Upload batch 137 (20 files, last=huggingface_dataset/Dataset_Card/zZWipeoutZz_rogue_style.md)
Browse files- huggingface_dataset/Dataset_Card/AdamOswald1_autotrain-data-alt.md +53 -0
- huggingface_dataset/Dataset_Card/Datatang_Face_Recognition_Data_with_Gauze_Mask.md +126 -0
- huggingface_dataset/Dataset_Card/Den4ikAI_fact_detection.md +10 -0
- huggingface_dataset/Dataset_Card/LeandraFichtel_KAMEL.md +99 -0
- huggingface_dataset/Dataset_Card/MicPie_unpredictable_cluster26.md +250 -0
- huggingface_dataset/Dataset_Card/Nerfgun3_miyuki-shiba_LoRA.md +68 -0
- huggingface_dataset/Dataset_Card/Nerfgun3_shatter_style.md +45 -0
- huggingface_dataset/Dataset_Card/TobiTob_CityLearn.md +12 -0
- huggingface_dataset/Dataset_Card/autoevaluate_autoeval-eval-futin__guess-en-6ca7d2-2087467164.md +34 -0
- huggingface_dataset/Dataset_Card/autoevaluate_autoeval-eval-inverse-scaling__NeQA-inverse-scaling__NeQA-1e740e-1694759589.md +34 -0
- huggingface_dataset/Dataset_Card/autoevaluate_autoeval-staging-eval-project-multi_news-416d7689-12805701.md +33 -0
- huggingface_dataset/Dataset_Card/derek-thomas_ScienceQA.md +301 -0
- huggingface_dataset/Dataset_Card/djghosh_wds_vtab-dsprites_label_x_position_test.md +15 -0
- huggingface_dataset/Dataset_Card/huggingartists_pharaoh.md +204 -0
- huggingface_dataset/Dataset_Card/huggingartists_tiamat.md +204 -0
- huggingface_dataset/Dataset_Card/jakartaresearch_cerpen-corpus.md +149 -0
- huggingface_dataset/Dataset_Card/vogloblinsky_skateboarding-tricks.md +24 -0
- huggingface_dataset/Dataset_Card/waifu-research-department_regularization.md +19 -0
- huggingface_dataset/Dataset_Card/youtube_caption_corrections.md +204 -0
- huggingface_dataset/Dataset_Card/zZWipeoutZz_rogue_style.md +37 -0
huggingface_dataset/Dataset_Card/AdamOswald1_autotrain-data-alt.md
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---
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task_categories:
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- image-classification
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---
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# AutoTrain Dataset for project: alt
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## Dataset Description
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This dataset has been automatically processed by AutoTrain for project alt.
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### Languages
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The BCP-47 code for the dataset's language is unk.
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## Dataset Structure
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| 17 |
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### Data Instances
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A sample from this dataset looks as follows:
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```json
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[
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{
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"image": "<600x600 RGB PIL image>",
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"target": 1
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},
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{
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"image": "<1024x590 RGB PIL image>",
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"target": 1
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}
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]
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```
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### Dataset Fields
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| 36 |
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The dataset has the following fields (also called "features"):
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```json
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{
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"image": "Image(decode=True, id=None)",
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"target": "ClassLabel(names=['Adult Chara', 'Adult Chara and Young Chara', 'Chara', 'Female Kris', 'Kris', 'Kris and Adult Chara', 'Kris and Chara', 'Kris and Female Chara', 'Kris and Male Chara', 'Kris and The Player', 'Kris and a Soul', 'Kris next to the Ghost of Chara', 'Male Kris', 'Male Kris and Female Kris', 'StoryShift Chara', 'StoryShift Chara and Young Chara', 'Teen Chara and Young Chara', 'Teenager Chara and Young Chara', 'Young Chara'], id=None)"
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}
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```
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### Dataset Splits
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| 47 |
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This dataset is split into a train and validation split. The split sizes are as follow:
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| Split name | Num samples |
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| 51 |
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| ------------ | ------------------- |
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| train | 243 |
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| 53 |
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| valid | 243 |
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huggingface_dataset/Dataset_Card/Datatang_Face_Recognition_Data_with_Gauze_Mask.md
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---
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YAML tags:
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| 3 |
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- copy-paste the tags obtained with the tagging app: https://github.com/huggingface/datasets-tagging
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| 4 |
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---
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| 5 |
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| 6 |
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# Dataset Card for Datatang/Face_Recognition_Data_with_Gauze_Mask
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| 7 |
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| 8 |
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## Table of Contents
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| 9 |
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- [Table of Contents](#table-of-contents)
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| 10 |
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- [Dataset Description](#dataset-description)
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| 11 |
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- [Dataset Summary](#dataset-summary)
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| 12 |
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- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
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| 13 |
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- [Languages](#languages)
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| 14 |
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- [Dataset Structure](#dataset-structure)
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| 15 |
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- [Data Instances](#data-instances)
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| 16 |
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- [Data Fields](#data-fields)
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| 17 |
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- [Data Splits](#data-splits)
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| 18 |
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- [Dataset Creation](#dataset-creation)
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| 19 |
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- [Curation Rationale](#curation-rationale)
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| 20 |
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- [Source Data](#source-data)
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| 21 |
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- [Annotations](#annotations)
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| 22 |
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- [Personal and Sensitive Information](#personal-and-sensitive-information)
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| 23 |
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- [Considerations for Using the Data](#considerations-for-using-the-data)
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| 24 |
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- [Social Impact of Dataset](#social-impact-of-dataset)
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| 25 |
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- [Discussion of Biases](#discussion-of-biases)
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| 26 |
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- [Other Known Limitations](#other-known-limitations)
|
| 27 |
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- [Additional Information](#additional-information)
|
| 28 |
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- [Dataset Curators](#dataset-curators)
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| 29 |
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- [Licensing Information](#licensing-information)
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| 30 |
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- [Citation Information](#citation-information)
|
| 31 |
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- [Contributions](#contributions)
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| 32 |
+
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| 33 |
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## Dataset Description
|
| 34 |
+
|
| 35 |
+
- **Homepage:** https://bit.ly/3a0NLRL
|
| 36 |
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- **Repository:**
|
| 37 |
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- **Paper:**
|
| 38 |
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- **Leaderboard:**
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| 39 |
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- **Point of Contact:**
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| 40 |
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|
| 41 |
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### Dataset Summary
|
| 42 |
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| 43 |
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5,030 People - Face Recognition Data with Gauze Mask, for each subject, 7 images were collected. The dataset diversity includes multiple mask types, multiple ages, multiple light conditions and scenes.This data can be applied to computer vision tasks such as occluded face detection and recognition.
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| 44 |
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|
| 45 |
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For more details, please refer to the link: https://bit.ly/3a0NLRL
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| 46 |
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| 47 |
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### Supported Tasks and Leaderboards
|
| 48 |
+
|
| 49 |
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face-detection, computer-vision: The dataset can be used to train a model for face detection.
|
| 50 |
+
|
| 51 |
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### Languages
|
| 52 |
+
|
| 53 |
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English
|
| 54 |
+
## Dataset Structure
|
| 55 |
+
|
| 56 |
+
### Data Instances
|
| 57 |
+
|
| 58 |
+
[More Information Needed]
|
| 59 |
+
|
| 60 |
+
### Data Fields
|
| 61 |
+
|
| 62 |
+
[More Information Needed]
|
| 63 |
+
|
| 64 |
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### Data Splits
|
| 65 |
+
|
| 66 |
+
[More Information Needed]
|
| 67 |
+
|
| 68 |
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## Dataset Creation
|
| 69 |
+
|
| 70 |
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### Curation Rationale
|
| 71 |
+
|
| 72 |
+
[More Information Needed]
|
| 73 |
+
|
| 74 |
+
### Source Data
|
| 75 |
+
|
| 76 |
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#### Initial Data Collection and Normalization
|
| 77 |
+
|
| 78 |
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[More Information Needed]
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| 79 |
+
|
| 80 |
+
#### Who are the source language producers?
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| 81 |
+
|
| 82 |
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[More Information Needed]
|
| 83 |
+
|
| 84 |
+
### Annotations
|
| 85 |
+
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| 86 |
+
#### Annotation process
|
| 87 |
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|
| 88 |
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[More Information Needed]
|
| 89 |
+
|
| 90 |
+
#### Who are the annotators?
|
| 91 |
+
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| 92 |
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[More Information Needed]
|
| 93 |
+
|
| 94 |
+
### Personal and Sensitive Information
|
| 95 |
+
|
| 96 |
+
[More Information Needed]
|
| 97 |
+
|
| 98 |
+
## Considerations for Using the Data
|
| 99 |
+
|
| 100 |
+
### Social Impact of Dataset
|
| 101 |
+
|
| 102 |
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[More Information Needed]
|
| 103 |
+
|
| 104 |
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### Discussion of Biases
|
| 105 |
+
|
| 106 |
+
[More Information Needed]
|
| 107 |
+
|
| 108 |
+
### Other Known Limitations
|
| 109 |
+
|
| 110 |
+
[More Information Needed]
|
| 111 |
+
|
| 112 |
+
## Additional Information
|
| 113 |
+
|
| 114 |
+
### Dataset Curators
|
| 115 |
+
|
| 116 |
+
[More Information Needed]
|
| 117 |
+
|
| 118 |
+
### Licensing Information
|
| 119 |
+
|
| 120 |
+
Commerical License: https://drive.google.com/file/d/1saDCPm74D4UWfBL17VbkTsZLGfpOQj1J/view?usp=sharing
|
| 121 |
+
|
| 122 |
+
### Citation Information
|
| 123 |
+
|
| 124 |
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[More Information Needed]
|
| 125 |
+
|
| 126 |
+
### Contributions
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huggingface_dataset/Dataset_Card/Den4ikAI_fact_detection.md
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---
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license: mit
|
| 3 |
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language:
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| 4 |
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- ru
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| 5 |
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---
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| 6 |
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| 7 |
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В качестве фактов использовались предложения из Википедии, а в качестве негативных - худлит и новости
|
| 8 |
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Модель обученная на этом датасете [Den4ikAI/ruBert_base_fact_detection](https://huggingface.co/Den4ikAI/ruBert_base_fact_detection)
|
| 9 |
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|
| 10 |
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delimiter='|'
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huggingface_dataset/Dataset_Card/LeandraFichtel_KAMEL.md
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| 1 |
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---
|
| 2 |
+
|
| 3 |
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# Dataset Card for KAMEL: Knowledge Analysis with Multitoken Entities in Language Models
|
| 4 |
+
## Table of Contents
|
| 5 |
+
- [Dataset Description](#dataset-description)
|
| 6 |
+
- [Dataset Summary](#dataset-summary)
|
| 7 |
+
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
|
| 8 |
+
- [Languages](#languages)
|
| 9 |
+
- [Dataset Structure](#dataset-structure)
|
| 10 |
+
- [Data Instances](#data-instances)
|
| 11 |
+
- [Data Fields](#data-fields)
|
| 12 |
+
- [Data Splits](#data-splits)
|
| 13 |
+
- [Dataset Creation](#dataset-creation)
|
| 14 |
+
- [Curation Rationale](#curation-rationale)
|
| 15 |
+
- [Source Data](#source-data)
|
| 16 |
+
- [Annotations](#annotations)
|
| 17 |
+
- [Personal and Sensitive Information](#personal-and-sensitive-information)
|
| 18 |
+
- [Considerations for Using the Data](#considerations-for-using-the-data)
|
| 19 |
+
- [Social Impact of Dataset](#social-impact-of-dataset)
|
| 20 |
+
- [Discussion of Biases](#discussion-of-biases)
|
| 21 |
+
- [Other Known Limitations](#other-known-limitations)
|
| 22 |
+
- [Additional Information](#additional-information)
|
| 23 |
+
- [Dataset Curators](#dataset-curators)
|
| 24 |
+
- [Licensing Information](#licensing-information)
|
| 25 |
+
- [Citation Information](#citation-information)
|
| 26 |
+
- [Contributions](#contributions)
|
| 27 |
+
## Dataset Description
|
| 28 |
+
- **Homepage:**
|
| 29 |
+
https://github.com/JanKalo/KAMEL
|
| 30 |
+
- **Repository:**
|
| 31 |
+
https://github.com/JanKalo/KAMEL
|
| 32 |
+
- **Paper:**
|
| 33 |
+
@inproceedings{kalo2022kamel,
|
| 34 |
+
title={KAMEL: Knowledge Analysis with Multitoken Entities in Language Models},
|
| 35 |
+
author={Kalo, Jan-Christoph and Fichtel, Leandra},
|
| 36 |
+
booktitle={Automated Knowledge Base Construction},
|
| 37 |
+
year={2022}
|
| 38 |
+
}
|
| 39 |
+
### Dataset Summary
|
| 40 |
+
This dataset provides the data for KAMEL, a probing dataset for language models that contains factual knowledge
|
| 41 |
+
from Wikidata and Wikipedia.
