). In some cases this differs from the gold label given by our language experts.
**disagreement_in_case**
True if label_annotated_maj does not match label_gold for the entry.
**disagreement_in_template**
True if the test case is generated from an IDENT template and there is at least one case with disagreement_in_case generated from the same template. This can be used to exclude entire templates from MHC. | 3,488 | [
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0.006702423095703125,
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... |
CShorten/Last-Week-on-ML-ArXiv | 2022-07-12T21:03:47.000Z | [
"region:us"
] | CShorten | null | null | 0 | 3 | 2022-07-07T12:01:47 | Please check here to see when the dataset was last updated. <br />
<h1> Last Updated July 12th, 2022 </h1> | 106 | [
[
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biglam/atypical_animacy | 2022-07-22T17:29:12.000Z | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"task_ids:intent-classification",
"annotations_creators:expert-generated",
"language_creators:machine-generated",
"multilinguality:monolingual",
"size_categories:n<1K",
"source_datasets:original",
"language:en",
"license:c... | biglam | Atypical animacy detection dataset, based on nineteenth-century sentences in English extracted from an open dataset of nineteenth-century books digitized by the British Library (available via https://doi.org/10.21250/db14, British Library Labs, 2014).
This dataset contains 598 sentences containing mentions of machines. Each sentence has been annotated according to the animacy and humanness of the machine in the sentence. | @article{DBLP:journals/corr/abs-2005-11140,
author = {Mariona Coll Ardanuy and
Federico Nanni and
Kaspar Beelen and
Kasra Hosseini and
Ruth Ahnert and
Jon Lawrence and
Katherine McDonough and
Giorgia Tolfo and
Daniel C. S. Wilson and
Barbara McGillivray},
title = {Living Machines: {A} study of atypical animacy},
journal = {CoRR},
volume = {abs/2005.11140},
year = {2020},
url = {https://arxiv.org/abs/2005.11140},
eprinttype = {arXiv},
eprint = {2005.11140},
timestamp = {Sat, 23 Jan 2021 01:12:25 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-2005-11140.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
} | 3 | 3 | 2022-07-11T21:33:07 | ---
annotations_creators:
- expert-generated
language:
- en
language_creators:
- machine-generated
license:
- cc0-1.0
multilinguality:
- monolingual
paperswithcode_id: null
pretty_name: Atypical Animacy
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
- intent-classification
---
# Dataset Card for atypical_animacy
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** https://bl.iro.bl.uk/concern/datasets/323177af-6081-4e93-8aaf-7932ca4a390a?locale=en
- **Repository:** https://github.com/Living-with-machines/AtypicalAnimacy
- **Paper:** https://arxiv.org/abs/2005.11140
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Mariona Coll Ardanuy](mailto:mcollardanuy@turing.ac.uk), [Daniel CS Wilson](mailto:dwilson@turing.ac.uk)
### Dataset Summary
Atypical animacy detection dataset, based on nineteenth-century sentences in English extracted from an open dataset of nineteenth-century books digitized by the British Library. This dataset contains 598 sentences containing mentions of machines. Each sentence has been annotated according to the animacy and humanness of the machine in the sentence.
### Supported Tasks and Leaderboards
- `text-classification` - This dataset can be used to determine if a mention of an entity in a document was humanlike or not
- `entity-recognition` - The dataset can be used to fine tune large models for NER, albeit for a very specific use case
### Languages
The text in the dataset is in English, as written by authors of books digitized by the British Library. The associated BCP-47 code in `en`
## Dataset Structure
The dataset has a single configuration
### Data Instances
An example data point
```
{'id': '002757962_01_184_16',
'sentence': '100 shows a Cornish boiler improperly seated with one small side flue and a bottom flue.',
'context': 'Fig. 100 shows a Cornish boiler improperly seated with one small side flue and a bottom flue. The effect of this on a long boiler is to cause springing and leakage of the seams from the heat being applied to one side of the boiler only.',
'target': 'boiler',
'animacy': 0.0,
'humanness': 1.0,
'offsets': [20, 26],
'date': '1893'}
```
### Data Fields
- id: sentence identifier according to internal Living with Machines BL books indexing.
- sentence: sentence where target expression occurs.
- context: sentence where target expression occurs, plus one sentence to the left and one sentence to the right.
- target: target expression
- animacy: animacy of the target expression
- humanness: humanness of the target expression
### Data Splits
Train | 598
## Dataset Creation
The dataset was created by manually annotating books that had been digitized by the British Library. According to the paper's authors,
> "we provide a basis for examining how machines were imagined during the nineteenth century as everything from lifeless mechanical objects to living beings, or even human-like agents that feel, think, and love. We focus on texts from nineteenth-century Britain, a society being transformed by industrialization, as a good candidate for studying the broader issue"
### Curation Rationale
From the paper:
> The Stories dataset is largely composed of target expressions that correspond to either typically animate or typically inanimate entities. Even though some cases of unconventional animacy can be found(folktales, in particular, are richer in typically inanimate entities that become animate), these accountfor a very small proportion of the data.6 We decided to create our own dataset (henceforth 19thC Machines dataset) to gain a better sense of the suitability of our method to the problem of atypical animacy detection, with particular attention to the case of animacy of machines in nineteenth-century texts.
### Source Data
#### Initial Data Collection and Normalization
The dataset was generated by manually annotating books that have been digitized by the British Library
#### Who are the source language producers?
The data was originally produced by British authors in the 19th century. The books were then digitzed whcih produces some noise due to the OCR method. The annotators are from The Alan Turing Institute, The British Library, University of Cambridge, University of Exeter and Queen Mary University of London
### Annotations
#### Annotation process
Annotation was carried out in two parts.
For the intial annotation process, from the paper:
> "For human annotators, even history and literature experts, language subtleties made this task extremely subjective. In the first task, we masked the target word (i.e. the machine) in each sentence and asked the annotator to fill the slot with the most likely entity between ‘human’, ‘horse’, and ‘machine’, representing three levels in the animacy hierarchy: human, animal, and object (Comrie, 1989, 185). We asked annotators to stick to the most literal meaning and avoid metaphorical interpretations when possible. The second task was more straightforwardly related to determining the animacy of the target entity, given the same 100 sentences. We asked annotators to provide a score between -2 and 2, with -2 being definitely inanimate, -1 possibly inanimate, 1 possibly animate, and 2 definitely animate. Neutral judgements were not allowed. "
For the final annotations, from the paper:
> A subgroup of five annotators collaboratively wrote the guidelines based on their experience annotating the first batch of sentences, taking into account common discrepancies. After discussion, it was decided that a machine would be tagged as animate if it is described as having traits distinctive of biologically animate beings or human-specific skills, or portrayed as having feelings, emotions, or a soul. Sentences like the ones in example 2 would be considered animate, but an additional annotation layer would be provided to capture the notion of humanness, which would be true if the machine is portrayed as sentient and capable of specifically human emotions, and false if it used to suggest some degree of dehumanization.
#### Who are the annotators?
Annotations were carried out by the following people
- Giorgia Tolfo
- Ruth Ahnert
- Kaspar Beelen
- Mariona Coll Ardanuy
- Jon Lawrence
- Katherine McDonough
- Federico Nanni
- Daniel CS Wilson
### Personal and Sensitive Information
This dataset does not have any personal information since they are digitizations of books from the 19th century. Some passages might be sensitive, but it is not explicitly mentioned in the paper.
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
The curators for this dataset are:
- Kaspar Beelen
- Mariona Coll Ardanuy
- Federico Nanni
- Giorgia Tolfo
### Licensing Information
CC0 1.0 Universal Public Domain
### Citation Information
```
@article{DBLP:journals/corr/abs-2005-11140,
author = {Mariona Coll Ardanuy and
Federico Nanni and
Kaspar Beelen and
Kasra Hosseini and
Ruth Ahnert and
Jon Lawrence and
Katherine McDonough and
Giorgia Tolfo and
Daniel C. S. Wilson and
Barbara McGillivray},
title = {Living Machines: {A} study of atypical animacy},
journal = {CoRR},
volume = {abs/2005.11140},
year = {2020},
url = {https://arxiv.org/abs/2005.11140},
eprinttype = {arXiv},
eprint = {2005.11140},
timestamp = {Sat, 23 Jan 2021 01:12:25 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-2005-11140.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
``` | 8,863 | [
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arbml/ashaar | 2022-09-03T18:05:56.000Z | [
"region:us"
] | arbml | null | null | 0 | 3 | 2022-07-12T14:42:57 | # ashaar
introducing ashaar, the largest dataset for arabic poetry
# general statistics
| metric | value |
|-----------------|-----------|
| number of poems | 254,630 |
| number of baits | 3,857,429 |
| number of poets | 7,167 |
# License
This dataset is released under fair use for research development only. Poets have the sole right to take down any access to their work. The authors of the websites, also, have the right to take down any material that does not conform with that. This work should not be used for any commercial purposes.
| 570 | [
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ilmariky/WikiQA-100-fi | 2022-10-25T15:47:21.000Z | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"multilinguality:monolingual",
"size_categories:n<1k",
"language:fi",
"license:gpl-3.0",
"question-generation",
"region:us"
] | ilmariky | null | null | 0 | 3 | 2022-07-12T18:51:02 | ---
language:
- fi
license:
- gpl-3.0
multilinguality:
- monolingual
size_categories:
- n<1k
task_categories:
- question-answering
task_ids:
- extractive-qa
pretty_name: WikiQA-100-fi
tags:
- question-generation
train-eval-index:
- config: plain_text
task: question-answering
task_id: extractive_question_answering
splits:
train_split: train
eval_split: validation
col_mapping:
question: question
context: context
answers:
text: text
answer_start: answer_start
---
# Dataset Card for "WikiQA-100-fi"
### Dataset Summary
WikiQA-100-fi dataset contains 100 questions related to Finnish Wikipedia articles. The dataset is in the [SQuAD](https://rajpurkar.github.io/SQuAD-explorer/) format, and there are 10 questions for each category identified by the authors of SQuAD. Unlike SQuAD2.0, WikiQA-100-fi contains only answerable questions. The dataset is tiny compared to actual QA test sets, but it still gives an impression of the models' performance on purely native text data collected by a native speaker. The dataset was originally created as an evaluation set for models that had been mostly fine-tuned with automatically translated QA data. More information about the dataset and models created with it can be found [here](https://helda.helsinki.fi/handle/10138/344973).
## Dataset Structure
### Data Instances
Example data:
```
{
"title": "Folksonomia",
"paragraphs": [
{
"qas": [
{
"question": "Minkälaista sisältöä käyttäjät voivat luokitella folksonomian avulla?",
"id": "6t4ufel624",
"answers": [
{
"text": "www-sivuja, valokuvia ja linkkejä",
"answer_start": 155
}
],
"is_impossible": false
}
],
"context": "Folksonomia (engl. folksonomy) on yhteisöllisesti tuotettu, avoin luokittelujärjestelmä, jonka avulla internet-käyttäjät voivat luokitella sisältöä, kuten www-sivuja, valokuvia ja linkkejä. Etymologisesti folksonomia on peräisin sanojen \"folk\" (suom. väki) ja \"taxonomy\" (suom. taksonomia) leikkimielisestä yhdistelmästä."
