Upload batch 95 (20 files, last=huggingface_dataset/Dataset_Card/SimulaMet-HOST_VISEM-Tracking.md)
Browse files- huggingface_dataset/Dataset_Card/3ee_regularization-forest.md +16 -0
- huggingface_dataset/Dataset_Card/ACOSharma_literature.md +52 -0
- huggingface_dataset/Dataset_Card/Alex3_01-cane.md +21 -0
- huggingface_dataset/Dataset_Card/Datatang_Canadian_Speaking_English_Speech_Data_by_Mobile_Phone.md +126 -0
- huggingface_dataset/Dataset_Card/HuggingFaceM4_TGIF.md +101 -0
- huggingface_dataset/Dataset_Card/Maxmioti_GDRP-fines.md +9 -0
- huggingface_dataset/Dataset_Card/SimulaMet-HOST_VISEM-Tracking.md +37 -0
- huggingface_dataset/Dataset_Card/afmck_peanuts.md +130 -0
- huggingface_dataset/Dataset_Card/autoevaluate_autoeval-eval-HadiPourmousa__TextSummarization-HadiPourmousa__TextSum-31dfb4-1463253931.md +33 -0
- huggingface_dataset/Dataset_Card/autoevaluate_autoeval-eval-mathemakitten__winobias_antistereotype_test_cot_v4-math-54ae93-2018366738.md +34 -0
- huggingface_dataset/Dataset_Card/autoevaluate_autoeval-eval-samsum-samsum-2c3c14-1486454326.md +33 -0
- huggingface_dataset/Dataset_Card/autoevaluate_autoeval-eval-squad_v2-squad_v2-5d46e4-1992966291.md +35 -0
- huggingface_dataset/Dataset_Card/autoevaluate_autoeval-staging-eval-project-Blaise-g__SumPubmed-d94a9931-12545675.md +33 -0
- huggingface_dataset/Dataset_Card/autoevaluate_autoeval-staging-eval-project-squad_v2-e85023ec-11745565.md +35 -0
- huggingface_dataset/Dataset_Card/huggingartists_jim-morrison.md +204 -0
- huggingface_dataset/Dataset_Card/irds_gov2_trec-tb-2006_efficiency_10k.md +44 -0
- huggingface_dataset/Dataset_Card/notional_notional-python.md +78 -0
- huggingface_dataset/Dataset_Card/pere_italian_tweets_10M.md +8 -0
- huggingface_dataset/Dataset_Card/tapaco.md +1831 -0
- huggingface_dataset/Dataset_Card/thiemowa_empathyreviewcorpus.md +21 -0
huggingface_dataset/Dataset_Card/3ee_regularization-forest.md
ADDED
|
@@ -0,0 +1,16 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
license: mit
|
| 3 |
+
tags:
|
| 4 |
+
- stable-diffusion
|
| 5 |
+
- regularization-images
|
| 6 |
+
- text-to-image
|
| 7 |
+
- image-to-image
|
| 8 |
+
- dreambooth
|
| 9 |
+
- class-instance
|
| 10 |
+
- preservation-loss-training
|
| 11 |
+
- forest
|
| 12 |
+
---
|
| 13 |
+
|
| 14 |
+
# Forest Regularization Images
|
| 15 |
+
|
| 16 |
+
A collection of regularization & class instance datasets of forests for the Stable Diffusion 1.5 model to use for DreamBooth prior preservation loss training.
|
huggingface_dataset/Dataset_Card/ACOSharma_literature.md
ADDED
|
@@ -0,0 +1,52 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
license: cc-by-sa-4.0
|
| 3 |
+
---
|
| 4 |
+
|
| 5 |
+
# Literature Dataset
|
| 6 |
+
## Files
|
| 7 |
+
A dataset containing novels, epics and essays.
|
| 8 |
+
The files are as follows:
|
| 9 |
+
- main.txt, a file with all the texts, every text on a newline, all English
|
| 10 |
+
- vocab.txt, a file with the trained (BERT) vocab, a newline a new word
|
| 11 |
+
- train.csv, a file with length 129 sequences of tokens, csv of ints, containing 48,758 samples (6,289,782 tokens)
|
| 12 |
+
- test.csv, the test split in the same way, 5,417 samples (698,793 tokens)
|
| 13 |
+
- DatasetDistribution.png, a file with all the texts and a plot with character length
|
| 14 |
+
|
| 15 |
+
## Texts
|
| 16 |
+
The texts used are these:
|
| 17 |
+
- Wuthering Heights
|
| 18 |
+
- Ulysses
|
| 19 |
+
- Treasure Island
|
| 20 |
+
- The War of the Worlds
|
| 21 |
+
- The Republic
|
| 22 |
+
- The Prophet
|
| 23 |
+
- The Prince
|
| 24 |
+
- The Picture of Dorian Gray
|
| 25 |
+
- The Odyssey
|
| 26 |
+
- The Great Gatsby
|
| 27 |
+
- The Brothers Karamazov
|
| 28 |
+
- Second Treatise of Goverment
|
| 29 |
+
- Pride and Prejudice
|
| 30 |
+
- Peter Pan
|
| 31 |
+
- Moby Dick
|
| 32 |
+
- Metamorphosis
|
| 33 |
+
- Little Women
|
| 34 |
+
- Les Misérables
|
| 35 |
+
- Japanese Girls and Women
|
| 36 |
+
- Iliad
|
| 37 |
+
- Heart of Darkness
|
| 38 |
+
- Grimms' Fairy Tales
|
| 39 |
+
- Great Expectations
|
| 40 |
+
- Frankenstein
|
| 41 |
+
- Emma
|
| 42 |
+
- Dracula
|
| 43 |
+
- Don Quixote
|
| 44 |
+
- Crime and Punishment
|
| 45 |
+
- Christmas Carol
|
| 46 |
+
- Beyond Good and Evil
|
| 47 |
+
- Anna Karenina
|
| 48 |
+
- Adventures of Sherlock Holmes
|
| 49 |
+
- Adventures of Huckleberry Finn
|
| 50 |
+
- Adventures in Wonderland
|
| 51 |
+
- A Tale of Two Cities
|
| 52 |
+
- A Room with A View
|
huggingface_dataset/Dataset_Card/Alex3_01-cane.md
ADDED
|
@@ -0,0 +1,21 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
annotations_creators:
|
| 2 |
+
- other
|
| 3 |
+
language:
|
| 4 |
+
- en
|
| 5 |
+
language_creators:
|
| 6 |
+
- other
|
| 7 |
+
license:
|
| 8 |
+
- artistic-2.0
|
| 9 |
+
multilinguality:
|
| 10 |
+
- monolingual
|
| 11 |
+
pretty_name: Cane
|
| 12 |
+
size_categories:
|
| 13 |
+
- n<1K
|
| 14 |
+
source_datasets:
|
| 15 |
+
- original
|
| 16 |
+
tags: []
|
| 17 |
+
task_categories:
|
| 18 |
+
- text-to-image
|
| 19 |
+
task_ids: []
|
| 20 |
+
|
| 21 |
+
|
huggingface_dataset/Dataset_Card/Datatang_Canadian_Speaking_English_Speech_Data_by_Mobile_Phone.md
ADDED
|
@@ -0,0 +1,126 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
YAML tags:
|
| 3 |
+
- copy-paste the tags obtained with the tagging app: https://github.com/huggingface/datasets-tagging
|
| 4 |
+
---
|
| 5 |
+
|
| 6 |
+
# Dataset Card for Datatang/Canadian_Speaking_English_Speech_Data_by_Mobile_Phone
|
| 7 |
+
|
| 8 |
+
## Table of Contents
|
| 9 |
+
- [Table of Contents](#table-of-contents)
|
| 10 |
+
- [Dataset Description](#dataset-description)
|
| 11 |
+
- [Dataset Summary](#dataset-summary)
|
| 12 |
+
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
|
| 13 |
+
- [Languages](#languages)
|
| 14 |
+
- [Dataset Structure](#dataset-structure)
|
| 15 |
+
- [Data Instances](#data-instances)
|
| 16 |
+
- [Data Fields](#data-fields)
|
| 17 |
+
- [Data Splits](#data-splits)
|
| 18 |
+
- [Dataset Creation](#dataset-creation)
|
| 19 |
+
- [Curation Rationale](#curation-rationale)
|
| 20 |
+
- [Source Data](#source-data)
|
| 21 |
+
- [Annotations](#annotations)
|
| 22 |
+
- [Personal and Sensitive Information](#personal-and-sensitive-information)
|
| 23 |
+
- [Considerations for Using the Data](#considerations-for-using-the-data)
|
| 24 |
+
- [Social Impact of Dataset](#social-impact-of-dataset)
|
| 25 |
+
- [Discussion of Biases](#discussion-of-biases)
|
| 26 |
+
- [Other Known Limitations](#other-known-limitations)
|
| 27 |
+
- [Additional Information](#additional-information)
|
| 28 |
+
- [Dataset Curators](#dataset-curators)
|
| 29 |
+
- [Licensing Information](#licensing-information)
|
| 30 |
+
- [Citation Information](#citation-information)
|
| 31 |
+
- [Contributions](#contributions)
|
| 32 |
+
|
| 33 |
+
## Dataset Description
|
| 34 |
+
|
| 35 |
+
- **Homepage:** https://bit.ly/3b4l9as
|
| 36 |
+
- **Repository:**
|
| 37 |
+
- **Paper:**
|
| 38 |
+
- **Leaderboard:**
|
| 39 |
+
- **Point of Contact:**
|
| 40 |
+
|
| 41 |
+
### Dataset Summary
|
| 42 |
+
|
| 43 |
+
466 native Canadian speakers involved, balanced for gender. The recording corpus is rich in content, and it covers a wide domain such as generic command and control category, human-machine interaction category; smart home category; in-car category. The transcription corpus has been manually proofread to ensure high accuracy.
|
| 44 |
+
|
| 45 |
+
For more details, please refer to the link: https://bit.ly/3b4l9as
|
| 46 |
+
|
| 47 |
+
### Supported Tasks and Leaderboards
|
| 48 |
+
|
| 49 |
+
automatic-speech-recognition, audio-speaker-identification: The dataset can be used to train a model for Automatic Speech Recognition (ASR).