|
| 42 |
+
|
| 43 |
+
See the paper for more details. For more information, also see:
|
| 44 |
+
https://github.com/JanKalo/KAMEL
|
| 45 |
+
### Languages
|
| 46 |
+
en
|
| 47 |
+
## Dataset Structure
|
| 48 |
+
### Data Instances
|
| 49 |
+
|
| 50 |
+
|
| 51 |
+
### Data Fields
|
| 52 |
+
KAMEL has the following fields:
|
| 53 |
+
* index: the id
|
| 54 |
+
* sub_label: a label for the subject
|
| 55 |
+
* obj_uri: Wikidata uri for the object
|
| 56 |
+
* obj_labels: multiple labels for the object
|
| 57 |
+
* chosen_label: the preferred label
|
| 58 |
+
* rel_uri: Wikidata uri for the relation
|
| 59 |
+
* rel_label: a label for the relation
|
| 60 |
+
|
| 61 |
+
### Data Splits
|
| 62 |
+
The dataset is split into a training, validation, and test dataset.
|
| 63 |
+
It contains 234 Wikidata relations.
|
| 64 |
+
For each relation there exist 200 training, 100 validation,
|
| 65 |
+
and 100 test instances.
|
| 66 |
+
|
| 67 |
+
## Dataset Creation
|
| 68 |
+
### Curation Rationale
|
| 69 |
+
This dataset was gathered and created to explore what knowledge graph facts are memorized by large language models.
|
| 70 |
+
### Source Data
|
| 71 |
+
#### Initial Data Collection and Normalization
|
| 72 |
+
See the reaserch paper and website for more detail. The dataset was
|
| 73 |
+
created from Wikidata and Wikipedia.
|
| 74 |
+
### Annotations
|
| 75 |
+
#### Annotation process
|
| 76 |
+
There is no human annotation, but only automatic linking from Wikidata facts to Wikipedia articles.
|
| 77 |
+
The details about the process can be found in the paper.
|
| 78 |
+
#### Who are the annotators?
|
| 79 |
+
Machine Annotations
|
| 80 |
+
### Personal and Sensitive Information
|
| 81 |
+
Unkown, but likely information about famous people mentioned in the English Wikipedia.
|
| 82 |
+
## Considerations for Using the Data
|
| 83 |
+
### Social Impact of Dataset
|
| 84 |
+
The goal for the work is to probe the understanding of language models.
|
| 85 |
+
### Discussion of Biases
|
| 86 |
+
Since the data is created from Wikipedia and Wikidata, the existing biases from these two data sources may also be reflected in KAMEL.
|
| 87 |
+
## Additional Information
|
| 88 |
+
### Dataset Curators
|
| 89 |
+
The authors of KAMEL at Vrije Universiteit Amsterdam and Technische Universität Braunschweig.
|
| 90 |
+
### Licensing Information
|
| 91 |
+
The Creative Commons Attribution-Noncommercial 4.0 International License. see https://github.com/facebookresearch/LAMA/blob/master/LICENSE
|
| 92 |
+
### Citation Information
|
| 93 |
+
@inproceedings{kalo2022kamel,
|
| 94 |
+
title={KAMEL: Knowledge Analysis with Multitoken Entities in Language Models},
|
| 95 |
+
author={Kalo, Jan-Christoph and Fichtel, Leandra},
|
| 96 |
+
booktitle={Automated Knowledge Base Construction},
|
| 97 |
+
year={2022}
|
| 98 |
+
}
|
| 99 |
+
|
huggingface_dataset/Dataset_Card/MicPie_unpredictable_cluster26.md
ADDED
|
@@ -0,0 +1,250 @@
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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 |
+
- 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-cluster26
|
| 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-cluster26" - 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/Nerfgun3_miyuki-shiba_LoRA.md
ADDED
|
@@ -0,0 +1,68 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
language:
|
| 3 |
+
- en
|
| 4 |
+
license: creativeml-openrail-m
|
| 5 |
+
thumbnail: "https://huggingface.co/datasets/Nerfgun3/miyuki-shiba_LoRA/resolve/main/preview/preview%20(1).png"
|
| 6 |
+
tags:
|
| 7 |
+
- stable-diffusion
|
| 8 |
+
- text-to-image
|
| 9 |
+
- image-to-image
|
| 10 |
+
inference: false
|
| 11 |
+
---
|
| 12 |
+
|
| 13 |
+
# Miyuki Character LoRA
|
| 14 |
+
|
| 15 |
+
# Use Cases
|
| 16 |
+
|
| 17 |
+
The LoRA is in itself very compatible with the most diverse model. However, it is most effective when used with Kenshi or AbyssOrangeMix2.
|
| 18 |
+
|
| 19 |
+
The LoRA itself was trained with the token: ```miyuki```.
|
| 20 |
+
I would suggest using the token with AbyssOrangeMix2, but not with Kenshi, since I got better results that way.
|
| 21 |
+
|
| 22 |
+
The models mentioned right now
|
| 23 |
+
1. AbyssOrangeMix2 from [WarriorMama777](https://huggingface.co/WarriorMama777/OrangeMixs)
|
| 24 |
+
2. Kenshi Model from [Luna](https://huggingface.co/SweetLuna/Kenshi)
|
| 25 |
+
|
| 26 |
+
## Strength
|
| 27 |
+
|
| 28 |
+
I would personally use these strength with the assosiated model:
|
| 29 |
+
- 0.6-0.75 for AbyssOrangeMix2
|
| 30 |
+
- 0.4-0.65 for Kenshi
|
| 31 |
+
|
| 32 |
+
# Showcase
|
| 33 |
+
|
| 34 |
+
**Example 1**
|
| 35 |
+
|
| 36 |
+
<img alt="Showcase" src="https://huggingface.co/datasets/Nerfgun3/miyuki-shiba_LoRA/resolve/main/preview/preview%20(2).png"/>
|
| 37 |
+
|
| 38 |
+
```
|
| 39 |
+
miyuki,
|
| 40 |
+
1girl, (masterpiece:1.2), (best quality:1.2), (sharp detail:1.2), (highres:1.2), (in a graden of flowers), sitting, waving
|
| 41 |
+
Steps: 32, Sampler: Euler a, CFG scale: 7
|
| 42 |
+
```
|
| 43 |
+
|
| 44 |
+
**Example 2**
|
| 45 |
+
<img alt="Showcase" src="https://huggingface.co/datasets/Nerfgun3/miyuki-shiba_LoRA/resolve/main/preview/preview%20(3).png"/>
|
| 46 |
+
|
| 47 |
+
```
|
| 48 |
+
miyuki, 1girl, (masterpiece:1.2), (best quality:1.2), (sharp detail:1.2), (highres:1.2), (in a graden of flowers), sitting, waving
|
| 49 |
+
Steps: 32, Sampler: Euler a, CFG scale: 7
|
| 50 |
+
```
|
| 51 |
+
|
| 52 |
+
**Example 3**
|
| 53 |
+
<img alt="Showcase" src="https://huggingface.co/datasets/Nerfgun3/miyuki-shiba_LoRA/resolve/main/preview/preview%20(4).png"/>
|
| 54 |
+
|
| 55 |
+
```
|
| 56 |
+
miyuki, 1girl, (masterpiece:1.2), (best quality:1.2), (sharp detail:1.2), (highres:1.2), (in a graden of flowers), sitting, hands behind her back
|
| 57 |
+
Steps: 20, Sampler: DPM++ SDE Karras, CFG scale: 7
|
| 58 |
+
```
|
| 59 |
+
|
| 60 |
+
# License
|
| 61 |
+
|
| 62 |
+
This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage.
|
| 63 |
+
The CreativeML OpenRAIL License specifies:
|
| 64 |
+
|
| 65 |
+
1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content
|
| 66 |
+
2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license
|
| 67 |
+
3. You may re-distribute the weights and use the embedding commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully)
|
| 68 |
+
[Please read the full license here](https://huggingface.co/spaces/CompVis/stable-diffusion-license)
|
huggingface_dataset/Dataset_Card/Nerfgun3_shatter_style.md
ADDED
|
@@ -0,0 +1,45 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
language:
|
| 3 |
+
- en
|
| 4 |
+
tags:
|
| 5 |
+
- stable-diffusion
|
| 6 |
+
- text-to-image
|
| 7 |
+
license: creativeml-openrail-m
|
| 8 |
+
inference: false
|
| 9 |
+
|
| 10 |
+
---
|
| 11 |
+
|
| 12 |
+
# Shatter Style Embedding / Textual Inversion
|
| 13 |
+
|
| 14 |
+
## Usage
|
| 15 |
+
To use this embedding you have to download the file aswell as drop it into the "\stable-diffusion-webui\embeddings" folder
|
| 16 |
+
|
| 17 |
+
To use it in a prompt: ```"drawn by shatter_style"```
|
| 18 |
+
|
| 19 |
+
If it is to strong just add [] around it.