}
]
}
```
### Data Fields
#### plain_text
- `id`: a `string` feature.
- `title`: a `string` feature.
- `context`: a `string` feature.
- `question`: a `string` feature.
- `answers`: a dictionary feature containing:
- `text`: a `string` feature.
- `answer_start`: a `int32` feature.
### Data Splits
| name | test|
|----------|----:|
|plain_text| 100|
### Citation Information
```
@MastersThesis{3241c198b3f147faacbc6d8b64ed9419,
author = "Kylli{\"a}inen, {Ilmari}",
title = "Neural Factoid Question Answering and Question Generation for Finnish",
language = "en",
address = "Helsinki, Finland",
school = "University of Helsinki",
year = "2022",
month = "jun",
day = "15",
url = "https://helda.helsinki.fi/handle/10138/344973"
}
``` | 3,067 | [
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MariaIsabel/FR_NFR_Spanish_requirements_classification | 2022-07-22T07:19:16.000Z | [
"task_categories:text-classification",
"task_ids:multi-class-classification",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:es",
"license:cc-by-4.0",
"region:us"
] | MariaIsabel | null | null | 0 | 3 | 2022-07-15T12:01:21 | ---
annotations_creators:
- other
language:
- es
language_creators:
- other
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: Spanish requirements labeled in functional and non-functional classes.
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-class-classification
---
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
Published version of dataset used for paper 'Towards an automatic requirements classification in a new Spanish dataset'
### Languages
Spanish
## Dataset Structure
### Data Fields
Project: Project's Identifier from which the requirements were obtained.
Requirement: Description of the software requirement.
Final label: Label of the requirement: F (functional requirement) and NF (non-functional requirement).
## Dataset Creation
### Initial Data Collection and Normalization
This dataset was created from a collection of functional and non-functional requirements extracted from 13 final degree and 2 master’s projects carried out from the University of A Coruna. It consist in 300 functional and 89 non-funtcional requirements.
## Additional Information
### Citation Information
https://doi.org/10.5281/zenodo.6556541
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rajistics/indian_food_images | 2022-08-04T17:58:49.000Z | [
"task_categories:image-classification",
"region:us"
] | rajistics | null | null | 0 | 3 | 2022-07-15T14:40:09 | ---
task_categories:
- image-classification
---
Source of dataset: [Kaggle](https://www.kaggle.com/datasets/l33tc0d3r/indian-food-classification)
This Dataset contains different images of food in 20 different classes. Some of the classes are of Indian food. All the images are extracted from google. Images per classes are little so Data augmentation and transfer learning will be best suited here.
Classes of the model: "burger", "butter_naan", "chai", "chapati", "chole_bhature", "dal_makhani", "dhokla", "fried_rice", "idli", "jalebi", "kaathi_rolls", "kadai_paneer", "kulfi", "masala_dosa", "momos", "paani_puri", "pakode", "pav_bhaji", "pizza", "samosa" | 660 | [
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Maxmioti/GDRP-fines | 2022-07-17T10:03:34.000Z | [
"license:other",
"region:us"
] | Maxmioti | null | null | 0 | 3 | 2022-07-17T09:57:46 | ---
license: other
---
Opensource DataSet form a Kaggle competition https://www.kaggle.com/datasets/andreibuliga1/gdpr-fines-20182020-updated-23012021
GDPR-fines is a dataset with summary of GDPR cases from companies that were find between 2018 and 2021. You will find the summary plus the Articles violated in the cases (3 most importants + "Others" regrouping the rest of articles).
Raw text and lemmatized text available plus multi-labels. | 445 | [
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biglam/lancaster_newsbooks | 2022-08-18T16:03:54.000Z | [
"annotations_creators:no-annotation",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:n<1K",
"source_datasets:original",
"language:en",
"license:cc-by-sa-3.0",
"newsbooks",
"1654",
"lancaster",
"oxford text",
"region:us"
] | biglam | This corpus consists of two collections of seventeenth-century English "newsbooks". Both were drawn from the Thomason Tracts collection, which is held at the British Library and available in graphical form via Early English Books Online (EEBO). The construction of these keyboarded versions were in both cases funded by the British Academy.
The FIRST collection (1654_newsbooks) consists of every newsbook published in London and still surviving in the Thomason Tracts from the first half of 1654 (to be precise, for the second half of December 1653 to the end of May 1654, with one or two additions from the first week in June, 1654). This was constructed for the project "Looking at text re-use in a corpus of seventeenth-century news reportage", funded by the British Academy, grant reference SG-33825.
The SECOND collection (mercurius_fumigosus) consists of every surviving issue published of the highly idiosyncratic newsbook "Mercurius Fumigosus", written by John Crouch between summer 1654 and early autumn 1655. This was constructed for the project "Decoding the news - Mercurius Fumigosus as a source of news in the interregnum, 1654-1655", funded by the British Academy, grant reference LRG-35423.
This is version 1.0 of the corpus, released April 2007; it supercedes earlier versions circulated informally.
For more information about the corpus, see www.ling.lancs.ac.uk/newsbooks | @misc{20.500.12024/2531,
title = {The Lancaster Newsbooks Corpus},
author = {Thomason, George, d. 1666},
url = {http://hdl.handle.net/20.500.12024/2531},
note = {Oxford Text Archive},
copyright = {Distributed by the University of Oxford under a Creative Commons Attribution-{NonCommercial}-{ShareAlike} 3.0 Unported License.},
year = {2005} } | 1 | 3 | 2022-07-19T19:48:58 | ---
annotations_creators:
- no-annotation
paperswithcode_id: null
language:
- en
language_creators:
- expert-generated
license:
- cc-by-sa-3.0
multilinguality:
- monolingual
pretty_name: Lancaster Newsbooks
size_categories:
- n<1K
source_datasets:
- original
tags:
- newsbooks
- '1654'
- lancaster
- oxford text
task_categories: []
task_ids: []
---
# Dataset Card for lancaster_newsbooks
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** https://ota.bodleian.ox.ac.uk/repository/xmlui/handle/20.500.12024/2531
- **Repository:** [Needs More Information]
- **Paper:** [Needs More Information]
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** Tony McEnery
### Dataset Summary
This corpus consists of two collections of seventeenth-century English "newsbooks". Both were drawn from the Thomason Tracts collection, which is held at the British Library and available in graphical form via Early English Books Online (EEBO). The construction of these keyboarded versions were in both cases funded by the British Academy.
The FIRST collection (1654_newsbooks) consists of every newsbook published in London and still surviving in the Thomason Tracts from the first half of 1654 (to be precise, for the second half of December 1653 to the end of May 1654, with one or two additions from the first week in June, 1654). This was constructed for the project "Looking at text re-use in a corpus of seventeenth-century news reportage", funded by the British Academy, grant reference SG-33825.
The SECOND collection (mercurius_fumigosus) consists of every surviving issue published of the highly idiosyncratic newsbook "Mercurius Fumigosus", written by John Crouch between summer 1654 and early autumn 1655. This was constructed for the project "Decoding the news - Mercurius Fumigosus as a source of news in the interregnum, 1654-1655", funded by the British Academy, grant reference LRG-35423.
This is version 1.0 of the corpus, released April 2007; it supercedes earlier versions circulated informally.
For more information about the corpus, see www.ling.lancs.ac.uk/newsbooks
### Supported Tasks and Leaderboards
`text-classification`: This dataset can be used to augent existing datasets to find stylistic differences between texts from different time periods
### Languages
The language in this dataset is English from 1654. The associated BCP-47 code is `en:GB`
## Dataset Structure
### Data Instances
```
{
'id': 'PerfAcc170',
'text': "Another late fight in Scotland, betwixt Col. Morgan and the Highlanders; with the number that were slain and taken Prisoners. The removing of Lieut. Col. John Lilburn from the Tower of London. The readiness of our Fleet for new action, though Peace be agreed on with Holland and Denmark. The taking of several more Prizes at sea. An Order of the Commissioners for the Trial and Approbation of public Preachers. Several proceedings of His Highness the Lord Protector and his Council, and another Ordinance touching
the adjourning of the Term. Together with variety of choice Intelligence from several Foreign parts. From Wednesday APRIL 5 TO Wednesday April 12. 1654. Many Addresses were made to his Highness the Lord Protector, in the name of the City and County of York, and other places, wherein they acknowledge the great blessing of God to this Nation, that they have so great, so good and able a Protector. This day the Sessions began in the Old Bailey, and one of those that committed the late Robbery on Black-Heath, being called to his Trial, he refused to plead; but more hereafter. This evening about 9 of the Clock, the Dutch Ambassadors signed and sealed the Ratification of the Articles of Peace so long spoken of; so did likewise the Commissioners appointed to treat with them by his Highness the Lord Protector. Paris April 11, 1654. The Cardinal de Retz being removed from Vincennes by the Marshal de la Mesteray, is now safe arrived at Nantes, and put into the Castle. The Court Emissaries give out that he is not to be long there, but in a few days to be set at liberty, only that his Majesty desireth satisfaction upon some certain points, although the main drift is to make him surrender his place of Archbishop of this City. The Commissioners of Languedoc cannot yet prevail in anything upon their Complaints, but are like the Commissioners of Catalonia, who hitherto have prevailed no further than to receive many fair words, but nothing effectual, the main work now in hand, is to find monies speedily for the setting forth of the Army, that they may be in the field as soon as may be, and to that end the Partisans are not wanting to find out new ways for exacting of monies, preferring large sums to be put into the King's Coffers, the difficulty lieth only in the effecting of it, by reason that the Country is in most places so exhausted of monies, that they are scarce able to live: The design for the King's Coronation is now on foot again, and if I am rightly informed, it will be done about the middle of May next, which being done, his Majesty shall go upon the borders and down to Picardy to forward his Army in their Action, so much the rather, by reason
that the Prince of Conde, whom we hear was last week at Valenciennes, and then taking a view of his Army, is returned to Bruxels, there to confer with the Archduke Leopoldus for to obtain money and other necessaries for the march of his Army, that so they may fall to action as soon as the weather and season will give them leave, his Lady and son are still at Rocroy, where they are expecting some alteration to their present condition. The Earl of Harcourt hath not yet received any answer from the Court upon those proposals which he lately
sent to the Court. We have news, that the Duke Francis hath at last accepted the command of his Brother the Duke of Lorrain's Army, and is expected there in a few days, which our Cardinal doth very well relish. The forces that were in the Country of Liege are now marching homewards, and are to be quartered in Lorrain. The great preparation for an Armado to go from Marseilles and Touloon, is much at a stand, only there are lately 5 men of War gone to Sea, and 3 more are to follow, but upon no design than to rob and plunder upon the sea, sparing scarce any they encounter, whether they be friends or foes. This day his Highness the Lord Protector and his Council, passed an Ordinance for adjourning of Easter Term, from and after the first Return thereof, called Quindena Pasch, until the first Return of Trinity Term, called Crastino Trinatatis. Dalkieth, April 3. Cap. Sherwin Commander of the Primrose, and Cap. Smith Commander of the Duchess, in their return from Orkney, took a Dutch vessel laden with French and Spanish Wines, linen Cloth, and other good commodities, bound for