|
| 50 |
+
|
| 51 |
+
### Languages
|
| 52 |
+
|
| 53 |
+
Canadian English
|
| 54 |
+
## Dataset Structure
|
| 55 |
+
|
| 56 |
+
### Data Instances
|
| 57 |
+
|
| 58 |
+
[More Information Needed]
|
| 59 |
+
|
| 60 |
+
### Data Fields
|
| 61 |
+
|
| 62 |
+
[More Information Needed]
|
| 63 |
+
|
| 64 |
+
### Data Splits
|
| 65 |
+
|
| 66 |
+
[More Information Needed]
|
| 67 |
+
|
| 68 |
+
## Dataset Creation
|
| 69 |
+
|
| 70 |
+
### Curation Rationale
|
| 71 |
+
|
| 72 |
+
[More Information Needed]
|
| 73 |
+
|
| 74 |
+
### Source Data
|
| 75 |
+
|
| 76 |
+
#### Initial Data Collection and Normalization
|
| 77 |
+
|
| 78 |
+
[More Information Needed]
|
| 79 |
+
|
| 80 |
+
#### Who are the source language producers?
|
| 81 |
+
|
| 82 |
+
[More Information Needed]
|
| 83 |
+
|
| 84 |
+
### Annotations
|
| 85 |
+
|
| 86 |
+
#### Annotation process
|
| 87 |
+
|
| 88 |
+
[More Information Needed]
|
| 89 |
+
|
| 90 |
+
#### Who are the annotators?
|
| 91 |
+
|
| 92 |
+
[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 |
+
[More Information Needed]
|
| 103 |
+
|
| 104 |
+
### 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 |
+
[More Information Needed]
|
| 125 |
+
|
| 126 |
+
### Contributions
|
huggingface_dataset/Dataset_Card/HuggingFaceM4_TGIF.md
ADDED
|
@@ -0,0 +1,101 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
annotations_creators:
|
| 3 |
+
- expert-generated
|
| 4 |
+
language_creators:
|
| 5 |
+
- crowdsourced
|
| 6 |
+
language:
|
| 7 |
+
- en
|
| 8 |
+
license:
|
| 9 |
+
- other
|
| 10 |
+
multilinguality:
|
| 11 |
+
- monolingual
|
| 12 |
+
pretty_name: TGIF
|
| 13 |
+
size_categories:
|
| 14 |
+
- 100K<n<1M
|
| 15 |
+
source_datasets:
|
| 16 |
+
- original
|
| 17 |
+
task_categories:
|
| 18 |
+
- question-answering
|
| 19 |
+
- visual-question-answering
|
| 20 |
+
task_ids:
|
| 21 |
+
- closed-domain-qa
|
| 22 |
+
---
|
| 23 |
+
|
| 24 |
+
|
| 25 |
+
# Dataset Card for [Dataset Name]
|
| 26 |
+
## Table of Contents
|
| 27 |
+
- [Table of Contents](#table-of-contents)
|
| 28 |
+
- [Dataset Description](#dataset-description)
|
| 29 |
+
- [Dataset Summary](#dataset-summary)
|
| 30 |
+
- [Languages](#languages)
|
| 31 |
+
- [Dataset Structure](#dataset-structure)
|
| 32 |
+
- [Data Fields](#data-fields)
|
| 33 |
+
- [Data Splits](#data-splits)
|
| 34 |
+
- [Dataset Creation](#dataset-creation)
|
| 35 |
+
|
| 36 |
+
- [Personal and Sensitive Information](#personal-and-sensitive-information)
|
| 37 |
+
- [Considerations for Using the Data](#considerations-for-using-the-data)
|
| 38 |
+
- [Social Impact of Dataset](#social-impact-of-dataset)
|
| 39 |
+
- [Discussion of Biases](#discussion-of-biases)
|
| 40 |
+
- [Other Known Limitations](#other-known-limitations)
|
| 41 |
+
- [Additional Information](#additional-information)
|
| 42 |
+
- [Licensing Information](#licensing-information)
|
| 43 |
+
- [Citation Information](#citation-information)
|
| 44 |
+
- [Contributions](#contributions)
|
| 45 |
+
## Dataset Description
|
| 46 |
+
- **Homepage:** http://raingo.github.io/TGIF-Release/
|
| 47 |
+
- **Repository:** https://github.com/raingo/TGIF-Release
|
| 48 |
+
- **Paper:** https://arxiv.org/abs/1604.02748
|
| 49 |
+
- **Point of Contact:** mailto: yli@cs.rochester.edu
|
| 50 |
+
### Dataset Summary
|
| 51 |
+
The Tumblr GIF (TGIF) dataset contains 100K animated GIFs and 120K sentences describing visual content of the animated GIFs. The animated GIFs have been collected from Tumblr, from randomly selected posts published between May and June of 2015. We provide the URLs of animated GIFs in this release. The sentences are collected via crowdsourcing, with a carefully designed annotation interface that ensures high quality dataset. We provide one sentence per animated GIF for the training and validation splits, and three sentences per GIF for the test split. The dataset shall be used to evaluate animated GIF/video description techniques.
|
| 52 |
+
### Languages
|
| 53 |
+
The captions in the dataset are in English.
|
| 54 |
+
## Dataset Structure
|
| 55 |
+
### Data Fields
|
| 56 |
+
- `video_path`: `str` "https://31.media.tumblr.com/001a8b092b9752d260ffec73c0bc29cd/tumblr_ndotjhRiX51t8n92fo1_500.gif"
|
| 57 |
+
-`video_bytes`: `large_bytes` video file in bytes format
|
| 58 |
+
- `en_global_captions`: `list_str` List of english captions describing the entire video
|
| 59 |
+
|
| 60 |
+
### Data Splits
|
| 61 |
+
| |train |validation| test | Overall |
|
| 62 |
+
|-------------|------:|---------:|------:|------:|
|
| 63 |
+
|# of GIFs|80,000 |10,708 |11,360 |102,068 |
|
| 64 |
+
### Annotations
|
| 65 |
+
Quoting [TGIF paper](https://arxiv.org/abs/1604.02748): \
|
| 66 |
+
"We annotated animated GIFs with natural language descriptions using the crowdsourcing service CrowdFlower.
|
| 67 |
+
We carefully designed our annotation task with various
|
| 68 |
+
quality control mechanisms to ensure the sentences are both
|
| 69 |
+
syntactically and semantically of high quality.
|
| 70 |
+
A total of 931 workers participated in our annotation
|
| 71 |
+
task. We allowed workers only from Australia, Canada, New Zealand, UK and USA in an effort to collect fluent descriptions from native English speakers. Figure 2 shows the
|
| 72 |
+
instructions given to the workers. Each task showed 5 animated GIFs and asked the worker to describe each with one
|
| 73 |
+
sentence. To promote language style diversity, each worker
|
| 74 |
+
could rate no more than 800 images (0.7% of our corpus).
|
| 75 |
+
We paid 0.02 USD per sentence; the entire crowdsourcing
|
| 76 |
+
cost less than 4K USD. We provide details of our annotation
|
| 77 |
+
task in the supplementary material."
|
| 78 |
+
### Personal and Sensitive Information
|
| 79 |
+
Nothing specifically mentioned in the paper.
|
| 80 |
+
## Considerations for Using the Data
|
| 81 |
+
### Social Impact of Dataset
|
| 82 |
+
[More Information Needed]
|
| 83 |
+
### Discussion of Biases
|
| 84 |
+
[More Information Needed]
|
| 85 |
+
### Other Known Limitations
|
| 86 |
+
[More Information Needed]
|
| 87 |
+
## Additional Information
|
| 88 |
+
### Licensing Information
|
| 89 |
+
This dataset is provided to be used for approved non-commercial research purposes. No personally identifying information is available in this dataset.
|
| 90 |
+
### Citation Information
|
| 91 |
+
```bibtex
|
| 92 |
+
@InProceedings{tgif-cvpr2016,
|
| 93 |
+
author = {Li, Yuncheng and Song, Yale and Cao, Liangliang and Tetreault, Joel and Goldberg, Larry and Jaimes, Alejandro and Luo, Jiebo},
|
| 94 |
+
title = "{TGIF: A New Dataset and Benchmark on Animated GIF Description}",
|
| 95 |
+
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
|
| 96 |
+
month = {June},
|
| 97 |
+
year = {2016}
|
| 98 |
+
}
|
| 99 |
+
```
|
| 100 |
+
### Contributions
|
| 101 |
+
Thanks to [@leot13](https://github.com/leot13) for adding this dataset.
|
huggingface_dataset/Dataset_Card/Maxmioti_GDRP-fines.md
ADDED
|
@@ -0,0 +1,9 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
license: other
|
| 3 |
+
---
|
| 4 |
+
|
| 5 |
+
Opensource DataSet form a Kaggle competition https://www.kaggle.com/datasets/andreibuliga1/gdpr-fines-20182020-updated-23012021
|
| 6 |
+
|
| 7 |
+
GDPR-fines is a dataset with summary of GDPR cases from companies that were find between 2018 and 2021. You will find the summary plus the Articles violated in the cases (3 most importants + "Others" regrouping the rest of articles).
|
| 8 |
+
|
| 9 |
+
Raw text and lemmatized text available plus multi-labels.