|
| 20 |
+
|
| 21 |
+
Trained until 6000 steps
|
| 22 |
+
|
| 23 |
+
Have fun :)
|
| 24 |
+
|
| 25 |
+
## Example Pictures
|
| 26 |
+
|
| 27 |
+
<table>
|
| 28 |
+
<tr>
|
| 29 |
+
<td><img src=https://i.imgur.com/ebXN3C2.png width=100% height=100%/></td>
|
| 30 |
+
<td><img src=https://i.imgur.com/7zUtEDQ.png width=100% height=100%/></td>
|
| 31 |
+
<td><img src=https://i.imgur.com/uEuKyBP.png width=100% height=100%/></td>
|
| 32 |
+
<td><img src=https://i.imgur.com/qRJ5o3E.png width=100% height=100%/></td>
|
| 33 |
+
<td><img src=https://i.imgur.com/FybZxbO.png width=100% height=100%/></td>
|
| 34 |
+
</tr>
|
| 35 |
+
</table>
|
| 36 |
+
|
| 37 |
+
## License
|
| 38 |
+
|
| 39 |
+
This embedding is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage.
|
| 40 |
+
The CreativeML OpenRAIL License specifies:
|
| 41 |
+
|
| 42 |
+
1. You can't use the embedding to deliberately produce nor share illegal or harmful outputs or content
|
| 43 |
+
2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license
|
| 44 |
+
3. You may re-distribute the weights and use the embedding commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully)
|
| 45 |
+
[Please read the full license here](https://huggingface.co/spaces/CompVis/stable-diffusion-license)
|
huggingface_dataset/Dataset_Card/TobiTob_CityLearn.md
ADDED
|
@@ -0,0 +1,12 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/datasetcard.md?plain=1
|
| 3 |
+
# Doc / guide: https://huggingface.co/docs/hub/datasets-cards
|
| 4 |
+
{}
|
| 5 |
+
---
|
| 6 |
+
|
| 7 |
+
# Dataset Card for Dataset CityLearn
|
| 8 |
+
|
| 9 |
+
This dataset is used to train a decision Transformer for the CityLearn 2022 environment https://www.aicrowd.com/challenges/neurips-2022-citylearn-challenge.
|
| 10 |
+
You can load data from this dataset via:
|
| 11 |
+
|
| 12 |
+
datasets.load_dataset('TobiTob/CityLearn', 'data_name')
|
huggingface_dataset/Dataset_Card/autoevaluate_autoeval-eval-futin__guess-en-6ca7d2-2087467164.md
ADDED
|
@@ -0,0 +1,34 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
type: predictions
|
| 3 |
+
tags:
|
| 4 |
+
- autotrain
|
| 5 |
+
- evaluation
|
| 6 |
+
datasets:
|
| 7 |
+
- futin/guess
|
| 8 |
+
eval_info:
|
| 9 |
+
task: text_zero_shot_classification
|
| 10 |
+
model: bigscience/bloomz-560m
|
| 11 |
+
metrics: []
|
| 12 |
+
dataset_name: futin/guess
|
| 13 |
+
dataset_config: en
|
| 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/bloomz-560m
|
| 26 |
+
* Dataset: futin/guess
|
| 27 |
+
* Config: en
|
| 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 [@futin](https://huggingface.co/futin) for evaluating this model.
|
huggingface_dataset/Dataset_Card/autoevaluate_autoeval-eval-inverse-scaling__NeQA-inverse-scaling__NeQA-1e740e-1694759589.md
ADDED
|
@@ -0,0 +1,34 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
| 1 |
+
---
|
| 2 |
+
type: predictions
|
| 3 |
+
tags:
|
| 4 |
+
- autotrain
|
| 5 |
+
- evaluation
|
| 6 |
+
datasets:
|
| 7 |
+
- inverse-scaling/NeQA
|
| 8 |
+
eval_info:
|
| 9 |
+
task: text_zero_shot_classification
|
| 10 |
+
model: inverse-scaling/opt-66b_eval
|
| 11 |
+
metrics: []
|
| 12 |
+
dataset_name: inverse-scaling/NeQA
|
| 13 |
+
dataset_config: inverse-scaling--NeQA
|
| 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-66b_eval
|
| 26 |
+
* Dataset: inverse-scaling/NeQA
|
| 27 |
+
* Config: inverse-scaling--NeQA
|
| 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-staging-eval-project-multi_news-416d7689-12805701.md
ADDED
|
@@ -0,0 +1,33 @@
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|
| 1 |
+
---
|
| 2 |
+
type: predictions
|
| 3 |
+
tags:
|
| 4 |
+
- autotrain
|
| 5 |
+
- evaluation
|
| 6 |
+
datasets:
|
| 7 |
+
- multi_news
|
| 8 |
+
eval_info:
|
| 9 |
+
task: summarization
|
| 10 |
+
model: datien228/distilbart-cnn-12-6-ftn-multi_news
|
| 11 |
+
metrics: []
|
| 12 |
+
dataset_name: multi_news
|
| 13 |
+
dataset_config: default
|
| 14 |
+
dataset_split: test
|
| 15 |
+
col_mapping:
|
| 16 |
+
text: document
|
| 17 |
+
target: summary
|
| 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: datien228/distilbart-cnn-12-6-ftn-multi_news
|
| 25 |
+
* Dataset: multi_news
|
| 26 |
+
* Config: default
|
| 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 [@ccdv](https://huggingface.co/ccdv) for evaluating this model.
|
huggingface_dataset/Dataset_Card/derek-thomas_ScienceQA.md
ADDED
|
@@ -0,0 +1,301 @@
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|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
license: cc-by-sa-4.0
|
| 3 |
+
annotations_creators:
|
| 4 |
+
- expert-generated
|
| 5 |
+
- found
|
| 6 |
+
language:
|
| 7 |
+
- en
|
| 8 |
+
language_creators:
|
| 9 |
+
- expert-generated
|
| 10 |
+
- found
|
| 11 |
+
multilinguality:
|
| 12 |
+
- monolingual
|
| 13 |
+
paperswithcode_id: scienceqa
|
| 14 |
+
pretty_name: ScienceQA
|
| 15 |
+
size_categories:
|
| 16 |
+
- 10K<n<100K
|
| 17 |
+
source_datasets:
|
| 18 |
+
- original
|
| 19 |
+
tags:
|
| 20 |
+
- multi-modal-qa
|
| 21 |
+
- science
|
| 22 |
+
- chemistry
|
| 23 |
+
- biology
|
| 24 |
+
- physics
|
| 25 |
+
- earth-science
|
| 26 |
+
- engineering
|
| 27 |
+
- geography
|
| 28 |
+
- history
|
| 29 |
+
- world-history
|
| 30 |
+
- civics
|
| 31 |
+
- economics
|
| 32 |
+
- global-studies
|
| 33 |
+
- grammar
|
| 34 |
+
- writing
|
| 35 |
+
- vocabulary
|
| 36 |
+
- natural-science
|
| 37 |
+
- language-science
|
| 38 |
+
- social-science
|
| 39 |
+
task_categories:
|
| 40 |
+
- multiple-choice
|
| 41 |
+
- question-answering
|
| 42 |
+
- other
|
| 43 |
+
- visual-question-answering
|
| 44 |
+
- text-classification
|
| 45 |
+
task_ids:
|
| 46 |
+
- multiple-choice-qa
|
| 47 |
+
- closed-domain-qa
|
| 48 |
+
- open-domain-qa
|
| 49 |
+
- visual-question-answering
|
| 50 |
+
- multi-class-classification
|
| 51 |
+
dataset_info:
|
| 52 |
+
features:
|
| 53 |
+
- name: image
|
| 54 |
+
dtype: image
|
| 55 |
+
- name: question
|
| 56 |
+
dtype: string
|
| 57 |
+
- name: choices
|
| 58 |
+
sequence: string
|
| 59 |
+
- name: answer
|
| 60 |
+
dtype: int8
|
| 61 |
+
- name: hint
|
| 62 |
+
dtype: string
|
| 63 |
+
- name: task
|
| 64 |
+
dtype: string
|
| 65 |
+
- name: grade
|
| 66 |
+
dtype: string
|
| 67 |
+
- name: subject
|
| 68 |
+
dtype: string
|
| 69 |
+
- name: topic
|
| 70 |
+
dtype: string
|
| 71 |
+
- name: category
|
| 72 |
+
dtype: string
|
| 73 |
+
- name: skill
|
| 74 |
+
dtype: string
|
| 75 |
+
- name: lecture
|
| 76 |
+
dtype: string
|
| 77 |
+
- name: solution
|
| 78 |
+
dtype: string
|
| 79 |
+
splits:
|
| 80 |
+
- name: train
|
| 81 |
+
num_bytes: 16416902
|
| 82 |
+
num_examples: 12726
|
| 83 |
+
- name: validation
|
| 84 |
+
num_bytes: 5404896
|
| 85 |
+
num_examples: 4241
|
| 86 |
+
- name: test
|
| 87 |
+
num_bytes: 5441676
|
| 88 |
+
num_examples: 4241
|
| 89 |
+
download_size: 0
|
| 90 |
+
dataset_size: 27263474
|
| 91 |
+
---
|
| 92 |
+
|
| 93 |
+
# Dataset Card Creation Guide
|
| 94 |
+
|
| 95 |
+
## Table of Contents
|
| 96 |
+
- [Dataset Card Creation Guide](#dataset-card-creation-guide)
|
| 97 |
+
- [Table of Contents](#table-of-contents)
|
| 98 |
+
- [Dataset Description](#dataset-description)
|
| 99 |
+
- [Dataset Summary](#dataset-summary)
|
| 100 |
+
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
|
| 101 |
+
- [Languages](#languages)
|
| 102 |
+
- [Dataset Structure](#dataset-structure)
|
| 103 |
+
- [Data Instances](#data-instances)
|
| 104 |
+
- [Data Fields](#data-fields)
|
| 105 |
+
- [Data Splits](#data-splits)
|
| 106 |
+
- [Dataset Creation](#dataset-creation)
|
| 107 |
+
- [Curation Rationale](#curation-rationale)
|
| 108 |
+
- [Source Data](#source-data)
|
| 109 |
+
- [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
|
| 110 |
+
- [Who are the source language producers?](#who-are-the-source-language-producers)
|
| 111 |
+
- [Annotations](#annotations)
|
| 112 |
+
- [Annotation process](#annotation-process)
|
| 113 |
+
- [Who are the annotators?](#who-are-the-annotators)
|
| 114 |
+
- [Personal and Sensitive Information](#personal-and-sensitive-information)
|
| 115 |
+
- [Considerations for Using the Data](#considerations-for-using-the-data)
|
| 116 |
+
- [Social Impact of Dataset](#social-impact-of-dataset)
|
| 117 |
+
- [Discussion of Biases](#discussion-of-biases)
|
| 118 |
+
- [Other Known Limitations](#other-known-limitations)
|
| 119 |
+
- [Additional Information](#additional-information)
|
| 120 |
+
- [Dataset Curators](#dataset-curators)
|
| 121 |
+
- [Licensing Information](#licensing-information)
|
| 122 |
+
- [Citation Information](#citation-information)
|
| 123 |
+
- [Contributions](#contributions)
|
| 124 |
+
|
| 125 |
+
## Dataset Description
|
| 126 |
+
|
| 127 |
+
- **Homepage:** [https://scienceqa.github.io/index.html#home](https://scienceqa.github.io/index.html#home)
|
| 128 |
+
- **Repository:** [https://github.com/lupantech/ScienceQA](https://github.com/lupantech/ScienceQA)
|
| 129 |
+
- **Paper:** [https://arxiv.org/abs/2209.09513](https://arxiv.org/abs/2209.09513)
|
| 130 |
+
- **Leaderboard:** [https://paperswithcode.com/dataset/scienceqa](https://paperswithcode.com/dataset/scienceqa)
|
| 131 |
+
- **Point of Contact:** [Pan Lu](https://lupantech.github.io/) or file an issue on [Github](https://github.com/lupantech/ScienceQA/issues)
|
| 132 |
+
|
| 133 |
+
### Dataset Summary
|
| 134 |
+
|
| 135 |
+
Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering
|
| 136 |
+
|
| 137 |
+
### Supported Tasks and Leaderboards
|
| 138 |
+
|
| 139 |
+
Multi-modal Multiple Choice
|
| 140 |
+
|
| 141 |
+
### Languages
|
| 142 |
+
|
| 143 |
+
English
|
| 144 |
+
|
| 145 |
+
## Dataset Structure
|
| 146 |
+
|
| 147 |
+
### Data Instances
|
| 148 |
+
|
| 149 |
+
Explore more samples [here](https://scienceqa.github.io/explore.html).