the West Indies; they sent her into Aberdeen. Some young Lairds and others purposing to glean a party of horse in Lothian, and repair to the enemy, are taken, and brought hither prisoners. Aberdeen, April 1. The Earl of Athol is come to Glencarn with about 700 horse and foot, Seaford and some new raised forces are daily expected to join with them. Glencarn with his whole force, consisting of 2000 horse and foot, is at Dingwel, two miles from Brahan, not undeserving the name of an Island, so that we hope to engage them there. In order whereunto Lieut. Col. Mitchell is marched towards Inverness with 9 companies of Foot, and Col. Morgan hath followed him with 5 troops of Col Rich his Regiment, and 4 troops of Dragoons; he intends to take Col. Tomlinson's Regiment, which is in his way, and to draw 5 companies of Foot out of Inverness. From Cows in the Isle of Wight, April 6. A private man of War hath, about two days since, taken and brought in hither two French vessels, one of which is laden with Salt, the other hath but little except ballast; Our Fleet is for the most part near St. Helens point and the rest as the Spits head, being in all near 100 sail, gallant ships, and bravely accommodated. One of our Frigates hath taken a Holland ship, and carried her to Portsmouth; she hath in her 8 Bales of Paper, and some small quantity of Indico. Many ships that were here, went away yesterday morning towards the Downs; and several Merchants' ships are at present here in this road, being detained by contrary winds; they expect some favourable Easterly gales, that so they may proceed on their intended voyages. Deal, April 7. A man of War of ours is this morning gone for Holland, to get the Ratification of the Peace made with them, and an Express from the Dutch Ambassador, touching the Agreement. Most part of the ships which remained in this Road, are gone up into the River of Thames; here is only some few left that are bound to the Southward. A Fleet consisting of about 40 or 50 sail of ships, great and small, passed by this place, which we suppose to be the Dunkirk fleet bound for London. Because many will not give credit to the Agreement of Peace between the Commonwealths of England and Holland, (though their Unbelief proceeds from several causes, some prejudicately fearing the worst, and others wishing and desiring rather than the Fountain of Blood may still be open) We can, and do assure you, That the Articles (as
we said before) were signed and sealed by the Commissioners on both sides, on Wednesday night last, and within 14 days are to be signed and sealed by the Lord Protector, and the States of Holland, and then to publicly proclaimed and published, both in England and Holland
in one day. The Agreement with Denmark is also taken in upon the Articles: And for satisfaction of the loss which our English Merchants sustained by that King's command, whose demands amount to about 150000l. it is referred to four Merchants, two whereof to be English, and the other two Dutch; which four Merchants shall have absolute power to determine those demands within the space of twenty days; the place where they are to sit, is Guildhall. As touching the business of Amboyna, it is referred to eight Commissioners, who have six months time to agree thereon, and in case they agree not, then Umpires are nominated to determine that business. Let those that delight themselves in blood, have blood to drink, for they are worthy. From Legorn, March 23. thus. This week in the sight of this City was a sore fight between two ships at Sea, the one Dutchman of War of 32 guns, and the other an English ship called the Expedition, who came from Zant with Currans; the fight lasted 6 hours, but night having parted them, both ships sunk; most of the men were saved, but nothing else, though the fight was near the shore. It is advertised from Cullen, That the Treaty between that Elector and the Spanish Commissioners, is brought to perfection, and signed, which is, That both French and Spanish shall have free passage through the Country of Liege, not committing any acts of hostility upon each other; and the Spaniards in point of satisfaction for the losses received from them and the Lorrainers, shall pay to the said Elector 200000 Rixdollars out of the Duke of Lorrain's estate, and for security of performance, the Lordship of Kerpen, and another in Gulick shall be put into his hands until full payment. From Poland thus. The General of the Cossacks hath delivered up three very considerable places to the Muscovite, and caused himself to be re baptized after the Muscovia manner, which is so
ill resented by all sorts of people in that Country, that the Commanders sent to the King of Poland, That if he pleased to send them a general pardon for what they had done, and the rest of the Army, they will return with the major part of the Army into his Majesty's service; which hath so incensed the General, that having caused them to be apprehended he hath made each of them shorter by the head, which hath caused much heart burning among the people. Whereas many abuses and corruptions are crept into the ordinary course and administration of Justice, both in Law and Equity, the reformation whereof hath not yet been attained; Out of a tender care and desire that so necessary and good a work may at length be brought to effect, it is held convenient that so necessary and good a work may at length be brought to effect, it is held convenient that so necessary and good a work may at length be brought to effect, it is held convenient and necessary to adjourn part of the next Term of Easter; be if therefore Ordained by his Highness the Lord Protector, by and with the consent of his Council, That part of the said Term of Easter now next coming be adjourned, that is to say, from and after the first Return, called Quindena Pasch, unto the last Return of the said Easter Term, called Crastino Ascensionis; And all and every person or persons, which have cause, or commandment to appear in any of the Courts at Westminster, in or at any day or time, from and after the said Return, called Quindena Pasch, may tarry at their dwellings, or where their business shall lie, without resorting to any of the said Courts for that
Cause, until the said last Return, called Crastino Ascensionis, without danger or forfeiture, penalty or contempt to be in that behalf. And be it also ordained by the Authority aforesaid, That Writs of Adjournment shall be directed to the Justices of the said Courts, and
Barons of the Exchequer, giving them authority to adjourn the said part of the said Term of Easter, as aforesaid, that is to say, from and after the said first Return called Quindena Pasch, until the said last Return of the said Term, called Crastino Ascensionis, as before is said, and the said adjournment shall be made, as aforesaid. And be it further Ordained, That all Matters, Causes and Suits, depending in any of the said Courts, shall have continuance, and the parties shall have day, from the day of the said Adjournment, until the said Return of Crastino Ascensionis, as is aforesaid; and the Lord's Commissioners of the Great Seal are required to issue forth Writs accordingly. And be it further Ordained, That a former Ordinance of the sixth day of this instant April, for the Adjourning of part of the
said Term, until the first Return of Trinity Term next, called Crastino Trinitatis, be from henceforth Repealed and void. And it is lastly Ordained by the Authority aforesaid, That the Sheriffs of London and Middlesex, and all other Sheriffs both in England and Wales, do
forthwith proclaim and publish this Ordinance in the chief Market Towns and usual places within their several and respective Counties. Lieutenant Colonel John Lilburn being said to have again attempted something against the State, is removed from the Tower to be prisoner
in some more remote place. The titular King of Scots is still at Paris, and of late something more merry than ordinary. The Deputies for Languedoc telling him, that if there were a Peace concluded with England, it would be well for all the Protestants in France; He made answer that he was glad of it, for it would then be the better for himself. This day was the Gaol delivery; three were hanged, one whereof died most desperately, and going up the Cart, drank a health to the Devil's Majesty: One was pressed last Saturday, and being afterwards heard to groan, was carried down to the Press-yard again to have the execution dispatched. The Commissioners for Approbation of public Ministers, sate at Whitehall, and divers Certificates were presented unto them in behalf of several particular persons, for approbation; and in regard that none hereafter should out of carelessness of partiality set their hands to a Certificate for any person that hereafter should out of carelessness or partiality let their hands to a Certificate for any person that hereafter may be found unworthy to be admitted, and so become prejudicial to the Church of Christ, and frustrate the intentions of our Governors which made this Ordinance; the said Commissioners do earnestly beseech all whom it may concern (in the bowels of Christ) as they tender the honour of the great God
himself, whose servants we all are, the prejudice of the souls of his people purchased by the blood of his Son, the advancement and propagation of his Gospel, through all the parts of this Land and Nation, whereunto we belong, so to lend assistance both of their fervent prayers, and due informations, that thereby the work may be carried on more prosperously, and the Commissioners more encouraged to attend it. Signed in the name, and at the request of the Commissioners for Approbation of public Preachers. By Francis Rouse, Io. Arrowsmith.
William Goss. Stephen Marshall. The last Letters from Edinburgh speak of another Engagement betwixt Col. Morgan, and the Enemy; but they tell us not the particulars, only they say, that the Enemy is once more dispersed, and driven further up into the mountains, with the loss of about 200 men. The peace with Holland being concluded (as you heard before) our Merchants are lading of goods on shipboard, as fast as Lighters can be gotten to carry them where the ships ride at anchor. We likewise hear of the like preparations in Holland for transporting of goods of several sorts hither. And now all the rest of Europe are at a stand, or at leastwise stand gazing upon us, and begin to cast about with themselves, what action may be great and considerable enough for to be undertaken next by those great Fleets, which are as ready for action as any opportunity can be to offer itself. How they will be disposed of Time will discover. London, Printed by E. Alsop 1654.",
'title': 'A Perfect Account, Issue 170'}
```
### Data Fields
```
{
"id": Unique identifier for that data point("string"),
"text": Text in that datapoint("string"),
"title": The title of the news article("string")
}
```
### Data Splits
Train: 303
## Dataset Creation
### Curation Rationale
The FIRST collection (1654_newsbooks) consists of every newsbook published in London and still surviving in the Thomason Tracts from the first half of 1654 (to be precise, for the second half of December 1653 to the end of May 1654, with one or two additions from the first week in June, 1654) and was constructed for the project "Looking at text re-use in a corpus of seventeenth-century news reportage", funded by the British Academy, grant reference SG-33825.
The SECOND collection (mercurius_fumigosus) consists of every surviving issue published of the highly idiosyncratic newsbook "Mercurius Fumigosus", written by John Crouch between summer 1654 and early autumn 1655. This was constructed for the project "Decoding the news - Mercurius Fumigosus as a source of news in the interregnum, 1654-1655", funded by the British Academy, grant reference LRG-35423.
### Source Data
#### Initial Data Collection and Normalization
This corpus was created by the Department of Linguistics and English Language, Lancaster University.
#### Who are the source language producers?
The original data was humna-generated from existing newsbooks
### Annotations
#### Annotation process
[N/A]
#### Who are the annotators?
[N/A]
### Personal and Sensitive Information
None, since this dataset is from 1654
## Considerations for Using the Data
### Social Impact of Dataset
This dataset provides an insight into the news and social systems from 17th century England
### Discussion of Biases
The dataset is from the 17th century and some articles might reflect social biases of the time in terms of sexuality, gender, race, etc.
### Other Known Limitations
[N/A]
## Additional Information
### Dataset Curators
This corpus was created by the Department of Linguistics and English Language, Lancaster University.