|
huggingface_dataset/Dataset_Card/SimulaMet-HOST_VISEM-Tracking.md
ADDED
|
@@ -0,0 +1,37 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
license: cc-by-4.0
|
| 3 |
+
task_categories:
|
| 4 |
+
- object-detection
|
| 5 |
+
tags:
|
| 6 |
+
- sperm
|
| 7 |
+
- VISEM-Tracking
|
| 8 |
+
- sperm tracking
|
| 9 |
+
- tracking
|
| 10 |
+
pretty_name: VISEM-Tracking
|
| 11 |
+
size_categories:
|
| 12 |
+
- 1B<n<10B
|
| 13 |
+
---
|
| 14 |
+
## To use this dataset for your research, please cite the following preprint. Full-paper will be available soon.
|
| 15 |
+
|
| 16 |
+
[Preprint](https://arxiv.org/abs/2212.02842)
|
| 17 |
+
|
| 18 |
+
### Citation:
|
| 19 |
+
@article{thambawita2022visem,
|
| 20 |
+
title={VISEM-Tracking: Human Spermatozoa Tracking Dataset},
|
| 21 |
+
author={Thambawita, Vajira and Hicks, Steven A and Stor{\aa}s, Andrea M and Nguyen, Thu and Andersen, Jorunn M and Witczak, Oliwia and Haugen, Trine B and Hammer, Hugo L, and Halvorsen, P{\aa}l and Riegler, Michael A},
|
| 22 |
+
journal={arXiv preprint arXiv:2212.02842}, year={2022}
|
| 23 |
+
}
|
| 24 |
+
☝️ ☝️ ☝️
|
| 25 |
+
|
| 26 |
+
### Motivation and background
|
| 27 |
+
|
| 28 |
+
Manual evaluation of a sperm sample using a microscope is time-consuming and requires costly experts who have extensive training. In addition, the validity of manual sperm analysis becomes unreliable due to limited reproducibility and high inter-personnel variations due to the complexity of tracking, identifying, and counting sperm in fresh samples. The existing computer-aided sperm analyzer systems are not working well enough for application in a real clinical setting due to unreliability caused by the consistency of the semen sample. Therefore, we need to research new methods for automated sperm analysis.
|
| 29 |
+
|
| 30 |
+
### Target group
|
| 31 |
+
|
| 32 |
+
The task is of interest to researchers in the areas of machine learning (classification and detection), visual content analysis, and multimodal fusion. Overall, this task is intended to encourage the multimedia community to help improve the healthcare system through the application of their knowledge and methods to reach the next level of computer and multimedia-assisted diagnosis, detection, and interpretation.
|
| 33 |
+
|
| 34 |
+
### Class Label Mapping
|
| 35 |
+
sperm: 0
|
| 36 |
+
cluster: 1
|
| 37 |
+
small or pinhead: 2
|
huggingface_dataset/Dataset_Card/afmck_peanuts.md
ADDED
|
@@ -0,0 +1,130 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
license: other
|
| 3 |
+
task_categories:
|
| 4 |
+
- text-to-image
|
| 5 |
+
language:
|
| 6 |
+
- en
|
| 7 |
+
pretty_name: Peanuts Dataset (Snoopy and Co.)
|
| 8 |
+
size_categories:
|
| 9 |
+
- 10K<n<100K
|
| 10 |
+
dataset_info:
|
| 11 |
+
features:
|
| 12 |
+
- name: image
|
| 13 |
+
dtype: image
|
| 14 |
+
- name: panel_name
|
| 15 |
+
dtype: string
|
| 16 |
+
- name: characters
|
| 17 |
+
sequence: string
|
| 18 |
+
- name: themes
|
| 19 |
+
sequence: string
|
| 20 |
+
- name: color
|
| 21 |
+
dtype: string
|
| 22 |
+
- name: year
|
| 23 |
+
dtype: int64
|
| 24 |
+
- name: caption
|
| 25 |
+
dtype: string
|
| 26 |
+
splits:
|
| 27 |
+
- name: train
|
| 28 |
+
num_bytes: 2948640650.848
|
| 29 |
+
num_examples: 77456
|
| 30 |
+
download_size: 4601323640
|
| 31 |
+
dataset_size: 2948640650.848
|
| 32 |
+
---
|
| 33 |
+
|
| 34 |
+
# Peanut Comic Strip Dataset (Snoopy & Co.)
|
| 35 |
+
|
| 36 |
+
|
| 37 |
+
|
| 38 |
+
This is a dataset Peanuts comic strips from `1950/10/02` to `2000/02/13`.
|
| 39 |
+
There are `77,457` panels extracted from `17,816` comic strips.
|
| 40 |
+
The dataset size is approximately `4.4G`.
|
| 41 |
+
|
| 42 |
+
Each row in the dataset contains the following fields:
|
| 43 |
+
- `image`: `PIL.Image` containing the extracted panel.
|
| 44 |
+
- `panel_name`: unique identifier for the row.
|
| 45 |
+
- `characters`: `tuple[str, ...]` of characters included in the comic strip the panel is part of.
|
| 46 |
+
- `themes`: `tuple[str, ...]` of theme in the comic strip the panel is part of.
|
| 47 |
+
- `color`: `str` indicating whether the panel is grayscale or in color.
|
| 48 |
+
- `caption`: [BLIP-2_OPT_6.7B](https://huggingface.co/docs/transformers/main/model_doc/blip-2) generated caption from the panel.
|
| 49 |
+
- `year`: `int` storing the year the specific panel was released.
|
| 50 |
+
|
| 51 |
+
Character and theme information was extracted from [Peanuts Wiki (Fandom)](https://peanuts.fandom.com/wiki/Peanuts_Wiki) using [Beautiful Soup](https://www.crummy.com/software/BeautifulSoup/bs4/doc/).
|
| 52 |
+
Images were extracted from [Peanuts Search](https://peanuts-search.com/).
|
| 53 |
+
|
| 54 |
+
Only strips with the following characters were extracted:
|
| 55 |
+
```
|
| 56 |
+
- "Charlie Brown"
|
| 57 |
+
- "Sally Brown"
|
| 58 |
+
- "Joe Cool" # Snoopy alter-ego
|
| 59 |
+
- "Franklin"
|
| 60 |
+
- "Violet Gray"
|
| 61 |
+
- "Eudora"
|
| 62 |
+
- "Frieda"
|
| 63 |
+
- "Marcie"
|
| 64 |
+
- "Peppermint Patty"
|
| 65 |
+
- "Patty"
|
| 66 |
+
- "Pig-Pen"
|
| 67 |
+
- "Linus van Pelt"
|
| 68 |
+
- "Lucy van Pelt"
|
| 69 |
+
- "Rerun van Pelt"
|
| 70 |
+
- "Schroeder"
|
| 71 |
+
- "Snoopy"
|
| 72 |
+
- "Shermy"
|
| 73 |
+
- "Spike"
|
| 74 |
+
- "Woodstock"
|
| 75 |
+
- "the World War I Flying Ace" # Snoopy alter-ego
|
| 76 |
+
```
|
| 77 |
+
|
| 78 |
+
### Extraction Details
|
| 79 |
+
Panel detection and extraction was done using the following codeblock:
|
| 80 |
+
```python
|
| 81 |
+
def check_contour(cnt):
|
| 82 |
+
area = cv2.contourArea(cnt)
|
| 83 |
+
if area < 600:
|
| 84 |
+
return False
|
| 85 |
+
|
| 86 |
+
_, _, w, h = cv2.boundingRect(cnt)
|
| 87 |
+
if w / h < 1 / 2: return False
|
| 88 |
+
if w / h > 2 / 1: return False
|
| 89 |
+
|
| 90 |
+
return True
|
| 91 |
+
|
| 92 |
+
def get_panels_from_image(path):
|
| 93 |
+
panels = []
|
| 94 |
+
original_img = cv2.imread(path)
|
| 95 |
+
gray = cv2.cvtColor(original_img, cv2.COLOR_BGR2GRAY)
|
| 96 |
+
blur = cv2.GaussianBlur(gray, (5,5), 0)
|
| 97 |
+
thresh = cv2.threshold(blur, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1]
|
| 98 |
+
|
| 99 |
+
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3,3))
|
| 100 |
+
opening = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel, iterations=1)
|
| 101 |
+
invert = 255 - opening
|
| 102 |
+
|
| 103 |
+
cnts, _ = cv2.findContours(invert, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
|
| 104 |
+
|
| 105 |
+
idx = 0
|
| 106 |
+
for cnt in cnts:
|
| 107 |
+
if not check_contour(cnt): continue
|
| 108 |
+
idx += 1
|
| 109 |
+
x,y,w,h = cv2.boundingRect(cnt)
|
| 110 |
+
roi = original_img[y:y+h,x:x+w]
|
| 111 |
+
panels.append(roi)
|
| 112 |
+
|
| 113 |
+
return panels
|
| 114 |
+
```
|
| 115 |
+
`check_contour` will reject panels with `area < 600` or with aspect ratios larger than `2` or smaller than `0.5`.
|
| 116 |
+
|
| 117 |
+
Grayscale detection was done using the following codeblock:
|
| 118 |
+
```python
|
| 119 |
+
def is_grayscale(panel):
|
| 120 |
+
LAB_THRESHOLD = 10.
|
| 121 |
+
img = cv2.cvtColor(panel, cv2.COLOR_RGB2LAB)
|
| 122 |
+
_, ea, eb = cv2.split(img)
|
| 123 |
+
de = abs(ea - eb)
|
| 124 |
+
mean_e = np.mean(de)
|
| 125 |
+
return mean_e < LAB_THRESHOLD
|
| 126 |
+
|
| 127 |
+
```
|
| 128 |
+
|
| 129 |
+
Captioning was done using the standard BLIP-2 pipeline shown in the [Huggingface docs](https://huggingface.co/docs/transformers/main/model_doc/blip-2) using beam search over 10 beams and a repetition penalty of `2.0`.
|
| 130 |
+
Raw captions are extracted and no postprocessing is applied. You may wish to normalise captions (such as replacing "cartoon" with "peanuts cartoon") or incorporate extra metadata into prompts.
|
huggingface_dataset/Dataset_Card/autoevaluate_autoeval-eval-HadiPourmousa__TextSummarization-HadiPourmousa__TextSum-31dfb4-1463253931.md
ADDED
|
@@ -0,0 +1,33 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
type: predictions
|
| 3 |
+
tags:
|
| 4 |
+
- autotrain
|
| 5 |
+
- evaluation
|
| 6 |
+
datasets:
|
| 7 |
+
- HadiPourmousa/TextSummarization
|
| 8 |
+
eval_info:
|
| 9 |
+
task: summarization
|
| 10 |
+
model: t5-base
|
| 11 |
+
metrics: []
|
| 12 |
+
dataset_name: HadiPourmousa/TextSummarization
|
| 13 |
+
dataset_config: HadiPourmousa--TextSummarization
|
| 14 |
+
dataset_split: train
|
| 15 |
+
col_mapping:
|
| 16 |
+
text: Text
|
| 17 |
+
target: Title
|
| 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: t5-base
|
| 25 |
+
* Dataset: HadiPourmousa/TextSummarization
|
| 26 |
+
* Config: HadiPourmousa--TextSummarization
|
| 27 |
+
* Split: train
|
| 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 [@marcmaxmeister](https://huggingface.co/marcmaxmeister) for evaluating this model.
