Upload batch 80 (20 files, last=huggingface_dataset/Dataset_Card/TheGreatRambler_mm2_level_comments.md)
Browse files- huggingface_dataset/Dataset_Card/DReAMy-lib_DreamBank-dreams.md +114 -0
- huggingface_dataset/Dataset_Card/DTU54DL_common-accent.md +173 -0
- huggingface_dataset/Dataset_Card/MicPie_unpredictable_ensembl-org.md +250 -0
- huggingface_dataset/Dataset_Card/Rosenberg_CMeEE.md +13 -0
- huggingface_dataset/Dataset_Card/TheGreatRambler_mm2_level_comments.md +166 -0
- huggingface_dataset/Dataset_Card/UKPLab_liar.md +1 -0
- huggingface_dataset/Dataset_Card/autoevaluate_autoeval-eval-inverse-scaling__redefine-math-inverse-scaling__redefin-f7efd9-1695359602.md +34 -0
- huggingface_dataset/Dataset_Card/autoevaluate_autoeval-eval-phpthinh__examplei-match-bd10ea-1748761024.md +34 -0
- huggingface_dataset/Dataset_Card/autoevaluate_autoeval-staging-eval-project-c967fc98-8385125.md +31 -0
- huggingface_dataset/Dataset_Card/autoevaluate_autoeval-staging-eval-project-conll2003-e2bfcc2b-10665436.md +33 -0
- huggingface_dataset/Dataset_Card/autoevaluate_autoeval-staging-eval-project-cuad-e5412c0a-12275642.md +35 -0
- huggingface_dataset/Dataset_Card/autoevaluate_autoeval-staging-eval-squad_v2-squad_v2-76c05b-14906070.md +35 -0
- huggingface_dataset/Dataset_Card/cats_vs_dogs.md +217 -0
- huggingface_dataset/Dataset_Card/irds_mmarco_es_dev.md +49 -0
- huggingface_dataset/Dataset_Card/norwegian_ner.md +336 -0
- huggingface_dataset/Dataset_Card/open-source-metrics_optimum-dependents.md +124 -0
- huggingface_dataset/Dataset_Card/scjnugacj_scjn_dataset_ner.md +84 -0
- huggingface_dataset/Dataset_Card/soymia_boudoir-dataset.md +25 -0
- huggingface_dataset/Dataset_Card/thaisum.md +215 -0
- huggingface_dataset/Dataset_Card/thejaminator_imdb_rewarded.md +11 -0
huggingface_dataset/Dataset_Card/DReAMy-lib_DreamBank-dreams.md
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| 1 |
+
---
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dataset_info:
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+
features:
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- name: dreams
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dtype: string
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| 6 |
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- name: series
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dtype: string
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- name: description
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dtype: string
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splits:
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| 11 |
+
- name: train
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+
num_bytes: 27263345
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| 13 |
+
num_examples: 29345
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| 14 |
+
download_size: 15525739
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dataset_size: 27263345
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license: apache-2.0
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task_categories:
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- text-generation
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| 19 |
+
language:
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| 20 |
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- en
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| 21 |
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- de
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size_categories:
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- 10K<n<100K
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---
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+
# DreamBank - Dreams
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| 26 |
+
|
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+
The dataset is a collection of ~30k textual reports of dreams, originally scraped from the [DreamBank](https://www.dreambank.net/) databased by
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+
[`mattbierner`](https://github.com/mattbierner/DreamScrape). The DreamBank reports are divided into `series`,
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+
which are collections of individuals or research projects/groups that have gathered the dreams. The vast majority of the series are in the
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English language, but a small part of the are in German. These series are indicated by the presence of `.de` in their name.
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## Content
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The dataset revolves around three main features:
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- `dreams`: the content of each dream report.
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+
- `series`: the series to which a report belongs
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- `description`: a brief description of the `series`
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+
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## Series distribution
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+
The following is a summary of (alphabetically ordered) DreamBank's series together with their total amount of dream reports.
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+
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+
- alta: 422
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+
- angie: 48
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| 43 |
+
- arlie: 212
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| 44 |
+
- b: 3114
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| 45 |
+
- b-baseline: 250
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| 46 |
+
- b2: 1138
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| 47 |
+
- bay_area_girls_456: 234
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| 48 |
+
- bay_area_girls_789: 154
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| 49 |
+
- bea1: 223
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| 50 |
+
- bea2: 63
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| 51 |
+
- blind-f: 238
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| 52 |
+
- blind-m: 143
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| 53 |
+
- bosnak: 53
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| 54 |
+
- chris: 100
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| 55 |
+
- chuck: 75
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| 56 |
+
- dahlia: 24
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| 57 |
+
- david: 166
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| 58 |
+
- dorothea: 899
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| 59 |
+
- ed: 143
|
| 60 |
+
- edna: 19
|
| 61 |
+
- elizabeth: 1707
|
| 62 |
+
- emma: 1221
|
| 63 |
+
- emmas_husband: 72
|
| 64 |
+
- esther: 110
|
| 65 |
+
- german-f.de: 397
|
| 66 |
+
- german-m.de: 140
|
| 67 |
+
- hall_female: 681
|
| 68 |
+
- jasmine1: 39
|
| 69 |
+
- jasmine2: 269
|
| 70 |
+
- jasmine3: 259
|
| 71 |
+
- jasmine4: 94
|
| 72 |
+
- jeff: 87
|
| 73 |
+
- joan: 42
|
| 74 |
+
- kenneth: 2021
|
| 75 |
+
- lawrence: 206
|
| 76 |
+
- mack: 38
|
| 77 |
+
- madeline1-hs: 98
|
| 78 |
+
- madeline2-dorms: 186
|
| 79 |
+
- madeline3-offcampus: 348
|
| 80 |
+
- madeline4-postgrad: 294
|
| 81 |
+
- mark: 23
|
| 82 |
+
- melissa: 89
|
| 83 |
+
- melora: 211
|
| 84 |
+
- melvin: 128
|
| 85 |
+
- merri: 315
|
| 86 |
+
- miami-home: 171
|
| 87 |
+
- miami-lab: 274
|
| 88 |
+
- midwest_teens-f: 111
|
| 89 |
+
- midwest_teens-m: 83
|
| 90 |
+
- nancy: 44
|
| 91 |
+
- natural_scientist: 234
|
| 92 |
+
- norman: 1235
|
| 93 |
+
- norms-f: 490
|
| 94 |
+
- norms-m: 491
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| 95 |
+
- pegasus: 1093
|
| 96 |
+
- peru-f: 381
|
| 97 |
+
- peru-m: 384
|
| 98 |
+
- phil1: 106
|
| 99 |
+
- phil2: 220
|
| 100 |
+
- phil3: 180
|
| 101 |
+
- physiologist: 86
|
| 102 |
+
- ringo: 16
|
| 103 |
+
- samantha: 63
|
| 104 |
+
- seventh_graders: 69
|
| 105 |
+
- toby: 33
|
| 106 |
+
- tom: 27
|
| 107 |
+
- ucsc_women: 81
|
| 108 |
+
- vickie: 35
|
| 109 |
+
- vietnam_vet: 98
|
| 110 |
+
- vonuslar.de: 6094
|
| 111 |
+
- wedding: 65
|
| 112 |
+
- west_coast_teens: 89
|
| 113 |
+
- zurich-f.de: 164
|
| 114 |
+
- zurich-m.de: 135
|
huggingface_dataset/Dataset_Card/DTU54DL_common-accent.md
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| 1 |
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---
|
| 2 |
+
annotations_creators:
|
| 3 |
+
- expert-generated
|
| 4 |
+
language:
|
| 5 |
+
- en
|
| 6 |
+
language_creators:
|
| 7 |
+
- found
|
| 8 |
+
license:
|
| 9 |
+
- mit
|
| 10 |
+
multilinguality:
|
| 11 |
+
- monolingual
|
| 12 |
+
paperswithcode_id: acronym-identification
|
| 13 |
+
pretty_name: Acronym Identification Dataset
|
| 14 |
+
size_categories:
|
| 15 |
+
- 10K<n<100K
|
| 16 |
+
source_datasets:
|
| 17 |
+
- original
|
| 18 |
+
task_categories:
|
| 19 |
+
- token-classification
|
| 20 |
+
task_ids:
|
| 21 |
+
- token-classification-other-acronym-identification
|
| 22 |
+
train-eval-index:
|
| 23 |
+
- col_mapping:
|
| 24 |
+
labels: tags
|
| 25 |
+
tokens: tokens
|
| 26 |
+
config: default
|
| 27 |
+
splits:
|
| 28 |
+
eval_split: test
|
| 29 |
+
task: token-classification
|
| 30 |
+
task_id: entity_extraction
|
| 31 |
+
dataset_info:
|
| 32 |
+
features:
|
| 33 |
+
- name: audio
|
| 34 |
+
dtype:
|
| 35 |
+
audio:
|
| 36 |
+
sampling_rate: 16000
|
| 37 |
+
- name: sentence
|
| 38 |
+
dtype: string
|
| 39 |
+
- name: accent
|
| 40 |
+
dtype: string
|
| 41 |
+
splits:
|
| 42 |
+
- name: train
|
| 43 |
+
num_bytes: 471755846.3910719
|
| 44 |
+
num_examples: 10000
|
| 45 |
+
- name: test
|
| 46 |
+
num_bytes: 19497172.25755167
|
| 47 |
+
num_examples: 451
|
| 48 |
+
download_size: 436911322
|
| 49 |
+
dataset_size: 491253018.6486236
|
| 50 |
+
---
|
| 51 |
+
|
| 52 |
+
# Dataset Card for [Dataset Name]
|
| 53 |
+
|
| 54 |
+
## Table of Contents
|
| 55 |
+
- [Table of Contents](#table-of-contents)
|
| 56 |
+
- [Dataset Description](#dataset-description)
|
| 57 |
+
- [Dataset Summary](#dataset-summary)
|
| 58 |
+
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
|
| 59 |
+
- [Languages](#languages)
|
| 60 |
+
- [Dataset Structure](#dataset-structure)
|
| 61 |
+
- [Data Instances](#data-instances)
|
| 62 |
+
- [Data Fields](#data-fields)
|
| 63 |
+
- [Data Splits](#data-splits)
|
| 64 |
+
- [Dataset Creation](#dataset-creation)
|
| 65 |
+
- [Curation Rationale](#curation-rationale)
|
| 66 |
+
- [Source Data](#source-data)
|
| 67 |
+
- [Annotations](#annotations)
|
| 68 |
+
- [Personal and Sensitive Information](#personal-and-sensitive-information)
|
| 69 |
+
- [Considerations for Using the Data](#considerations-for-using-the-data)
|
| 70 |
+
- [Social Impact of Dataset](#social-impact-of-dataset)
|
| 71 |
+
- [Discussion of Biases](#discussion-of-biases)
|
| 72 |
+
- [Other Known Limitations](#other-known-limitations)
|
| 73 |
+
- [Additional Information](#additional-information)
|
| 74 |
+
- [Dataset Curators](#dataset-curators)
|
| 75 |
+
- [Licensing Information](#licensing-information)
|
| 76 |
+
- [Citation Information](#citation-information)
|
| 77 |
+
- [Contributions](#contributions)
|
| 78 |
+
|
| 79 |
+
## Dataset Description
|
| 80 |
+
|
| 81 |
+
- **Homepage:**
|
| 82 |
+
- **Repository:**
|
| 83 |
+
- **Paper:**
|
| 84 |
+
- **Leaderboard:**
|
| 85 |
+
- **Point of Contact:**
|
| 86 |
+
|
| 87 |
+
### Dataset Summary
|
| 88 |
+
|
| 89 |
+
[More Information Needed]
|
| 90 |
+
|
| 91 |
+
### Supported Tasks and Leaderboards
|
| 92 |
+
|
| 93 |
+
[More Information Needed]
|
| 94 |
+
|
| 95 |
+
### Languages
|
| 96 |
+
|
| 97 |
+
[More Information Needed]
|
| 98 |
+
|
| 99 |
+
## Dataset Structure
|
| 100 |
+
|
| 101 |
+
### Data Instances
|
| 102 |
+
|
| 103 |
+
[More Information Needed]
|
| 104 |
+
|
| 105 |
+
### Data Fields
|
| 106 |
+
|
| 107 |
+
[More Information Needed]
|
| 108 |
+
|
| 109 |
+
### Data Splits
|
| 110 |
+
|
| 111 |
+
[More Information Needed]
|
| 112 |
+
|
| 113 |
+
## Dataset Creation
|
| 114 |
+
|
| 115 |
+
### Curation Rationale
|
| 116 |
+
|
| 117 |
+
[More Information Needed]
|
| 118 |
+
|
| 119 |
+
### Source Data
|
| 120 |
+
|
| 121 |
+
#### Initial Data Collection and Normalization
|
| 122 |
+
|
| 123 |
+
[More Information Needed]
|
| 124 |
+
|
| 125 |
+
#### Who are the source language producers?
|
| 126 |
+
|
| 127 |
+
[More Information Needed]
|
| 128 |
+
|
| 129 |
+
### Annotations
|
| 130 |
+
|
| 131 |
+
#### Annotation process
|
| 132 |
+
|
| 133 |
+
[More Information Needed]
|
| 134 |
+
|
| 135 |
+
#### Who are the annotators?
|
| 136 |
+
|
| 137 |
+
[More Information Needed]
|
| 138 |
+
|
| 139 |
+
### Personal and Sensitive Information
|
| 140 |
+
|
| 141 |
+
[More Information Needed]
|
| 142 |
+
|
| 143 |
+
## Considerations for Using the Data
|
| 144 |
+
|
| 145 |
+
### Social Impact of Dataset
|
| 146 |
+
|
| 147 |
+
[More Information Needed]
|
| 148 |
+
|
| 149 |
+
### Discussion of Biases
|
| 150 |
+
|
| 151 |
+
[More Information Needed]
|
| 152 |
+
|
| 153 |
+
### Other Known Limitations
|
| 154 |
+
|
| 155 |
+
[More Information Needed]
|
| 156 |
+
|
| 157 |
+
## Additional Information
|
| 158 |
+
|
| 159 |
+
### Dataset Curators
|
| 160 |
+
|
| 161 |
+
[More Information Needed]
|
| 162 |
+
|
| 163 |
+
### Licensing Information
|
| 164 |
+
|
| 165 |
+
[More Information Needed]
|
| 166 |
+
|
| 167 |
+
### Citation Information
|
| 168 |
+
|
| 169 |
+
[More Information Needed]
|
| 170 |
+
|
| 171 |
+
### Contributions
|
| 172 |
+
|
| 173 |
+
Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset.