Project leader: Tony McEnery
Corpus editor: Andrew Hardie
### Licensing Information
Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License
### Citation Information
@misc{20.500.12024/2531,
title = {The Lancaster Newsbooks Corpus},
author = {Thomason, George, d. 1666},
url = {http://hdl.handle.net/20.500.12024/2531},
note = {Oxford Text Archive},
copyright = {Distributed by the University of Oxford under a Creative Commons Attribution-{NonCommercial}-{ShareAlike} 3.0 Unported License.},
year = {2005} } | 21,212 | [
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0.059539794921875,
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-0.0... |
sadrasabouri/ShahNegar | 2022-10-21T11:54:05.000Z | [
"task_categories:image-to-text",
"task_categories:text-to-image",
"task_ids:image-captioning",
"annotations_creators:machine-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:mit",
"r... | sadrasabouri | null | null | 4 | 3 | 2022-07-20T05:13:00 | ---
annotations_creators:
- machine-generated
language_creators:
- expert-generated
language:
- en
license:
- mit
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- image-to-text
- text-to-image
task_ids:
- image-captioning
pretty_name: ShahNegar
---
# ShahNegar (A Plotted version of The Shahnameh)
This dataset is a plotted version of Ferdowsi's Shahnameh (which is a highly-regarded ancient set of Farsi poems) generated using DALL-E mini (aka [craiyon](https://www.craiyon.com/)). You can use this dataset using the code below:
```python
from datasets import load_dataset
dataset = load_dataset("sadrasabouri/ShahNegar")
```
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Paper:**
- **Point of Contact:** [Sadra Sabouri](mailto:sabouri.sadra@gmail.com)
### Dataset Summary
This dataset contains more than 30K images with their corresponding text from the Shahnameh. For each Shahnameh paragraph, we generated at most 9 images. Images corresponding to the same paragraphs have the same `id` field. There was a human annotation post-process in which we removed some harmful/private generated images from the dataset. After all we reached to more than 30K, 256 * 256 images.
### Supported Tasks and Leaderboards
The main purpose of making this dataset open source is because of its artistic value, but it can also be used for the below tasks:
+ text-to-image
+ image-to-text (image captioning)
### Languages
The Shahnameh was generally written in Farsi (Persian) but the translated version we used for this dataset - [satoor](https://www.sattor.com/english/Shahnameh.pdf) - was completely in English with no alignments for the corresponding Farsi poem. We are planning to add another field to dataset entries which is the corresponding Farsi poem as soon as possible.
## Dataset Structure
### Data Fields
Here is an instance of our dataset:
```json
{
"image": <PIL Image Bytes>,
"id": 0,
"text": "He took up his abode in the mountains, and clad himself and his people in tiger-skins, and from him sprang all kindly nurture and the arts of clothing, till then unknown."
}
```
+ `image`: the image for given text.
+ `id`: the id for the text (**Not for the image**).
+ `text`: the English text for the image.
### Data Splits
This dataset has only a split (`train` split).
## Dataset Creation
The translated version of the Shahnameh was generally derived from the [satoor](https://www.sattor.com/english/Shahnameh.pdf) website. We first extracted texts from the pdf. After that, we divided paragraphs into sentences and give each sentence to the DALL-E mini model through its online API. It generated nine images for each sentence. After a few annotations, we came up with more than 30000 images.
### Annotations
#### Annotation process
Through the process of image generation, we noticed a bias in the DALL-E models towards the word `iran`. It was biased so that each sentence with this given word would have pictures from Iran's political figures which were usually totally irrelevant. The annotation process mainly focused to deal with these pictures. We removed those images which seems to be harmful to those figures and/or were irrelevant to the context.
#### Who are the annotators?
Mahsa Namdar and Sadra Sabouri were the annotators of this dataset.
### Personal and Sensitive Information
Since the textual data is easily downloadable and the images were generated through an image generation model there shouldn't be any personal information in this dataset. Just in case you find something harmful or violating of one's personal information please let us know. We will take proper action as soon as possible.
## Considerations for Using the Data
### Social Impact of Dataset
This dataset is mainly aimed to release for its artistic value. The process of generating images for the Shahnameh - which is one of the most important Farsi poem books - is our precious contribution. This dataset is not only used for this purpose but also can as a dataset in image-to-text and text-to-image tasks.
### Discussion of Biases
The dataset's possible biases would come from the DALL-E mini biases. It's actually a good practice to check the dataset entries in order to find biases in that model. One it's worth mentioning in this work is the DALL-E mini model's bias for the word `iran` which nearly always comes up with images from political figures of this country.
### Other Known Limitations
There are constant debates in the literature about the limitations of machine-generated datasets. Some believe that since nowadays models are not perfect - and so do their output, it wouldn't be a good idea to use these artificially generated datasets as input to the new model. They suggest that by doing so we are actually limiting our accuracy by the model's accuracy which provided the primary dataset.
## Additional Information
### Dataset Curators
+ Emad Fatemizadeh: The general idea for generating a graphical version of Farsi poems was firstly introduced by him.
+ Sadra Sabouri: He looked up a translated version of the Shahnameh, extract and tokenized poems from it, and used the online DALL-E mini API to generate images from poems.
+ Mahsa Namdar: The process of annotation as a post-process on data has been held by her.
### Licensing Information
MIT
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@sadrasabouri](https://github.com/sadrasabouri) for adding this dataset.
| 6,521 | [
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0.0252990722... |
tokeron/Piyyut | 2023-04-08T10:36:57.000Z | [
"task_categories:text-classification",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:heb",
"license:afl-3.0",
"metaphor-detection",
"region:us"
] | tokeron | null | null | 0 | 3 | 2022-07-20T09:01:23 | ---
license: afl-3.0
language:
- heb
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
tags:
- metaphor-detection
viewer: true
---
| 223 | [
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ydmeira/segment-pokemon | 2022-07-23T10:28:38.000Z | [
"region:us"
] | ydmeira | null | null | 1 | 3 | 2022-07-23T10:25:00 | Entry not found | 15 | [
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bigscience/evaluation-results | 2023-05-28T00:13:53.000Z | [
"task_categories:other",
"size_categories:100M<n<1B",
"region:us"
] | bigscience | @misc{muennighoff2022crosslingual,
title={Crosslingual Generalization through Multitask Finetuning},
author={Niklas Muennighoff and Thomas Wang and Lintang Sutawika and Adam Roberts and Stella Biderman and Teven Le Scao and M Saiful Bari and Sheng Shen and Zheng-Xin Yong and Hailey Schoelkopf and Xiangru Tang and Dragomir Radev and Alham Fikri Aji and Khalid Almubarak and Samuel Albanie and Zaid Alyafeai and Albert Webson and Edward Raff and Colin Raffel},
year={2022},
eprint={2211.01786},
archivePrefix={arXiv},
primaryClass={cs.CL}
} | @article{scao2022bloom,
title={BLOOM: A 176B-Parameter Open-Access Multilingual Language Model},
author={Scao, Teven Le and Fan, Angela and Akiki, Christopher and Pavlick, Ellie and Ili{\'c}, Suzana and Hesslow, Daniel and Castagn{\'e}, Roman and Luccioni, Alexandra Sasha and Yvon, Fran{\c{c}}ois and Gall{\'e}, Matthias and others},
journal={arXiv preprint arXiv:2211.05100},
year={2022}
} | 8 | 3 | 2022-08-01T18:35:58 | ---
pretty_name: evaluation-results
size_categories:
- 100M<n<1B
task_categories:
- other
---
# BigScience BLOOM Evaluation Results
This repository contains evaluation results & original predictions of BLOOM & friends.
## Usage
You can load numeric results via:
```python
from datasets import load_dataset
ds = load_dataset("bigscience/evaluation-results", "bloom")
```
If it takes too long, it may be faster to clone the repository and load the data from disk:
```python
!git clone https://huggingface.co/datasets/bigscience/evaluation-results
ds = load_dataset("evaluation-results", "bloom")
```
For example generations (.jsonl files), you need to manually browse the repository.
## Structure
For `bigsciencelmevalharness`, `lmevalharness` & `codeeval` evaluation_frameworks the structure is:
`model_name > evaluation_framework > checkpoint_type > dataset_name > data`
## Evaluation Procedure
- `bigsciencelmevalharness` files were created using the below:
- https://github.com/bigscience-workshop/Megatron-DeepSpeed/pull/291
- https://github.com/bigscience-workshop/lm-evaluation-harness
- `lmevalharness` files were created using the below:
- https://github.com/bigscience-workshop/Megatron-DeepSpeed
- https://github.com/EleutherAI/lm-evaluation-harness
- `codeeval` files were created using the HumanEval code dataset with the below:
- https://github.com/loubnabnl/bloom-code-evaluation
| 1,424 | [
[
-0.025115966796875,
-0.027740478515625,
0.049835205078125,
0.031463623046875,
0.0032329559326171875,
0.006626129150390625,
-0.01053619384765625,
-0.023040771484375,
0.0252227783203125,
0.02789306640625,
-0.052520751953125,
-0.037872314453125,
-0.0310516357421875... |
VanessaSchenkel/handmade-dataset | 2022-08-06T22:11:34.000Z | [
"task_categories:translation",
"annotations_creators:found",
"language_creators:found",
"multilinguality:translation",
"size_categories:n<1K",
"source_datasets:original",
"language:en",
"language:pt",
"license:afl-3.0",
"region:us"
] | VanessaSchenkel | null | null | 1 | 3 | 2022-08-06T22:02:15 | ---
annotations_creators:
- found
language:
- en
- pt
language_creators:
- found
license:
- afl-3.0
multilinguality:
- translation
pretty_name: VanessaSchenkel/handmade-dataset
size_categories:
- n<1K
source_datasets:
- original
tags: []
task_categories:
- translation
task_ids: []
---
Dataset with sentences regarding professions, half of the translations are to feminine and half for masculine sentences.
How to use it:
```
from datasets import load_dataset
remote_dataset = load_dataset("VanessaSchenkel/handmade-dataset", field="data")
remote_dataset
```
Output:
```
DatasetDict({
train: Dataset({
features: ['id', 'translation'],
num_rows: 388
})
})
```
Exemple:
```
remote_dataset["train"][5]
```
Output:
```
{'id': '5',
'translation': {'english': 'the postman finished her work .',
'portuguese': 'A carteira terminou seu trabalho .'}}
``` | 876 | [
[
0.005218505859375,
-0.0345458984375,
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-0.0287933349609375,
-0.024871826171875,
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0.0013532638549804688,
0.011749267578125,
0.053436279296875,
-0.0714111328125,
-0.0584716796875,
-0.0435791015625,
0.0... |
Chr0my/public_flickr_photos_license_1 | 2022-08-08T20:39:40.000Z | [
"license:cc-by-nc-sa-3.0",
"region:us"
] | Chr0my | null | null | 2 | 3 | 2022-08-08T20:27:28 | ---
license: cc-by-nc-sa-3.0
---
119893266 photos from flickr (https://www.flickr.com/creativecommons/by-nc-sa-2.0/)
---
all photos are under license id = 1 name=Attribution-NonCommercial-ShareAlike License url=https://creativecommons.org/licenses/by-nc-sa/2.0/ | 262 | [
[
-0.045013427734375,
0.0017824172973632812,
0.05731201171875,
0.048828125,
-0.05615234375,
-0.024993896484375,
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-0.04376220703125,
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0.02685546875,
-0.033172607421875,
-0.02099609375,
-0.044830322265625,
0.0252685546875,
... |
anandu/eurostat_demo | 2022-08-13T19:38:55.000Z | [
"region:us"
] | anandu | null | null | 0 | 3 | 2022-08-13T19:38:32 | Entry not found | 15 | [
[
-0.02142333984375,
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0.0169830322265625,
-0.052032470703125,
-0.014984130859375,
-0.060455322265625,
0.037... |
Gabriel/pubmed_swe | 2022-10-29T11:54:25.000Z | [
"task_categories:summarization",
"task_categories:text2text-generation",
"size_categories:10K<n<100K",
"source_datasets:https://github.com/huggingface/datasets/tree/master/datasets/pubmed",
"language:sv",
"license:other",
"conditional-text-generation",
"region:us"
] | Gabriel | null | null | 0 | 3 | 2022-08-14T14:06:26 | ---
language:
- sv
license:
- other
size_categories:
- 10K<n<100K
source_datasets:
- https://github.com/huggingface/datasets/tree/master/datasets/pubmed
task_categories:
- summarization
- text2text-generation
task_ids: []
tags:
- conditional-text-generation
---
# Dataset Card for Swedish pubmed Dataset
The Swedish pubmed dataset has only been machine-translated to improve downstream fine-tuning on Swedish summarization tasks.