|
| 150 |
+
|
| 151 |
+
``` json
|
| 152 |
+
{'image': Image,
|
| 153 |
+
'question': 'Which of these states is farthest north?',
|
| 154 |
+
'choices': ['West Virginia', 'Louisiana', 'Arizona', 'Oklahoma'],
|
| 155 |
+
'answer': 0,
|
| 156 |
+
'hint': '',
|
| 157 |
+
'task': 'closed choice',
|
| 158 |
+
'grade': 'grade2',
|
| 159 |
+
'subject': 'social science',
|
| 160 |
+
'topic': 'geography',
|
| 161 |
+
'category': 'Geography',
|
| 162 |
+
'skill': 'Read a map: cardinal directions',
|
| 163 |
+
'lecture': 'Maps have four cardinal directions, or main directions. Those directions are north, south, east, and west.\nA compass rose is a set of arrows that point to the cardinal directions. A compass rose usually shows only the first letter of each cardinal direction.\nThe north arrow points to the North Pole. On most maps, north is at the top of the map.',
|
| 164 |
+
'solution': 'To find the answer, look at the compass rose. Look at which way the north arrow is pointing. West Virginia is farthest north.'}
|
| 165 |
+
```
|
| 166 |
+
|
| 167 |
+
Some records might be missing any or all of image, lecture, solution.
|
| 168 |
+
|
| 169 |
+
### Data Fields
|
| 170 |
+
|
| 171 |
+
- `image` : Contextual image
|
| 172 |
+
- `question` : Prompt relating to the `lecture`
|
| 173 |
+
- `choices` : Multiple choice answer with 1 correct to the `question`
|
| 174 |
+
- `answer` : Index of choices corresponding to the correct answer
|
| 175 |
+
- `hint` : Hint to help answer the `question`
|
| 176 |
+
- `task` : Task description
|
| 177 |
+
- `grade` : Grade level from K-12
|
| 178 |
+
- `subject` : High level
|
| 179 |
+
- `topic` : natural-sciences, social-science, or language-science
|
| 180 |
+
- `category` : A subcategory of `topic`
|
| 181 |
+
- `skill` : A description of the task required
|
| 182 |
+
- `lecture` : A relevant lecture that a `question` is generated from
|
| 183 |
+
- `solution` : Instructions on how to solve the `question`
|
| 184 |
+
|
| 185 |
+
|
| 186 |
+
Note that the descriptions can be initialized with the **Show Markdown Data Fields** output of the [Datasets Tagging app](https://huggingface.co/spaces/huggingface/datasets-tagging), you will then only need to refine the generated descriptions.
|
| 187 |
+
|
| 188 |
+
### Data Splits
|
| 189 |
+
- name: train
|
| 190 |
+
- num_bytes: 16416902
|
| 191 |
+
- num_examples: 12726
|
| 192 |
+
- name: validation
|
| 193 |
+
- num_bytes: 5404896
|
| 194 |
+
- num_examples: 4241
|
| 195 |
+
- name: test
|
| 196 |
+
- num_bytes: 5441676
|
| 197 |
+
- num_examples: 4241
|
| 198 |
+
|
| 199 |
+
## Dataset Creation
|
| 200 |
+
|
| 201 |
+
### Curation Rationale
|
| 202 |
+
|
| 203 |
+
When answering a question, humans utilize the information available across different modalities to synthesize a consistent and complete chain of thought (CoT). This process is normally a black box in the case of deep learning models like large-scale language models. Recently, science question benchmarks have been used to diagnose the multi-hop reasoning ability and interpretability of an AI system. However, existing datasets fail to provide annotations for the answers, or are restricted to the textual-only modality, small scales, and limited domain diversity. To this end, we present Science Question Answering (ScienceQA).
|
| 204 |
+
|
| 205 |
+
### Source Data
|
| 206 |
+
|
| 207 |
+
ScienceQA is collected from elementary and high school science curricula.
|
| 208 |
+
|
| 209 |
+
#### Initial Data Collection and Normalization
|
| 210 |
+
|
| 211 |
+
See Below
|
| 212 |
+
|
| 213 |
+
#### Who are the source language producers?
|
| 214 |
+
|
| 215 |
+
See Below
|
| 216 |
+
|
| 217 |
+
### Annotations
|
| 218 |
+
|
| 219 |
+
Questions in the ScienceQA dataset are sourced from open resources managed by IXL Learning,
|
| 220 |
+
an online learning platform curated by experts in the field of K-12 education. The dataset includes
|
| 221 |
+
problems that align with California Common Core Content Standards. To construct ScienceQA, we
|
| 222 |
+
downloaded the original science problems and then extracted individual components (e.g. questions,
|
| 223 |
+
hints, images, options, answers, lectures, and solutions) from them based on heuristic rules.
|
| 224 |
+
We manually removed invalid questions, such as questions that have only one choice, questions that
|
| 225 |
+
contain faulty data, and questions that are duplicated, to comply with fair use and transformative
|
| 226 |
+
use of the law. If there were multiple correct answers that applied, we kept only one correct answer.
|
| 227 |
+
Also, we shuffled the answer options of each question to ensure the choices do not follow any
|
| 228 |
+
specific pattern. To make the dataset easy to use, we then used semi-automated scripts to reformat
|
| 229 |
+
the lectures and solutions. Therefore, special structures in the texts, such as tables and lists, are
|
| 230 |
+
easily distinguishable from simple text passages. Similar to ImageNet, ReClor, and PMR datasets,
|
| 231 |
+
ScienceQA is available for non-commercial research purposes only and the copyright belongs to
|
| 232 |
+
the original authors. To ensure data quality, we developed a data exploration tool to review examples
|
| 233 |
+
in the collected dataset, and incorrect annotations were further manually revised by experts. The tool
|
| 234 |
+
can be accessed at https://scienceqa.github.io/explore.html.
|
| 235 |
+
|
| 236 |
+
#### Annotation process
|
| 237 |
+
|
| 238 |
+
See above
|
| 239 |
+
|
| 240 |
+
#### Who are the annotators?
|
| 241 |
+
|
| 242 |
+
See above
|
| 243 |
+
|
| 244 |
+
### Personal and Sensitive Information
|
| 245 |
+
|
| 246 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 247 |
+
|
| 248 |
+
## Considerations for Using the Data
|
| 249 |
+
|
| 250 |
+
### Social Impact of Dataset
|
| 251 |
+
|
| 252 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 253 |
+
|
| 254 |
+
### Discussion of Biases
|
| 255 |
+
|
| 256 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 257 |
+
|
| 258 |
+
### Other Known Limitations
|
| 259 |
+
|
| 260 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 261 |
+
|
| 262 |
+
## Additional Information
|
| 263 |
+
|
| 264 |
+
### Dataset Curators
|
| 265 |
+
|
| 266 |
+
- Pan Lu1,3
|
| 267 |
+
- Swaroop Mishra2,3
|
| 268 |
+
- Tony Xia1
|
| 269 |
+
- Liang Qiu1
|
| 270 |
+
- Kai-Wei Chang1
|
| 271 |
+
- Song-Chun Zhu1
|
| 272 |
+
- Oyvind Tafjord3
|
| 273 |
+
- Peter Clark3
|
| 274 |
+
- Ashwin Kalyan3
|
| 275 |
+
|
| 276 |
+
From:
|
| 277 |
+
1. University of California, Los Angeles
|
| 278 |
+
2. Arizona State University
|
| 279 |
+
3. Allen Institute for AI
|
| 280 |
+
|
| 281 |
+
|
| 282 |
+
|
| 283 |
+
### Licensing Information
|
| 284 |
+
|
| 285 |
+
[Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
|
| 286 |
+
](https://creativecommons.org/licenses/by-nc-sa/4.0/)
|
| 287 |
+
|
| 288 |
+
### Citation Information
|
| 289 |
+
|
| 290 |
+
Provide the [BibTex](http://www.bibtex.org/)-formatted reference for the dataset. For example:
|
| 291 |
+
```
|
| 292 |
+
@inproceedings{lu2022learn,
|
| 293 |
+
title={Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering},
|
| 294 |
+
author={Lu, Pan and Mishra, Swaroop and Xia, Tony and Qiu, Liang and Chang, Kai-Wei and Zhu, Song-Chun and Tafjord, Oyvind and Clark, Peter and Ashwin Kalyan},
|
| 295 |
+
booktitle={The 36th Conference on Neural Information Processing Systems (NeurIPS)},
|
| 296 |
+
year={2022}
|
| 297 |
+
}
|
| 298 |
+
```
|
| 299 |
+
### Contributions
|
| 300 |
+
|
| 301 |
+
Thanks to [Derek Thomas](https://huggingface.co/derek-thomas) [@datavistics](https://github.com/datavistics) for adding this dataset.