|
huggingface_dataset/Dataset_Card/autoevaluate_autoeval-eval-mathemakitten__winobias_antistereotype_test_cot_v4-math-54ae93-2018366738.md
ADDED
|
@@ -0,0 +1,34 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
type: predictions
|
| 3 |
+
tags:
|
| 4 |
+
- autotrain
|
| 5 |
+
- evaluation
|
| 6 |
+
datasets:
|
| 7 |
+
- mathemakitten/winobias_antistereotype_test_cot_v4
|
| 8 |
+
eval_info:
|
| 9 |
+
task: text_zero_shot_classification
|
| 10 |
+
model: inverse-scaling/opt-6.7b_eval
|
| 11 |
+
metrics: []
|
| 12 |
+
dataset_name: mathemakitten/winobias_antistereotype_test_cot_v4
|
| 13 |
+
dataset_config: mathemakitten--winobias_antistereotype_test_cot_v4
|
| 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: inverse-scaling/opt-6.7b_eval
|
| 26 |
+
* Dataset: mathemakitten/winobias_antistereotype_test_cot_v4
|
| 27 |
+
* Config: mathemakitten--winobias_antistereotype_test_cot_v4
|
| 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 [@mathemakitten](https://huggingface.co/mathemakitten) for evaluating this model.
|
huggingface_dataset/Dataset_Card/autoevaluate_autoeval-eval-samsum-samsum-2c3c14-1486454326.md
ADDED
|
@@ -0,0 +1,33 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
type: predictions
|
| 3 |
+
tags:
|
| 4 |
+
- autotrain
|
| 5 |
+
- evaluation
|
| 6 |
+
datasets:
|
| 7 |
+
- samsum
|
| 8 |
+
eval_info:
|
| 9 |
+
task: summarization
|
| 10 |
+
model: SamuelAllen123/t5-efficient-large-nl36_fine_tune_sum
|
| 11 |
+
metrics: []
|
| 12 |
+
dataset_name: samsum
|
| 13 |
+
dataset_config: samsum
|
| 14 |
+
dataset_split: train
|
| 15 |
+
col_mapping:
|
| 16 |
+
text: dialogue
|
| 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: SamuelAllen123/t5-efficient-large-nl36_fine_tune_sum
|
| 25 |
+
* Dataset: samsum
|
| 26 |
+
* Config: samsum
|
| 27 |
+
* Split: train
|
| 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 [@samuelallen123](https://huggingface.co/samuelallen123) for evaluating this model.
|
huggingface_dataset/Dataset_Card/autoevaluate_autoeval-eval-squad_v2-squad_v2-5d46e4-1992966291.md
ADDED
|
@@ -0,0 +1,35 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
type: predictions
|
| 3 |
+
tags:
|
| 4 |
+
- autotrain
|
| 5 |
+
- evaluation
|
| 6 |
+
datasets:
|
| 7 |
+
- squad_v2
|
| 8 |
+
eval_info:
|
| 9 |
+
task: extractive_question_answering
|
| 10 |
+
model: deepset/bert-base-uncased-squad2
|
| 11 |
+
metrics: ['accuracy', 'bleu', 'precision', 'recall', 'rouge']
|
| 12 |
+
dataset_name: squad_v2
|
| 13 |
+
dataset_config: squad_v2
|
| 14 |
+
dataset_split: validation
|
| 15 |
+
col_mapping:
|
| 16 |
+
context: context
|
| 17 |
+
question: question
|
| 18 |
+
answers-text: answers.text
|
| 19 |
+
answers-answer_start: answers.answer_start
|
| 20 |
+
---
|
| 21 |
+
# Dataset Card for AutoTrain Evaluator
|
| 22 |
+
|
| 23 |
+
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
|
| 24 |
+
|
| 25 |
+
* Task: Question Answering
|
| 26 |
+
* Model: deepset/bert-base-uncased-squad2
|
| 27 |
+
* Dataset: squad_v2
|
| 28 |
+
* Config: squad_v2
|
| 29 |
+
* Split: validation
|
| 30 |
+
|
| 31 |
+
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
|
| 32 |
+
|
| 33 |
+
## Contributions
|
| 34 |
+
|
| 35 |
+
Thanks to [@anchal](https://huggingface.co/anchal) for evaluating this model.
|
huggingface_dataset/Dataset_Card/autoevaluate_autoeval-staging-eval-project-Blaise-g__SumPubmed-d94a9931-12545675.md
ADDED
|
@@ -0,0 +1,33 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
type: predictions
|
| 3 |
+
tags:
|
| 4 |
+
- autotrain
|
| 5 |
+
- evaluation
|
| 6 |
+
datasets:
|
| 7 |
+
- Blaise-g/SumPubmed
|
| 8 |
+
eval_info:
|
| 9 |
+
task: summarization
|
| 10 |
+
model: Jacobsith/autotrain-Hello_there-1209845735
|
| 11 |
+
metrics: []
|
| 12 |
+
dataset_name: Blaise-g/SumPubmed
|
| 13 |
+
dataset_config: Blaise-g--SumPubmed
|
| 14 |
+
dataset_split: test
|
| 15 |
+
col_mapping:
|
| 16 |
+
text: text
|
| 17 |
+
target: abstract
|
| 18 |
+
---
|
| 19 |
+
# Dataset Card for AutoTrain Evaluator
|
| 20 |
+
|
| 21 |
+
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
|
| 22 |
+
|
| 23 |
+
* Task: Summarization
|
| 24 |
+
* Model: Jacobsith/autotrain-Hello_there-1209845735
|
| 25 |
+
* Dataset: Blaise-g/SumPubmed
|
| 26 |
+
* Config: Blaise-g--SumPubmed
|
| 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 [@Jacobsith](https://huggingface.co/Jacobsith) for evaluating this model.
|
huggingface_dataset/Dataset_Card/autoevaluate_autoeval-staging-eval-project-squad_v2-e85023ec-11745565.md
ADDED
|
@@ -0,0 +1,35 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
type: predictions
|
| 3 |
+
tags:
|
| 4 |
+
- autotrain
|
| 5 |
+
- evaluation
|
| 6 |
+
datasets:
|
| 7 |
+
- squad_v2
|
| 8 |
+
eval_info:
|
| 9 |
+
task: extractive_question_answering
|
| 10 |
+
model: deepset/roberta-large-squad2
|
| 11 |
+
metrics: []
|
| 12 |
+
dataset_name: squad_v2
|
| 13 |
+
dataset_config: squad_v2
|
| 14 |
+
dataset_split: validation
|
| 15 |
+
col_mapping:
|
| 16 |
+
context: context
|
| 17 |
+
question: question
|
| 18 |
+
answers-text: answers.text
|
| 19 |
+
answers-answer_start: answers.answer_start
|
| 20 |
+
---
|
| 21 |
+
# Dataset Card for AutoTrain Evaluator
|
| 22 |
+
|
| 23 |
+
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
|
| 24 |
+
|
| 25 |
+
* Task: Question Answering
|
| 26 |
+
* Model: deepset/roberta-large-squad2
|
| 27 |
+
* Dataset: squad_v2
|
| 28 |
+
* Config: squad_v2
|
| 29 |
+
* Split: validation
|
| 30 |
+
|
| 31 |
+
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
|
| 32 |
+
|
| 33 |
+
## Contributions
|
| 34 |
+
|
| 35 |
+
Thanks to [@sjrlee](https://huggingface.co/sjrlee) for evaluating this model.
|
huggingface_dataset/Dataset_Card/huggingartists_jim-morrison.md
ADDED
|
@@ -0,0 +1,204 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
language:
|
| 3 |
+
- en
|
| 4 |
+
tags:
|
| 5 |
+
- huggingartists
|
| 6 |
+
- lyrics
|
| 7 |
+
---
|
| 8 |
+
|
| 9 |
+
# Dataset Card for "huggingartists/jim-morrison"
|
| 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:** 0.279131 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/ca9d975b4af890b1a7dedd5171157994.570x570x1.jpg')">
|
| 47 |
+
</div>
|
| 48 |
+
</div>
|
| 49 |
+
<a href="https://huggingface.co/huggingartists/jim-morrison">
|
| 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">Jim Morrison</div>
|
| 53 |
+
<a href="https://genius.com/artists/jim-morrison">
|
| 54 |
+
<div style="text-align: center; font-size: 14px;">@jim-morrison</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/jim-morrison).
|
| 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/jim-morrison")
|
| 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 |
+
|252| -| -|
|
| 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/jim-morrison")
|
| 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/irds_gov2_trec-tb-2006_efficiency_10k.md
ADDED
|
@@ -0,0 +1,44 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
pretty_name: '`gov2/trec-tb-2006/efficiency/10k`'
|
| 3 |
+
viewer: false
|
| 4 |
+
source_datasets: ['irds/gov2']
|
| 5 |
+
task_categories:
|
| 6 |
+
- text-retrieval
|
| 7 |
+
---
|
| 8 |
+
|
| 9 |
+
# Dataset Card for `gov2/trec-tb-2006/efficiency/10k`
|
| 10 |
+
|
| 11 |
+
The `gov2/trec-tb-2006/efficiency/10k` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
|
| 12 |
+
For more information about the dataset, see the [documentation](https://ir-datasets.com/gov2#gov2/trec-tb-2006/efficiency/10k).
|
| 13 |
+
|
| 14 |
+
# Data
|
| 15 |
+
|
| 16 |
+
This dataset provides:
|
| 17 |
+
- `queries` (i.e., topics); count=10,000
|
| 18 |
+
|
| 19 |
+
- For `docs`, use [`irds/gov2`](https://huggingface.co/datasets/irds/gov2)
|
| 20 |
+
|
| 21 |
+
## Usage
|
| 22 |
+
|
| 23 |
+
```python
|
| 24 |
+
from datasets import load_dataset
|
| 25 |
+
|
| 26 |
+
queries = load_dataset('irds/gov2_trec-tb-2006_efficiency_10k', 'queries')
|
| 27 |
+
for record in queries:
|
| 28 |
+
record # {'query_id': ..., 'text': ...}
|
| 29 |
+
|
| 30 |
+
```
|
| 31 |
+
|
| 32 |
+
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
|
| 33 |
+
data in 🤗 Dataset format.