## Dataset Summary
Read about the full details at original English version: https://huggingface.co/datasets/pubmed
### Data Fields
- `document`: a string containing the body of the paper
- `summary`: a string containing the abstract of the paper
### Data Splits
The Swedish pubmed dataset follows the same splits as the original English version and has 1 splits: _train_.
| Dataset Split | Number of Instances in Split |
| ------------- | ------------------------------------------- |
| Train | 90,000 |
| 999 | [
[
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0.0333251953125,
0.04840087890625,
-0.04425048828125,
-0.057769775390625,
-0.05181884765625,
... |
hugginglearners/data-science-job-salaries | 2022-08-17T18:42:40.000Z | [
"license:cc0-1.0",
"region:us"
] | hugginglearners | null | null | 2 | 3 | 2022-08-15T00:00:27 | ---
license:
- cc0-1.0
kaggle_id: ruchi798/data-science-job-salaries
---
# Dataset Card for Data Science Job Salaries
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://kaggle.com/datasets/ruchi798/data-science-job-salaries
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
### Content
| Column | Description |
|--------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| work_year | The year the salary was paid. |
| experience_level | The experience level in the job during the year with the following possible values: EN Entry-level / Junior MI Mid-level / Intermediate SE Senior-level / Expert EX Executive-level / Director |
| employment_type | The type of employement for the role: PT Part-time FT Full-time CT Contract FL Freelance |
| job_title | The role worked in during the year. |
| salary | The total gross salary amount paid. |
| salary_currency | The currency of the salary paid as an ISO 4217 currency code. |
| salary_in_usd | The salary in USD (FX rate divided by avg. USD rate for the respective year via fxdata.foorilla.com). |
| employee_residence | Employee's primary country of residence in during the work year as an ISO 3166 country code. |
| remote_ratio | The overall amount of work done remotely, possible values are as follows: 0 No remote work (less than 20%) 50 Partially remote 100 Fully remote (more than 80%) |
| company_location | The country of the employer's main office or contracting branch as an ISO 3166 country code. |
| company_size | The average number of people that worked for the company during the year: S less than 50 employees (small) M 50 to 250 employees (medium) L more than 250 employees (large) |
### Acknowledgements
I'd like to thank ai-jobs.net Salaries for aggregating this data!
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
This dataset was shared by [@ruchi798](https://kaggle.com/ruchi798)
### Licensing Information
The license for this dataset is cc0-1.0
### Citation Information
```bibtex
[More Information Needed]
```
### Contributions
[More Information Needed] | 5,488 | [
[
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0.046661376953125,
0.033203125,
-0.0601806640625,
-0.054656982421875,
-0.048126220703125,
0.... |
Hobson/surname-nationality | 2022-12-29T23:14:09.000Z | [
"task_categories:token-classification",
"task_categories:text-classification",
"task_ids:named-entity-recognition",
"size_categories:List[str]",
"source_datasets:List[str]",
"license:mit",
"multilingual",
"RNN",
"name",
"tagging",
"nlp",
"transliterated",
"character-level",
"text-tagging",... | Hobson | null | null | 2 | 3 | 2022-08-15T03:52:58 | ---
license: mit
size_categories: List[str]
source_datasets: List[str]
task_categories:
- token-classification
- text-classification
task_ids:
- named-entity-recognition
pretty_name: Popular Surname Nationality Mapping
tags:
- multilingual
- RNN
- name
- tagging
- nlp
- transliterated
- character-level
- text-tagging
- bias
- classification
- language model
- surname
- ethnicity
- multilabel classification
- natural language
---
# Popular Surname Nationality Mapping
Sample of popular surnames for 30+ countries labeled with nationality (language)
| 555 | [
[
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-0.012939453125,
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0.065185546875,
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-0.0797119140625,
-0.052154541015625,
0.00005906... |
Luka-Wang/COCO | 2022-08-18T07:36:16.000Z | [
"task_categories:token-classification",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:mit",
"region:us"
] | Luka-Wang | null | null | 0 | 3 | 2022-08-17T13:40:37 | ---
annotations_creators:
- expert-generated
language:
- en
language_creators:
- found
license:
- mit
multilinguality:
- monolingual
paperswithcode_id: acronym-identification
pretty_name: Acronym Identification Dataset
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- token-classification-other-acronym-identification
train-eval-index:
- col_mapping:
labels: tags
tokens: tokens
config: default
splits:
eval_split: test
task: token-classification
task_id: entity_extraction
---
# Dataset Card for [COCO]
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@github-scuwyh2000](https://github.com/scuwyh2000) for adding this dataset. | 3,029 | [
[
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0.005992889404296875,
0.03289794921875,
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-0.08355712890625,
-0.05029296875,
0.00732040... |
acul3/KoPI-CC_News | 2023-03-03T07:48:00.000Z | [
"task_categories:text-generation",
"task_ids:language-modeling",
"annotations_creators:no-annotation",
"language_creators:found",
"source_datasets:original",
"language:id",
"license:cc",
"region:us"
] | acul3 | null | null | 1 | 3 | 2022-08-17T14:50:15 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- id
license: cc
source_datasets:
- original
task_categories:
- text-generation
task_ids:
- language-modeling
paperswithcode_id: oscar
---
### Dataset Summary
KoPI(Korpus Perayapan Indonesia)-CC_News is Indonesian Only Extract from CC NEWS Common Crawl from 2016-2022(july) ,each snapshots get extracted using warcio,trafilatura and filter using fasttext
detail soon
```
| 459 | [
[
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-0.0535888671875,
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0.01126... |
BigBang/galaxyzoo-decals | 2022-08-29T18:03:24.000Z | [
"license:cc-by-4.0",
"region:us"
] | BigBang | null | null | 0 | 3 | 2022-08-19T15:50:22 | ---
license: cc-by-4.0
---
# Galaxy Zoo DECaLS: Detailed Visual Morphology Measurements from Volunteers and Deep Learning for 314,000 Galaxies
- https://github.com/mwalmsley/zoobot
- https://zenodo.org/record/4573248
# Dataset Schema
This schema describes the columns in the GZ DECaLS catalogues; `gz_decals_auto_posteriors`, `gz_decals_volunteers_1_and_2`, and `gz_decals_volunteers_5`.
In all catalogues, galaxies are identified by their `iauname`. Galaxies are unique within a catalogue. `gz_decals_auto_posteriors` contains all galaxies with appropriate imaging and photometry in DECaLS DR5, while `gz_decals_volunteers_1_and_2`, and `gz_decals_volunteers_5` contain subsets classified by volunteers in the respective campaigns.
The columns reporting morphology measurements are named like `{some-question}_{an-answer}`. For example, for the first question, both volunteer catalogues include the following:
| Column | Description |
| ----------- | ----------- |
| smooth-or-featured_total | Total number of volunteers who answered the "Smooth of Featured" question |
| smooth-or-featured_smooth | Count of volunteers who responded "Smooth" to the "Smooth or Featured" question |
| smooth-or-featured_featured-or-disk | Count of volunteers who responded "Featured or Disk", similarly |
| smooth-or-featured_artifact | Count of volunteers who responded "Artifact", similarly |
| smooth-or-featured_smooth_fraction | Fraction of volunteers who responded "Smooth" to the "Smooth or Featured" question, out of all respondes (i.e. smooth count / total) |
| smooth-or-featured_featured-or-disk_fraction | Fraction of volunteers who responded "Featured or Disk", similarly |
| smooth-or-featured_artifact_fraction | Fraction of volunteers who responded "Artifact", similarly |
The questions and answers are slightly different for `gz_decals_volunteers_1_and_2` than `gz_decals_volunteers_5`. See the paper for more.
The volunteer catalogues include `{question}_{answer}_debiased` columns which attempt to estimate what the vote fractions would be if the same galaxy were imaged at lower redshift. See the paper for more. Note that the debiased measurements are highly uncertain on an individual galaxy basis and therefore should be used with caution. Debiased estimates are only available for galaxies with 0.02<z<0.15, -21.5>M_r>-23, and at least 30 votes for the first question (`Smooth or Featured') after volunteer weighting.
The automated catalogue, `gz_decals_auto_posteriors`, includes predictions for all galaxies and all questions even when that question may not be appropriate (e.g. number of spiral arms for a smooth elliptical). To assess relevance, we include `{question}_proportion_volunteers_asked` columns showing the estimated fraction of volunteers that would have been asked each question (i.e. the product of the vote fractions for the preceding answers). We suggest a cut of `{question}_proportion_volunteers_asked` > 0.5 as a starting point.
The automated catalogue does not include volunteer counts or totals (naturally).
Each catalogue includes a pair of columns to warn where galaxies may have been classified using an inappropriately large field-of-view (due to incorrect radii measurements in the NSA, on which the field-of-view is calculated). We suggest excluding galaxies (<1%) with such warnings.
| Column | Description |
| ----------- | ----------- |
| wrong_size_statistic | Mean distance from center of all pixels above double the 20th percentile (i.e. probable source pixels) |
| wrong_size_warning | True if wrong_size_statistic > 161.0, our suggested starting cut. Approximately the mean distance of all pixels from center|
For convenience, each catalogue includes the same set of basic astrophysical measurements copied from the NASA Sloan Atlas (NSA). Additional measurements can be added my crossmatching on `iauname` with the NSA. See [here](https://data.sdss.org/datamodel/files/ATLAS_DATA/ATLAS_MAJOR_VERSION/nsa.html) for the NSA schema. If you use these columns, you should cite the NSA.
| Column | Description |
| ----------- | ----------- |
| ra | Right ascension (degrees) |
| dec | Declination (degrees) |
| iauname | Unique identifier listed in NSA v1.0.1 |
| petro_theta | "Azimuthally-averaged SDSS-style Petrosian radius (derived from r band" |
| petro_th50 | "Azimuthally-averaged SDSS-style 50% light radius (r-band)" |
| petro_th90 | "Azimuthally-averaged SDSS-style 50% light radius (r-band)" |
| elpetro_absmag_r | "Absolute magnitude from elliptical Petrosian fluxes in rest-frame" in SDSS r |
| sersic_nmgy_r | "Galactic-extinction corrected AB flux" in SDSS r |
| redshift | "Heliocentric redshift" ("z" column in NSA) |
| mag_r | 22.5 - 2.5 log10(sersic_nmgy_r). *Not* the same as the NSA mag column! |
```
@dataset{walmsley_mike_2020_4573248,
author = {Walmsley, Mike and
Lintott, Chris and
Tobias, Geron and
Kruk, Sandor J and
Krawczyk, Coleman and
Willett, Kyle and
Bamford, Steven and
Kelvin, Lee S and
Fortson, Lucy and
Gal, Yarin and
Keel, William and
Masters, Karen and
Mehta, Vihang and
Simmons, Brooke and
Smethurst, Rebecca J and
Smith, Lewis and
Baeten, Elisabeth M L and
Macmillan, Christine},
title = {{Galaxy Zoo DECaLS: Detailed Visual Morphology
Measurements from Volunteers and Deep Learning for
314,000 Galaxies}},
month = dec,
year = 2020,
publisher = {Zenodo},
version = {0.0.2},
doi = {10.5281/zenodo.4573248},
url = {https://doi.org/10.5281/zenodo.4573248}
}
``` | 6,035 | [
[
-0.0684814453125,
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0.035430908203125,
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0.007495880126953125,
-0.044677734375,
0.06988525390625,
0.04302978515625,
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-0.0660400390625,
-0.035430908203125,
0.0115... |
ariesutiono/entailment-bank-v3 | 2022-08-21T06:05:29.000Z | [
"license:cc-by-4.0",
"region:us"
] | ariesutiono | null | null | 0 | 3 | 2022-08-21T05:48:22 |
---
license: cc-by-4.0
---
# Entailment bank dataset
This dataset raw source can be found at [allenai's Github](https://github.com/allenai/entailment_bank/).