|
huggingface_dataset/Dataset_Card/djghosh_wds_vtab-dsprites_label_x_position_test.md
ADDED
|
@@ -0,0 +1,15 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
# dSprites X Position (Test set only)
|
| 2 |
+
|
| 3 |
+
Original paper: [beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework](https://openreview.net/forum?id=Sy2fzU9gl)
|
| 4 |
+
|
| 5 |
+
Homepage: https://github.com/deepmind/dsprites-dataset
|
| 6 |
+
|
| 7 |
+
Bibtex:
|
| 8 |
+
```
|
| 9 |
+
@misc{dsprites17,
|
| 10 |
+
author = {Loic Matthey and Irina Higgins and Demis Hassabis and Alexander Lerchner},
|
| 11 |
+
title = {dSprites: Disentanglement testing Sprites dataset},
|
| 12 |
+
howpublished= {https://github.com/deepmind/dsprites-dataset/},
|
| 13 |
+
year = "2017",
|
| 14 |
+
}
|
| 15 |
+
```
|
huggingface_dataset/Dataset_Card/huggingartists_pharaoh.md
ADDED
|
@@ -0,0 +1,204 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
language:
|
| 3 |
+
- en
|
| 4 |
+
tags:
|
| 5 |
+
- huggingartists
|
| 6 |
+
- lyrics
|
| 7 |
+
---
|
| 8 |
+
|
| 9 |
+
# Dataset Card for "huggingartists/pharaoh"
|
| 10 |
+
|
| 11 |
+
## Table of Contents
|
| 12 |
+
- [Dataset Description](#dataset-description)
|
| 13 |
+
- [Dataset Summary](#dataset-summary)
|
| 14 |
+
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
|
| 15 |
+
- [Languages](#languages)
|
| 16 |
+
- [How to use](#how-to-use)
|
| 17 |
+
- [Dataset Structure](#dataset-structure)
|
| 18 |
+
- [Data Fields](#data-fields)
|
| 19 |
+
- [Data Splits](#data-splits)
|
| 20 |
+
- [Dataset Creation](#dataset-creation)
|
| 21 |
+
- [Curation Rationale](#curation-rationale)
|
| 22 |
+
- [Source Data](#source-data)
|
| 23 |
+
- [Annotations](#annotations)
|
| 24 |
+
- [Personal and Sensitive Information](#personal-and-sensitive-information)
|
| 25 |
+
- [Considerations for Using the Data](#considerations-for-using-the-data)
|
| 26 |
+
- [Social Impact of Dataset](#social-impact-of-dataset)
|
| 27 |
+
- [Discussion of Biases](#discussion-of-biases)
|
| 28 |
+
- [Other Known Limitations](#other-known-limitations)
|
| 29 |
+
- [Additional Information](#additional-information)
|
| 30 |
+
- [Dataset Curators](#dataset-curators)
|
| 31 |
+
- [Licensing Information](#licensing-information)
|
| 32 |
+
- [Citation Information](#citation-information)
|
| 33 |
+
- [About](#about)
|
| 34 |
+
|
| 35 |
+
## Dataset Description
|
| 36 |
+
|
| 37 |
+
- **Homepage:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
|
| 38 |
+
- **Repository:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
|
| 39 |
+
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 40 |
+
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 41 |
+
- **Size of the generated dataset:** 0.809639 MB
|
| 42 |
+
|
| 43 |
+
|
| 44 |
+
<div class="inline-flex flex-col" style="line-height: 1.5;">
|
| 45 |
+
<div class="flex">
|
| 46 |
+
<div style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/3bb9817ec1fbf2b9f944e9da3662bee6.1000x1000x1.jpg')">
|
| 47 |
+
</div>
|
| 48 |
+
</div>
|
| 49 |
+
<a href="https://huggingface.co/huggingartists/pharaoh">
|
| 50 |
+
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
|
| 51 |
+
</a>
|
| 52 |
+
<div style="text-align: center; font-size: 16px; font-weight: 800">PHARAOH</div>
|
| 53 |
+
<a href="https://genius.com/artists/pharaoh">
|
| 54 |
+
<div style="text-align: center; font-size: 14px;">@pharaoh</div>
|
| 55 |
+
</a>
|
| 56 |
+
</div>
|
| 57 |
+
|
| 58 |
+
### Dataset Summary
|
| 59 |
+
|
| 60 |
+
The Lyrics dataset parsed from Genius. This dataset is designed to generate lyrics with HuggingArtists.
|
| 61 |
+
Model is available [here](https://huggingface.co/huggingartists/pharaoh).
|
| 62 |
+
|
| 63 |
+
### Supported Tasks and Leaderboards
|
| 64 |
+
|
| 65 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 66 |
+
|
| 67 |
+
### Languages
|
| 68 |
+
|
| 69 |
+
en
|
| 70 |
+
|
| 71 |
+
## How to use
|
| 72 |
+
|
| 73 |
+
How to load this dataset directly with the datasets library:
|
| 74 |
+
|
| 75 |
+
```python
|
| 76 |
+
from datasets import load_dataset
|
| 77 |
+
|
| 78 |
+
dataset = load_dataset("huggingartists/pharaoh")
|
| 79 |
+
```
|
| 80 |
+
|
| 81 |
+
## Dataset Structure
|
| 82 |
+
|
| 83 |
+
An example of 'train' looks as follows.
|
| 84 |
+
```
|
| 85 |
+
This example was too long and was cropped:
|
| 86 |
+
|
| 87 |
+
{
|
| 88 |
+
"text": "Look, I was gonna go easy on you\nNot to hurt your feelings\nBut I'm only going to get this one chance\nSomething's wrong, I can feel it..."
|
| 89 |
+
}
|
| 90 |
+
```
|
| 91 |
+
|
| 92 |
+
### Data Fields
|
| 93 |
+
|
| 94 |
+
The data fields are the same among all splits.
|
| 95 |
+
|
| 96 |
+
- `text`: a `string` feature.
|
| 97 |
+
|
| 98 |
+
|
| 99 |
+
### Data Splits
|
| 100 |
+
|
| 101 |
+
| train |validation|test|
|
| 102 |
+
|------:|---------:|---:|
|
| 103 |
+
|300| -| -|
|
| 104 |
+
|
| 105 |
+
'Train' can be easily divided into 'train' & 'validation' & 'test' with few lines of code:
|
| 106 |
+
|
| 107 |
+
```python
|
| 108 |
+
from datasets import load_dataset, Dataset, DatasetDict
|
| 109 |
+
import numpy as np
|
| 110 |
+
|
| 111 |
+
datasets = load_dataset("huggingartists/pharaoh")
|
| 112 |
+
|
| 113 |
+
train_percentage = 0.9
|
| 114 |
+
validation_percentage = 0.07
|
| 115 |
+
test_percentage = 0.03
|
| 116 |
+
|
| 117 |
+
train, validation, test = np.split(datasets['train']['text'], [int(len(datasets['train']['text'])*train_percentage), int(len(datasets['train']['text'])*(train_percentage + validation_percentage))])
|
| 118 |
+
|
| 119 |
+
datasets = DatasetDict(
|
| 120 |
+
{
|
| 121 |
+
'train': Dataset.from_dict({'text': list(train)}),
|
| 122 |
+
'validation': Dataset.from_dict({'text': list(validation)}),
|
| 123 |
+
'test': Dataset.from_dict({'text': list(test)})
|
| 124 |
+
}
|
| 125 |
+
)
|
| 126 |
+
```
|
| 127 |
+
|
| 128 |
+
## Dataset Creation
|
| 129 |
+
|
| 130 |
+
### Curation Rationale
|
| 131 |
+
|
| 132 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 133 |
+
|
| 134 |
+
### Source Data
|
| 135 |
+
|
| 136 |
+
#### Initial Data Collection and Normalization
|
| 137 |
+
|
| 138 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 139 |
+
|
| 140 |
+
#### Who are the source language producers?
|
| 141 |
+
|
| 142 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 143 |
+
|
| 144 |
+
### Annotations
|
| 145 |
+
|
| 146 |
+
#### Annotation process
|
| 147 |
+
|
| 148 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 149 |
+
|
| 150 |
+
#### Who are the annotators?
|
| 151 |
+
|
| 152 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 153 |
+
|
| 154 |
+
### Personal and Sensitive Information
|
| 155 |
+
|
| 156 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 157 |
+
|
| 158 |
+
## Considerations for Using the Data
|
| 159 |
+
|
| 160 |
+
### Social Impact of Dataset
|
| 161 |
+
|
| 162 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 163 |
+
|
| 164 |
+
### Discussion of Biases
|
| 165 |
+
|
| 166 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 167 |
+
|
| 168 |
+
### Other Known Limitations
|
| 169 |
+
|
| 170 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 171 |
+
|
| 172 |
+
## Additional Information
|
| 173 |
+
|
| 174 |
+
### Dataset Curators
|
| 175 |
+
|
| 176 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 177 |
+
|
| 178 |
+
### Licensing Information
|
| 179 |
+
|
| 180 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 181 |
+
|
| 182 |
+
### Citation Information
|
| 183 |
+
|
| 184 |
+
```
|
| 185 |
+
@InProceedings{huggingartists,
|
| 186 |
+
author={Aleksey Korshuk}
|
| 187 |
+
year=2021
|
| 188 |
+
}
|
| 189 |
+
```
|
| 190 |
+
|
| 191 |
+
|
| 192 |
+
## About
|
| 193 |
+
|
| 194 |
+
*Built by Aleksey Korshuk*
|
| 195 |
+
|
| 196 |
+
[](https://github.com/AlekseyKorshuk)
|
| 197 |
+
|
| 198 |
+
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
|
| 199 |
+
|
| 200 |
+
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
|
| 201 |
+
|
| 202 |
+
For more details, visit the project repository.