|
| 34 |
+
|
| 35 |
+
## Citation Information
|
| 36 |
+
|
| 37 |
+
```
|
| 38 |
+
@inproceedings{Buttcher2006TrecTerabyte,
|
| 39 |
+
title={The TREC 2006 Terabyte Track},
|
| 40 |
+
author={Stefan B\"uttcher and Charles L. A. Clarke and Ian Soboroff},
|
| 41 |
+
booktitle={TREC},
|
| 42 |
+
year={2006}
|
| 43 |
+
}
|
| 44 |
+
```
|
huggingface_dataset/Dataset_Card/notional_notional-python.md
ADDED
|
@@ -0,0 +1,78 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
annotations_creators:
|
| 3 |
+
- no-annotation
|
| 4 |
+
language:
|
| 5 |
+
- py
|
| 6 |
+
language_creators:
|
| 7 |
+
- found
|
| 8 |
+
license:
|
| 9 |
+
- unknown
|
| 10 |
+
multilinguality:
|
| 11 |
+
- monolingual
|
| 12 |
+
size_categories:
|
| 13 |
+
- 10K<n<100K
|
| 14 |
+
source_datasets:
|
| 15 |
+
- original
|
| 16 |
+
task_categories:
|
| 17 |
+
- code-generation
|
| 18 |
+
- conditional-text-generation
|
| 19 |
+
task_ids:
|
| 20 |
+
- language-modeling
|
| 21 |
+
- code-generation
|
| 22 |
+
---
|
| 23 |
+
|
| 24 |
+
# Dataset Card for notional-python
|
| 25 |
+
|
| 26 |
+
## Table of Contents
|
| 27 |
+
- [Dataset Description](#dataset-description)
|
| 28 |
+
- [Dataset Summary](#dataset-summary)
|
| 29 |
+
- [Languages](#languages)
|
| 30 |
+
- [Dataset Creation](#dataset-creation)
|
| 31 |
+
- [Curation Rationale](#curation-rationale)
|
| 32 |
+
- [Source Data](#source-data)
|
| 33 |
+
- [Considerations for Using the Data](#considerations-for-using-the-data)
|
| 34 |
+
- [Social Impact of Dataset](#social-impact-of-dataset)
|
| 35 |
+
- [Discussion of Biases](#discussion-of-biases)
|
| 36 |
+
- [Other Known Limitations](#other-known-limitations)
|
| 37 |
+
- [Additional Information](#additional-information)
|
| 38 |
+
- [Dataset Curators](#dataset-curators)
|
| 39 |
+
- [Licensing Information](#licensing-information)
|
| 40 |
+
- [Citation Information](#citation-information)
|
| 41 |
+
|
| 42 |
+
## Dataset Description
|
| 43 |
+
|
| 44 |
+
- **Homepage:** https://notional.ai/
|
| 45 |
+
- **Repository:** [Needs More Information]
|
| 46 |
+
- **Paper:** [Needs More Information]
|
| 47 |
+
- **Leaderboard:** [Needs More Information]
|
| 48 |
+
- **Point of Contact:** [Needs More Information]
|
| 49 |
+
|
| 50 |
+
### Dataset Summary
|
| 51 |
+
|
| 52 |
+
The Notional-python dataset contains python code files from 100 well-known repositories gathered from Google Bigquery Github Dataset. The dataset was created to test the ability of programming language models.
|
| 53 |
+
Follow [our repo]() to do the model evaluation using notional-python dataset.
|
| 54 |
+
|
| 55 |
+
### Languages
|
| 56 |
+
|
| 57 |
+
Python
|
| 58 |
+
|
| 59 |
+
## Dataset Creation
|
| 60 |
+
|
| 61 |
+
### Curation Rationale
|
| 62 |
+
|
| 63 |
+
Notional-python was built to provide a dataset for testing the ability of the machine to generate python code.
|
| 64 |
+
|
| 65 |
+
### Source Data
|
| 66 |
+
|
| 67 |
+
#### Initial Data Collection and Normalization
|
| 68 |
+
|
| 69 |
+
The data was obtained by filtering code from [Google Bigquery Github data](https://cloud.google.com/blog/topics/public-datasets/github-on-bigquery-analyze-all-the-open-source-code)
|
| 70 |
+
In order to improve the quality of the dataset, only python code files that meet the below conditions are added to the dataset:
|
| 71 |
+
- Code with more than 60% of executable lines
|
| 72 |
+
- Code with logic, not config files or comment-only files
|
| 73 |
+
- Code with more than 30% of attribute declaration lines (E.G.: Some files contain just only class names and their class attributes, usually used for configuration of the project, these files were not selected)
|
| 74 |
+
- Code without `TODO` and `FIXME`.
|
| 75 |
+
|
| 76 |
+
#### Who are the source language producers?
|
| 77 |
+
|
| 78 |
+
The producers are users of github.
|
huggingface_dataset/Dataset_Card/pere_italian_tweets_10M.md
ADDED
|
@@ -0,0 +1,8 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
# Italian Tweets Test Dataset
|
| 2 |
+
This is a dataset with 10M italian tweets. It still contains errors. Please do not use.
|
| 3 |
+
|
| 4 |
+
## How to Use
|
| 5 |
+
```python
|
| 6 |
+
from datasets import load_dataset
|
| 7 |
+
data = load_dataset("pere/italian_tweets_10M")
|
| 8 |
+
```
|
huggingface_dataset/Dataset_Card/tapaco.md
ADDED
|
@@ -0,0 +1,1831 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
annotations_creators:
|
| 3 |
+
- machine-generated
|
| 4 |
+
language_creators:
|
| 5 |
+
- crowdsourced
|
| 6 |
+
language:
|
| 7 |
+
- af
|
| 8 |
+
- ar
|
| 9 |
+
- az
|
| 10 |
+
- be
|
| 11 |
+
- ber
|
| 12 |
+
- bg
|
| 13 |
+
- bn
|
| 14 |
+
- br
|
| 15 |
+
- ca
|
| 16 |
+
- cbk
|
| 17 |
+
- cmn
|
| 18 |
+
- cs
|
| 19 |
+
- da
|
| 20 |
+
- de
|
| 21 |
+
- el
|
| 22 |
+
- en
|
| 23 |
+
- eo
|
| 24 |
+
- es
|
| 25 |
+
- et
|
| 26 |
+
- eu
|
| 27 |
+
- fi
|
| 28 |
+
- fr
|
| 29 |
+
- gl
|
| 30 |
+
- gos
|
| 31 |
+
- he
|
| 32 |
+
- hi
|
| 33 |
+
- hr
|
| 34 |
+
- hu
|
| 35 |
+
- hy
|
| 36 |
+
- ia
|
| 37 |
+
- id
|
| 38 |
+
- ie
|
| 39 |
+
- io
|
| 40 |
+
- is
|
| 41 |
+
- it
|
| 42 |
+
- ja
|
| 43 |
+
- jbo
|
| 44 |
+
- kab
|
| 45 |
+
- ko
|
| 46 |
+
- kw
|
| 47 |
+
- la
|
| 48 |
+
- lfn
|
| 49 |
+
- lt
|
| 50 |
+
- mk
|
| 51 |
+
- mr
|
| 52 |
+
- nb
|
| 53 |
+
- nds
|
| 54 |
+
- nl
|
| 55 |
+
- orv
|
| 56 |
+
- ota
|
| 57 |
+
- pes
|
| 58 |
+
- pl
|
| 59 |
+
- pt
|
| 60 |
+
- rn
|
| 61 |
+
- ro
|
| 62 |
+
- ru
|
| 63 |
+
- sl
|
| 64 |
+
- sr
|
| 65 |
+
- sv
|
| 66 |
+
- tk
|
| 67 |
+
- tl
|
| 68 |
+
- tlh
|
| 69 |
+
- tok
|
| 70 |
+
- tr
|
| 71 |
+
- tt
|
| 72 |
+
- ug
|
| 73 |
+
- uk
|
| 74 |
+
- ur
|
| 75 |
+
- vi
|
| 76 |
+
- vo
|
| 77 |
+
- war
|
| 78 |
+
- wuu
|
| 79 |
+
- yue
|
| 80 |
+
license:
|
| 81 |
+
- cc-by-2.0
|
| 82 |
+
multilinguality:
|
| 83 |
+
- multilingual
|
| 84 |
+
size_categories:
|
| 85 |
+
- 100K<n<1M
|
| 86 |
+
- 10K<n<100K
|
| 87 |
+
- 1K<n<10K
|
| 88 |
+
- 1M<n<10M
|
| 89 |
+
- n<1K
|
| 90 |
+
source_datasets:
|
| 91 |
+
- extended|other-tatoeba
|
| 92 |
+
task_categories:
|
| 93 |
+
- text2text-generation
|
| 94 |
+
- translation
|
| 95 |
+
- text-classification
|
| 96 |
+
task_ids:
|
| 97 |
+
- semantic-similarity-classification
|
| 98 |
+
paperswithcode_id: tapaco
|
| 99 |
+
pretty_name: TaPaCo Corpus
|
| 100 |
+
configs:
|
| 101 |
+
- af
|
| 102 |
+
- all_languages
|
| 103 |
+
- ar
|
| 104 |
+
- az
|
| 105 |
+
- be
|
| 106 |
+
- ber
|
| 107 |
+
- bg
|
| 108 |
+
- bn
|
| 109 |
+
- br
|
| 110 |
+
- ca
|
| 111 |
+
- cbk
|
| 112 |
+
- cmn
|
| 113 |
+
- cs
|
| 114 |
+
- da
|
| 115 |
+
- de
|
| 116 |
+
- el
|
| 117 |
+
- en
|
| 118 |
+
- eo
|
| 119 |
+
- es
|
| 120 |
+
- et
|
| 121 |
+
- eu
|
| 122 |
+
- fi
|
| 123 |
+
- fr
|
| 124 |
+
- gl
|
| 125 |
+
- gos
|
| 126 |
+
- he
|
| 127 |
+
- hi
|
| 128 |
+
- hr
|
| 129 |
+
- hu
|
| 130 |
+
- hy
|
| 131 |