If you use this dataset, it is best to cite the original paper
```
@article{entalmentbank2021,
title={Explaining Answers with Entailment Trees},
author={Dalvi, Bhavana and Jansen, Peter and Tafjord, Oyvind and Xie, Zhengnan and Smith, Hannah and Pipatanangkura, Leighanna and Clark, Peter},
journal={EMNLP},
year={2021}
}
``` | 499 | [
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... |
merkalo-ziri/qa_shreded | 2022-08-26T01:27:18.000Z | [
"task_categories:question-answering",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:rus",
"license:other",
"region:us"
] | merkalo-ziri | null | null | 0 | 3 | 2022-08-26T01:25:51 | ---
annotations_creators:
- found
language:
- rus
language_creators:
- found
license:
- other
multilinguality:
- monolingual
pretty_name: qa_main
size_categories:
- 1K<n<10K
source_datasets:
- original
tags: []
task_categories:
- question-answering
task_ids: []
---
# Dataset Card for [Dataset Name]
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset.
| 2,702 | [
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BDas/EnglishNLPDataset | 2022-08-27T11:13:01.000Z | [
"task_categories:text-classification",
"task_ids:multi-class-classification",
"task_ids:multi-label-classification",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
... | BDas | The dataset, prepared in English, includes 10.000 tests, 10.000 validations and 80000 train data.
The data is composed of customer comments and created from e-commerce sites. | ----EnglishNLPDataset---- | 0 | 3 | 2022-08-27T10:58:22 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- other
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-class-classification
- multi-label-classification
pretty_name: 'EnglishNLPDataset'
---
# Dataset Card for "EnglishNLPDataset"
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Dataset Preprocessing](#dataset-preprocessing)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://github.com/BihterDass/EnglishTextClassificationDataset]
- **Repository:** [https://github.com/BihterDass/EnglishTextClassificationDataset]
- **Size of downloaded dataset files:** 8.71 MB
- **Size of the generated dataset:** 8.71 MB
### Dataset Summary
The dataset was compiled from user comments from e-commerce sites. It consists of 10,000 validations, 10,000 tests and 80000 train data. Data were classified into 3 classes (positive(pos), negative(neg) and natural(nor). The data is available to you on github.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
#### english-dataset-v1
- **Size of downloaded dataset files:** 8.71 MB
- **Size of the generated dataset:** 8.71 MB
### Data Fields
The data fields are the same among all splits.
#### english-dataset-v-v1
- `text`: a `string` feature.
- `label`: a classification label, with possible values including `positive` (2), `natural` (1), `negative` (0).
### Data Splits
| |train |validation|test |
|----|--------:|---------:|---------:|
|Data| 80000 | 10000 | 10000 |
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@PnrSvc](https://github.com/PnrSvc) for adding this dataset. | 3,653 | [
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sagawa/ord-uniq-canonicalized | 2022-09-04T02:41:10.000Z | [
"task_categories:text2text-generation",
"task_categories:translation",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"license:apache-2.0",
"ord",
"chemical",
"reaction",
"region:us"
] | sagawa | null | null | 0 | 3 | 2022-09-03T04:28:23 | ---
annotations_creators: []
language_creators: []
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: canonicalized ORD
size_categories:
- 1M<n<10M
source_datasets:
- original
tags:
- ord
- chemical
- reaction
task_categories:
- text2text-generation
- translation
task_ids: []
---
### dataset description
We downloaded open-reaction-database(ORD) dataset from [here](https://github.com/open-reaction-database/ord-data). As a preprocess, we removed overlapping data and canonicalized them using RDKit.
We used the following function to canonicalize the data and removed some SMILES that cannot be read by RDKit.
```python:
from rdkit import Chem
def canonicalize(mol):
mol = Chem.MolToSmiles(Chem.MolFromSmiles(mol),True)
return mol
```
We randomly split the preprocessed data into train, validation and test. The ratio is 8:1:1. | 859 | [
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... |
batterydata/battery-device-data-qa | 2022-09-05T15:54:40.000Z | [
"task_categories:question-answering",
"language:en",
"license:apache-2.0",
"region:us"
] | batterydata | null | null | 0 | 3 | 2022-09-05T15:30:32 | ---
language:
- en
license:
- apache-2.0
task_categories:
- question-answering
pretty_name: 'Battery Device Question Answering Dataset'
---
# Battery Device QA Data
Battery device records, including anode, cathode, and electrolyte.
Examples of the question answering evaluation dataset:
\{'question': 'What is the cathode?', 'answer': 'Al foil', 'context': 'The blended slurry was then cast onto a clean current collector (Al foil for the cathode and Cu foil for the anode) and dried at 90 °C under vacuum overnight.', 'start index': 645\}
\{'question': 'What is the anode?', 'answer': 'Cu foil', 'context': 'The blended slurry was then cast onto a clean current collector (Al foil for the cathode and Cu foil for the anode) and dried at 90 °C under vacuum overnight. Finally, the obtained electrodes were cut into desired shapes on demand. It should be noted that the electrode mass ratio of cathode/anode is set to about 4, thus achieving the battery balance.', 'start index': 673\}
\{'question': 'What is the cathode?', 'answer': 'SiC/RGO nanocomposite', 'context': 'In conclusion, the SiC/RGO nanocomposite, integrating the synergistic effect of SiC flakes and RGO, was synthesized by an in situ gas–solid fabrication method. Taking advantage of the enhanced photogenerated charge separation, large CO2 adsorption, and numerous exposed active sites, SiC/RGO nanocomposite served as the cathode material for the photo-assisted Li–CO2 battery.', 'start index': 284\}
# Usage
```
from datasets import load_dataset
dataset = load_dataset("batterydata/battery-device-data-qa")
```
# Citation
```
@article{huang2022batterybert,
title={BatteryBERT: A Pretrained Language Model for Battery Database Enhancement},
author={Huang, Shu and Cole, Jacqueline M},
journal={J. Chem. Inf. Model.},
year={2022},
doi={10.1021/acs.jcim.2c00035},
url={DOI:10.1021/acs.jcim.2c00035},
pages={DOI: 10.1021/acs.jcim.2c00035},
publisher={ACS Publications}
}
``` | 1,968 | [
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Mijavier/donut_custom_dataset | 2022-09-05T17:15:39.000Z | [
"region:us"
] | Mijavier | null | null | 0 | 3 | 2022-09-05T17:07:15 | Entry not found | 15 | [
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priyank-m/IAM_words_text_recognition | 2022-09-07T13:25:48.000Z | [
"region:us"
] | priyank-m | null | null | 0 | 3 | 2022-09-07T13:10:31 | Entry not found | 15 | [
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nbroad/basic_text_dataset | 2022-09-08T04:21:31.000Z | [
"region:us"
] | nbroad | null | null | 0 | 3 | 2022-09-08T03:43:50 | Entry not found | 15 | [
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Mandi/CourtSegmentation | 2022-09-09T07:38:52.000Z | [
"region:us"
] | Mandi | null | null | 0 | 3 | 2022-09-08T15:20:30 | Entry not found | 15 | [
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0.0379... |
biu-nlp/Controlled-Text-Reduction-dataset | 2022-10-25T13:25:49.000Z | [
"arxiv:2210.13449",
"region:us"
] | biu-nlp | The dataset contains document-summary pairs with document spans (referred to as "highlights"), indicating the "pre-selected" spans that lead to the creation of the summary.
The evaluation and test datasets were constructed via controlled crowdsourcing.
The train datasets were automatically generated using the summary-source proposition-level alignment model SuperPAL (Ernst et al., 2021). | """
# _CITATION = | 1 | 3 | 2022-09-11T09:44:55 | # Controlled Text Reduction
This dataset contains Controlled Text Reduction triplets - document-summary pairs, and the spans in the document that cover the summary.
The task input is consists of a document with pre-selected spans in it ("highlights"). The output is a text covering all and only the highlighted content.
The script downloads the data from the original [GitHub repository](https://github.com/lovodkin93/Controlled_Text_Reduction).
### Format
The dataset contains the following important features:
* `doc_text` - the input text.
* `summary_text` - the output text.
* `highlight_spans` - the spans in the input text (the doc_text) that lead to the output text (the summary_text).
```json
{'doc_text': 'The motion picture industry\'s most coveted award...with 32.',
'summary_text': 'The Oscar, created 60 years ago by MGM...awarded person (32).',
'highlight_spans':'[[0, 48], [50, 55], [57, 81], [184, 247], ..., [953, 975], [1033, 1081]]'}
```
where for each document-summary pair, we save the spans in the input document that lead to the summary.
Notice that the dataset consists of two subsets:
1. `DUC-2001-2002` - which is further divided into 3 splits (train, validation and test).
2. `CNN-DM` - which has a single split.
Citation
========
If you find the Controlled Text Reduction dataset useful in your research, please cite the following paper:
```
@misc{https://doi.org/10.48550/arxiv.2210.13449,
doi = {10.48550/ARXIV.2210.13449},
url = {https://arxiv.org/abs/2210.13449},
author = {Slobodkin, Aviv and Roit, Paul and Hirsch, Eran and Ernst, Ori and Dagan, Ido},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Controlled Text Reduction},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Zero v1.0 Universal}
}
``` | 1,877 | [
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pbwt/all-thai | 2022-09-13T09:36:32.000Z | [
"region:us"
] | pbwt | null | null | 1 | 3 | 2022-09-13T07:43:56 | Entry not found | 15 | [
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allenai/cochrane_sparse_oracle | 2022-11-24T14:54:01.000Z | [
"task_categories:summarization",
"task_categories:text2text-generation",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|other-MS^2",
"source_datasets:extended|other-Cochrane",
"lang... | allenai | null | null | 0 | 3 | 2022-09-14T13:16:16 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|other-MS^2
- extended|other-Cochrane
task_categories:
- summarization
- text2text-generation
paperswithcode_id: multi-document-summarization
pretty_name: MSLR Shared Task
---
This is a copy of the [Cochrane](https://huggingface.co/datasets/allenai/mslr2022) dataset, except the input source documents of its `validation` split have been replaced by a __sparse__ retriever. The retrieval pipeline used:
- __query__: The `target` field of each example
- __corpus__: The union of all documents in the `train`, `validation` and `test` splits. A document is the concatenation of the `title` and `abstract`.