|
huggingface_dataset/Dataset_Card/MicPie_unpredictable_ensembl-org.md
ADDED
|
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|
| 1 |
+
---
|
| 2 |
+
annotations_creators:
|
| 3 |
+
- no-annotation
|
| 4 |
+
language_creators:
|
| 5 |
+
- found
|
| 6 |
+
language:
|
| 7 |
+
- en
|
| 8 |
+
license:
|
| 9 |
+
- apache-2.0
|
| 10 |
+
multilinguality:
|
| 11 |
+
- monolingual
|
| 12 |
+
pretty_name: UnpredicTable-ensembl-org
|
| 13 |
+
size_categories:
|
| 14 |
+
- 100K<n<1M
|
| 15 |
+
source_datasets: []
|
| 16 |
+
task_categories:
|
| 17 |
+
- multiple-choice
|
| 18 |
+
- question-answering
|
| 19 |
+
- zero-shot-classification
|
| 20 |
+
- text2text-generation
|
| 21 |
+
- table-question-answering
|
| 22 |
+
- text-generation
|
| 23 |
+
- text-classification
|
| 24 |
+
- tabular-classification
|
| 25 |
+
task_ids:
|
| 26 |
+
- multiple-choice-qa
|
| 27 |
+
- extractive-qa
|
| 28 |
+
- open-domain-qa
|
| 29 |
+
- closed-domain-qa
|
| 30 |
+
- closed-book-qa
|
| 31 |
+
- open-book-qa
|
| 32 |
+
- language-modeling
|
| 33 |
+
- multi-class-classification
|
| 34 |
+
- natural-language-inference
|
| 35 |
+
- topic-classification
|
| 36 |
+
- multi-label-classification
|
| 37 |
+
- tabular-multi-class-classification
|
| 38 |
+
- tabular-multi-label-classification
|
| 39 |
+
---
|
| 40 |
+
|
| 41 |
+
|
| 42 |
+
# Dataset Card for "UnpredicTable-ensembl-org" - Dataset of Few-shot Tasks from Tables
|
| 43 |
+
|
| 44 |
+
## Table of Contents
|
| 45 |
+
- [Dataset Description](#dataset-description)
|
| 46 |
+
- [Dataset Summary](#dataset-summary)
|
| 47 |
+
- [Supported Tasks](#supported-tasks-and-leaderboards)
|
| 48 |
+
- [Languages](#languages)
|
| 49 |
+
- [Dataset Structure](#dataset-structure)
|
| 50 |
+
- [Data Instances](#data-instances)
|
| 51 |
+
- [Data Fields](#data-instances)
|
| 52 |
+
- [Data Splits](#data-instances)
|
| 53 |
+
- [Dataset Creation](#dataset-creation)
|
| 54 |
+
- [Curation Rationale](#curation-rationale)
|
| 55 |
+
- [Source Data](#source-data)
|
| 56 |
+
- [Annotations](#annotations)
|
| 57 |
+
- [Personal and Sensitive Information](#personal-and-sensitive-information)
|
| 58 |
+
- [Considerations for Using the Data](#considerations-for-using-the-data)
|
| 59 |
+
- [Social Impact of Dataset](#social-impact-of-dataset)
|
| 60 |
+
- [Discussion of Biases](#discussion-of-biases)
|
| 61 |
+
- [Other Known Limitations](#other-known-limitations)
|
| 62 |
+
- [Additional Information](#additional-information)
|
| 63 |
+
- [Dataset Curators](#dataset-curators)
|
| 64 |
+
- [Licensing Information](#licensing-information)
|
| 65 |
+
- [Citation Information](#citation-information)
|
| 66 |
+
|
| 67 |
+
## Dataset Description
|
| 68 |
+
|
| 69 |
+
- **Homepage:** https://ethanperez.net/unpredictable
|
| 70 |
+
- **Repository:** https://github.com/JunShern/few-shot-adaptation
|
| 71 |
+
- **Paper:** Few-shot Adaptation Works with UnpredicTable Data
|
| 72 |
+
- **Point of Contact:** junshern@nyu.edu, perez@nyu.edu
|
| 73 |
+
|
| 74 |
+
### Dataset Summary
|
| 75 |
+
|
| 76 |
+
The UnpredicTable dataset consists of web tables formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance.
|
| 77 |
+
|
| 78 |
+
There are several dataset versions available:
|
| 79 |
+
|
| 80 |
+
* [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full): Starting from the initial WTC corpus of 50M tables, we apply our tables-to-tasks procedure to produce our resulting dataset, [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full), which comprises 413,299 tasks from 23,744 unique websites.
|
| 81 |
+
|
| 82 |
+
* [UnpredicTable-unique](https://huggingface.co/datasets/MicPie/unpredictable_unique): This is the same as [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full) but filtered to have a maximum of one task per website. [UnpredicTable-unique](https://huggingface.co/datasets/MicPie/unpredictable_unique) contains exactly 23,744 tasks from 23,744 websites.
|
| 83 |
+
|
| 84 |
+
* [UnpredicTable-5k](https://huggingface.co/datasets/MicPie/unpredictable_5k): This dataset contains 5k random tables from the full dataset.
|
| 85 |
+
|
| 86 |
+
* UnpredicTable data subsets based on a manual human quality rating (please see our publication for details of the ratings):
|
| 87 |
+
* [UnpredicTable-rated-low](https://huggingface.co/datasets/MicPie/unpredictable_rated-low)
|
| 88 |
+
* [UnpredicTable-rated-medium](https://huggingface.co/datasets/MicPie/unpredictable_rated-medium)
|
| 89 |
+
* [UnpredicTable-rated-high](https://huggingface.co/datasets/MicPie/unpredictable_rated-high)
|
| 90 |
+
|
| 91 |
+
* UnpredicTable data subsets based on the website of origin:
|
| 92 |
+
* [UnpredicTable-baseball-fantasysports-yahoo-com](https://huggingface.co/datasets/MicPie/unpredictable_baseball-fantasysports-yahoo-com)
|
| 93 |
+
* [UnpredicTable-bulbapedia-bulbagarden-net](https://huggingface.co/datasets/MicPie/unpredictable_bulbapedia-bulbagarden-net)
|
| 94 |
+
* [UnpredicTable-cappex-com](https://huggingface.co/datasets/MicPie/unpredictable_cappex-com)
|
| 95 |
+
* [UnpredicTable-cram-com](https://huggingface.co/datasets/MicPie/unpredictable_cram-com)
|
| 96 |
+
* [UnpredicTable-dividend-com](https://huggingface.co/datasets/MicPie/unpredictable_dividend-com)
|
| 97 |
+
* [UnpredicTable-dummies-com](https://huggingface.co/datasets/MicPie/unpredictable_dummies-com)
|
| 98 |
+
* [UnpredicTable-en-wikipedia-org](https://huggingface.co/datasets/MicPie/unpredictable_en-wikipedia-org)
|
| 99 |
+
* [UnpredicTable-ensembl-org](https://huggingface.co/datasets/MicPie/unpredictable_ensembl-org)
|
| 100 |
+
* [UnpredicTable-gamefaqs-com](https://huggingface.co/datasets/MicPie/unpredictable_gamefaqs-com)
|
| 101 |
+
* [UnpredicTable-mgoblog-com](https://huggingface.co/datasets/MicPie/unpredictable_mgoblog-com)
|
| 102 |
+
* [UnpredicTable-mmo-champion-com](https://huggingface.co/datasets/MicPie/unpredictable_mmo-champion-com)
|
| 103 |
+
* [UnpredicTable-msdn-microsoft-com](https://huggingface.co/datasets/MicPie/unpredictable_msdn-microsoft-com)
|
| 104 |
+
* [UnpredicTable-phonearena-com](https://huggingface.co/datasets/MicPie/unpredictable_phonearena-com)
|
| 105 |
+
* [UnpredicTable-sittercity-com](https://huggingface.co/datasets/MicPie/unpredictable_sittercity-com)
|
| 106 |
+
* [UnpredicTable-sporcle-com](https://huggingface.co/datasets/MicPie/unpredictable_sporcle-com)
|
| 107 |
+
* [UnpredicTable-studystack-com](https://huggingface.co/datasets/MicPie/unpredictable_studystack-com)
|
| 108 |
+
* [UnpredicTable-support-google-com](https://huggingface.co/datasets/MicPie/unpredictable_support-google-com)
|
| 109 |
+
* [UnpredicTable-w3-org](https://huggingface.co/datasets/MicPie/unpredictable_w3-org)
|
| 110 |
+
* [UnpredicTable-wiki-openmoko-org](https://huggingface.co/datasets/MicPie/unpredictable_wiki-openmoko-org)
|
| 111 |
+
* [UnpredicTable-wkdu-org](https://huggingface.co/datasets/MicPie/unpredictable_wkdu-org)
|
| 112 |
+
|
| 113 |
+
|
| 114 |
+
* UnpredicTable data subsets based on clustering (for the clustering details please see our publication):
|
| 115 |
+
* [UnpredicTable-cluster00](https://huggingface.co/datasets/MicPie/unpredictable_cluster00)
|
| 116 |
+
* [UnpredicTable-cluster01](https://huggingface.co/datasets/MicPie/unpredictable_cluster01)
|
| 117 |
+
* [UnpredicTable-cluster02](https://huggingface.co/datasets/MicPie/unpredictable_cluster02)
|
| 118 |
+
* [UnpredicTable-cluster03](https://huggingface.co/datasets/MicPie/unpredictable_cluster03)
|
| 119 |
+
* [UnpredicTable-cluster04](https://huggingface.co/datasets/MicPie/unpredictable_cluster04)
|
| 120 |
+
* [UnpredicTable-cluster05](https://huggingface.co/datasets/MicPie/unpredictable_cluster05)
|
| 121 |
+
* [UnpredicTable-cluster06](https://huggingface.co/datasets/MicPie/unpredictable_cluster06)
|
| 122 |
+
* [UnpredicTable-cluster07](https://huggingface.co/datasets/MicPie/unpredictable_cluster07)
|
| 123 |
+
* [UnpredicTable-cluster08](https://huggingface.co/datasets/MicPie/unpredictable_cluster08)
|
| 124 |
+
* [UnpredicTable-cluster09](https://huggingface.co/datasets/MicPie/unpredictable_cluster09)
|
| 125 |
+
* [UnpredicTable-cluster10](https://huggingface.co/datasets/MicPie/unpredictable_cluster10)
|
| 126 |
+
* [UnpredicTable-cluster11](https://huggingface.co/datasets/MicPie/unpredictable_cluster11)
|
| 127 |
+
* [UnpredicTable-cluster12](https://huggingface.co/datasets/MicPie/unpredictable_cluster12)
|
| 128 |
+
* [UnpredicTable-cluster13](https://huggingface.co/datasets/MicPie/unpredictable_cluster13)
|
| 129 |
+
* [UnpredicTable-cluster14](https://huggingface.co/datasets/MicPie/unpredictable_cluster14)
|
| 130 |
+
* [UnpredicTable-cluster15](https://huggingface.co/datasets/MicPie/unpredictable_cluster15)
|
| 131 |
+
* [UnpredicTable-cluster16](https://huggingface.co/datasets/MicPie/unpredictable_cluster16)
|
| 132 |
+
* [UnpredicTable-cluster17](https://huggingface.co/datasets/MicPie/unpredictable_cluster17)
|
| 133 |
+
* [UnpredicTable-cluster18](https://huggingface.co/datasets/MicPie/unpredictable_cluster18)
|
| 134 |
+
* [UnpredicTable-cluster19](https://huggingface.co/datasets/MicPie/unpredictable_cluster19)
|
| 135 |
+
* [UnpredicTable-cluster20](https://huggingface.co/datasets/MicPie/unpredictable_cluster20)
|
| 136 |
+
* [UnpredicTable-cluster21](https://huggingface.co/datasets/MicPie/unpredictable_cluster21)
|
| 137 |
+
* [UnpredicTable-cluster22](https://huggingface.co/datasets/MicPie/unpredictable_cluster22)
|
| 138 |
+
* [UnpredicTable-cluster23](https://huggingface.co/datasets/MicPie/unpredictable_cluster23)
|
| 139 |
+
* [UnpredicTable-cluster24](https://huggingface.co/datasets/MicPie/unpredictable_cluster24)
|
| 140 |
+
* [UnpredicTable-cluster25](https://huggingface.co/datasets/MicPie/unpredictable_cluster25)
|
| 141 |
+
* [UnpredicTable-cluster26](https://huggingface.co/datasets/MicPie/unpredictable_cluster26)
|
| 142 |
+
* [UnpredicTable-cluster27](https://huggingface.co/datasets/MicPie/unpredictable_cluster27)
|
| 143 |
+
* [UnpredicTable-cluster28](https://huggingface.co/datasets/MicPie/unpredictable_cluster28)
|
| 144 |
+
* [UnpredicTable-cluster29](https://huggingface.co/datasets/MicPie/unpredictable_cluster29)
|
| 145 |
+
* [UnpredicTable-cluster-noise](https://huggingface.co/datasets/MicPie/unpredictable_cluster-noise)
|
| 146 |
+
|
| 147 |
+
### Supported Tasks and Leaderboards
|
| 148 |
+
|
| 149 |
+
Since the tables come from the web, the distribution of tasks and topics is very broad. The shape of our dataset is very wide, i.e., we have 1000's of tasks, while each task has only a few examples, compared to most current NLP datasets which are very deep, i.e., 10s of tasks with many examples. This implies that our dataset covers a broad range of potential tasks, e.g., multiple-choice, question-answering, table-question-answering, text-classification, etc.
|
| 150 |
+
|
| 151 |
+
The intended use of this dataset is to improve few-shot performance by fine-tuning/pre-training on our dataset.
|
| 152 |
+
|
| 153 |
+
### Languages
|
| 154 |
+
|
| 155 |
+
English
|
| 156 |
+
|
| 157 |
+
## Dataset Structure
|
| 158 |
+
|
| 159 |
+
### Data Instances
|
| 160 |
+
|
| 161 |
+
Each task is represented as a jsonline file and consists of several few-shot examples. Each example is a dictionary containing a field 'task', which identifies the task, followed by an 'input', 'options', and 'output' field. The 'input' field contains several column elements of the same row in the table, while the 'output' field is a target which represents an individual column of the same row. Each task contains several such examples which can be concatenated as a few-shot task. In the case of multiple choice classification, the 'options' field contains the possible classes that a model needs to choose from.
|
| 162 |
+
|
| 163 |
+
There are also additional meta-data fields such as 'pageTitle', 'title', 'outputColName', 'url', 'wdcFile'.
|
| 164 |
+
|
| 165 |
+
### Data Fields
|
| 166 |
+
|
| 167 |
+
'task': task identifier
|
| 168 |
+
|
| 169 |
+
'input': column elements of a specific row in the table.
|
| 170 |
+
|
| 171 |
+
'options': for multiple choice classification, it provides the options to choose from.
|
| 172 |
+
|
| 173 |
+
'output': target column element of the same row as input.
|
| 174 |
+
|
| 175 |
+
'pageTitle': the title of the page containing the table.
|
| 176 |
+
|
| 177 |
+
'outputColName': output column name
|
| 178 |
+
|
| 179 |
+
'url': url to the website containing the table
|
| 180 |
+
|
| 181 |
+
'wdcFile': WDC Web Table Corpus file
|
| 182 |
+
|
| 183 |
+
### Data Splits
|
| 184 |
+
|
| 185 |
+
The UnpredicTable datasets do not come with additional data splits.