+
- ia
|
| 132 |
+
- id
|
| 133 |
+
- ie
|
| 134 |
+
- io
|
| 135 |
+
- is
|
| 136 |
+
- it
|
| 137 |
+
- ja
|
| 138 |
+
- jbo
|
| 139 |
+
- kab
|
| 140 |
+
- ko
|
| 141 |
+
- kw
|
| 142 |
+
- la
|
| 143 |
+
- lfn
|
| 144 |
+
- lt
|
| 145 |
+
- mk
|
| 146 |
+
- mr
|
| 147 |
+
- nb
|
| 148 |
+
- nds
|
| 149 |
+
- nl
|
| 150 |
+
- orv
|
| 151 |
+
- ota
|
| 152 |
+
- pes
|
| 153 |
+
- pl
|
| 154 |
+
- pt
|
| 155 |
+
- rn
|
| 156 |
+
- ro
|
| 157 |
+
- ru
|
| 158 |
+
- sl
|
| 159 |
+
- sr
|
| 160 |
+
- sv
|
| 161 |
+
- tk
|
| 162 |
+
- tl
|
| 163 |
+
- tlh
|
| 164 |
+
- tok
|
| 165 |
+
- tr
|
| 166 |
+
- tt
|
| 167 |
+
- ug
|
| 168 |
+
- uk
|
| 169 |
+
- ur
|
| 170 |
+
- vi
|
| 171 |
+
- vo
|
| 172 |
+
- war
|
| 173 |
+
- wuu
|
| 174 |
+
- yue
|
| 175 |
+
tags:
|
| 176 |
+
- paraphrase-generation
|
| 177 |
+
dataset_info:
|
| 178 |
+
- config_name: all_languages
|
| 179 |
+
features:
|
| 180 |
+
- name: paraphrase_set_id
|
| 181 |
+
dtype: string
|
| 182 |
+
- name: sentence_id
|
| 183 |
+
dtype: string
|
| 184 |
+
- name: paraphrase
|
| 185 |
+
dtype: string
|
| 186 |
+
- name: lists
|
| 187 |
+
sequence: string
|
| 188 |
+
- name: tags
|
| 189 |
+
sequence: string
|
| 190 |
+
- name: language
|
| 191 |
+
dtype: string
|
| 192 |
+
splits:
|
| 193 |
+
- name: train
|
| 194 |
+
num_bytes: 162802556
|
| 195 |
+
num_examples: 1926192
|
| 196 |
+
download_size: 32213126
|
| 197 |
+
dataset_size: 162802556
|
| 198 |
+
- config_name: af
|
| 199 |
+
features:
|
| 200 |
+
- name: paraphrase_set_id
|
| 201 |
+
dtype: string
|
| 202 |
+
- name: sentence_id
|
| 203 |
+
dtype: string
|
| 204 |
+
- name: paraphrase
|
| 205 |
+
dtype: string
|
| 206 |
+
- name: lists
|
| 207 |
+
sequence: string
|
| 208 |
+
- name: tags
|
| 209 |
+
sequence: string
|
| 210 |
+
- name: language
|
| 211 |
+
dtype: string
|
| 212 |
+
splits:
|
| 213 |
+
- name: train
|
| 214 |
+
num_bytes: 21219
|
| 215 |
+
num_examples: 307
|
| 216 |
+
download_size: 32213126
|
| 217 |
+
dataset_size: 21219
|
| 218 |
+
- config_name: ar
|
| 219 |
+
features:
|
| 220 |
+
- name: paraphrase_set_id
|
| 221 |
+
dtype: string
|
| 222 |
+
- name: sentence_id
|
| 223 |
+
dtype: string
|
| 224 |
+
- name: paraphrase
|
| 225 |
+
dtype: string
|
| 226 |
+
- name: lists
|
| 227 |
+
sequence: string
|
| 228 |
+
- name: tags
|
| 229 |
+
sequence: string
|
| 230 |
+
- name: language
|
| 231 |
+
dtype: string
|
| 232 |
+
splits:
|
| 233 |
+
- name: train
|
| 234 |
+
num_bytes: 546200
|
| 235 |
+
num_examples: 6446
|
| 236 |
+
download_size: 32213126
|
| 237 |
+
dataset_size: 546200
|
| 238 |
+
- config_name: az
|
| 239 |
+
features:
|
| 240 |
+
- name: paraphrase_set_id
|
| 241 |
+
dtype: string
|
| 242 |
+
- name: sentence_id
|
| 243 |
+
dtype: string
|
| 244 |
+
- name: paraphrase
|
| 245 |
+
dtype: string
|
| 246 |
+
- name: lists
|
| 247 |
+
sequence: string
|
| 248 |
+
- name: tags
|
| 249 |
+
sequence: string
|
| 250 |
+
- name: language
|
| 251 |
+
dtype: string
|
| 252 |
+
splits:
|
| 253 |
+
- name: train
|
| 254 |
+
num_bytes: 44461
|
| 255 |
+
num_examples: 624
|
| 256 |
+
download_size: 32213126
|
| 257 |
+
dataset_size: 44461
|
| 258 |
+
- config_name: be
|
| 259 |
+
features:
|
| 260 |
+
- name: paraphrase_set_id
|
| 261 |
+
dtype: string
|
| 262 |
+
- name: sentence_id
|
| 263 |
+
dtype: string
|
| 264 |
+
- name: paraphrase
|
| 265 |
+
dtype: string
|
| 266 |
+
- name: lists
|
| 267 |
+
sequence: string
|
| 268 |
+
- name: tags
|
| 269 |
+
sequence: string
|
| 270 |
+
- name: language
|
| 271 |
+
dtype: string
|
| 272 |
+
splits:
|
| 273 |
+
- name: train
|
| 274 |
+
num_bytes: 140376
|
| 275 |
+
num_examples: 1512
|
| 276 |
+
download_size: 32213126
|
| 277 |
+
dataset_size: 140376
|
| 278 |
+
- config_name: ber
|
| 279 |
+
features:
|
| 280 |
+
- name: paraphrase_set_id
|
| 281 |
+
dtype: string
|
| 282 |
+
- name: sentence_id
|
| 283 |
+
dtype: string
|
| 284 |
+
- name: paraphrase
|
| 285 |
+
dtype: string
|
| 286 |
+
- name: lists
|
| 287 |
+
sequence: string
|
| 288 |
+
- name: tags
|
| 289 |
+
sequence: string
|
| 290 |
+
- name: language
|
| 291 |
+
dtype: string
|
| 292 |
+
splits:
|
| 293 |
+
- name: train
|
| 294 |
+
num_bytes: 5118620
|
| 295 |
+
num_examples: 67484
|
| 296 |
+
download_size: 32213126
|
| 297 |
+
dataset_size: 5118620
|
| 298 |
+
- config_name: bg
|
| 299 |
+
features:
|
| 300 |
+
- name: paraphrase_set_id
|
| 301 |
+
dtype: string
|
| 302 |
+
- name: sentence_id
|
| 303 |
+
dtype: string
|
| 304 |
+
- name: paraphrase
|
| 305 |
+
dtype: string
|
| 306 |
+
- name: lists
|
| 307 |
+
sequence: string
|
| 308 |
+
- name: tags
|
| 309 |
+
sequence: string
|
| 310 |
+
- name: language
|
| 311 |
+
dtype: string
|
| 312 |
+
splits:
|
| 313 |
+
- name: train
|
| 314 |
+
num_bytes: 590535
|
| 315 |
+
num_examples: 6324
|
| 316 |
+
download_size: 32213126
|
| 317 |
+
dataset_size: 590535
|
| 318 |
+
- config_name: bn
|
| 319 |
+
features:
|
| 320 |
+
- name: paraphrase_set_id
|
| 321 |
+
dtype: string
|
| 322 |
+
- name: sentence_id
|
| 323 |
+
dtype: string
|
| 324 |
+
- name: paraphrase
|
| 325 |
+
dtype: string
|
| 326 |
+
- name: lists
|
| 327 |
+
sequence: string
|
| 328 |
+
- name: tags
|
| 329 |
+
sequence: string
|
| 330 |
+
- name: language
|
| 331 |
+
dtype: string
|
| 332 |
+
splits:
|
| 333 |
+
- name: train
|
| 334 |
+
num_bytes: 146654
|
| 335 |
+
num_examples: 1440
|
| 336 |
+
download_size: 32213126
|
| 337 |
+
dataset_size: 146654
|
| 338 |
+
- config_name: br
|
| 339 |
+
features:
|
| 340 |
+
- name: paraphrase_set_id
|
| 341 |
+
dtype: string
|
| 342 |
+
- name: sentence_id
|
| 343 |
+
dtype: string
|
| 344 |
+
- name: paraphrase
|
| 345 |
+
dtype: string
|
| 346 |
+
- name: lists
|
| 347 |
+
sequence: string
|
| 348 |
+
- name: tags
|
| 349 |
+
sequence: string
|
| 350 |
+
- name: language
|
| 351 |
+
dtype: string
|
| 352 |
+
splits:
|
| 353 |
+
- name: train
|
| 354 |
+
num_bytes: 177919
|
| 355 |
+
num_examples: 2536
|
| 356 |
+
download_size: 32213126
|
| 357 |
+
dataset_size: 177919
|
| 358 |
+
- config_name: ca
|
| 359 |
+
features:
|
| 360 |
+
- name: paraphrase_set_id
|
| 361 |
+
dtype: string
|
| 362 |
+
- name: sentence_id
|
| 363 |
+
dtype: string
|
| 364 |
+
- name: paraphrase
|
| 365 |
+
dtype: string
|
| 366 |
+
- name: lists
|
| 367 |
+
sequence: string
|
| 368 |
+
- name: tags
|
| 369 |
+
sequence: string
|
| 370 |
+
- name: language
|
| 371 |
+
dtype: string
|
| 372 |
+
splits:
|
| 373 |
+
- name: train
|
| 374 |
+
num_bytes: 39404
|
| 375 |
+
num_examples: 518
|
| 376 |
+
download_size: 32213126
|
| 377 |
+
dataset_size: 39404
|
| 378 |
+
- config_name: cbk
|
| 379 |
+
features:
|
| 380 |
+
- name: paraphrase_set_id
|
| 381 |
+
dtype: string
|
| 382 |
+
- name: sentence_id
|
| 383 |
+
dtype: string
|
| 384 |
+
- name: paraphrase
|
| 385 |
+
dtype: string
|
| 386 |
+