|
| 203 |
+
|
| 204 |
+
[](https://github.com/AlekseyKorshuk/huggingartists)
|
huggingface_dataset/Dataset_Card/huggingartists_tiamat.md
ADDED
|
@@ -0,0 +1,204 @@
|
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|
| 1 |
+
---
|
| 2 |
+
language:
|
| 3 |
+
- en
|
| 4 |
+
tags:
|
| 5 |
+
- huggingartists
|
| 6 |
+
- lyrics
|
| 7 |
+
---
|
| 8 |
+
|
| 9 |
+
# Dataset Card for "huggingartists/tiamat"
|
| 10 |
+
|
| 11 |
+
## Table of Contents
|
| 12 |
+
- [Dataset Description](#dataset-description)
|
| 13 |
+
- [Dataset Summary](#dataset-summary)
|
| 14 |
+
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
|
| 15 |
+
- [Languages](#languages)
|
| 16 |
+
- [How to use](#how-to-use)
|
| 17 |
+
- [Dataset Structure](#dataset-structure)
|
| 18 |
+
- [Data Fields](#data-fields)
|
| 19 |
+
- [Data Splits](#data-splits)
|
| 20 |
+
- [Dataset Creation](#dataset-creation)
|
| 21 |
+
- [Curation Rationale](#curation-rationale)
|
| 22 |
+
- [Source Data](#source-data)
|
| 23 |
+
- [Annotations](#annotations)
|
| 24 |
+
- [Personal and Sensitive Information](#personal-and-sensitive-information)
|
| 25 |
+
- [Considerations for Using the Data](#considerations-for-using-the-data)
|
| 26 |
+
- [Social Impact of Dataset](#social-impact-of-dataset)
|
| 27 |
+
- [Discussion of Biases](#discussion-of-biases)
|
| 28 |
+
- [Other Known Limitations](#other-known-limitations)
|
| 29 |
+
- [Additional Information](#additional-information)
|
| 30 |
+
- [Dataset Curators](#dataset-curators)
|
| 31 |
+
- [Licensing Information](#licensing-information)
|
| 32 |
+
- [Citation Information](#citation-information)
|
| 33 |
+
- [About](#about)
|
| 34 |
+
|
| 35 |
+
## Dataset Description
|
| 36 |
+
|
| 37 |
+
- **Homepage:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
|
| 38 |
+
- **Repository:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
|
| 39 |
+
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 40 |
+
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 41 |
+
- **Size of the generated dataset:** 0.115111 MB
|
| 42 |
+
|
| 43 |
+
|
| 44 |
+
<div class="inline-flex flex-col" style="line-height: 1.5;">
|
| 45 |
+
<div class="flex">
|
| 46 |
+
<div style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/9ca13ed308504f6f9ac7c3cabdb54138.556x556x1.jpg')">
|
| 47 |
+
</div>
|
| 48 |
+
</div>
|
| 49 |
+
<a href="https://huggingface.co/huggingartists/tiamat">
|
| 50 |
+
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
|
| 51 |
+
</a>
|
| 52 |
+
<div style="text-align: center; font-size: 16px; font-weight: 800">Tiamat</div>
|
| 53 |
+
<a href="https://genius.com/artists/tiamat">
|
| 54 |
+
<div style="text-align: center; font-size: 14px;">@tiamat</div>
|
| 55 |
+
</a>
|
| 56 |
+
</div>
|
| 57 |
+
|
| 58 |
+
### Dataset Summary
|
| 59 |
+
|
| 60 |
+
The Lyrics dataset parsed from Genius. This dataset is designed to generate lyrics with HuggingArtists.
|
| 61 |
+
Model is available [here](https://huggingface.co/huggingartists/tiamat).
|
| 62 |
+
|
| 63 |
+
### Supported Tasks and Leaderboards
|
| 64 |
+
|
| 65 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 66 |
+
|
| 67 |
+
### Languages
|
| 68 |
+
|
| 69 |
+
en
|
| 70 |
+
|
| 71 |
+
## How to use
|
| 72 |
+
|
| 73 |
+
How to load this dataset directly with the datasets library:
|
| 74 |
+
|
| 75 |
+
```python
|
| 76 |
+
from datasets import load_dataset
|
| 77 |
+
|
| 78 |
+
dataset = load_dataset("huggingartists/tiamat")
|
| 79 |
+
```
|
| 80 |
+
|
| 81 |
+
## Dataset Structure
|
| 82 |
+
|
| 83 |
+
An example of 'train' looks as follows.
|
| 84 |
+
```
|
| 85 |
+
This example was too long and was cropped:
|
| 86 |
+
|
| 87 |
+
{
|
| 88 |
+
"text": "Look, I was gonna go easy on you\nNot to hurt your feelings\nBut I'm only going to get this one chance\nSomething's wrong, I can feel it..."
|
| 89 |
+
}
|
| 90 |
+
```
|
| 91 |
+
|
| 92 |
+
### Data Fields
|
| 93 |
+
|
| 94 |
+
The data fields are the same among all splits.
|
| 95 |
+
|
| 96 |
+
- `text`: a `string` feature.
|
| 97 |
+
|
| 98 |
+
|
| 99 |
+
### Data Splits
|
| 100 |
+
|
| 101 |
+
| train |validation|test|
|
| 102 |
+
|------:|---------:|---:|
|
| 103 |
+
|122| -| -|
|
| 104 |
+
|
| 105 |
+
'Train' can be easily divided into 'train' & 'validation' & 'test' with few lines of code:
|
| 106 |
+
|
| 107 |
+
```python
|
| 108 |
+
from datasets import load_dataset, Dataset, DatasetDict
|
| 109 |
+
import numpy as np
|
| 110 |
+
|
| 111 |
+
datasets = load_dataset("huggingartists/tiamat")
|
| 112 |
+
|
| 113 |
+
train_percentage = 0.9
|
| 114 |
+
validation_percentage = 0.07
|
| 115 |
+
test_percentage = 0.03
|
| 116 |
+
|
| 117 |
+
train, validation, test = np.split(datasets['train']['text'], [int(len(datasets['train']['text'])*train_percentage), int(len(datasets['train']['text'])*(train_percentage + validation_percentage))])
|
| 118 |
+
|
| 119 |
+
datasets = DatasetDict(
|
| 120 |
+
{
|
| 121 |
+
'train': Dataset.from_dict({'text': list(train)}),
|
| 122 |
+
'validation': Dataset.from_dict({'text': list(validation)}),
|
| 123 |
+
'test': Dataset.from_dict({'text': list(test)})
|
| 124 |
+
}
|
| 125 |
+
)
|
| 126 |
+
```
|
| 127 |
+
|
| 128 |
+
## Dataset Creation
|
| 129 |
+
|
| 130 |
+
### Curation Rationale
|
| 131 |
+
|
| 132 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 133 |
+
|
| 134 |
+
### Source Data
|
| 135 |
+
|
| 136 |
+
#### Initial Data Collection and Normalization
|
| 137 |
+
|
| 138 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 139 |
+
|
| 140 |
+
#### Who are the source language producers?
|
| 141 |
+
|
| 142 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 143 |
+
|
| 144 |
+
### Annotations
|
| 145 |
+
|
| 146 |
+
#### Annotation process
|
| 147 |
+
|
| 148 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 149 |
+
|
| 150 |
+
#### Who are the annotators?
|
| 151 |
+
|
| 152 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 153 |
+
|
| 154 |
+
### Personal and Sensitive Information
|
| 155 |
+
|
| 156 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 157 |
+
|
| 158 |
+
## Considerations for Using the Data
|
| 159 |
+
|
| 160 |
+
### Social Impact of Dataset
|
| 161 |
+
|
| 162 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 163 |
+
|
| 164 |
+
### Discussion of Biases
|
| 165 |
+
|
| 166 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 167 |
+
|
| 168 |
+
### Other Known Limitations
|
| 169 |
+
|
| 170 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 171 |
+
|
| 172 |
+
## Additional Information
|
| 173 |
+
|
| 174 |
+
### Dataset Curators
|
| 175 |
+
|
| 176 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 177 |
+
|
| 178 |
+
### Licensing Information
|
| 179 |
+
|
| 180 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 181 |
+
|
| 182 |
+
### Citation Information
|
| 183 |
+
|
| 184 |
+
```
|
| 185 |
+
@InProceedings{huggingartists,
|
| 186 |
+
author={Aleksey Korshuk}
|
| 187 |
+
year=2021
|
| 188 |
+
}
|
| 189 |
+
```
|
| 190 |
+
|
| 191 |
+
|
| 192 |
+
## About
|
| 193 |
+
|
| 194 |
+
*Built by Aleksey Korshuk*
|
| 195 |
+
|
| 196 |
+
[](https://github.com/AlekseyKorshuk)
|
| 197 |
+
|
| 198 |
+
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
|
| 199 |
+
|
| 200 |
+
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
|
| 201 |
+
|
| 202 |
+
For more details, visit the project repository.
|
| 203 |
+
|
| 204 |
+
[](https://github.com/AlekseyKorshuk/huggingartists)
|
huggingface_dataset/Dataset_Card/jakartaresearch_cerpen-corpus.md
ADDED
|
@@ -0,0 +1,149 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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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 |
+
- no-annotation
|
| 4 |
+
language:
|
| 5 |
+
- id
|
| 6 |
+
language_creators:
|
| 7 |
+
- found
|
| 8 |
+
license:
|
| 9 |
+
- cc-by-4.0
|
| 10 |
+
multilinguality:
|
| 11 |
+
- monolingual
|
| 12 |
+
pretty_name: Small Indonesian Short Story Corpus
|
| 13 |
+
size_categories:
|
| 14 |
+
- n<1K
|
| 15 |
+
- 10K<n<100K
|
| 16 |
+
source_datasets:
|
| 17 |
+
- original
|
| 18 |
+
tags:
|
| 19 |
+
- cerpen
|
| 20 |
+
- short-story
|
| 21 |
+
task_categories:
|
| 22 |
+
- text-generation
|
| 23 |
+
task_ids:
|
| 24 |
+
- language-modeling
|
| 25 |
+
---
|
| 26 |
+
|
| 27 |
+
# Dataset Card for Cerpen Corpus
|
| 28 |
+
|
| 29 |
+
## Table of Contents
|
| 30 |
+
- [Table of Contents](#table-of-contents)
|
| 31 |
+
- [Dataset Description](#dataset-description)
|
| 32 |
+
- [Dataset Summary](#dataset-summary)
|
| 33 |
+
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
|
| 34 |
+
- [Languages](#languages)
|
| 35 |
+
- [Dataset Structure](#dataset-structure)
|
| 36 |
+
- [Data Instances](#data-instances)
|
| 37 |
+
- [Data Fields](#data-fields)
|
| 38 |
+
- [Data Splits](#data-splits)
|
| 39 |
+
- [Dataset Creation](#dataset-creation)
|
| 40 |
+
- [Curation Rationale](#curation-rationale)
|
| 41 |
+
- [Source Data](#source-data)
|
| 42 |
+
- [Annotations](#annotations)
|
| 43 |
+
- [Personal and Sensitive Information](#personal-and-sensitive-information)
|
| 44 |
+
- [Considerations for Using the Data](#considerations-for-using-the-data)
|
| 45 |
+
- [Social Impact of Dataset](#social-impact-of-dataset)
|
| 46 |
+
- [Discussion of Biases](#discussion-of-biases)
|
| 47 |
+
- [Other Known Limitations](#other-known-limitations)
|
| 48 |
+
- [Additional Information](#additional-information)
|
| 49 |
+
- [Dataset Curators](#dataset-curators)
|
| 50 |
+
- [Licensing Information](#licensing-information)
|
| 51 |
+
- [Citation Information](#citation-information)
|
| 52 |
+
- [Contributions](#contributions)
|
| 53 |
+
|
| 54 |
+
## Dataset Description
|
| 55 |
+
|
| 56 |
+
- **Homepage:**
|
| 57 |
+
- **Repository:**
|
| 58 |
+
- **Paper:**
|
| 59 |
+
- **Leaderboard:**
|
| 60 |
+
- **Point of Contact:**
|
| 61 |
+
|
| 62 |
+
### Dataset Summary
|
| 63 |
+
|
| 64 |
+
This is a small size for Indonesian short story gathered from the internet.