|
| 186 |
+
|
| 187 |
+
## Dataset Creation
|
| 188 |
+
|
| 189 |
+
### Curation Rationale
|
| 190 |
+
|
| 191 |
+
Few-shot training on multi-task datasets has been demonstrated to improve language models' few-shot learning (FSL) performance on new tasks, but it is unclear which training tasks lead to effective downstream task adaptation. Few-shot learning datasets are typically produced with expensive human curation, limiting the scale and diversity of the training tasks available to study. As an alternative source of few-shot data, we automatically extract 413,299 tasks from diverse internet tables. We provide this as a research resource to investigate the relationship between training data and few-shot learning.
|
| 192 |
+
|
| 193 |
+
### Source Data
|
| 194 |
+
|
| 195 |
+
#### Initial Data Collection and Normalization
|
| 196 |
+
|
| 197 |
+
We use internet tables from the English-language Relational Subset of the WDC Web Table Corpus 2015 (WTC). The WTC dataset tables were extracted from the July 2015 Common Crawl web corpus (http://webdatacommons.org/webtables/2015/EnglishStatistics.html). The dataset contains 50,820,165 tables from 323,160 web domains. We then convert the tables into few-shot learning tasks. Please see our publication for more details on the data collection and conversion pipeline.
|
| 198 |
+
|
| 199 |
+
#### Who are the source language producers?
|
| 200 |
+
|
| 201 |
+
The dataset is extracted from [WDC Web Table Corpora](http://webdatacommons.org/webtables/).
|
| 202 |
+
|
| 203 |
+
### Annotations
|
| 204 |
+
|
| 205 |
+
#### Annotation process
|
| 206 |
+
|
| 207 |
+
Manual annotation was only carried out for the [UnpredicTable-rated-low](https://huggingface.co/datasets/MicPie/unpredictable_rated-low),
|
| 208 |
+
[UnpredicTable-rated-medium](https://huggingface.co/datasets/MicPie/unpredictable_rated-medium), and [UnpredicTable-rated-high](https://huggingface.co/datasets/MicPie/unpredictable_rated-high) data subsets to rate task quality. Detailed instructions of the annotation instructions can be found in our publication.
|
| 209 |
+
|
| 210 |
+
#### Who are the annotators?
|
| 211 |
+
|
| 212 |
+
Annotations were carried out by a lab assistant.
|
| 213 |
+
|
| 214 |
+
### Personal and Sensitive Information
|
| 215 |
+
|
| 216 |
+
The data was extracted from [WDC Web Table Corpora](http://webdatacommons.org/webtables/), which in turn extracted tables from the [Common Crawl](https://commoncrawl.org/). We did not filter the data in any way. Thus any user identities or otherwise sensitive information (e.g., data that reveals racial or ethnic origins, sexual orientations, religious beliefs, political opinions or union memberships, or locations; financial or health data; biometric or genetic data; forms of government identification, such as social security numbers; criminal history, etc.) might be contained in our dataset.
|
| 217 |
+
|
| 218 |
+
## Considerations for Using the Data
|
| 219 |
+
|
| 220 |
+
### Social Impact of Dataset
|
| 221 |
+
|
| 222 |
+
This dataset is intended for use as a research resource to investigate the relationship between training data and few-shot learning. As such, it contains high- and low-quality data, as well as diverse content that may be untruthful or inappropriate. Without careful investigation, it should not be used for training models that will be deployed for use in decision-critical or user-facing situations.
|
| 223 |
+
|
| 224 |
+
### Discussion of Biases
|
| 225 |
+
|
| 226 |
+
Since our dataset contains tables that are scraped from the web, it will also contain many toxic, racist, sexist, and otherwise harmful biases and texts. We have not run any analysis on the biases prevalent in our datasets. Neither have we explicitly filtered the content. This implies that a model trained on our dataset may potentially reflect harmful biases and toxic text that exist in our dataset.
|
| 227 |
+
|
| 228 |
+
### Other Known Limitations
|
| 229 |
+
|
| 230 |
+
No additional known limitations.
|
| 231 |
+
|
| 232 |
+
## Additional Information
|
| 233 |
+
|
| 234 |
+
### Dataset Curators
|
| 235 |
+
Jun Shern Chan, Michael Pieler, Jonathan Jao, Jérémy Scheurer, Ethan Perez
|
| 236 |
+
|
| 237 |
+
### Licensing Information
|
| 238 |
+
Apache 2.0
|
| 239 |
+
|
| 240 |
+
### Citation Information
|
| 241 |
+
|
| 242 |
+
```
|
| 243 |
+
@misc{chan2022few,
|
| 244 |
+
author = {Chan, Jun Shern and Pieler, Michael and Jao, Jonathan and Scheurer, Jérémy and Perez, Ethan},
|
| 245 |
+
title = {Few-shot Adaptation Works with UnpredicTable Data},
|
| 246 |
+
publisher={arXiv},
|
| 247 |
+
year = {2022},
|
| 248 |
+
url = {https://arxiv.org/abs/2208.01009}
|
| 249 |
+
}
|
| 250 |
+
```
|
huggingface_dataset/Dataset_Card/Rosenberg_CMeEE.md
ADDED
|
@@ -0,0 +1,13 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
license: mit
|
| 3 |
+
---
|
| 4 |
+
# Mainfest
|
| 5 |
+
- CMeEE_train.json: 训练集
|
| 6 |
+
- CMeEE_dev.json: 验证集
|
| 7 |
+
- CMeEE_test.json: 测试集
|
| 8 |
+
- 提交的时候需要为每条记录填充"entities"字段，类型为列表。每个识别出来的实体必须包含"start_idx", "end_idx", "type"3个字段。
|
| 9 |
+
- 提交的文件名为：CMeEE_test.json
|
| 10 |
+
- example_gold.json: 标准答案示例
|
| 11 |
+
- example_pred.json: 提交结果示例
|
| 12 |
+
|
| 13 |
+
评估指标以严格Micro-F1值为准
|
huggingface_dataset/Dataset_Card/TheGreatRambler_mm2_level_comments.md
ADDED
|
@@ -0,0 +1,166 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
language:
|
| 3 |
+
- multilingual
|
| 4 |
+
license:
|
| 5 |
+
- cc-by-nc-sa-4.0
|
| 6 |
+
multilinguality:
|
| 7 |
+
- multilingual
|
| 8 |
+
size_categories:
|
| 9 |
+
- 10M<n<100M
|
| 10 |
+
source_datasets:
|
| 11 |
+
- original
|
| 12 |
+
task_categories:
|
| 13 |
+
- other
|
| 14 |
+
- object-detection
|
| 15 |
+
- text-retrieval
|
| 16 |
+
- token-classification
|
| 17 |
+
- text-generation
|
| 18 |
+
task_ids: []
|
| 19 |
+
pretty_name: Mario Maker 2 level comments
|
| 20 |
+
tags:
|
| 21 |
+
- text-mining
|
| 22 |
+
---
|
| 23 |
+
|
| 24 |
+
# Mario Maker 2 level comments
|
| 25 |
+
Part of the [Mario Maker 2 Dataset Collection](https://tgrcode.com/posts/mario_maker_2_datasets)
|
| 26 |
+
|
| 27 |
+
## Dataset Description
|
| 28 |
+
The Mario Maker 2 level comment dataset consists of 31.9 million level comments from Nintendo's online service totaling around 20GB of data. 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.
|
| 29 |
+
|
| 30 |
+
### How to use it
|
| 31 |
+
The Mario Maker 2 level comment dataset is a very large dataset so for most use cases it is recommended to make use of the streaming API of `datasets`. You can load and iterate through the dataset with the following code:
|
| 32 |
+
|
| 33 |
+
```python
|
| 34 |
+
from datasets import load_dataset
|
| 35 |
+
|
| 36 |
+
ds = load_dataset("TheGreatRambler/mm2_level_comments", streaming=True, split="train")
|
| 37 |
+
print(next(iter(ds)))
|
| 38 |
+
|
| 39 |
+
#OUTPUT:
|
| 40 |
+
{
|
| 41 |
+
'data_id': 3000006,
|
| 42 |
+
'comment_id': '20200430072710528979_302de3722145c7a2_2dc6c6',
|
| 43 |
+
'type': 2,
|
| 44 |
+
'pid': '3471680967096518562',
|
| 45 |
+
'posted': 1561652887,
|
| 46 |
+
'clear_required': 0,
|
| 47 |
+
'text': '',
|
| 48 |
+
'reaction_image_id': 10,
|
| 49 |
+
'custom_image': [some binary data],
|
| 50 |
+
'has_beaten': 0,
|
| 51 |
+
'x': 557,
|
| 52 |
+
'y': 64,
|
| 53 |
+
'reaction_face': 0,
|
| 54 |
+
'unk8': 0,
|
| 55 |
+
'unk10': 0,
|
| 56 |
+
'unk12': 0,
|
| 57 |
+
'unk14': [some binary data],
|
| 58 |
+
'unk17': 0
|
| 59 |
+
}
|
| 60 |
+
```
|
| 61 |
+
Comments can be one of three types: text, reaction image or custom image. `type` can be used with the enum below to identify different kinds of comments. Custom images are binary PNGs.
|
| 62 |
+
|
| 63 |
+
You can also download the full dataset. Note that this will download ~20GB:
|
| 64 |
+
```python
|
| 65 |
+
ds = load_dataset("TheGreatRambler/mm2_level_comments", split="train")
|
| 66 |
+
```
|
| 67 |
+
|
| 68 |
+
## Data Structure
|
| 69 |
+
|
| 70 |
+
### Data Instances
|
| 71 |
+
|
| 72 |
+
```python
|
| 73 |
+
{
|
| 74 |
+
'data_id': 3000006,
|
| 75 |
+
'comment_id': '20200430072710528979_302de3722145c7a2_2dc6c6',
|
| 76 |
+
'type': 2,
|
| 77 |
+
'pid': '3471680967096518562',
|
| 78 |
+
'posted': 1561652887,
|
| 79 |
+
'clear_required': 0,
|
| 80 |
+
'text': '',
|
| 81 |
+
'reaction_image_id': 10,
|
| 82 |
+
'custom_image': [some binary data],
|
| 83 |
+
'has_beaten': 0,
|
| 84 |
+
'x': 557,
|
| 85 |
+
'y': 64,
|
| 86 |
+
'reaction_face': 0,
|
| 87 |
+
'unk8': 0,
|
| 88 |
+
'unk10': 0,
|
| 89 |
+
'unk12': 0,
|
| 90 |
+
'unk14': [some binary data],
|
| 91 |
+
'unk17': 0
|
| 92 |
+
}
|
| 93 |
+
```
|
| 94 |
+
|
| 95 |
+
### Data Fields
|
| 96 |
+
|
| 97 |
+
|Field|Type|Description|
|
| 98 |
+
|---|---|---|
|
| 99 |
+
|data_id|int|The data ID of the level this comment appears on|
|
| 100 |
+
|comment_id|string|Comment ID|
|
| 101 |
+
|type|int|Type of comment, enum below|
|
| 102 |
+
|pid|string|Player ID of the comment creator|
|
| 103 |
+
|posted|int|UTC timestamp of when this comment was created|
|
| 104 |
+
|clear_required|bool|Whether this comment requires a clear to view|
|
| 105 |
+
|text|string|If the comment type is text, the text of the comment|
|
| 106 |
+
|reaction_image_id|int|If this comment is a reaction image, the id of the reaction image, enum below|
|
| 107 |
+
|custom_image|bytes|If this comment is a custom drawing, the custom drawing as a PNG binary|
|
| 108 |
+
|has_beaten|int|Whether the user had beaten the level when they created the comment|
|
| 109 |
+
|x|int|The X position of the comment in game|
|
| 110 |
+
|y|int|The Y position of the comment in game|
|
| 111 |
+
|reaction_face|int|The reaction face of the mii of this user, enum below|
|
| 112 |
+
|unk8|int|Unknown|
|
| 113 |
+
|unk10|int|Unknown|
|
| 114 |
+
|unk12|int|Unknown|
|
| 115 |
+
|unk14|bytes|Unknown|
|
| 116 |
+
|unk17|int|Unknown|
|
| 117 |
+
|
| 118 |
+
### Data Splits
|
| 119 |
+
|
| 120 |
+
The dataset only contains a train split.
|
| 121 |
+
|
| 122 |
+
## Enums
|
| 123 |
+
|
| 124 |
+
The dataset contains some enum integer fields. This can be used to convert back to their string equivalents:
|
| 125 |
+
|
| 126 |
+
```python
|
| 127 |
+
CommentType = {
|
| 128 |
+
0: "Custom Image",
|
| 129 |
+
1: "Text",
|
| 130 |
+
2: "Reaction Image"
|
| 131 |
+
}
|
| 132 |
+
|
| 133 |
+
CommentReactionImage = {
|
| 134 |
+
0: "Nice!",
|
| 135 |
+
1: "Good stuff!",
|
| 136 |
+
2: "So tough...",
|
| 137 |
+
3: "EASY",
|
| 138 |
+
4: "Seriously?!",
|
| 139 |
+
5: "Wow!",
|
| 140 |
+
6: "Cool idea!",
|
| 141 |
+
7: "SPEEDRUN!",
|
| 142 |
+
8: "How?!",
|
| 143 |
+
9: "Be careful!",
|
| 144 |
+
10: "So close!",
|
| 145 |
+
11: "Beat it!"
|
| 146 |
+
}
|
| 147 |
+
|
| 148 |
+
CommentReactionFace = {
|
| 149 |
+
0: "Normal",
|
| 150 |
+
16: "Wink",
|
| 151 |
+
1: "Happy",
|
| 152 |
+
4: "Surprised",
|
| 153 |
+
18: "Scared",
|
| 154 |
+
3: "Confused"
|
| 155 |
+
}
|
| 156 |
+
```
|
| 157 |
+
|
| 158 |
+
<!-- TODO create detailed statistics -->
|
| 159 |
+
|
| 160 |
+
## Dataset Creation
|
| 161 |
+
|
| 162 |
+
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.
|
| 163 |
+
|
| 164 |
+
## Considerations for Using the Data
|
| 165 |
+
|
| 166 |
+
The dataset consists of comments from many different Mario Maker 2 players globally and as such their text could contain harmful language. Harmful depictions could also be present in the custom images.
|
huggingface_dataset/Dataset_Card/UKPLab_liar.md
ADDED
|
@@ -0,0 +1 @@
|
|
|
|
|
|
|
| 1 |
+
This is a binary version of the LIAR dataset from https://aclanthology.org/P17-2067/, where the labels have been collapsed into either true or false.