- name: lists
|
| 387 |
+
sequence: string
|
| 388 |
+
- name: tags
|
| 389 |
+
sequence: string
|
| 390 |
+
- name: language
|
| 391 |
+
dtype: string
|
| 392 |
+
splits:
|
| 393 |
+
- name: train
|
| 394 |
+
num_bytes: 19404
|
| 395 |
+
num_examples: 262
|
| 396 |
+
download_size: 32213126
|
| 397 |
+
dataset_size: 19404
|
| 398 |
+
- config_name: cmn
|
| 399 |
+
features:
|
| 400 |
+
- name: paraphrase_set_id
|
| 401 |
+
dtype: string
|
| 402 |
+
- name: sentence_id
|
| 403 |
+
dtype: string
|
| 404 |
+
- name: paraphrase
|
| 405 |
+
dtype: string
|
| 406 |
+
- name: lists
|
| 407 |
+
sequence: string
|
| 408 |
+
- name: tags
|
| 409 |
+
sequence: string
|
| 410 |
+
- name: language
|
| 411 |
+
dtype: string
|
| 412 |
+
splits:
|
| 413 |
+
- name: train
|
| 414 |
+
num_bytes: 964514
|
| 415 |
+
num_examples: 12549
|
| 416 |
+
download_size: 32213126
|
| 417 |
+
dataset_size: 964514
|
| 418 |
+
- config_name: cs
|
| 419 |
+
features:
|
| 420 |
+
- name: paraphrase_set_id
|
| 421 |
+
dtype: string
|
| 422 |
+
- name: sentence_id
|
| 423 |
+
dtype: string
|
| 424 |
+
- name: paraphrase
|
| 425 |
+
dtype: string
|
| 426 |
+
- name: lists
|
| 427 |
+
sequence: string
|
| 428 |
+
- name: tags
|
| 429 |
+
sequence: string
|
| 430 |
+
- name: language
|
| 431 |
+
dtype: string
|
| 432 |
+
splits:
|
| 433 |
+
- name: train
|
| 434 |
+
num_bytes: 482292
|
| 435 |
+
num_examples: 6659
|
| 436 |
+
download_size: 32213126
|
| 437 |
+
dataset_size: 482292
|
| 438 |
+
- config_name: da
|
| 439 |
+
features:
|
| 440 |
+
- name: paraphrase_set_id
|
| 441 |
+
dtype: string
|
| 442 |
+
- name: sentence_id
|
| 443 |
+
dtype: string
|
| 444 |
+
- name: paraphrase
|
| 445 |
+
dtype: string
|
| 446 |
+
- name: lists
|
| 447 |
+
sequence: string
|
| 448 |
+
- name: tags
|
| 449 |
+
sequence: string
|
| 450 |
+
- name: language
|
| 451 |
+
dtype: string
|
| 452 |
+
splits:
|
| 453 |
+
- name: train
|
| 454 |
+
num_bytes: 848886
|
| 455 |
+
num_examples: 11220
|
| 456 |
+
download_size: 32213126
|
| 457 |
+
dataset_size: 848886
|
| 458 |
+
- config_name: de
|
| 459 |
+
features:
|
| 460 |
+
- name: paraphrase_set_id
|
| 461 |
+
dtype: string
|
| 462 |
+
- name: sentence_id
|
| 463 |
+
dtype: string
|
| 464 |
+
- name: paraphrase
|
| 465 |
+
dtype: string
|
| 466 |
+
- name: lists
|
| 467 |
+
sequence: string
|
| 468 |
+
- name: tags
|
| 469 |
+
sequence: string
|
| 470 |
+
- name: language
|
| 471 |
+
dtype: string
|
| 472 |
+
splits:
|
| 473 |
+
- name: train
|
| 474 |
+
num_bytes: 10593377
|
| 475 |
+
num_examples: 125091
|
| 476 |
+
download_size: 32213126
|
| 477 |
+
dataset_size: 10593377
|
| 478 |
+
- config_name: el
|
| 479 |
+
features:
|
| 480 |
+
- name: paraphrase_set_id
|
| 481 |
+
dtype: string
|
| 482 |
+
- name: sentence_id
|
| 483 |
+
dtype: string
|
| 484 |
+
- name: paraphrase
|
| 485 |
+
dtype: string
|
| 486 |
+
- name: lists
|
| 487 |
+
sequence: string
|
| 488 |
+
- name: tags
|
| 489 |
+
sequence: string
|
| 490 |
+
- name: language
|
| 491 |
+
dtype: string
|
| 492 |
+
splits:
|
| 493 |
+
- name: train
|
| 494 |
+
num_bytes: 926054
|
| 495 |
+
num_examples: 10072
|
| 496 |
+
download_size: 32213126
|
| 497 |
+
dataset_size: 926054
|
| 498 |
+
- config_name: en
|
| 499 |
+
features:
|
| 500 |
+
- name: paraphrase_set_id
|
| 501 |
+
dtype: string
|
| 502 |
+
- name: sentence_id
|
| 503 |
+
dtype: string
|
| 504 |
+
- name: paraphrase
|
| 505 |
+
dtype: string
|
| 506 |
+
- name: lists
|
| 507 |
+
sequence: string
|
| 508 |
+
- name: tags
|
| 509 |
+
sequence: string
|
| 510 |
+
- name: language
|
| 511 |
+
dtype: string
|
| 512 |
+
splits:
|
| 513 |
+
- name: train
|
| 514 |
+
num_bytes: 15070349
|
| 515 |
+
num_examples: 158053
|
| 516 |
+
download_size: 32213126
|
| 517 |
+
dataset_size: 15070349
|
| 518 |
+
- config_name: eo
|
| 519 |
+
features:
|
| 520 |
+
- name: paraphrase_set_id
|
| 521 |
+
dtype: string
|
| 522 |
+
- name: sentence_id
|
| 523 |
+
dtype: string
|
| 524 |
+
- name: paraphrase
|
| 525 |
+
dtype: string
|
| 526 |
+
- name: lists
|
| 527 |
+
sequence: string
|
| 528 |
+
- name: tags
|
| 529 |
+
sequence: string
|
| 530 |
+
- name: language
|
| 531 |
+
dtype: string
|
| 532 |
+
splits:
|
| 533 |
+
- name: train
|
| 534 |
+
num_bytes: 16810965
|
| 535 |
+
num_examples: 207105
|
| 536 |
+
download_size: 32213126
|
| 537 |
+
dataset_size: 16810965
|
| 538 |
+
- config_name: es
|
| 539 |
+
features:
|
| 540 |
+
- name: paraphrase_set_id
|
| 541 |
+
dtype: string
|
| 542 |
+
- name: sentence_id
|
| 543 |
+
dtype: string
|
| 544 |
+
- name: paraphrase
|
| 545 |
+
dtype: string
|
| 546 |
+
- name: lists
|
| 547 |
+
sequence: string
|
| 548 |
+
- name: tags
|
| 549 |
+
sequence: string
|
| 550 |
+
- name: language
|
| 551 |
+
dtype: string
|
| 552 |
+
splits:
|
| 553 |
+
- name: train
|
| 554 |
+
num_bytes: 6851135
|
| 555 |
+
num_examples: 85064
|
| 556 |
+
download_size: 32213126
|
| 557 |
+
dataset_size: 6851135
|
| 558 |
+
- config_name: et
|
| 559 |
+
features:
|
| 560 |
+
- name: paraphrase_set_id
|
| 561 |
+
dtype: string
|
| 562 |
+
- name: sentence_id
|
| 563 |
+
dtype: string
|
| 564 |
+
- name: paraphrase
|
| 565 |
+
dtype: string
|
| 566 |
+
- name: lists
|
| 567 |
+
sequence: string
|
| 568 |
+
- name: tags
|
| 569 |
+
sequence: string
|
| 570 |
+
- name: language
|
| 571 |
+
dtype: string
|
| 572 |
+
splits:
|
| 573 |
+
- name: train
|
| 574 |
+
num_bytes: 17127
|
| 575 |
+
num_examples: 241
|
| 576 |
+
download_size: 32213126
|
| 577 |
+
dataset_size: 17127
|
| 578 |
+
- config_name: eu
|
| 579 |
+
features:
|
| 580 |
+
- name: paraphrase_set_id
|
| 581 |
+
dtype: string
|
| 582 |
+
- name: sentence_id
|
| 583 |
+
dtype: string
|
| 584 |
+
- name: paraphrase
|
| 585 |
+
dtype: string
|
| 586 |
+
- name: lists
|
| 587 |
+
sequence: string
|
| 588 |
+
- name: tags
|
| 589 |
+
sequence: string
|
| 590 |
+
- name: language
|
| 591 |
+
dtype: string
|
| 592 |
+
splits:
|
| 593 |
+
- name: train
|
| 594 |
+
num_bytes: 42702
|
| 595 |
+
num_examples: 573
|
| 596 |
+
download_size: 32213126
|
| 597 |
+
dataset_size: 42702
|
| 598 |
+
- config_name: fi
|
| 599 |
+
features:
|
| 600 |
+
- name: paraphrase_set_id
|
| 601 |
+
dtype: string
|
| 602 |
+
- name: sentence_id
|
| 603 |
+
dtype: string
|
| 604 |
+
- name: paraphrase
|
| 605 |
+
dtype: string
|
| 606 |
+
- name: lists
|
| 607 |
+
sequence: string
|
| 608 |
+
- name: tags
|
| 609 |
+
sequence: string
|
| 610 |
+
- name: language
|
| 611 |
+
dtype: string
|
| 612 |
+
splits:
|
| 613 |
+
- name: train
|
| 614 |
+
num_bytes: 2520167
|
| 615 |
+
num_examples: 31753
|
| 616 |
+
download_size: 32213126
|
| 617 |
+
dataset_size: 2520167
|
| 618 |
+
- config_name: fr
|
| 619 |
+
features:
|
| 620 |
+
- name: paraphrase_set_id
|
| 621 |
+
dtype: string
|
| 622 |
+
- name: sentence_id
|
| 623 |
+
dtype: string
|
| 624 |