|
| 65 |
+
We keep the large size for internal research. if you are interested, please join to [our discord server](https://discord.gg/6v28dq8dRE)
|
| 66 |
+
|
| 67 |
+
### Supported Tasks and Leaderboards
|
| 68 |
+
|
| 69 |
+
[More Information Needed]
|
| 70 |
+
|
| 71 |
+
### Languages
|
| 72 |
+
|
| 73 |
+
[More Information Needed]
|
| 74 |
+
|
| 75 |
+
## Dataset Structure
|
| 76 |
+
|
| 77 |
+
### Data Instances
|
| 78 |
+
|
| 79 |
+
[More Information Needed]
|
| 80 |
+
|
| 81 |
+
### Data Fields
|
| 82 |
+
|
| 83 |
+
[More Information Needed]
|
| 84 |
+
|
| 85 |
+
### Data Splits
|
| 86 |
+
|
| 87 |
+
[More Information Needed]
|
| 88 |
+
|
| 89 |
+
## Dataset Creation
|
| 90 |
+
|
| 91 |
+
### Curation Rationale
|
| 92 |
+
|
| 93 |
+
[More Information Needed]
|
| 94 |
+
|
| 95 |
+
### Source Data
|
| 96 |
+
|
| 97 |
+
#### Initial Data Collection and Normalization
|
| 98 |
+
|
| 99 |
+
[More Information Needed]
|
| 100 |
+
|
| 101 |
+
#### Who are the source language producers?
|
| 102 |
+
|
| 103 |
+
[More Information Needed]
|
| 104 |
+
|
| 105 |
+
### Annotations
|
| 106 |
+
|
| 107 |
+
#### Annotation process
|
| 108 |
+
|
| 109 |
+
[More Information Needed]
|
| 110 |
+
|
| 111 |
+
#### Who are the annotators?
|
| 112 |
+
|
| 113 |
+
[More Information Needed]
|
| 114 |
+
|
| 115 |
+
### Personal and Sensitive Information
|
| 116 |
+
|
| 117 |
+
[More Information Needed]
|
| 118 |
+
|
| 119 |
+
## Considerations for Using the Data
|
| 120 |
+
|
| 121 |
+
### Social Impact of Dataset
|
| 122 |
+
|
| 123 |
+
[More Information Needed]
|
| 124 |
+
|
| 125 |
+
### Discussion of Biases
|
| 126 |
+
|
| 127 |
+
[More Information Needed]
|
| 128 |
+
|
| 129 |
+
### Other Known Limitations
|
| 130 |
+
|
| 131 |
+
[More Information Needed]
|
| 132 |
+
|
| 133 |
+
## Additional Information
|
| 134 |
+
|
| 135 |
+
### Dataset Curators
|
| 136 |
+
|
| 137 |
+
[More Information Needed]
|
| 138 |
+
|
| 139 |
+
### Licensing Information
|
| 140 |
+
|
| 141 |
+
[More Information Needed]
|
| 142 |
+
|
| 143 |
+
### Citation Information
|
| 144 |
+
|
| 145 |
+
[More Information Needed]
|
| 146 |
+
|
| 147 |
+
### Contributions
|
| 148 |
+
|
| 149 |
+
Thanks to [@andreaschandra](https://github.com/andreaschandra) for adding this dataset.
|
huggingface_dataset/Dataset_Card/vogloblinsky_skateboarding-tricks.md
ADDED
|
@@ -0,0 +1,24 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
license: mit
|
| 3 |
+
annotations_creators:
|
| 4 |
+
- machine-generated
|
| 5 |
+
language:
|
| 6 |
+
- en
|
| 7 |
+
language_creators:
|
| 8 |
+
- other
|
| 9 |
+
multilinguality:
|
| 10 |
+
- monolingual
|
| 11 |
+
pretty_name: 'Skateboarding tricks'
|
| 12 |
+
size_categories:
|
| 13 |
+
- n<1K
|
| 14 |
+
tags: []
|
| 15 |
+
task_categories:
|
| 16 |
+
- text-to-image
|
| 17 |
+
task_ids: []
|
| 18 |
+
---
|
| 19 |
+
# Dataset Card for Skateboarding tricks
|
| 20 |
+
Dataset used to train [Text to skateboarding image model](https://github.com/LambdaLabsML/examples/tree/main/stable-diffusion-finetuning).
|
| 21 |
+
|
| 22 |
+
For each row the dataset contains `image` and `text` keys.
|
| 23 |
+
|
| 24 |
+
`image` is a varying size PIL jpeg, and `text` is the accompanying text caption.
|
huggingface_dataset/Dataset_Card/waifu-research-department_regularization.md
ADDED
|
@@ -0,0 +1,19 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
license: mit
|
| 3 |
+
---
|
| 4 |
+
# Info
|
| 5 |
+
> This is a repository for anime regularization. If you wish to contribute to the dataset, contact me at naotsue#9786. I will add them to the dataset and update it.
|
| 6 |
+
|
| 7 |
+
# Criteria
|
| 8 |
+
> 512x512
|
| 9 |
+
|
| 10 |
+
> No excessive deformations
|
| 11 |
+
|
| 12 |
+
> Vaguely resembles an anime artstyle
|
| 13 |
+
|
| 14 |
+
# Contribution Leaderboard
|
| 15 |
+
> 1. bWm_nubby: 5838 images
|
| 16 |
+
|
| 17 |
+
> 2. naotsue: 888 images
|
| 18 |
+
|
| 19 |
+
|
huggingface_dataset/Dataset_Card/youtube_caption_corrections.md
ADDED
|
@@ -0,0 +1,204 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
annotations_creators:
|
| 3 |
+
- expert-generated
|
| 4 |
+
- machine-generated
|
| 5 |
+
language_creators:
|
| 6 |
+
- machine-generated
|
| 7 |
+
language:
|
| 8 |
+
- en
|
| 9 |
+
license:
|
| 10 |
+
- mit
|
| 11 |
+
multilinguality:
|
| 12 |
+
- monolingual
|
| 13 |
+
size_categories:
|
| 14 |
+
- 10K<n<100K
|
| 15 |
+
source_datasets:
|
| 16 |
+
- original
|
| 17 |
+
task_categories:
|
| 18 |
+
- other
|
| 19 |
+
- text-generation
|
| 20 |
+
- fill-mask
|
| 21 |
+
task_ids:
|
| 22 |
+
- slot-filling
|
| 23 |
+
pretty_name: YouTube Caption Corrections
|
| 24 |
+
tags:
|
| 25 |
+
- token-classification-of-text-errors
|
| 26 |
+
dataset_info:
|
| 27 |
+
features:
|
| 28 |
+
- name: video_ids
|
| 29 |
+
dtype: string
|
| 30 |
+
- name: default_seq
|
| 31 |
+
sequence: string
|
| 32 |
+
- name: correction_seq
|
| 33 |
+
sequence: string
|
| 34 |
+
- name: diff_type
|
| 35 |
+
sequence:
|
| 36 |
+
class_label:
|
| 37 |
+
names:
|
| 38 |
+
'0': NO_DIFF
|
| 39 |
+
'1': CASE_DIFF
|
| 40 |
+
'2': PUNCUATION_DIFF
|
| 41 |
+
'3': CASE_AND_PUNCUATION_DIFF
|
| 42 |
+
'4': STEM_BASED_DIFF
|
| 43 |
+
'5': DIGIT_DIFF
|
| 44 |
+
'6': INTRAWORD_PUNC_DIFF
|
| 45 |
+
'7': UNKNOWN_TYPE_DIFF
|
| 46 |
+
'8': RESERVED_DIFF
|
| 47 |
+
splits:
|
| 48 |
+
- name: train
|
| 49 |
+
num_bytes: 355978939
|
| 50 |
+
num_examples: 10769
|
| 51 |
+
download_size: 222479455
|
| 52 |
+
dataset_size: 355978939
|
| 53 |
+
---
|
| 54 |
+
|
| 55 |
+
# Dataset Card for YouTube Caption Corrections
|
| 56 |
+
|
| 57 |
+
## Table of Contents
|
| 58 |
+
- [Dataset Description](#dataset-description)
|
| 59 |
+
- [Dataset Summary](#dataset-summary)
|
| 60 |
+
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
|
| 61 |
+
- [Languages](#languages)
|
| 62 |
+
- [Dataset Structure](#dataset-structure)
|
| 63 |
+
- [Data Instances](#data-instances)
|
| 64 |
+
- [Data Fields](#data-fields)
|
| 65 |
+
- [Data Splits](#data-splits)
|
| 66 |
+
- [Dataset Creation](#dataset-creation)
|
| 67 |
+
- [Curation Rationale](#curation-rationale)
|
| 68 |
+
- [Source Data](#source-data)
|
| 69 |
+
- [Annotations](#annotations)
|
| 70 |
+
- [Personal and Sensitive Information](#personal-and-sensitive-information)
|
| 71 |
+
- [Considerations for Using the Data](#considerations-for-using-the-data)
|
| 72 |
+
- [Social Impact of Dataset](#social-impact-of-dataset)
|
| 73 |
+
- [Discussion of Biases](#discussion-of-biases)
|
| 74 |
+
- [Other Known Limitations](#other-known-limitations)
|
| 75 |
+
- [Additional Information](#additional-information)
|
| 76 |
+
- [Dataset Curators](#dataset-curators)
|
| 77 |
+
- [Licensing Information](#licensing-information)
|
| 78 |
+
- [Citation Information](#citation-information)
|
| 79 |
+
- [Contributions](#contributions)
|
| 80 |
+
|
| 81 |
+
## Dataset Description
|
| 82 |
+
|
| 83 |
+
- **Homepage:** https://github.com/2dot71mily/youtube_captions_corrections
|
| 84 |
+
- **Repository:** https://github.com/2dot71mily/youtube_captions_corrections
|
| 85 |
+
- **Paper:** [N/A]
|
| 86 |
+
- **Leaderboard:** [N/A]
|
| 87 |
+
- **Point of Contact:** Emily McMilin
|
| 88 |
+
|
| 89 |
+
### Dataset Summary
|
| 90 |
+
|
| 91 |
+
This dataset is built from pairs of YouTube captions where both an auto-generated and a manually-corrected caption are available for a single specified language. It currently only in English, but scripts at repo support other languages. The motivation for creating it was from viewing errors in auto-generated captions at a recent virtual conference, with the hope that there could be some way to help correct those errors.