|
huggingface_dataset/Dataset_Card/autoevaluate_autoeval-eval-inverse-scaling__redefine-math-inverse-scaling__redefin-f7efd9-1695359602.md
ADDED
|
@@ -0,0 +1,34 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
type: predictions
|
| 3 |
+
tags:
|
| 4 |
+
- autotrain
|
| 5 |
+
- evaluation
|
| 6 |
+
datasets:
|
| 7 |
+
- inverse-scaling/redefine-math
|
| 8 |
+
eval_info:
|
| 9 |
+
task: text_zero_shot_classification
|
| 10 |
+
model: inverse-scaling/opt-6.7b_eval
|
| 11 |
+
metrics: []
|
| 12 |
+
dataset_name: inverse-scaling/redefine-math
|
| 13 |
+
dataset_config: inverse-scaling--redefine-math
|
| 14 |
+
dataset_split: train
|
| 15 |
+
col_mapping:
|
| 16 |
+
text: prompt
|
| 17 |
+
classes: classes
|
| 18 |
+
target: answer_index
|
| 19 |
+
---
|
| 20 |
+
# Dataset Card for AutoTrain Evaluator
|
| 21 |
+
|
| 22 |
+
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
|
| 23 |
+
|
| 24 |
+
* Task: Zero-Shot Text Classification
|
| 25 |
+
* Model: inverse-scaling/opt-6.7b_eval
|
| 26 |
+
* Dataset: inverse-scaling/redefine-math
|
| 27 |
+
* Config: inverse-scaling--redefine-math
|
| 28 |
+
* Split: train
|
| 29 |
+
|
| 30 |
+
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
|
| 31 |
+
|
| 32 |
+
## Contributions
|
| 33 |
+
|
| 34 |
+
Thanks to [@MicPie](https://huggingface.co/MicPie) for evaluating this model.
|
huggingface_dataset/Dataset_Card/autoevaluate_autoeval-eval-phpthinh__examplei-match-bd10ea-1748761024.md
ADDED
|
@@ -0,0 +1,34 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
type: predictions
|
| 3 |
+
tags:
|
| 4 |
+
- autotrain
|
| 5 |
+
- evaluation
|
| 6 |
+
datasets:
|
| 7 |
+
- phpthinh/examplei
|
| 8 |
+
eval_info:
|
| 9 |
+
task: text_zero_shot_classification
|
| 10 |
+
model: bigscience/bloom-1b1
|
| 11 |
+
metrics: ['f1']
|
| 12 |
+
dataset_name: phpthinh/examplei
|
| 13 |
+
dataset_config: match
|
| 14 |
+
dataset_split: test
|
| 15 |
+
col_mapping:
|
| 16 |
+
text: text
|
| 17 |
+
classes: classes
|
| 18 |
+
target: target
|
| 19 |
+
---
|
| 20 |
+
# Dataset Card for AutoTrain Evaluator
|
| 21 |
+
|
| 22 |
+
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
|
| 23 |
+
|
| 24 |
+
* Task: Zero-Shot Text Classification
|
| 25 |
+
* Model: bigscience/bloom-1b1
|
| 26 |
+
* Dataset: phpthinh/examplei
|
| 27 |
+
* Config: match
|
| 28 |
+
* Split: test
|
| 29 |
+
|
| 30 |
+
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
|
| 31 |
+
|
| 32 |
+
## Contributions
|
| 33 |
+
|
| 34 |
+
Thanks to [@phpthinh](https://huggingface.co/phpthinh) for evaluating this model.
|
huggingface_dataset/Dataset_Card/autoevaluate_autoeval-staging-eval-project-c967fc98-8385125.md
ADDED
|
@@ -0,0 +1,31 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
type: predictions
|
| 3 |
+
tags:
|
| 4 |
+
- autotrain
|
| 5 |
+
- evaluation
|
| 6 |
+
datasets:
|
| 7 |
+
- scientific_papers
|
| 8 |
+
eval_info:
|
| 9 |
+
task: summarization
|
| 10 |
+
model: google/bigbird-pegasus-large-arxiv
|
| 11 |
+
metrics: ['bertscore', 'meteor']
|
| 12 |
+
dataset_name: scientific_papers
|
| 13 |
+
dataset_config: pubmed
|
| 14 |
+
dataset_split: test
|
| 15 |
+
col_mapping:
|
| 16 |
+
text: article
|
| 17 |
+
target: abstract
|
| 18 |
+
---
|
| 19 |
+
# Dataset Card for AutoTrain Evaluator
|
| 20 |
+
|
| 21 |
+
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
|
| 22 |
+
|
| 23 |
+
* Task: Summarization
|
| 24 |
+
* Model: google/bigbird-pegasus-large-arxiv
|
| 25 |
+
* Dataset: scientific_papers
|
| 26 |
+
|
| 27 |
+
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
|
| 28 |
+
|
| 29 |
+
## Contributions
|
| 30 |
+
|
| 31 |
+
Thanks to [@Blaise_g](https://huggingface.co/Blaise_g) for evaluating this model.
|
huggingface_dataset/Dataset_Card/autoevaluate_autoeval-staging-eval-project-conll2003-e2bfcc2b-10665436.md
ADDED
|
@@ -0,0 +1,33 @@
|
|
|
|
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|
|
|
|
|
|
|
|
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|
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|
|
|
|
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|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
type: predictions
|
| 3 |
+
tags:
|
| 4 |
+
- autotrain
|
| 5 |
+
- evaluation
|
| 6 |
+
datasets:
|
| 7 |
+
- conll2003
|
| 8 |
+
eval_info:
|
| 9 |
+
task: entity_extraction
|
| 10 |
+
model: huggingface-course/bert-finetuned-ner
|
| 11 |
+
metrics: ['jordyvl/ece']
|
| 12 |
+
dataset_name: conll2003
|
| 13 |
+
dataset_config: conll2003
|
| 14 |
+
dataset_split: test
|
| 15 |
+
col_mapping:
|
| 16 |
+
tokens: tokens
|
| 17 |
+
tags: ner_tags
|
| 18 |
+
---
|
| 19 |
+
# Dataset Card for AutoTrain Evaluator
|
| 20 |
+
|
| 21 |
+
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
|
| 22 |
+
|
| 23 |
+
* Task: Token Classification
|
| 24 |
+
* Model: huggingface-course/bert-finetuned-ner
|
| 25 |
+
* Dataset: conll2003
|
| 26 |
+
* Config: conll2003
|
| 27 |
+
* Split: test
|
| 28 |
+
|
| 29 |
+
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
|
| 30 |
+
|
| 31 |
+
## Contributions
|
| 32 |
+
|
| 33 |
+
Thanks to [@jordyvl](https://huggingface.co/jordyvl) for evaluating this model.
|
huggingface_dataset/Dataset_Card/autoevaluate_autoeval-staging-eval-project-cuad-e5412c0a-12275642.md
ADDED
|
@@ -0,0 +1,35 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
|
|
|
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|
|
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|
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|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
type: predictions
|
| 3 |
+
tags:
|
| 4 |
+
- autotrain
|
| 5 |
+
- evaluation
|
| 6 |
+
datasets:
|
| 7 |
+
- cuad
|
| 8 |
+
eval_info:
|
| 9 |
+
task: extractive_question_answering
|
| 10 |
+
model: 21iridescent/RoBERTa-base-finetuned-squad2-lwt
|
| 11 |
+
metrics: []
|
| 12 |
+
dataset_name: cuad
|
| 13 |
+
dataset_config: default
|
| 14 |
+
dataset_split: test
|
| 15 |
+
col_mapping:
|
| 16 |
+
context: context
|
| 17 |
+
question: question
|
| 18 |
+
answers-text: answers.text
|
| 19 |
+
answers-answer_start: answers.answer_start
|
| 20 |
+
---
|
| 21 |
+
# Dataset Card for AutoTrain Evaluator
|
| 22 |
+
|
| 23 |
+
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
|
| 24 |
+
|
| 25 |
+
* Task: Question Answering
|
| 26 |
+
* Model: 21iridescent/RoBERTa-base-finetuned-squad2-lwt
|
| 27 |
+
* Dataset: cuad
|
| 28 |
+
* Config: default
|
| 29 |
+
* Split: test
|
| 30 |
+
|
| 31 |
+
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
|
| 32 |
+
|
| 33 |
+
## Contributions
|
| 34 |
+
|
| 35 |
+
Thanks to [@halima](https://huggingface.co/halima) for evaluating this model.
|
huggingface_dataset/Dataset_Card/autoevaluate_autoeval-staging-eval-squad_v2-squad_v2-76c05b-14906070.md
ADDED
|
@@ -0,0 +1,35 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
type: predictions
|
| 3 |
+
tags:
|
| 4 |
+
- autotrain
|
| 5 |
+
- evaluation
|
| 6 |
+
datasets:
|
| 7 |
+
- squad_v2
|
| 8 |
+
eval_info:
|
| 9 |
+
task: extractive_question_answering
|
| 10 |
+
model: deepset/roberta-base-squad2-covid
|
| 11 |
+
metrics: ['bertscore']
|
| 12 |
+
dataset_name: squad_v2
|
| 13 |
+
dataset_config: squad_v2
|
| 14 |
+
dataset_split: validation
|
| 15 |
+
col_mapping:
|
| 16 |
+
context: context
|
| 17 |
+
question: question
|
| 18 |
+
answers-text: answers.text
|
| 19 |
+
answers-answer_start: answers.answer_start
|
| 20 |
+
---
|
| 21 |
+
# Dataset Card for AutoTrain Evaluator
|
| 22 |
+
|
| 23 |
+
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
|
| 24 |
+
|
| 25 |
+
* Task: Question Answering
|
| 26 |
+
* Model: deepset/roberta-base-squad2-covid
|
| 27 |
+
* Dataset: squad_v2
|
| 28 |
+
* Config: squad_v2
|
| 29 |
+
* Split: validation
|
| 30 |
+
|
| 31 |
+
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
|
| 32 |
+
|
| 33 |
+
## Contributions
|
| 34 |
+
|
| 35 |
+
Thanks to [@nonchalant-nagavalli](https://huggingface.co/nonchalant-nagavalli) for evaluating this model.
|
huggingface_dataset/Dataset_Card/cats_vs_dogs.md
ADDED
|
@@ -0,0 +1,217 @@
|
|
|
|
|
|
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|
|
|
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|
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|
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|
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|
|
|
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|
|
|
|
|
|
|
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|
|
|
|
|
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|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
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|
|
|
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|
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|
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|
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|
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|
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|
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|
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|
|
|
|
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|
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|
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|
|
|
|
|
|
|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
annotations_creators:
|
| 3 |
+
- crowdsourced
|
| 4 |
+
language_creators:
|
| 5 |
+
- crowdsourced
|
| 6 |
+
language:
|
| 7 |
+
- en
|
| 8 |
+
license:
|
| 9 |
+
- unknown
|
| 10 |
+
multilinguality:
|
| 11 |
+
- monolingual
|
| 12 |
+
size_categories:
|
| 13 |
+
- 10K<n<100K
|
| 14 |
+
source_datasets:
|
| 15 |
+
- original
|
| 16 |
+
task_categories:
|
| 17 |
+
- image-classification
|
| 18 |
+
task_ids:
|
| 19 |
+
- multi-class-image-classification
|
| 20 |
+
paperswithcode_id: cats-vs-dogs
|
| 21 |
+
pretty_name: Cats Vs. Dogs
|
| 22 |
+
dataset_info:
|
| 23 |
+
features:
|
| 24 |
+
- name: image
|
| 25 |
+
dtype: image
|
| 26 |
+
- name: labels
|
| 27 |
+
dtype:
|
| 28 |
+
class_label:
|
| 29 |
+
names:
|
| 30 |
+
'0': cat
|
| 31 |
+
'1': dog
|
| 32 |
+
splits:
|
| 33 |
+
- name: train
|
| 34 |
+
num_bytes: 4219400
|
| 35 |
+
num_examples: 23410
|
| 36 |
+
download_size: 824887076
|
| 37 |
+
dataset_size: 4219400
|
| 38 |
+
---
|
| 39 |
+
|
| 40 |
+
# Dataset Card for Cats Vs. Dogs
|
| 41 |
+
|
| 42 |
+
## Table of Contents
|
| 43 |
+
- [Table of Contents](#table-of-contents)
|
| 44 |
+
- [Dataset Description](#dataset-description)
|
| 45 |
+
- [Dataset Summary](#dataset-summary)
|
| 46 |
+
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
|
| 47 |
+
- [Languages](#languages)
|
| 48 |
+
- [Dataset Structure](#dataset-structure)
|
| 49 |
+
- [Data Instances](#data-instances)
|
| 50 |
+
- [Data Fields](#data-fields)
|
| 51 |
+
- [Data Splits](#data-splits)
|
| 52 |
+
- [Dataset Creation](#dataset-creation)
|
| 53 |
+
- [Curation Rationale](#curation-rationale)
|
| 54 |
+
- [Source Data](#source-data)
|
| 55 |
+
- [Annotations](#annotations)
|
| 56 |
+
- [Personal and Sensitive Information](#personal-and-sensitive-information)
|
| 57 |
+
- [Considerations for Using the Data](#considerations-for-using-the-data)
|
| 58 |
+
- [Social Impact of Dataset](#social-impact-of-dataset)
|
| 59 |
+
- [Discussion of Biases](#discussion-of-biases)
|
| 60 |
+
- [Other Known Limitations](#other-known-limitations)
|
| 61 |
+
- [Additional Information](#additional-information)
|
| 62 |
+
- [Dataset Curators](#dataset-curators)
|
| 63 |
+
- [Licensing Information](#licensing-information)
|
| 64 |
+
- [Citation Information](#citation-information)
|
| 65 |
+
- [Contributions](#contributions)
|
| 66 |
+
|
| 67 |
+
## Dataset Description
|
| 68 |
+
|
| 69 |
+
- **Homepage:** [Cats vs Dogs Dataset](https://www.microsoft.com/en-us/download/details.aspx?id=54765)
|
| 70 |
+
- **Repository:**
|
| 71 |
+
- **Paper:** [Asirra: A CAPTCHA that Exploits Interest-Aligned Manual Image Categorization](https://www.microsoft.com/en-us/research/wp-content/uploads/2007/10/CCS2007.pdf)
|
| 72 |
+
- **Leaderboard:** [Dogs vs. Cats](https://www.kaggle.com/competitions/dogs-vs-cats)
|
| 73 |
+
- **Point of Contact:**
|
| 74 |
+
|
| 75 |
+
### Dataset Summary
|
| 76 |
+
|
| 77 |
+
A large set of images of cats and dogs. There are 1738 corrupted images that are dropped. This dataset is part of a now-closed Kaggle competition and represents a subset of the so-called Asirra dataset.
|
| 78 |
+
|
| 79 |
+
From the competition page:
|
| 80 |
+
|
| 81 |
+
> The Asirra data set
|
| 82 |
+
>
|
| 83 |
+
> Web services are often protected with a challenge that's supposed to be easy for people to solve, but difficult for computers. Such a challenge is often called a [CAPTCHA](http://www.captcha.net/) (Completely Automated Public Turing test to tell Computers and Humans Apart) or HIP (Human Interactive Proof). HIPs are used for many purposes, such as to reduce email and blog spam and prevent brute-force attacks on web site passwords.