- __retriever__: BM25 via [PyTerrier](https://pyterrier.readthedocs.io/en/latest/) with default settings
- __top-k strategy__: `"oracle"`, i.e. the number of documents retrieved, `k`, is set as the original number of input documents for each example
Retrieval results on the `train` set:
| Recall@100 | Rprec | Precision@k | Recall@k |
| ----------- | ----------- | ----------- | ----------- |
| 0.7014 | 0.3841 | 0.3841 | 0.3841 |
Retrieval results on the `validation` set:
| Recall@100 | Rprec | Precision@k | Recall@k |
| ----------- | ----------- | ----------- | ----------- |
| 0.7226 | 0.4023 | 0.4023 | 0.4023 |
Retrieval results on the `test` set:
N/A. Test set is blind so we do not have any queries. | 1,528 | [
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TheGreatRambler/mm2_world_levels | 2022-11-11T08:03:22.000Z | [
"task_categories:other",
"task_categories:object-detection",
"task_categories:text-retrieval",
"task_categories:token-classification",
"task_categories:text-generation",
"multilinguality:multilingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:multilingual",
"license:cc-by-... | TheGreatRambler | null | null | 1 | 3 | 2022-09-18T20:19:22 | ---
language:
- multilingual
license:
- cc-by-nc-sa-4.0
multilinguality:
- multilingual
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- other
- object-detection
- text-retrieval
- token-classification
- text-generation
task_ids: []
pretty_name: Mario Maker 2 super world levels
tags:
- text-mining
---
# Mario Maker 2 super world levels
Part of the [Mario Maker 2 Dataset Collection](https://tgrcode.com/posts/mario_maker_2_datasets)
## Dataset Description
The Mario Maker 2 super world levels dataset consists of 3.3 million super world levels from Nintendo's online service and adds onto `TheGreatRambler/mm2_world`. The dataset was created using the self-hosted [Mario Maker 2 api](https://tgrcode.com/posts/mario_maker_2_api) over the course of 1 month in February 2022.
### How to use it
You can load and iterate through the dataset with the following code:
```python
from datasets import load_dataset
ds = load_dataset("TheGreatRambler/mm2_world_levels", split="train")
print(next(iter(ds)))
#OUTPUT:
{
'pid': '14510618610706594411',
'data_id': 19170881,
'ninjis': 23
}
```
Each row is a level within a super world owned by player `pid` that is denoted by `data_id`. Each level contains some number of ninjis `ninjis`, a rough metric for their popularity.
## Data Structure
### Data Instances
```python
{
'pid': '14510618610706594411',
'data_id': 19170881,
'ninjis': 23
}
```
### Data Fields
|Field|Type|Description|
|---|---|---|
|pid|string|The player ID of the user who created the super world with this level|
|data_id|int|The data ID of the level|
|ninjis|int|Number of ninjis shown on this level|
### Data Splits
The dataset only contains a train split.
<!-- TODO create detailed statistics -->
## Dataset Creation
The dataset was created over a little more than a month in Febuary 2022 using the self hosted [Mario Maker 2 api](https://tgrcode.com/posts/mario_maker_2_api). As requests made to Nintendo's servers require authentication the process had to be done with upmost care and limiting download speed as to not overload the API and risk a ban. There are no intentions to create an updated release of this dataset.
## Considerations for Using the Data
The dataset contains no harmful language or depictions.
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readerbench/squad_qgen | 2022-09-22T11:51:14.000Z | [
"region:us"
] | readerbench | null | null | 0 | 3 | 2022-09-22T11:39:35 | Entry not found | 15 | [
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0.0379... |
ayesha08/pake-freelancer-dataset | 2022-09-28T19:54:04.000Z | [
"region:us"
] | ayesha08 | null | null | 0 | 3 | 2022-09-28T18:42:15 | Entry not found | 15 | [
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privacy-tech-lab/ppAllTest | 2022-09-30T21:48:55.000Z | [
"region:us"
] | privacy-tech-lab | null | null | 0 | 3 | 2022-09-30T21:48:35 | Entry not found | 15 | [
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ihassan1/auditor-sentiment | 2022-10-02T08:44:54.000Z | [
"task_categories:text-classification",
"task_ids:sentiment-scoring",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"auditor",
"financial",
"sentiment",
"markets",
"region:us"
] | ihassan1 | null | null | 1 | 3 | 2022-10-01T15:10:00 | ---
annotations_creators:
- expert-generated
language: []
language_creators:
- expert-generated
license: []
multilinguality:
- monolingual
pretty_name: auditor-sentiment
size_categories: []
source_datasets: []
tags:
- auditor
- financial
- sentiment
- markets
task_categories:
- text-classification
task_ids:
- sentiment-scoring
---
# Dataset Card for Auditor Sentiment | 369 | [
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Harsit/xnli2.0_french | 2022-10-05T05:32:18.000Z | [
"region:us"
] | Harsit | null | null | 0 | 3 | 2022-10-05T05:32:00 | Entry not found | 15 | [
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Tidrael/tsl_news | 2022-10-10T14:23:36.000Z | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"language_creators:machine-generated",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:apache-2.0",
"region:us"
] | Tidrael | This new dataset is designed to solve this great NLP task and is crafted with a lot of care. | @InProceedings{huggingface:dataset,
title = {A great new dataset},
author={huggingface, Inc.
},
year={2020}
} | 1 | 3 | 2022-10-06T04:47:14 | ---
annotations_creators: []
language:
- en
language_creators:
- machine-generated
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: bussiness-news
size_categories:
- 1K<n<10K
source_datasets:
- original
tags: []
task_categories:
- text-classification
task_ids:
- sentiment-classification
---
# Dataset Card for [Dataset Name]
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
Top news headline in finance from bbc-news
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
English
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
Sentiment label: Using threshold below 0 is negative (0) and above 0 is positive (1)
[More Information Needed]
### Data Splits
Train/Split Ratio is 0.9/0.1
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset. | 2,879 | [
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arbml/darija | 2022-11-03T15:07:13.000Z | [
"region:us"
] | arbml | null | null | 0 | 3 | 2022-10-06T13:53:55 | Entry not found | 15 | [
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HuggingFaceM4/general-pmd-synthetic-testing | 2022-10-07T03:12:13.000Z | [
"license:bigscience-openrail-m",
"region:us"
] | HuggingFaceM4 | This dataset is designed to be used in testing. It's derived from general-pmd-10k dataset | @InProceedings{huggingface:dataset,
title = {Multimodal synthetic dataset for testing / general PMD},
author={HuggingFace, Inc.},
year={2022}
} | 0 | 3 | 2022-10-07T01:07:24 | ---
license: bigscience-openrail-m
---
This dataset is designed to be used in testing. It's derived from general-pmd/localized_narratives__ADE20k dataset
The current splits are: `['100.unique', '100.repeat', '300.unique', '300.repeat', '1k.unique', '1k.repeat', '10k.unique', '10k.repeat']`.
The `unique` ones ensure uniqueness across `text` entries.
The `repeat` ones are repeating the same 10 unique records: - these are useful for memory leaks debugging as the records are always the same and thus remove the record variation from the equation.
The default split is `100.unique`
The full process of this dataset creation, including which records were used to build it, is documented inside [general-pmd-synthetic-testing.py](https://huggingface.co/datasets/HuggingFaceM4/general-pmd-synthetic-testing/blob/main/general-pmd-synthetic-testing.py)
| 854 | [
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GuiGel/meddocan | 2022-10-07T08:58:07.000Z | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:es",
"license:cc-by-4.0",
"clinical",
"pr... | GuiGel | null | null | 0 | 3 | 2022-10-07T06:31:03 | ---
annotations_creators:
- expert-generated
language:
- es
language_creators:
- expert-generated
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: MEDDOCAN
size_categories:
- 10K<n<100K
source_datasets:
- original
tags:
- clinical
- protected health information
- health records
task_categories:
- token-classification
task_ids:
- named-entity-recognition
---
# Dataset Card for "meddocan"
## Table of Contents
- [Dataset Card for [Dataset Name]](#dataset-card-for-dataset-name)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
- [Who are the source language producers?](#who-are-the-source-language-producers)
- [Annotations](#annotations)
- [Annotation process](#annotation-process)
- [Who are the annotators?](#who-are-the-annotators)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://temu.bsc.es/meddocan/index.php/datasets/](https://temu.bsc.es/meddocan/index.php/datasets/)
- **Repository:** [https://github.com/PlanTL-GOB-ES/SPACCC_MEDDOCAN](https://github.com/PlanTL-GOB-ES/SPACCC_MEDDOCAN)
- **Paper:** [http://ceur-ws.org/Vol-2421/MEDDOCAN_overview.pdf](http://ceur-ws.org/Vol-2421/MEDDOCAN_overview.pdf)
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
A personal upload of the SPACC_MEDDOCAN corpus. The tokenization is made with the help of a custom [spaCy](https://spacy.io/) pipeline.
### Supported Tasks and Leaderboards
Name Entity Recognition
### Languages
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Dataset Structure
### Data Instances
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Data Fields
The data fields are the same among all splits.
### Data Splits
| name |train|validation|test|
|---------|----:|---------:|---:|
|meddocan|10312|5268|5155|
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
From the [SPACCC_MEDDOCAN: Spanish Clinical Case Corpus - Medical Document Anonymization](https://github.com/PlanTL-GOB-ES/SPACCC_MEDDOCAN) page:
> This work is licensed under a Creative Commons Attribution 4.0 International License.
>
> You are free to: Share — copy and redistribute the material in any medium or format Adapt — remix, transform, and build upon the material for any purpose, even commercially. Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
>
> For more information, please see https://creativecommons.org/licenses/by/4.0/
### Citation Information
```
@inproceedings{Marimon2019AutomaticDO,
title={Automatic De-identification of Medical Texts in Spanish: the MEDDOCAN Track, Corpus, Guidelines, Methods and Evaluation of Results},
author={Montserrat Marimon and Aitor Gonzalez-Agirre and Ander Intxaurrondo and Heidy Rodriguez and Jose Lopez Martin and Marta Villegas and Martin Krallinger},
booktitle={IberLEF@SEPLN},
year={2019}
}
```
### Contributions
Thanks to [@GuiGel](https://github.com/GuiGel) for adding this dataset. | 6,109 | [
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frankier/multiscale_rotten_tomatoes_critic_reviews | 2022-11-04T12:09:34.000Z | [
"task_categories:text-classification",
"task_ids:text-scoring",
"task_ids:sentiment-scoring",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"language:en",
"license:cc0-1.0",
"reviews",
"ratings",
"ordinal",
"text",
"region:us"
] | frankier | null | null | 0 | 3 | 2022-10-07T12:54:12 | ---
language:
- en
language_creators:
- found
license: cc0-1.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
tags:
- reviews
- ratings
- ordinal
- text
task_categories:
- text-classification
task_ids:
- text-scoring
- sentiment-scoring
---
Cleaned up version of the rotten tomatoes critic reviews dataset. The original
is obtained from Kaggle:
https://www.kaggle.com/datasets/stefanoleone992/rotten-tomatoes-movies-and-critic-reviews-dataset
Data has been scraped from the publicly available website
https://www.rottentomatoes.com as of 2020-10-31.