|
| 92 |
+
|
| 93 |
+
The dataset in the repo at https://github.com/2dot71mily/youtube_captions_corrections records in a non-destructive manner all the differences between an auto-generated and a manually-corrected caption for thousands of videos. The dataset here focuses on the subset of those differences which are mutual and have the same size in token length difference, which means it excludes token insertion or deletion differences between the two captions. Therefore dataset here remains a non-destructive representation of the original auto-generated captions, but excludes some of the differences that are found in the manually-corrected captions.
|
| 94 |
+
|
| 95 |
+
### Supported Tasks and Leaderboards
|
| 96 |
+
|
| 97 |
+
- `token-classification`: The tokens in `default_seq` are from the auto-generated YouTube captions. If `diff_type` is labeled greater than `0` at a given index, then the associated token in same index in the `default_seq` was found to be different to the token in the manually-corrected YouTube caption, and therefore we assume it is an error. A model can be trained to learn when there are errors in the auto-generated captions.
|
| 98 |
+
|
| 99 |
+
- `slot-filling`: The `correction_seq` is sparsely populated with tokens from the manually-corrected YouTube captions in the locations where there was found to be a difference to the token in the auto-generated YouTube captions. These 'incorrect' tokens in the `default_seq` can be masked in the locations where `diff_type` is labeled greater than `0`, so that a model can be trained to hopefully find a better word to fill in, rather than the 'incorrect' one.
|
| 100 |
+
|
| 101 |
+
End to end, the models could maybe first identify and then replace (with suitable alternatives) errors in YouTube and other auto-generated captions that are lacking manual corrections
|
| 102 |
+
|
| 103 |
+
### Languages
|
| 104 |
+
|
| 105 |
+
English
|
| 106 |
+
|
| 107 |
+
## Dataset Structure
|
| 108 |
+
|
| 109 |
+
### Data Instances
|
| 110 |
+
|
| 111 |
+
If `diff_type` is labeled greater than `0` at a given index, then the associated token in same index in the `default_seq` was found to have a difference to the token in the manually-corrected YouTube caption. The `correction_seq` is sparsely populated with tokens from the manually-corrected YouTube captions at those locations of differences.
|
| 112 |
+
|
| 113 |
+
`diff_type` labels for tokens are as follows:
|
| 114 |
+
0: No difference
|
| 115 |
+
1: Case based difference, e.g. `hello` vs `Hello`
|
| 116 |
+
2: Punctuation difference, e.g. `hello` vs `hello`
|
| 117 |
+
3: Case and punctuation difference, e.g. `hello` vs `Hello,`
|
| 118 |
+
4: Word difference with same stem, e.g. `thank` vs `thanked`
|
| 119 |
+
5: Digit difference, e.g. `2` vs `two`
|
| 120 |
+
6: Intra-word punctuation difference, e.g. `autogenerated` vs `auto-generated`
|
| 121 |
+
7: Unknown type of difference, e.g. `laughter` vs `draft`
|
| 122 |
+
8: Reserved for unspecified difference
|
| 123 |
+
|
| 124 |
+
{
|
| 125 |
+
'video_titles': '_QUEXsHfsA0',
|
| 126 |
+
'default_seq': ['you', 'see', "it's", 'a', 'laughter', 'but', 'by', 'the', 'time', 'you', 'see', 'this', 'it', "won't", 'be', 'so', 'we', 'have', 'a', 'big']
|
| 127 |
+
'correction_seq': ['', 'see,', '', '', 'draft,', '', '', '', '', '', 'read', 'this,', '', '', 'be.', 'So', '', '', '', '']
|
| 128 |
+
'diff_type': [0, 2, 0, 0, 7, 0, 0, 0, 0, 0, 7, 2, 0, 0, 2, 1, 0, 0, 0, 0]
|
| 129 |
+
}
|
| 130 |
+
|
| 131 |
+
### Data Fields
|
| 132 |
+
|
| 133 |
+
- 'video_ids': Unique ID used by YouTube for each video. Can paste into `https://www.youtube.com/watch?v=<{video_ids}` to see video
|
| 134 |
+
- 'default_seq': Tokenized auto-generated YouTube captions for the video
|
| 135 |
+
- 'correction_seq': Tokenized manually-corrected YouTube captions only at those locations, where there is a difference between the auto-generated and manually-corrected captions
|
| 136 |
+
- 'diff_type': A value greater than `0` at every token where there is a difference between the auto-generated and manually-corrected captions
|
| 137 |
+
|
| 138 |
+
### Data Splits
|
| 139 |
+
|
| 140 |
+
No data splits
|
| 141 |
+
|
| 142 |
+
## Dataset Creation
|
| 143 |
+
|
| 144 |
+
### Curation Rationale
|
| 145 |
+
|
| 146 |
+
It was created after viewing errors in auto-generated captions at a recent virtual conference, with the hope that there could be some way to help correct those errors.
|
| 147 |
+
|
| 148 |
+
### Source Data
|
| 149 |
+
|
| 150 |
+
#### Initial Data Collection and Normalization
|
| 151 |
+
|
| 152 |
+
All captions are requested via `googleapiclient` and `youtube_transcript_api` at the `channel_id` and language granularity, using scripts written at https://github.com/2dot71mily/youtube_captions_corrections.
|
| 153 |
+
|
| 154 |
+
The captions are tokenized on spaces and the manually-corrected sequence has here been reduced to only include differences between it and the auto-generated sequence.
|
| 155 |
+
|
| 156 |
+
#### Who are the source language producers?
|
| 157 |
+
|
| 158 |
+
Auto-generated scripts are from YouTube and the manually-corrected scripts are from creators, and any support they may have (e.g. community or software support)
|
| 159 |
+
|
| 160 |
+
### Annotations
|
| 161 |
+
|
| 162 |
+
#### Annotation process
|
| 163 |
+
|
| 164 |
+
Scripts at repo, https://github.com/2dot71mily/youtube_captions_corrections take a diff of the two captions and use this to create annotations.
|
| 165 |
+
|
| 166 |
+
#### Who are the annotators?
|
| 167 |
+
|
| 168 |
+
YouTube creators, and any support they may have (e.g. community or software support)
|
| 169 |
+
|
| 170 |
+
### Personal and Sensitive Information
|
| 171 |
+
|
| 172 |
+
All content publicly available on YouTube
|
| 173 |
+
|
| 174 |
+
## Considerations for Using the Data
|
| 175 |
+
|
| 176 |
+
### Social Impact of Dataset
|
| 177 |
+
|
| 178 |
+
[More Information Needed]
|
| 179 |
+
|
| 180 |
+
### Discussion of Biases
|
| 181 |
+
|
| 182 |
+
[More Information Needed]
|
| 183 |
+
|
| 184 |
+
### Other Known Limitations
|
| 185 |
+
|
| 186 |
+
[More Information Needed]
|
| 187 |
+
|
| 188 |
+
## Additional Information
|
| 189 |
+
|
| 190 |
+
### Dataset Curators
|
| 191 |
+
|
| 192 |
+
Emily McMilin
|
| 193 |
+
|
| 194 |
+
### Licensing Information
|
| 195 |
+
|
| 196 |
+
MIT License
|
| 197 |
+
|
| 198 |
+
### Citation Information
|
| 199 |
+
|
| 200 |
+
https://github.com/2dot71mily/youtube_captions_corrections
|
| 201 |
+
|
| 202 |
+
### Contributions
|
| 203 |
+
|
| 204 |
+
Thanks to [@2dot71mily](https://github.com/2dot71mily) for adding this dataset.
|
huggingface_dataset/Dataset_Card/zZWipeoutZz_rogue_style.md
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|
| 1 |
+
---
|
| 2 |
+
license: creativeml-openrail-m
|
| 3 |
+
---
|
| 4 |
+
|
| 5 |
+
<h4> Usage </h4>
|
| 6 |
+
To use this embedding you have to download the file and put it into the "\stable-diffusion-webui\embeddings" folder
|
| 7 |
+
|
| 8 |
+
To use it in a prompt add
|
| 9 |
+
<em style="font-weight:600">art by rogue_style </em>
|
| 10 |
+
|
| 11 |
+
add <b>[ ]</b> around it to reduce its weight.
|
| 12 |
+
|
| 13 |
+
<h4> Included Files </h4>
|
| 14 |
+
<ul>
|
| 15 |
+
<li>500 steps <em>Usage: art by rogue_style-500</em></li>
|
| 16 |
+
<li>3500 steps <em>Usage: art by rogue_style-3500</em></li>
|
| 17 |
+
<li>6500 steps <em>Usage: art by rogue_style</em> </li>
|
| 18 |
+
|
| 19 |
+
</ul>
|
| 20 |
+
|
| 21 |
+
cheers<br>
|
| 22 |
+
Wipeout
|
| 23 |
+
|
| 24 |
+
<h4> Example Pictures </h4>
|
| 25 |
+
<table>
|
| 26 |
+
<tbody>
|
| 27 |
+
<tr>
|
| 28 |
+
<td><img height="100%/" width="100%" src="https://i.imgur.com/JefZ3cA.png"></td>
|
| 29 |
+
<td><img height="100%/" width="100%" src="https://i.imgur.com/YBJzVIi.png"></td>
|
| 30 |
+
<td><img height="100%/" width="100%" src="https://i.imgur.com/96iutfu.png"></td>
|
| 31 |
+
<td><img height="100%/" width="100%" src="https://i.imgur.com/SBKfnc4.png"></td>
|
| 32 |
+
</tr>
|
| 33 |
+
</tbody>
|
| 34 |
+
</table>
|
| 35 |
+
<h4> prompt comparison </h4>
|
| 36 |
+
<em> click the image to enlarge</em>
|
| 37 |
+
<a href="https://i.imgur.com/a6te4zG.png" target="_blank"><img height="50%" width="50%" src="https://i.imgur.com/a6te4zG.png"></a>
|