|
| 84 |
+
>
|
| 85 |
+
> Asirra (Animal Species Image Recognition for Restricting Access) is a HIP that works by asking users to identify photographs of cats and dogs. This task is difficult for computers, but studies have shown that people can accomplish it quickly and accurately. Many even think it's fun! Here is an example of the Asirra interface:
|
| 86 |
+
>
|
| 87 |
+
> Asirra is unique because of its partnership with [Petfinder.com](https://www.petfinder.com/), the world's largest site devoted to finding homes for homeless pets. They've provided Microsoft Research with over three million images of cats and dogs, manually classified by people at thousands of animal shelters across the United States. Kaggle is fortunate to offer a subset of this data for fun and research.
|
| 88 |
+
|
| 89 |
+
### Supported Tasks and Leaderboards
|
| 90 |
+
|
| 91 |
+
- `image-classification`: The goal of this task is to classify a given image as either containing a cat or a dog. The leaderboard is available [here](https://paperswithcode.com/sota/image-classification-on-cats-vs-dogs).
|
| 92 |
+
|
| 93 |
+
### Languages
|
| 94 |
+
|
| 95 |
+
English.
|
| 96 |
+
|
| 97 |
+
## Dataset Structure
|
| 98 |
+
|
| 99 |
+
### Data Instances
|
| 100 |
+
|
| 101 |
+
A sample from the training set is provided below:
|
| 102 |
+
|
| 103 |
+
```
|
| 104 |
+
{
|
| 105 |
+
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=500x375 at 0x29CEAD71780>,
|
| 106 |
+
'labels': 0
|
| 107 |
+
}
|
| 108 |
+
```
|
| 109 |
+
|
| 110 |
+
### Data Fields
|
| 111 |
+
|
| 112 |
+
The data instances have the following fields:
|
| 113 |
+
|
| 114 |
+
- `image`: A `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`.
|
| 115 |
+
- `labels`: an `int` classification label.
|
| 116 |
+
|
| 117 |
+
Class Label Mappings:
|
| 118 |
+
|
| 119 |
+
```
|
| 120 |
+
{
|
| 121 |
+
"cat": 0,
|
| 122 |
+
"dog": 1,
|
| 123 |
+
}
|
| 124 |
+
```
|
| 125 |
+
|
| 126 |
+
### Data Splits
|
| 127 |
+
|
| 128 |
+
| | train |
|
| 129 |
+
|---------------|------:|
|
| 130 |
+
| # of examples | 23410 |
|
| 131 |
+
|
| 132 |
+
## Dataset Creation
|
| 133 |
+
|
| 134 |
+
### Curation Rationale
|
| 135 |
+
|
| 136 |
+
This subset was to built to test whether computer vision algorithms can beat the Asirra CAPTCHA:
|
| 137 |
+
|
| 138 |
+
From the competition page:
|
| 139 |
+
|
| 140 |
+
> Image recognition attacks
|
| 141 |
+
>
|
| 142 |
+
> While random guessing is the easiest form of attack, various forms of image recognition can allow an attacker to make guesses that are better than random. There is enormous diversity in the photo database (a wide variety of backgrounds, angles, poses, lighting, etc.), making accurate automatic classification difficult. In an informal poll conducted many years ago, computer vision experts posited that a classifier with better than 60% accuracy would be difficult without a major advance in the state of the art. For reference, a 60% classifier improves the guessing probability of a 12-image HIP from 1/4096 to 1/459.
|
| 143 |
+
|
| 144 |
+
### Source Data
|
| 145 |
+
|
| 146 |
+
#### Initial Data Collection and Normalization
|
| 147 |
+
|
| 148 |
+
This dataset is a subset of the Asirra dataset.
|
| 149 |
+
|
| 150 |
+
From the competition page:
|
| 151 |
+
|
| 152 |
+
> Asirra is unique because of its partnership with Petfinder.com, the world's largest site devoted to finding homes for homeless pets. They've provided Microsoft Research with over three million images of cats and dogs, manually classified by people at thousands of animal shelters across the United States.
|
| 153 |
+
|
| 154 |
+
#### Who are the source language producers?
|
| 155 |
+
|
| 156 |
+
The users of [Petfinder.com](https://www.petfinder.com/).
|
| 157 |
+
|
| 158 |
+
### Annotations
|
| 159 |
+
|
| 160 |
+
#### Annotation process
|
| 161 |
+
|
| 162 |
+
The images were annotated by selecting a pet category on [Petfinder.com](https://www.petfinder.com/).
|
| 163 |
+
|
| 164 |
+
#### Who are the annotators?
|
| 165 |
+
|
| 166 |
+
The users of [Petfinder.com](https://www.petfinder.com/).
|
| 167 |
+
|
| 168 |
+
### Personal and Sensitive Information
|
| 169 |
+
|
| 170 |
+
[More Information Needed]
|
| 171 |
+
|
| 172 |
+
## Considerations for Using the Data
|
| 173 |
+
|
| 174 |
+
### Social Impact of Dataset
|
| 175 |
+
|
| 176 |
+
[More Information Needed]
|
| 177 |
+
|
| 178 |
+
### Discussion of Biases
|
| 179 |
+
|
| 180 |
+
From the paper:
|
| 181 |
+
|
| 182 |
+
> Unlike many image-based CAPTCHAs which are abstract or subjective, Asirra’s challenges are concrete, inoffensive (cute, by some accounts), require no specialized or culturally biased knowledge, and have definite ground truth. This
|
| 183 |
+
makes Asirra less frustrating for humans. Some beta-testers found it fun. The four-year-old child of one asked several times to “play the cat and dog game again.”
|
| 184 |
+
|
| 185 |
+
|
| 186 |
+
### Other Known Limitations
|
| 187 |
+
|
| 188 |
+
[More Information Needed]
|
| 189 |
+
|
| 190 |
+
## Additional Information
|
| 191 |
+
|
| 192 |
+
### Dataset Curators
|
| 193 |
+
|
| 194 |
+
[More Information Needed]
|
| 195 |
+
|
| 196 |
+
### Licensing Information
|
| 197 |
+
|
| 198 |
+
[More Information Needed]
|
| 199 |
+
|
| 200 |
+
### Citation Information
|
| 201 |
+
|
| 202 |
+
```bibtex
|
| 203 |
+
@Inproceedings (Conference){asirra-a-captcha-that-exploits-interest-aligned-manual-image-categorization,
|
| 204 |
+
author = {Elson, Jeremy and Douceur, John (JD) and Howell, Jon and Saul, Jared},
|
| 205 |
+
title = {Asirra: A CAPTCHA that Exploits Interest-Aligned Manual Image Categorization},
|
| 206 |
+
booktitle = {Proceedings of 14th ACM Conference on Computer and Communications Security (CCS)},
|
| 207 |
+
year = {2007},
|
| 208 |
+
month = {October},
|
| 209 |
+
publisher = {Association for Computing Machinery, Inc.},
|
| 210 |
+
url = {https://www.microsoft.com/en-us/research/publication/asirra-a-captcha-that-exploits-interest-aligned-manual-image-categorization/},
|
| 211 |
+
edition = {Proceedings of 14th ACM Conference on Computer and Communications Security (CCS)},
|
| 212 |
+
}
|
| 213 |
+
```
|
| 214 |
+
|
| 215 |
+
### Contributions
|
| 216 |
+
|
| 217 |
+
Thanks to [@nateraw](https://github.com/nateraw) for adding this dataset.
|
huggingface_dataset/Dataset_Card/irds_mmarco_es_dev.md
ADDED
|
@@ -0,0 +1,49 @@
|
|
|
|
|
|
|
|
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|
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|
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|
|
|
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|
|
|
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|
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|
|
|
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|
|
|
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|
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|
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|
|
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|
|
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|
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|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
pretty_name: '`mmarco/es/dev`'
|
| 3 |
+
viewer: false
|
| 4 |
+
source_datasets: ['irds/mmarco_es']
|
| 5 |
+
task_categories:
|
| 6 |
+
- text-retrieval
|
| 7 |
+
---
|
| 8 |
+
|
| 9 |
+
# Dataset Card for `mmarco/es/dev`
|
| 10 |
+
|
| 11 |
+
The `mmarco/es/dev` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
|
| 12 |
+
For more information about the dataset, see the [documentation](https://ir-datasets.com/mmarco#mmarco/es/dev).
|
| 13 |
+
|
| 14 |
+
# Data
|
| 15 |
+
|
| 16 |
+
This dataset provides:
|
| 17 |
+
- `queries` (i.e., topics); count=101,092
|
| 18 |
+
- `qrels`: (relevance assessments); count=59,273
|
| 19 |
+
|
| 20 |
+
- For `docs`, use [`irds/mmarco_es`](https://huggingface.co/datasets/irds/mmarco_es)
|
| 21 |
+
|
| 22 |
+
## Usage
|
| 23 |
+
|
| 24 |
+
```python
|
| 25 |
+
from datasets import load_dataset
|
| 26 |
+
|
| 27 |
+
queries = load_dataset('irds/mmarco_es_dev', 'queries')
|
| 28 |
+
for record in queries:
|
| 29 |
+
record # {'query_id': ..., 'text': ...}
|
| 30 |
+
|
| 31 |
+
qrels = load_dataset('irds/mmarco_es_dev', 'qrels')
|
| 32 |
+
for record in qrels:
|
| 33 |
+
record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...}
|
| 34 |
+
|
| 35 |
+
```
|
| 36 |
+
|
| 37 |
+
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
|
| 38 |
+
data in 🤗 Dataset format.
|
| 39 |
+
|
| 40 |
+
## Citation Information
|
| 41 |
+
|
| 42 |
+
```
|
| 43 |
+
@article{Bonifacio2021MMarco,
|
| 44 |
+
title={{mMARCO}: A Multilingual Version of {MS MARCO} Passage Ranking Dataset},
|
| 45 |
+
author={Luiz Henrique Bonifacio and Israel Campiotti and Roberto Lotufo and Rodrigo Nogueira},
|
| 46 |
+
year={2021},
|
| 47 |
+
journal={arXiv:2108.13897}
|
| 48 |
+
}
|
| 49 |
+
```
|
huggingface_dataset/Dataset_Card/norwegian_ner.md
ADDED
|
@@ -0,0 +1,336 @@
|
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|
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|
|
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|
|
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|
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|
|
|
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|
|
|
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|
|
|
|
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|
|
|
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|
|
|
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|
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|
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|
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|
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|
|
|
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|
|
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|
|
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|
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|
|
|
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|
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|
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|
|
|
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|
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|
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|
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|
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|
|
|
|
| 1 |
+
---
|
| 2 |
+
annotations_creators:
|
| 3 |
+
- expert-generated
|
| 4 |
+
language_creators:
|
| 5 |
+
- crowdsourced
|
| 6 |
+
language:
|
| 7 |
+
- 'no'
|
| 8 |
+
license:
|
| 9 |
+
- unknown
|
| 10 |
+
multilinguality:
|
| 11 |
+
- monolingual
|
| 12 |
+
size_categories:
|
| 13 |
+
- 10K<n<100K
|
| 14 |
+
source_datasets:
|
| 15 |
+
- original
|
| 16 |
+
task_categories:
|
| 17 |
+
- token-classification
|
| 18 |
+
task_ids:
|
| 19 |
+
- named-entity-recognition
|
| 20 |
+
pretty_name: Norwegian NER
|
| 21 |
+
dataset_info:
|
| 22 |
+
- config_name: bokmaal
|
| 23 |
+
features:
|
| 24 |
+
- name: idx
|
| 25 |
+
dtype: string
|
| 26 |
+
- name: text
|
| 27 |
+
dtype: string
|
| 28 |
+
- name: tokens
|
| 29 |
+
sequence: string
|
| 30 |
+
- name: lemmas
|
| 31 |
+
sequence: string
|
| 32 |
+
- name: pos_tags
|
| 33 |
+
sequence:
|
| 34 |
+
class_label:
|
| 35 |
+
names:
|
| 36 |
+
'0': NOUN
|
| 37 |
+
'1': PUNCT
|
| 38 |
+
'2': ADP
|
| 39 |
+
'3': NUM
|
| 40 |
+
'4': SYM
|
| 41 |
+
'5': SCONJ
|