The clean up process drops anything without both a review and a rating, as well
as standardising the ratings onto several integer, ordinal scales.
Requires the `kaggle` library to be installed, and kaggle API keys passed
through environment variables or in ~/.kaggle/kaggle.json. See [the Kaggle
docs](https://www.kaggle.com/docs/api#authentication).
A processed version is available at
https://huggingface.co/datasets/frankier/processed_multiscale_rt_critics
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argilla/sentiment-banking | 2022-10-07T13:22:00.000Z | [
"region:us"
] | argilla | null | null | 0 | 3 | 2022-10-07T13:18:29 | Entry not found | 15 | [
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argilla/news | 2022-10-07T13:23:10.000Z | [
"region:us"
] | argilla | null | null | 0 | 3 | 2022-10-07T13:23:00 | Entry not found | 15 | [
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rjac/biobert-ner-diseases-dataset | 2022-11-04T11:12:13.000Z | [
"region:us"
] | rjac | null | null | 3 | 3 | 2022-10-08T15:34:44 | ---
dataset_info:
features:
- name: tokens
sequence: string
- name: tags
sequence:
class_label:
names:
0: O
1: B-Disease
2: I-Disease
id:
- 0
- 1
- 2
- name: sentence_id
dtype: string
splits:
- name: test
num_bytes: 2614997
num_examples: 5737
- name: train
num_bytes: 6947635
num_examples: 15488
download_size: 1508920
dataset_size: 9562632
---
# Dataset Card for "biobert-ner-diseases-dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 649 | [
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autoevaluate/autoeval-eval-KETI-AIR__korquad-v1.0-acb0d1-1711659840 | 2022-10-10T12:25:13.000Z | [
"autotrain",
"evaluation",
"region:us"
] | autoevaluate | null | null | 0 | 3 | 2022-10-10T11:38:00 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- KETI-AIR/korquad
eval_info:
task: extractive_question_answering
model: 21iridescent/RoBERTa-base-finetuned-squad2-lwt
metrics: ['angelina-wang/directional_bias_amplification']
dataset_name: KETI-AIR/korquad
dataset_config: v1.0
dataset_split: train
col_mapping:
context: context
question: question
answers-text: answers.text
answers-answer_start: answers.answer_start
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Question Answering
* Model: 21iridescent/RoBERTa-base-finetuned-squad2-lwt
* Dataset: KETI-AIR/korquad
* Config: v1.0
* Split: train
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@HANSOLYOO](https://huggingface.co/HANSOLYOO) for evaluating this model. | 1,028 | [
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umair894/rvl_cdip_300_examples_per_class_donut | 2022-10-11T07:12:23.000Z | [
"region:us"
] | umair894 | null | null | 0 | 3 | 2022-10-11T07:12:17 | Entry not found | 15 | [
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krm/for-ULPGL-Dissertation | 2022-10-16T07:53:00.000Z | [
"task_categories:summarization",
"task_ids:news-articles-summarization",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|orange_sum",
"language:fr",
"license:other",
"krm",
"ulpgl",
"orange",
"reg... | krm | null | null | 0 | 3 | 2022-10-13T11:01:24 | ---
annotations_creators:
- other
language:
- fr
language_creators:
- other
license:
- other
multilinguality:
- monolingual
pretty_name: for-ULPGL-Dissertation
size_categories:
- 10K<n<100K
source_datasets:
- extended|orange_sum
tags:
- krm
- ulpgl
- orange
task_categories:
- summarization
task_ids:
- news-articles-summarization
---
# Dataset Card for [for-ULPGL-Dissertation]
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** krm/for-ULPGL-Dissertation
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
Ce dataset est essentiellement basé sur le dataset *GEM/Orange_sum* dédié à la synthèse d'articles en français. Il est constitué des données abstract de ce dataset (Orange_sum) auxquelles a été ajouté un certain nombre de synthèses générées par le système **Mon Résumeur** de **David Krame**.
### Supported Tasks and Leaderboards
Synthèse automatique
### Languages
Français
## Dataset Structure
### Data Fields
*summary* et *text* sont les champs du dataset avec :
**text** contient les textes et
**summary** les synthèses correspondantes.
### Data Splits
Pour le moment (le 16 Octobre 2022), le dataset est constitué de :
> **21721** données d'entraînement (split dénommé **train**)
> **1545** données de validation (split dénommé **validation**)
> **1581** données de test (split dénommé **test**)
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
| 3,286 | [
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jamescalam/channel-metadata | 2022-10-26T01:05:55.000Z | [
"task_categories:other",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:afl-3.0",
"youtube",
"video",
"video metadata",
"tech",
"science and tech",
"region:us"... | jamescalam | null | null | 1 | 3 | 2022-10-14T05:29:45 | ---
annotations_creators:
- no-annotation
language:
- en
language_creators:
- found
license:
- afl-3.0
multilinguality:
- monolingual
pretty_name: Tech Channels Metadata
size_categories:
- 10K<n<100K
source_datasets:
- original
tags:
- youtube
- video
- video metadata
- tech
- science and tech
task_categories:
- other
task_ids: []
---
Dataset containing video metadata from a few tech channels, i.e.
* [James Briggs](https://youtube.com/c/JamesBriggs)
* [Yannic Kilcher](https://www.youtube.com/c/YannicKilcher)
* [sentdex](https://www.youtube.com/c/sentdex)
* [Daniel Bourke](https://www.youtube.com/channel/UCr8O8l5cCX85Oem1d18EezQ)
* [AI Coffee Break with Letitia](https://www.youtube.com/c/AICoffeeBreak)
* [Alex Ziskind](https://youtube.com/channel/UCajiMK_CY9icRhLepS8_3ug) | 783 | [
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0.... |
cjvt/sloie | 2022-10-21T07:36:18.000Z | [
"task_categories:text-classification",
"task_categories:token-classification",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"size_categories:100K<n<1M",
"language:sl",
"license:cc-by-nc-sa-4.0",
"idiom-detection",
... | cjvt | SloIE is a manually labelled dataset of Slovene idiomatic expressions.
It contains 29,400 sentences with 75 different expressions that can occur with either a literal or an idiomatic meaning,
with appropriate manual annotations for each token. The idiomatic expressions were selected from the Slovene Lexical
Database (http://hdl.handle.net/11356/1030). Only expressions that can occur with both a literal and an idiomatic
meaning were selected. The sentences were extracted from the Gigafida corpus. | @article{skvorc2022mice,
title = {MICE: Mining Idioms with Contextual Embeddings},
journal = {Knowledge-Based Systems},
volume = {235},
pages = {107606},
year = {2022},
issn = {0950-7051},
doi = {https://doi.org/10.1016/j.knosys.2021.107606},
url = {https://www.sciencedirect.com/science/article/pii/S0950705121008686},
author = {{\v S}kvorc, Tadej and Gantar, Polona and Robnik-{\v S}ikonja, Marko},
} | 0 | 3 | 2022-10-17T12:55:41 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- sl
license:
- cc-by-nc-sa-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
- 100K<n<1M
source_datasets: []
task_categories:
- text-classification
- token-classification
task_ids: []
pretty_name: Dataset of Slovene idiomatic expressions SloIE
tags:
- idiom-detection
- multiword-expression-detection
---
# Dataset Card for SloIE
### Dataset Summary
SloIE is a manually labelled dataset of Slovene idiomatic expressions. It contains 29399 sentences with 75 different expressions that can occur with either a literal or an idiomatic meaning, with appropriate manual annotations for each token. The idiomatic expressions were selected from the [Slovene Lexical Database]( (http://hdl.handle.net/11356/1030). Only expressions that can occur with both a literal and an idiomatic meaning were selected. The sentences were extracted from the Gigafida corpus.
For a more detailed description of the dataset, please see the paper Škvorc et al. (2022) - see below.
### Supported Tasks and Leaderboards
Idiom detection.
### Languages
Slovenian.
## Dataset Structure
### Data Instances
A sample instance from the dataset:
```json
{
'sentence': 'Fantje regljajo v enem kotu, deklice pa svoje obrazke barvajo s pisanimi barvami.',
'expression': 'barvati kaj s črnimi barvami',
'word_order': [11, 10, 12, 13, 14],
'sentence_words': ['Fantje', 'regljajo', 'v', 'enem', 'kotu,', 'deklice', 'pa', 'svoje', 'obrazke', 'barvajo', 's', 'pisanimi', 'barvami.'],
'is_idiom': ['*', '*', '*', '*', '*', '*', '*', '*', 'NE', 'NE', 'NE', 'NE', 'NE']
}
```
In this `sentence`, the words of the expression "barvati kaj s črnimi barvami" are used in a literal sense, as indicated by the "NE" annotations inside `is_idiom`. The "*" annotations indicate the words are not part of the expression.
### Data Fields
- `sentence`: raw sentence in string form - **WARNING**: this is at times slightly different from the words inside `sentence_words` (e.g., "..." here could be "." in `sentence_words`);
- `expression`: the annotated idiomatic expression;
- `word_order`: numbers indicating the positions of tokens that belong to the expression;
- `sentence_words`: words in the sentence;
- `is_idiom`: a string denoting whether each word has an idiomatic (`"DA"`), literal (`"NE"`), or ambiguous (`"NEJASEN ZGLED"`) meaning. `"*"` means that the word is not part of the expression.
## Additional Information
### Dataset Curators
Tadej Škvorc, Polona Gantar, Marko Robnik-Šikonja.
### Licensing Information
CC BY-NC-SA 4.0.
### Citation Information
```
@article{skvorc2022mice,
title = {MICE: Mining Idioms with Contextual Embeddings},
journal = {Knowledge-Based Systems},
volume = {235},
pages = {107606},
year = {2022},
doi = {https://doi.org/10.1016/j.knosys.2021.107606},
url = {https://www.sciencedirect.com/science/article/pii/S0950705121008686},
author = {{\v S}kvorc, Tadej and Gantar, Polona and Robnik-{\v S}ikonja, Marko},
}
```
### Contributions
Thanks to [@matejklemen](https://github.com/matejklemen) for adding this dataset.
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pachi107/autotrain-data-ethos-sentiments | 2022-10-17T16:28:44.000Z | [
"task_categories:text-classification",
"language:en",
"region:us"
] | pachi107 | null | null | 0 | 3 | 2022-10-17T16:28:10 | ---
language:
- en
task_categories:
- text-classification
---
# AutoTrain Dataset for project: ethos-sentiments
## Dataset Description
This dataset has been automatically processed by AutoTrain for project ethos-sentiments.
### Languages
The BCP-47 code for the dataset's language is en.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"text": "well it's clear now why europeans can't differ niggers and shitskins from human",
"target": 0
},
{
"text": "These boys will then grow up with people making fun of them and they will then hate their parents for ruining their lives.",
"target": 1
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"text": "Value(dtype='string', id=None)",
"target": "ClassLabel(num_classes=2, names=['hate_speech', 'no_hate_speech'], id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 798 |
| valid | 200 |
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-0.048583984375,
... |
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