| 42 |
+
'6': ADJ
|
| 43 |
+
'7': PART
|
| 44 |
+
'8': DET
|
| 45 |
+
'9': CCONJ
|
| 46 |
+
'10': PROPN
|
| 47 |
+
'11': PRON
|
| 48 |
+
'12': X
|
| 49 |
+
'13': ADV
|
| 50 |
+
'14': INTJ
|
| 51 |
+
'15': VERB
|
| 52 |
+
'16': AUX
|
| 53 |
+
- name: ner_tags
|
| 54 |
+
sequence:
|
| 55 |
+
class_label:
|
| 56 |
+
names:
|
| 57 |
+
'0': O
|
| 58 |
+
'1': B-OTH
|
| 59 |
+
'2': I-OTH
|
| 60 |
+
'3': E-OTH
|
| 61 |
+
'4': S-OTH
|
| 62 |
+
'5': B-ORG
|
| 63 |
+
'6': I-ORG
|
| 64 |
+
'7': E-ORG
|
| 65 |
+
'8': S-ORG
|
| 66 |
+
'9': B-PRS
|
| 67 |
+
'10': I-PRS
|
| 68 |
+
'11': E-PRS
|
| 69 |
+
'12': S-PRS
|
| 70 |
+
'13': B-GEO
|
| 71 |
+
'14': I-GEO
|
| 72 |
+
'15': E-GEO
|
| 73 |
+
'16': S-GEO
|
| 74 |
+
splits:
|
| 75 |
+
- name: train
|
| 76 |
+
num_bytes: 9859760
|
| 77 |
+
num_examples: 15696
|
| 78 |
+
- name: validation
|
| 79 |
+
num_bytes: 1475216
|
| 80 |
+
num_examples: 2410
|
| 81 |
+
- name: test
|
| 82 |
+
num_bytes: 1212939
|
| 83 |
+
num_examples: 1939
|
| 84 |
+
download_size: 8747760
|
| 85 |
+
dataset_size: 12547915
|
| 86 |
+
- config_name: nynorsk
|
| 87 |
+
features:
|
| 88 |
+
- name: idx
|
| 89 |
+
dtype: string
|
| 90 |
+
- name: text
|
| 91 |
+
dtype: string
|
| 92 |
+
- name: tokens
|
| 93 |
+
sequence: string
|
| 94 |
+
- name: lemmas
|
| 95 |
+
sequence: string
|
| 96 |
+
- name: pos_tags
|
| 97 |
+
sequence:
|
| 98 |
+
class_label:
|
| 99 |
+
names:
|
| 100 |
+
'0': NOUN
|
| 101 |
+
'1': PUNCT
|
| 102 |
+
'2': ADP
|
| 103 |
+
'3': NUM
|
| 104 |
+
'4': SYM
|
| 105 |
+
'5': SCONJ
|
| 106 |
+
'6': ADJ
|
| 107 |
+
'7': PART
|
| 108 |
+
'8': DET
|
| 109 |
+
'9': CCONJ
|
| 110 |
+
'10': PROPN
|
| 111 |
+
'11': PRON
|
| 112 |
+
'12': X
|
| 113 |
+
'13': ADV
|
| 114 |
+
'14': INTJ
|
| 115 |
+
'15': VERB
|
| 116 |
+
'16': AUX
|
| 117 |
+
- name: ner_tags
|
| 118 |
+
sequence:
|
| 119 |
+
class_label:
|
| 120 |
+
names:
|
| 121 |
+
'0': O
|
| 122 |
+
'1': B-OTH
|
| 123 |
+
'2': I-OTH
|
| 124 |
+
'3': E-OTH
|
| 125 |
+
'4': S-OTH
|
| 126 |
+
'5': B-ORG
|
| 127 |
+
'6': I-ORG
|
| 128 |
+
'7': E-ORG
|
| 129 |
+
'8': S-ORG
|
| 130 |
+
'9': B-PRS
|
| 131 |
+
'10': I-PRS
|
| 132 |
+
'11': E-PRS
|
| 133 |
+
'12': S-PRS
|
| 134 |
+
'13': B-GEO
|
| 135 |
+
'14': I-GEO
|
| 136 |
+
'15': E-GEO
|
| 137 |
+
'16': S-GEO
|
| 138 |
+
splits:
|
| 139 |
+
- name: train
|
| 140 |
+
num_bytes: 9916338
|
| 141 |
+
num_examples: 14174
|
| 142 |
+
- name: validation
|
| 143 |
+
num_bytes: 1257235
|
| 144 |
+
num_examples: 1890
|
| 145 |
+
- name: test
|
| 146 |
+
num_bytes: 1006733
|
| 147 |
+
num_examples: 1511
|
| 148 |
+
download_size: 8484545
|
| 149 |
+
dataset_size: 12180306
|
| 150 |
+
- config_name: samnorsk
|
| 151 |
+
features:
|
| 152 |
+
- name: idx
|
| 153 |
+
dtype: string
|
| 154 |
+
- name: text
|
| 155 |
+
dtype: string
|
| 156 |
+
- name: tokens
|
| 157 |
+
sequence: string
|
| 158 |
+
- name: lemmas
|
| 159 |
+
sequence: string
|
| 160 |
+
- name: pos_tags
|
| 161 |
+
sequence:
|
| 162 |
+
class_label:
|
| 163 |
+
names:
|
| 164 |
+
'0': NOUN
|
| 165 |
+
'1': PUNCT
|
| 166 |
+
'2': ADP
|
| 167 |
+
'3': NUM
|
| 168 |
+
'4': SYM
|
| 169 |
+
'5': SCONJ
|
| 170 |
+
'6': ADJ
|
| 171 |
+
'7': PART
|
| 172 |
+
'8': DET
|
| 173 |
+
'9': CCONJ
|
| 174 |
+
'10': PROPN
|
| 175 |
+
'11': PRON
|
| 176 |
+
'12': X
|
| 177 |
+
'13': ADV
|
| 178 |
+
'14': INTJ
|
| 179 |
+
'15': VERB
|
| 180 |
+
'16': AUX
|
| 181 |
+
- name: ner_tags
|
| 182 |
+
sequence:
|
| 183 |
+
class_label:
|
| 184 |
+
names:
|
| 185 |
+
'0': O
|
| 186 |
+
'1': B-OTH
|
| 187 |
+
'2': I-OTH
|
| 188 |
+
'3': E-OTH
|
| 189 |
+
'4': S-OTH
|
| 190 |
+
'5': B-ORG
|
| 191 |
+
'6': I-ORG
|
| 192 |
+
'7': E-ORG
|
| 193 |
+
'8': S-ORG
|
| 194 |
+
'9': B-PRS
|
| 195 |
+
'10': I-PRS
|
| 196 |
+
'11': E-PRS
|
| 197 |
+
'12': S-PRS
|
| 198 |
+
'13': B-GEO
|
| 199 |
+
'14': I-GEO
|
| 200 |
+
'15': E-GEO
|
| 201 |
+
'16': S-GEO
|
| 202 |
+
splits:
|
| 203 |
+
- name: train
|
| 204 |
+
num_bytes: 22508485
|
| 205 |
+
num_examples: 34170
|
| 206 |
+
- name: validation
|
| 207 |
+
num_bytes: 2732419
|
| 208 |
+
num_examples: 4300
|
| 209 |
+
- name: test
|
| 210 |
+
num_bytes: 2219640
|
| 211 |
+
num_examples: 3450
|
| 212 |
+
download_size: 19133049
|
| 213 |
+
dataset_size: 27460544
|
| 214 |
+
---
|
| 215 |
+
|
| 216 |
+
# Dataset Card for Norwegian NER
|
| 217 |
+
|
| 218 |
+
## Table of Contents
|
| 219 |
+
- [Dataset Description](#dataset-description)
|
| 220 |
+
- [Dataset Summary](#dataset-summary)
|
| 221 |
+
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
|
| 222 |
+
- [Languages](#languages)
|
| 223 |
+
- [Dataset Structure](#dataset-structure)
|
| 224 |
+
- [Data Instances](#data-instances)
|
| 225 |
+
- [Data Fields](#data-fields)
|
| 226 |
+
- [Data Splits](#data-splits)
|
| 227 |
+
- [Dataset Creation](#dataset-creation)
|
| 228 |
+
- [Curation Rationale](#curation-rationale)
|
| 229 |
+
- [Source Data](#source-data)
|
| 230 |
+
- [Annotations](#annotations)
|
| 231 |
+
- [Personal and Sensitive Information](#personal-and-sensitive-information)
|
| 232 |
+
- [Considerations for Using the Data](#considerations-for-using-the-data)
|
| 233 |
+
- [Social Impact of Dataset](#social-impact-of-dataset)
|
| 234 |
+
- [Discussion of Biases](#discussion-of-biases)
|
| 235 |
+
- [Other Known Limitations](#other-known-limitations)
|
| 236 |
+
- [Additional Information](#additional-information)
|
| 237 |
+
- [Dataset Curators](#dataset-curators)
|
| 238 |
+
- [Licensing Information](#licensing-information)
|
| 239 |
+
- [Citation Information](#citation-information)
|
| 240 |
+
- [Contributions](#contributions)
|
| 241 |
+
|
| 242 |
+
## Dataset Description
|
| 243 |
+
|
| 244 |
+
- **Homepage:** [Github](https://github.com/ljos/navnkjenner)
|
| 245 |
+
- **Repository:** [Github](https://github.com/ljos/navnkjenner)
|
| 246 |
+
- **Paper:**
|
| 247 |
+
- **Leaderboard:**
|
| 248 |
+
- **Point of Contact:**
|
| 249 |
+
|
| 250 |
+
### Dataset Summary
|
| 251 |
+
|
| 252 |
+
[More Information Needed]
|
| 253 |
+
|
| 254 |
+
### Supported Tasks and Leaderboards
|
| 255 |
+
|
| 256 |
+
[More Information Needed]
|
| 257 |
+
|
| 258 |
+
### Languages
|
| 259 |
+
|
| 260 |
+
[More Information Needed]
|
| 261 |
+
|
| 262 |
+
## Dataset Structure
|
| 263 |
+
|
| 264 |
+
### Data Instances
|
| 265 |
+
|
| 266 |
+
[More Information Needed]
|
| 267 |
+
|
| 268 |
+
### Data Fields
|
| 269 |
+
|
| 270 |
+
[More Information Needed]
|
| 271 |
+
|
| 272 |
+
### Data Splits
|
| 273 |
+
|
| 274 |
+
[More Information Needed]
|
| 275 |
+
|
| 276 |
+
## Dataset Creation
|
| 277 |
+
|
| 278 |
+
### Curation Rationale
|
| 279 |
+
|
| 280 |
+
[More Information Needed]
|
| 281 |
+
|
| 282 |
+
### Source Data
|
| 283 |
+
|
| 284 |
+
#### Initial Data Collection and Normalization
|
| 285 |
+
|
| 286 |
+
[More Information Needed]
|
| 287 |
+
|
| 288 |
+
#### Who are the source language producers?
|
| 289 |
+
|
| 290 |
+
[More Information Needed]
|
| 291 |
+
|
| 292 |
+
### Annotations
|
| 293 |
+
|
| 294 |
+
#### Annotation process
|
| 295 |
+
|
| 296 |
+
[More Information Needed]
|
| 297 |
+
|
| 298 |
+
#### Who are the annotators?
|
| 299 |
+
|
| 300 |
+
[More Information Needed]
|
| 301 |
+
|
| 302 |
+
### Personal and Sensitive Information
|
| 303 |
+
|
| 304 |
+
[More Information Needed]
|
| 305 |
+
|
| 306 |
+
## Considerations for Using the Data
|
| 307 |
+
|
| 308 |
+
### Social Impact of Dataset
|
| 309 |
+
|
| 310 |
+
[More Information Needed]
|
| 311 |
+
|
| 312 |
+
### Discussion of Biases
|
| 313 |
+
|
| 314 |
+
[More Information Needed]
|
| 315 |
+
|
| 316 |
+
### Other Known Limitations
|
| 317 |
+
|
| 318 |
+
[More Information Needed]
|
| 319 |
+
|
| 320 |
+
## Additional Information
|
| 321 |
+
|
| 322 |
+
### Dataset Curators
|
| 323 |
+
|
| 324 |
+
[More Information Needed]
|
| 325 |
+
|
| 326 |
+
### Licensing Information
|
| 327 |
+
|
| 328 |
+
[More Information Needed]
|
| 329 |
+
|
| 330 |
+
### Citation Information
|
| 331 |
+
|
| 332 |
+
[More Information Needed]
|
| 333 |
+
|
| 334 |
+
### Contributions
|
| 335 |
+
|
| 336 |
+
Thanks to [@jplu](https://github.com/jplu) for adding this dataset.
|
huggingface_dataset/Dataset_Card/open-source-metrics_optimum-dependents.md
ADDED
|
@@ -0,0 +1,124 @@
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
license: apache-2.0
|
| 3 |
+
pretty_name: optimum metrics
|
| 4 |
+
tags:
|
| 5 |
+
- github-stars
|
| 6 |
+
---
|
| 7 |
+
|
| 8 |
+
# optimum metrics
|
| 9 |
+
|
| 10 |
+
This dataset contains metrics about the huggingface/optimum package.
|
| 11 |
+
|
| 12 |
+
Number of repositories in the dataset: 19
|
| 13 |
+
Number of packages in the dataset: 6
|
| 14 |
+
|
| 15 |
+
## Package dependents
|
| 16 |
+
|
| 17 |
+
This contains the data available in the [used-by](https://github.com/huggingface/optimum/network/dependents)
|
| 18 |
+
tab on GitHub.
|
| 19 |
+
|
| 20 |
+
### Package & Repository star count
|
| 21 |
+
|
| 22 |
+
This section shows the package and repository star count, individually.
|
| 23 |
+
|
| 24 |
+
|
| 25 |
+
Package | Repository
|
| 26 |
+
:-------------------------:|:-------------------------:
|
| 27 |
+
 | 
|
| 28 |
+
|
| 29 |
+
There are 0 packages that have more than 1000 stars.
|
| 30 |
+
|
| 31 |
+
There are 0 repositories that have more than 1000 stars.
|
| 32 |
+
|
| 33 |
+
|
| 34 |
+
The top 10 in each category are the following:
|
| 35 |
+
|
| 36 |
+
*Package*
|
| 37 |
+
|
| 38 |
+
[SeldonIO/MLServer](https://github.com/SeldonIO/MLServer): 288
|
| 39 |
+
|
| 40 |
+
[AlekseyKorshuk/optimum-transformers](https://github.com/AlekseyKorshuk/optimum-transformers): 114
|
| 41 |
+
|
| 42 |
+
[huggingface/optimum-intel](https://github.com/huggingface/optimum-intel): 61
|
| 43 |
+
|
| 44 |
+
[huggingface/optimum-graphcore](https://github.com/huggingface/optimum-graphcore): 34
|
| 45 |
+
|
| 46 |
+
[huggingface/optimum-habana](https://github.com/huggingface/optimum-habana): 24
|
| 47 |
+
|
| 48 |
+
[bhavsarpratik/easy-transformers](https://github.com/bhavsarpratik/easy-transformers): 10
|
| 49 |
+
|
| 50 |
+
*Repository*
|
| 51 |
+
|
| 52 |
+
[SeldonIO/MLServer](https://github.com/SeldonIO/MLServer): 288
|
| 53 |
+
|
| 54 |
+
[marqo-ai/marqo](https://github.com/marqo-ai/marqo): 265
|
| 55 |
+
|
| 56 |
+
[AlekseyKorshuk/optimum-transformers](https://github.com/AlekseyKorshuk/optimum-transformers): 114
|
| 57 |
+
|
| 58 |
+
[graphcore/tutorials](https://github.com/graphcore/tutorials): 65
|
| 59 |
+
|
| 60 |
+
[huggingface/optimum-intel](https://github.com/huggingface/optimum-intel): 61
|
| 61 |
+
|
| 62 |
+
[huggingface/optimum-graphcore](https://github.com/huggingface/optimum-graphcore): 34
|
| 63 |
+
|
| 64 |
+
[huggingface/optimum-habana](https://github.com/huggingface/optimum-habana): 24
|
| 65 |
+
|
| 66 |
+
[philschmid/optimum-static-quantization](https://github.com/philschmid/optimum-static-quantization): 20
|
| 67 |
+
|
| 68 |
+
[philschmid/optimum-transformers-optimizations](https://github.com/philschmid/optimum-transformers-optimizations): 15
|
| 69 |
+
|
| 70 |
+
[girafe-ai/msai-python](https://github.com/girafe-ai/msai-python): 15
|
| 71 |
+
|
| 72 |
+
|
| 73 |
+
### Package & Repository fork count
|
| 74 |
+
|
| 75 |
+
This section shows the package and repository fork count, individually.
|
| 76 |
+
|
| 77 |
+
Package | Repository
|
| 78 |
+
:-------------------------:|:-------------------------:
|
| 79 |
+
 | 
|
| 80 |
+
|
| 81 |
+
There are 0 packages that have more than 200 forks.
|
| 82 |
+
|
| 83 |
+
There are 0 repositories that have more than 200 forks.
|
| 84 |
+
|
| 85 |
+
|
| 86 |
+
The top 10 in each category are the following:
|
| 87 |
+
|
| 88 |
+
*Package*
|
| 89 |
+
|
| 90 |
+
[SeldonIO/MLServer](https://github.com/SeldonIO/MLServer): 82
|
| 91 |
+
|
| 92 |
+
[huggingface/optimum-graphcore](https://github.com/huggingface/optimum-graphcore): 18
|
| 93 |
+
|
| 94 |
+
[huggingface/optimum-intel](https://github.com/huggingface/optimum-intel): 10
|
| 95 |
+
|
| 96 |
+
[AlekseyKorshuk/optimum-transformers](https://github.com/AlekseyKorshuk/optimum-transformers): 6
|
| 97 |
+
|
| 98 |
+
[huggingface/optimum-habana](https://github.com/huggingface/optimum-habana): 3
|
| 99 |
+
|
| 100 |
+
[bhavsarpratik/easy-transformers](https://github.com/bhavsarpratik/easy-transformers): 2
|
| 101 |
+
|
| 102 |
+
*Repository*
|
| 103 |
+
|
| 104 |
+
[SeldonIO/MLServer](https://github.com/SeldonIO/MLServer): 82
|
| 105 |
+
|
| 106 |
+
[graphcore/tutorials](https://github.com/graphcore/tutorials): 33
|
| 107 |
+
|
| 108 |
+
[huggingface/optimum-graphcore](https://github.com/huggingface/optimum-graphcore): 18
|
| 109 |
+
|
| 110 |
+
[girafe-ai/msai-python](https://github.com/girafe-ai/msai-python): 14
|
| 111 |
+
|
| 112 |
+
[huggingface/optimum-intel](https://github.com/huggingface/optimum-intel): 10
|
| 113 |
+
|
| 114 |
+
[marqo-ai/marqo](https://github.com/marqo-ai/marqo): 6
|
| 115 |
+
|
| 116 |
+
[AlekseyKorshuk/optimum-transformers](https://github.com/AlekseyKorshuk/optimum-transformers): 6
|
| 117 |
+
|
| 118 |
+
[whatofit/LevelWordWithFreq](https://github.com/whatofit/LevelWordWithFreq): 5
|
| 119 |
+
|
| 120 |
+
[philschmid/optimum-transformers-optimizations](https://github.com/philschmid/optimum-transformers-optimizations): 3
|
| 121 |
+
|
| 122 |
+
[huggingface/optimum-habana](https://github.com/huggingface/optimum-habana): 3
|
| 123 |
+
|
| 124 |
+
|
huggingface_dataset/Dataset_Card/scjnugacj_scjn_dataset_ner.md
ADDED
|
@@ -0,0 +1,84 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
annotations_creators:
|
| 3 |
+
- expert-generated
|
| 4 |
+
language_creators:
|
| 5 |
+
- other
|
| 6 |
+
language:
|
| 7 |
+
- es
|
| 8 |
+
license:
|
| 9 |
+
- cc-by-sa-4.0
|
| 10 |
+
multilinguality:
|
| 11 |
+
- monolingual
|
| 12 |
+
pretty_name: Corpus SCJN NER
|
| 13 |
+
size_categories:
|
| 14 |
+
- unknown
|
| 15 |
+
source_datasets:
|
| 16 |
+
- original
|
| 17 |
+
task_categories:
|
| 18 |
+
- Token Classification
|
| 19 |
+
task_ids:
|
| 20 |
+
- NER
|
| 21 |
+
---
|
| 22 |
+
|
| 23 |
+
# Corpus SCJN NER, para el reconocimiento de entidades nombradas
|
| 24 |
+
|
| 25 |
+
|
| 26 |
+
En su primera versión contiene etiquetas para identificar leyes y tratados internacionales de los que el Estado Mexicano es parte.
|
| 27 |
+
|
| 28 |
+
## Dataset Structure
|
| 29 |
+
|
| 30 |
+
### Data Instances
|
| 31 |
+
|
| 32 |
+
Un ejemplo de 'train' se ve de la siguiente forma:
|
| 33 |
+
|
| 34 |
+
```
|
| 35 |
+
{
|
| 36 |
+
'id': '3',
|
| 37 |
+
'ner_tags': [0, 0, 0, 0, 0, 1, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
|
| 38 |
+
'tokens': ['el', 'artículo', '15', 'de', 'la', 'ley', 'general', 'de', 'títulos', 'y', 'operaciones', 'de', 'crédito', 'exige', 'que', 'se', 'satisfagan', 'las', 'expresiones', 'omitidas', 'en', 'el', 'título', ',', 'antes', 'de', 'la', 'presentación', 'de', 'éste', 'para', 'su', 'aceptación', 'o', 'para', 'su', 'pago', '.', 'aunque', 'varios', 'autores', 'estiman', 'que', 'el', 'tenedor', 'puede', 'completar', 'los', 'requisitos', 'faltantes', 'a', 'la', 'cambial', ',', 'en', 'cualquier', 'instante', 'anterior', 'a', 'su', 'vencimiento', ',', 'este', 'criterio', 'no', 'es', 'aplicable', 'frente', 'a', 'la', 'disposición', 'terminante', 'de', 'la', 'ley', 'mexicana', ';', 'y', 'si', 'nuestro', 'legislador', 'hubiera', 'aceptado', 'la', 'posibilidad', 'de', 'llenar', 'los', 'requisitos', 'en', 'cualquier', 'momento', ',', 'hasta', 'antes', 'de', 'la', 'presentación', 'del', 'documento', 'para', ',', 'el', 'pago', ',', 'no', 'habría', 'hablado', 'de', 'la', 'presentación', 'para', 'la', 'aceptación', ';', 'máxime', ',', 'que', 'mientras', 'todas', 'las', 'letras', 'de', 'cambio', 'son', 'susceptibles', 'de', 'pago', ',', 'no', 'todas', 'lo', 'son', 'de', 'aceptación', '.', 'la', 'cambial', 'en', 'blanco', 'bien', 'puede', 'existir', 'y', 'circular', 'antes', 'de', 'que', 'sea', 'presentada', 'para', 'su', 'aceptación', ';', 'pero', 'cuando', 'ya', 'el', 'tenedor', 'va', 'a', 'hacer', 'valer', 'sus', 'derechos', '(', 'y', 'la', 'presentación', 'para', 'la', 'aceptación', 'es', 'el', 'ejercicio', 'de', 'uno', 'de', 'ellos', ')', ',', 'debe', 'llenar', 'los', 'extremos', 'necesarios', 'y', 'presentar', 'un', 'documento', 'completo', '.', 'cuando', 'el', 'girado', ',', 'al', 'aceptar', 'la', 'letra', ',', 'se', 'muestra', 'conforme', 'en', 'que', 'después', 'se', 'llene', 'la', 'expresión', 'de', 'su', 'importe', ',', 'ello', 'no', 'le', 'reporta', 'perjuicio', ',', 'si', 'el', 'beneficiario', 'lo', 'hace', 'dentro', 'de', 'los', 'límites', 'convenidos', ';', 'más', 'si', 'éste', 'se', 'excede', 'en', 'la', 'expresión', 'de', 'la', 'cantidad', 'convenida', ',', 'el', 'girado', 'sí', 'recibe', 'perjuicio', 'considerable', ',', 'ya', 'que', 'a', 'pesar', 'de', 'que', 'pueda', 'válidamente', 'oponer', 'las', 'excepciones', 'de', 'dolo', 'y', 'plus', 'petitio', 'correspondientes', ',', 'frente', 'al', 'beneficiario', 'que', 'violó', 'lo', 'pactado', ',', 'no', 'podrá', 'hacerlo', 'si', 'el', 'tenedor', 'es', 'un', 'tercero', 'que', 'de', 'buena', 'fe', 'adquirió', 'el', 'documento', ',', 'ignorando', 'las', 'circunstancias', 'precedentes', ';', 'en', 'cambio', ',', 'si', 'de', 'acuerdo', 'con', 'lo', 'preceptuado', 'por', 'nuestra', 'ley', ',', 'falta', 'el', 'título', 'de', 'crédito', ',', 'pues', 'el', 'documento', 'cuyos', 'requisitos', 'omitidos', 'no', 'se', 'satisficieron', 'oportunamente', ',', 'no', 'produce', 'efectos', 'como', 'tal', '(', 'artículo', '14', 'de', 'la', 'ley', 'de', 'la', 'materia', ')', ',', 'ésta', 'será', 'excepción', 'que', ',', 'demostrada', ',', 'puede', 'ser', 'oponible', 'a', 'cualquier', 'tenedor', ',', 'es', 'decir', ',', 'ya', 'no', 'será', 'una', 'excepción', 'personal', ',', 'sino', 'una', 'excepción', 'real', '.']
|
| 39 |
+
}
|
| 40 |
+
```
|
| 41 |
+
|
| 42 |
+
### Data Fields
|
| 43 |
+
|
| 44 |
+
Los campos son los mismos para todos los splits.
|
| 45 |
+
|
| 46 |
+
- `id`: a `string` feature.
|
| 47 |
+
- `tokens`: a `list` of `string` features.
|
| 48 |
+
- `ner_tags`: a `list` of classification labels (`int`). Full tagset with indices:
|
| 49 |
+
```python
|
| 50 |
+
{'O': 0, 'B-LEY': 1, 'I-LEY': 2, 'B-TRAT_INTL': 3, 'I-TRAT_INTL': 4}
|
| 51 |
+
```
|
| 52 |
+
|
| 53 |
+
### Data Splits
|
| 54 |
+
|
| 55 |
+
| name |train|validation|test|
|
| 56 |
+
|---------|----:|---------:|---:|
|
| 57 |
+
|SCJNNER|1396|345|0|
|
| 58 |
+
|
| 59 |
+
## Dataset Creation
|
| 60 |
+
|
| 61 |
+
### Annotations
|
| 62 |
+
|
| 63 |
+
| annotations|train|validation|test|
|
| 64 |
+
|---------|----:|---------:|---:|
|
| 65 |
+
|LEY|1084|329|0|
|
| 66 |
+
|TRAT_INTL|935|161|0|
|
| 67 |
+
|
| 68 |
+
### Dataset Curators
|
| 69 |
+
|
| 70 |
+
Ana Gabriela Palomeque Ortiz, from SCJN - Unidad General de Administración del Conocimiento Jurídico.
|
| 71 |
+
|
| 72 |
+
### Personal and Sensitive Information
|
| 73 |
+
|
| 74 |
+
No personal or sensitive information included.
|
| 75 |
+
|
| 76 |
+
## Considerations for Using the Data
|
| 77 |
+
|
| 78 |
+
### Other Known Limitations
|
| 79 |
+
|
| 80 |
+
La información contenida en este dataset es para efectos demostrativos y no representa una fuente oficial de la Suprema Corte de Justicia de la Nación.
|
| 81 |
+
|
| 82 |
+
## License
|
| 83 |
+
|
| 84 |
+
<br/>This work is licensed under a <a rel="license" href="https://creativecommons.org/licenses/by-sa/4.0/deed.es">Attribution-ShareAlike 4.0 International License</a>.
|
huggingface_dataset/Dataset_Card/soymia_boudoir-dataset.md
ADDED
|
@@ -0,0 +1,25 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
dataset_info:
|
| 3 |
+
features:
|
| 4 |
+
- name: image
|
| 5 |
+
dtype: image
|
| 6 |
+
- name: text
|
| 7 |
+
dtype: string
|
| 8 |
+
splits:
|
| 9 |
+
- name: train
|
| 10 |
+
num_bytes: 96479861.365
|
| 11 |
+
num_examples: 1055
|
| 12 |
+
download_size: 95036573
|
| 13 |
+
dataset_size: 96479861.365
|
| 14 |
+
license: apache-2.0
|
| 15 |
+
task_categories:
|
| 16 |
+
- text-to-image
|
| 17 |
+
pretty_name: Boudoir Dataset
|
| 18 |
+
size_categories:
|
| 19 |
+
- 1K<n<10K
|
| 20 |
+
---
|
| 21 |
+
# Dataset Card for "boudoir-dataset"
|
| 22 |
+
|
| 23 |
+
### Dataset Summary
|
| 24 |
+
|
| 25 |
+
Images scrapped from selected Galleries on Behance.
|
huggingface_dataset/Dataset_Card/thaisum.md
ADDED
|
@@ -0,0 +1,215 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
| 1 |
+
---
|
| 2 |
+
annotations_creators:
|
| 3 |
+
- no-annotation
|
| 4 |
+
language_creators:
|
| 5 |
+
- found
|
| 6 |
+
language:
|
| 7 |
+
- th
|
| 8 |
+
license:
|
| 9 |
+
- mit
|
| 10 |
+
multilinguality:
|
| 11 |
+
- monolingual
|
| 12 |
+
size_categories:
|
| 13 |
+
- 100K<n<1M
|
| 14 |
+
source_datasets:
|
| 15 |
+
- original
|
| 16 |
+
task_categories:
|
| 17 |
+
- summarization
|
| 18 |
+
- text-generation
|
| 19 |
+
- fill-mask
|
| 20 |
+
task_ids:
|
| 21 |
+
- language-modeling
|
| 22 |
+
- masked-language-modeling
|
| 23 |
+
paperswithcode_id: null
|
| 24 |
+
pretty_name: ThaiSum
|
| 25 |
+
dataset_info:
|
| 26 |
+
features:
|
| 27 |
+
- name: title
|
| 28 |
+
dtype: string
|
| 29 |
+
- name: body
|
| 30 |
+
dtype: string
|
| 31 |
+
- name: summary
|
| 32 |
+
dtype: string
|
| 33 |
+
- name: type
|
| 34 |
+
dtype: string
|
| 35 |
+
- name: tags
|
| 36 |
+
dtype: string
|
| 37 |
+
- name: url
|
| 38 |
+
dtype: string
|
| 39 |
+
config_name: thaisum
|
| 40 |
+
splits:
|
| 41 |
+
- name: train
|
| 42 |
+
num_bytes: 2945472406
|
| 43 |
+
num_examples: 358868
|
| 44 |
+
- name: validation
|
| 45 |
+
num_bytes: 118437310
|
| 46 |
+
num_examples: 11000
|
| 47 |
+
- name: test
|
| 48 |
+
num_bytes: 119496704
|
| 49 |
+
num_examples: 11000
|
| 50 |
+
download_size: 647582078
|
| 51 |
+
dataset_size: 3183406420
|
| 52 |
+
---
|
| 53 |
+
|
| 54 |
+
# Dataset Card for ThaiSum
|
| 55 |
+
|
| 56 |
+
## Table of Contents
|
| 57 |
+
- [Dataset Description](#dataset-description)
|
| 58 |
+
- [Dataset Summary](#dataset-summary)
|
| 59 |
+
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
|
| 60 |
+
- [Languages](#languages)
|
| 61 |
+
- [Dataset Structure](#dataset-structure)
|
| 62 |
+
- [Data Instances](#data-instances)
|
| 63 |
+
- [Data Fields](#data-fields)
|
| 64 |
+
- [Data Splits](#data-splits)
|
| 65 |
+
- [Dataset Creation](#dataset-creation)
|
| 66 |
+
- [Curation Rationale](#curation-rationale)
|
| 67 |
+
- [Source Data](#source-data)
|
| 68 |
+
- [Annotations](#annotations)
|
| 69 |
+
- [Personal and Sensitive Information](#personal-and-sensitive-information)
|
| 70 |
+
- [Considerations for Using the Data](#considerations-for-using-the-data)
|
| 71 |
+
- [Social Impact of Dataset](#social-impact-of-dataset)
|
| 72 |
+
- [Discussion of Biases](#discussion-of-biases)
|
| 73 |
+
- [Other Known Limitations](#other-known-limitations)
|
| 74 |
+
- [Additional Information](#additional-information)
|
| 75 |
+
- [Dataset Curators](#dataset-curators)
|
| 76 |
+
- [Licensing Information](#licensing-information)
|
| 77 |
+
- [Citation Information](#citation-information)
|
| 78 |
+
- [Contributions](#contributions)
|
| 79 |
+
|
| 80 |
+
## Dataset Description
|
| 81 |
+
|
| 82 |
+
- **Homepage:** https://github.com/nakhunchumpolsathien/ThaiSum
|
| 83 |
+
- **Repository:** https://github.com/nakhunchumpolsathien/ThaiSum
|
| 84 |
+
- **Paper:**
|
| 85 |
+
- **Leaderboard:**
|
| 86 |
+
- **Point of Contact:** https://github.com/nakhunchumpolsathien
|
| 87 |
+
|
| 88 |
+
### Dataset Summary
|
| 89 |
+
|
| 90 |
+
ThaiSum is a large-scale corpus for Thai text summarization obtained from several online news websites namely Thairath, ThaiPBS, Prachathai, and The Standard. This dataset consists of over 350,000 article and summary pairs written by journalists.
|
| 91 |
+
|
| 92 |
+
### Supported Tasks and Leaderboards
|
| 93 |
+
|
| 94 |
+
summarization, language modeling
|
| 95 |
+
|
| 96 |
+
### Languages
|
| 97 |
+
|
| 98 |
+
Thai
|
| 99 |
+
|
| 100 |
+
## Dataset Structure
|
| 101 |
+
|
| 102 |
+
### Data Instances
|
| 103 |
+
|
| 104 |
+
```
|
| 105 |
+
{'body': 'กีเก ซานเชซ ฟลอเรส\xa0 กุนซือเลือดกระทิงของทีมวัตฟอร์ด\xa0 เมินประเด็นจุดโทษปัญหาในเกมพรีเมียร์ลีก อังกฤษ นัดที่แตนอาละวาดเปิดบ้านพ่าย คริสตัล พาเลซ 0-1ชี้ทีมของเขาเล่นไม่ดีพอเอง,สำนักข่าวต่างประเทศรายงานวันที่ 27 ก.ย. ว่า กีเก ซานเชซ ฟลอเรส\xa0 ผู้จัดการทีมชาวสเปน ของ แตนอาละวาด วัตฟอร์ด\xa0 ยอมรับทีมของเขาเล่นได้ไม่ดีพอเอง ในเกมพรีเมียร์ลีก อังกฤษ นัดเปิดบ้านพ่าย อินทรีผงาด คริสตัล พาเลซ 0-1 เมื่อคืนวันอาทิตย์ที่ผ่านมา,เกมนี้จุดเปลี่ยนมาอยู่ที่การได้จุดโทษในช่วงครึ่งหลังของ คริสตัล พาเลซ ซึ่งไม่ค่อยชัดเจนเท่าไหร่ว่า อัลลัน นียอม นั้นไปทำฟาล์วใส่ วิลฟรีด ซาฮา ในเขตโทษหรือไม่ แต่ผู้ตัดสินก็ชี้เป็นจุดโทษ ซึ่ง โยอัน กาบาย สังหารไม่พลาด และเป็นประตูชัยช่วยให้ คริสตัล พาเลซ เอาชนะ วัตฟอร์ด ไป 1-0 และเป็นการพ่ายแพ้ในบ้านนัดแรกของวัตฟอร์ดในฤดูกาลนี้อีกด้วย,ฟลอเรส กล่าวว่า มันเป็นเรื่องยากในการหยุดเกมรุกของคริสตัล พาเลซ ซึ่งมันอึดอัดจริงๆสำหรับ���รา เราเล่นกันได้ไม่ดีนักในตอนที่ได้ครองบอล เราต้องเล่นทางริมเส้นให้มากกว่านี้ เราไม่สามารถหยุดเกมสวนกลับของพวกเขาได้ และแนวรับของเราก็ยืนไม่เป็นระเบียบสักเท่าไหร่ในช่วงครึ่งแรก ส่วนเรื่องจุดโทษการตัดสินใจขั้นสุดท้ายมันอยู่ที่ผู้ตัดสิน ซึ่งมันเป็นการตัดสินใจที่สำคัญ ผมเองก็ไม่รู้ว่าเขาตัดสินถูกหรือเปล่า บางทีมันอาจเป็นจุดที่ตัดสินเกมนี้เลย แต่เราไม่ได้แพ้เกมนี้เพราะจุดโทษ เราแพ้ในวันนี้เพราะเราเล่นไม่ดีและคริสตัล พาเลซ เล่นดีกว่าเรา เราไม่ได้มีฟอร์มการเล่นที่ดีในเกมนี้เลย', 'summary': 'กีเก ซานเชซ ฟลอเรส กุนซือเลือดกระทิงของทีมวัตฟอร์ด เมินประเด็นจุดโทษปัญหาในเกมพรีเมียร์ลีก อังกฤษ นัดที่แตนอาละวาดเปิดบ้านพ่าย คริสตัล พาเลซ 0-1ชี้ทีมของเขาเล่นไม่ดีพอเอง', 'tags': 'พรีเมียร์ลีก,วัตฟอร์ด,คริสตัล พาเลซ,กีเก ซานเชซ ฟลอเรส,ข่าวกีฬา,ข่าว,ไทยรัฐออนไลน์', 'title': 'ฟลอเรส รับ วัตฟอร์ดห่วยเองเกมพ่ายพาเลซคาบ้าน', 'type': '', 'url': 'https://www.thairath.co.th/content/528322'}
|
| 106 |
+
```
|
| 107 |
+
|
| 108 |
+
### Data Fields
|
| 109 |
+
|
| 110 |
+
- `title`: title of article
|
| 111 |
+
- `body`: body of article
|
| 112 |
+
- `summary`: summary of article
|
| 113 |
+
- `type`: type of article, if any
|
| 114 |
+
- `tags`: tags of article, separated by `,`
|
| 115 |
+
- `url`: URL of article
|
| 116 |
+
|
| 117 |
+
### Data Splits
|
| 118 |
+
|
| 119 |
+
train/valid/test: 358868 / 11000 / 11000
|
| 120 |
+
|
| 121 |
+
## Dataset Creation
|
| 122 |
+
|
| 123 |
+
### Curation Rationale
|
| 124 |
+
|
| 125 |
+
Sequence-to-sequence (Seq2Seq) models have shown great achievement in text summarization. However, Seq2Seq model often requires large-scale training data to achieve effective results. Although many impressive advancements in text summarization field have been made, most of summarization studies focus on resource-rich languages. The progress of Thai text summarization is still far behind. The dearth of large-scale dataset keeps Thai text summarization in its infancy. As far as our knowledge goes, there is not a large-scale dataset for Thai text summarization available anywhere. Thus, we present ThaiSum, a large-scale corpus for Thai text summarization obtained from several online news websites namely Thairath, ThaiPBS, Prachathai, and The Standard.
|
| 126 |
+
|
| 127 |
+
### Source Data
|
| 128 |
+
|
| 129 |
+
#### Initial Data Collection and Normalization
|
| 130 |
+
|
| 131 |
+
We used a python library named Scrapy to crawl articles from several news websites namely Thairath, Prachatai, ThaiPBS and, The Standard. We first collected news URLs provided in their sitemaps. During web-crawling, we used HTML markup and metadata available in HTML pages to identify article text, summary, headline, tags and label. Collected articles were published online from 2014 to August 2020. <br> <br>
|
| 132 |
+
We further performed data cleansing process to minimize noisy data. We filtered out articles that their article text or summary is missing. Articles that contains article text with less than 150 words or summary with less than 15 words were removed. We also discarded articles that contain at least one of these following tags: ‘ดวง’ (horoscope), ‘นิยาย’ (novel), ‘อินสตราแกรมดารา’ (celebrity Instagram), ‘คลิปสุดฮา’(funny video) and ‘สรุปข่าว’ (highlight news). Some summaries were completely irrelevant to their original article texts. To eliminate those irrelevant summaries, we calculated abstractedness score between summary and its article text. Abstractedness score is written formally as: <br>
|
| 133 |
+
<center><a href="https://www.codecogs.com/eqnedit.php?latex=\begin{equation}&space;\frac{|S-A|}{r}&space;\times&space;100&space;\end{equation}" target="_blank"><img src="https://latex.codecogs.com/gif.latex?\begin{equation}&space;\frac{|S-A|}{r}&space;\times&space;100&space;\end{equation}" title="\begin{equation} \frac{|S-A|}{r} \times 100 \end{equation}" /></a></center><br>
|
| 134 |
+
<br>Where 𝑆 denotes set of article tokens. 𝐴 denotes set of summary tokens. 𝑟 denotes a total number of summary tokens. We omitted articles that have abstractedness score at 1-grams higher than 60%.
|
| 135 |
+
<br><br>
|
| 136 |
+
|
| 137 |
+
It is important to point out that we used [PyThaiNLP](https://github.com/PyThaiNLP/pythainlp), version 2.2.4, tokenizing engine = newmm, to process Thai texts in this study. It is challenging to tokenize running Thai text into words or sentences because there are not clear word/sentence delimiters in Thai language. Therefore, using different tokenization engines may result in different segment of words/sentences.
|
| 138 |
+
|
| 139 |
+
After data-cleansing process, ThaiSum dataset contains over 358,000 articles. The size of this dataset is comparable to a well-known English document summarization dataset, CNN/Dily mail dataset. Moreover, we analyse the characteristics of this dataset by measuring the abstractedness level, compassion rate, and content diversity. For more details, see [thaisum_exploration.ipynb](https://github.com/nakhunchumpolsathien/ThaiSum/blob/master/thaisum_exploration.ipynb).
|
| 140 |
+
|
| 141 |
+
#### Dataset Statistics
|
| 142 |
+
|
| 143 |
+
ThaiSum dataset consists of 358,868 articles. Average lengths of article texts and summaries are approximately 530 and 37 words respectively. As mentioned earlier, we also collected headlines, tags and labels provided in each article. Tags are similar to keywords of the article. An article normally contains several tags but a few labels. Tags can be name of places or persons that article is about while labels indicate news category (politic, entertainment, etc.). Ultimatly, ThaiSum contains 538,059 unique tags and 59 unique labels. Note that not every article contains tags or labels.
|
| 144 |
+
|
| 145 |
+
|Dataset Size| 358,868 | articles |
|
| 146 |
+
|:---|---:|---:|
|
| 147 |
+
|Avg. Article Length| 529.5 | words|
|
| 148 |
+
|Avg. Summary Length | 37.3 | words|
|
| 149 |
+
|Avg. Headline Length | 12.6 | words|
|
| 150 |
+
|Unique Vocabulary Size | 407,355 | words|
|
| 151 |
+
|Occurring > 10 times | 81,761 | words|
|
| 152 |
+
|Unique News Tag Size | 538,059 | tags|
|
| 153 |
+
|Unique News Label Size | 59 | labels|
|
| 154 |
+
|
| 155 |
+
#### Who are the source language producers?
|
| 156 |
+
|
| 157 |
+
Journalists of respective articles
|
| 158 |
+
|
| 159 |
+
### Annotations
|
| 160 |
+
|
| 161 |
+
#### Annotation process
|
| 162 |
+
|
| 163 |
+
`summary`, `type` and `tags` are created by journalists who wrote the articles and/or their publishers.
|
| 164 |
+
|
| 165 |
+
#### Who are the annotators?
|
| 166 |
+
|
| 167 |
+
`summary`, `type` and `tags` are created by journalists who wrote the articles and/or their publishers.
|
| 168 |
+
|
| 169 |
+
### Personal and Sensitive Information
|
| 170 |
+
|
| 171 |
+
All data are public news articles. No personal and sensitive information is expected to be included.
|
| 172 |
+
|
| 173 |
+
## Considerations for Using the Data
|
| 174 |
+
|
| 175 |
+
### Social Impact of Dataset
|
| 176 |
+
|
| 177 |
+
- News summarization in Thai
|
| 178 |
+
- Language modeling for Thai news
|
| 179 |
+
|
| 180 |
+
### Discussion of Biases
|
| 181 |
+
|
| 182 |
+
|
| 183 |
+
- [ThaiPBS](https://www.thaipbs.or.th/home) [receives funding from Thai government](https://www.bangkokbiznews.com/blog/detail/648740).
|
| 184 |
+
- [Thairath](https://www.thairath.co.th/) is known as [the most popular newspaper in Thailand](https://mgronline.com/onlinesection/detail/9620000058532); no clear political leaning.
|
| 185 |
+
- [The Standard](https://thestandard.co/) is a left-leaning online magazine.
|
| 186 |
+
- [Prachathai](https://prachatai.com/) is a left-leaning, human-right-focused news site.
|
| 187 |
+
|
| 188 |
+
### Other Known Limitations
|
| 189 |
+
|
| 190 |
+
[More Information Needed]
|
| 191 |
+
|
| 192 |
+
## Additional Information
|
| 193 |
+
|
| 194 |
+
### Dataset Curators
|
| 195 |
+
|
| 196 |
+
[@nakhunchumpolsathien](https://github.com/nakhunchumpolsathien/)
|
| 197 |
+
[@caramelWaffle](https://github.com/caramelWaffle)
|
| 198 |
+
|
| 199 |
+
### Licensing Information
|
| 200 |
+
|
| 201 |
+
MIT License
|
| 202 |
+
|
| 203 |
+
### Citation Information
|
| 204 |
+
|
| 205 |
+
```
|
| 206 |
+
@mastersthesis{chumpolsathien_2020,
|
| 207 |
+
title={Using Knowledge Distillation from Keyword Extraction to Improve the Informativeness of Neural Cross-lingual Summarization},
|
| 208 |
+
author={Chumpolsathien, Nakhun},
|
| 209 |
+
year={2020},
|
| 210 |
+
school={Beijing Institute of Technology}
|
| 211 |
+
```
|
| 212 |
+
|
| 213 |
+
### Contributions
|
| 214 |
+
|
| 215 |
+
Thanks to [@cstorm125](https://github.com/cstorm125) for adding this dataset.
|
huggingface_dataset/Dataset_Card/thejaminator_imdb_rewarded.md
ADDED
|
@@ -0,0 +1,11 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
license: apache-2.0
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| 3 |
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task_categories:
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| 4 |
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- text-generation
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| 5 |
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language:
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| 6 |
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- en
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| 7 |
+
---
|
| 8 |
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This is the imdb dataset, https://huggingface.co/datasets/imdb
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| 9 |
+
|
| 10 |
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We've used a reward / sentiment model, https://huggingface.co/lvwerra/distilbert-imdb to compute the rewards of the offline data.
|
| 11 |
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This is so that we can use offline RL on the data.
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