+
- name: paraphrase
|
| 625 |
+
dtype: string
|
| 626 |
+
- name: lists
|
| 627 |
+
sequence: string
|
| 628 |
+
- name: tags
|
| 629 |
+
sequence: string
|
| 630 |
+
- name: language
|
| 631 |
+
dtype: string
|
| 632 |
+
splits:
|
| 633 |
+
- name: train
|
| 634 |
+
num_bytes: 9481426
|
| 635 |
+
num_examples: 116733
|
| 636 |
+
download_size: 32213126
|
| 637 |
+
dataset_size: 9481426
|
| 638 |
+
- config_name: gl
|
| 639 |
+
features:
|
| 640 |
+
- name: paraphrase_set_id
|
| 641 |
+
dtype: string
|
| 642 |
+
- name: sentence_id
|
| 643 |
+
dtype: string
|
| 644 |
+
- name: paraphrase
|
| 645 |
+
dtype: string
|
| 646 |
+
- name: lists
|
| 647 |
+
sequence: string
|
| 648 |
+
- name: tags
|
| 649 |
+
sequence: string
|
| 650 |
+
- name: language
|
| 651 |
+
dtype: string
|
| 652 |
+
splits:
|
| 653 |
+
- name: train
|
| 654 |
+
num_bytes: 26551
|
| 655 |
+
num_examples: 351
|
| 656 |
+
download_size: 32213126
|
| 657 |
+
dataset_size: 26551
|
| 658 |
+
- config_name: gos
|
| 659 |
+
features:
|
| 660 |
+
- name: paraphrase_set_id
|
| 661 |
+
dtype: string
|
| 662 |
+
- name: sentence_id
|
| 663 |
+
dtype: string
|
| 664 |
+
- name: paraphrase
|
| 665 |
+
dtype: string
|
| 666 |
+
- name: lists
|
| 667 |
+
sequence: string
|
| 668 |
+
- name: tags
|
| 669 |
+
sequence: string
|
| 670 |
+
- name: language
|
| 671 |
+
dtype: string
|
| 672 |
+
splits:
|
| 673 |
+
- name: train
|
| 674 |
+
num_bytes: 18442
|
| 675 |
+
num_examples: 279
|
| 676 |
+
download_size: 32213126
|
| 677 |
+
dataset_size: 18442
|
| 678 |
+
- config_name: he
|
| 679 |
+
features:
|
| 680 |
+
- name: paraphrase_set_id
|
| 681 |
+
dtype: string
|
| 682 |
+
- name: sentence_id
|
| 683 |
+
dtype: string
|
| 684 |
+
- name: paraphrase
|
| 685 |
+
dtype: string
|
| 686 |
+
- name: lists
|
| 687 |
+
sequence: string
|
| 688 |
+
- name: tags
|
| 689 |
+
sequence: string
|
| 690 |
+
- name: language
|
| 691 |
+
dtype: string
|
| 692 |
+
splits:
|
| 693 |
+
- name: train
|
| 694 |
+
num_bytes: 6024345
|
| 695 |
+
num_examples: 68350
|
| 696 |
+
download_size: 32213126
|
| 697 |
+
dataset_size: 6024345
|
| 698 |
+
- config_name: hi
|
| 699 |
+
features:
|
| 700 |
+
- name: paraphrase_set_id
|
| 701 |
+
dtype: string
|
| 702 |
+
- name: sentence_id
|
| 703 |
+
dtype: string
|
| 704 |
+
- name: paraphrase
|
| 705 |
+
dtype: string
|
| 706 |
+
- name: lists
|
| 707 |
+
sequence: string
|
| 708 |
+
- name: tags
|
| 709 |
+
sequence: string
|
| 710 |
+
- name: language
|
| 711 |
+
dtype: string
|
| 712 |
+
splits:
|
| 713 |
+
- name: train
|
| 714 |
+
num_bytes: 209382
|
| 715 |
+
num_examples: 1913
|
| 716 |
+
download_size: 32213126
|
| 717 |
+
dataset_size: 209382
|
| 718 |
+
- config_name: hr
|
| 719 |
+
features:
|
| 720 |
+
- name: paraphrase_set_id
|
| 721 |
+
dtype: string
|
| 722 |
+
- name: sentence_id
|
| 723 |
+
dtype: string
|
| 724 |
+
- name: paraphrase
|
| 725 |
+
dtype: string
|
| 726 |
+
- name: lists
|
| 727 |
+
sequence: string
|
| 728 |
+
- name: tags
|
| 729 |
+
sequence: string
|
| 730 |
+
- name: language
|
| 731 |
+
dtype: string
|
| 732 |
+
splits:
|
| 733 |
+
- name: train
|
| 734 |
+
num_bytes: 36638
|
| 735 |
+
num_examples: 505
|
| 736 |
+
download_size: 32213126
|
| 737 |
+
dataset_size: 36638
|
| 738 |
+
- config_name: hu
|
| 739 |
+
features:
|
| 740 |
+
- name: paraphrase_set_id
|
| 741 |
+
dtype: string
|
| 742 |
+
- name: sentence_id
|
| 743 |
+
dtype: string
|
| 744 |
+
- name: paraphrase
|
| 745 |
+
dtype: string
|
| 746 |
+
- name: lists
|
| 747 |
+
sequence: string
|
| 748 |
+
- name: tags
|
| 749 |
+
sequence: string
|
| 750 |
+
- name: language
|
| 751 |
+
dtype: string
|
| 752 |
+
splits:
|
| 753 |
+
- name: train
|
| 754 |
+
num_bytes: 5289610
|
| 755 |
+
num_examples: 67964
|
| 756 |
+
download_size: 32213126
|
| 757 |
+
dataset_size: 5289610
|
| 758 |
+
- config_name: hy
|
| 759 |
+
features:
|
| 760 |
+
- name: paraphrase_set_id
|
| 761 |
+
dtype: string
|
| 762 |
+
- name: sentence_id
|
| 763 |
+
dtype: string
|
| 764 |
+
- name: paraphrase
|
| 765 |
+
dtype: string
|
| 766 |
+
- name: lists
|
| 767 |
+
sequence: string
|
| 768 |
+
- name: tags
|
| 769 |
+
sequence: string
|
| 770 |
+
- name: language
|
| 771 |
+
dtype: string
|
| 772 |
+
splits:
|
| 773 |
+
- name: train
|
| 774 |
+
num_bytes: 49230
|
| 775 |
+
num_examples: 603
|
| 776 |
+
download_size: 32213126
|
| 777 |
+
dataset_size: 49230
|
| 778 |
+
- config_name: ia
|
| 779 |
+
features:
|
| 780 |
+
- name: paraphrase_set_id
|
| 781 |
+
dtype: string
|
| 782 |
+
- name: sentence_id
|
| 783 |
+
dtype: string
|
| 784 |
+
- name: paraphrase
|
| 785 |
+
dtype: string
|
| 786 |
+
- name: lists
|
| 787 |
+
sequence: string
|
| 788 |
+
- name: tags
|
| 789 |
+
sequence: string
|
| 790 |
+
- name: language
|
| 791 |
+
dtype: string
|
| 792 |
+
splits:
|
| 793 |
+
- name: train
|
| 794 |
+
num_bytes: 194035
|
| 795 |
+
num_examples: 2548
|
| 796 |
+
download_size: 32213126
|
| 797 |
+
dataset_size: 194035
|
| 798 |
+
- config_name: id
|
| 799 |
+
features:
|
| 800 |
+
- name: paraphrase_set_id
|
| 801 |
+
dtype: string
|
| 802 |
+
- name: sentence_id
|
| 803 |
+
dtype: string
|
| 804 |
+
- name: paraphrase
|
| 805 |
+
dtype: string
|
| 806 |
+
- name: lists
|
| 807 |
+
sequence: string
|
| 808 |
+
- name: tags
|
| 809 |
+
sequence: string
|
| 810 |
+
- name: language
|
| 811 |
+
dtype: string
|
| 812 |
+
splits:
|
| 813 |
+
- name: train
|
| 814 |
+
num_bytes: 124568
|
| 815 |
+
num_examples: 1602
|
| 816 |
+
download_size: 32213126
|
| 817 |
+
dataset_size: 124568
|
| 818 |
+
- config_name: ie
|
| 819 |
+
features:
|
| 820 |
+
- name: paraphrase_set_id
|
| 821 |
+
dtype: string
|
| 822 |
+
- name: sentence_id
|
| 823 |
+
dtype: string
|
| 824 |
+
- name: paraphrase
|
| 825 |
+
dtype: string
|
| 826 |
+
- name: lists
|
| 827 |
+
sequence: string
|
| 828 |
+
- name: tags
|
| 829 |
+
sequence: string
|
| 830 |
+
- name: language
|
| 831 |
+
dtype: string
|
| 832 |
+
splits:
|
| 833 |
+
- name: train
|
| 834 |
+
num_bytes: 31956
|
| 835 |
+
num_examples: 488
|
| 836 |
+
download_size: 32213126
|
| 837 |
+
dataset_size: 31956
|
| 838 |
+
- config_name: io
|
| 839 |
+
features:
|
| 840 |
+
- name: paraphrase_set_id
|
| 841 |
+
dtype: string
|
| 842 |
+
- name: sentence_id
|
| 843 |
+
dtype: string
|
| 844 |
+
- name: paraphrase
|
| 845 |
+
dtype: string
|
| 846 |
+
- name: lists
|
| 847 |
+
sequence: string
|
| 848 |
+
- name: tags
|
| 849 |
+
sequence: string
|
| 850 |
+
- name: language
|
| 851 |
+
dtype: string
|
| 852 |
+
splits:
|
| 853 |
+
- name: train
|
| 854 |
+
num_bytes: 33892
|
| 855 |
+
num_examples: 480
|
| 856 |
+
download_size: 32213126
|
| 857 |
+
dataset_size: 33892
|
| 858 |
+
- config_name: is
|
| 859 |
+
features:
|
| 860 |
+
- name: paraphrase_set_id
|
| 861 |
+
dtype: string
|
| 862 |
+
- name: sentence_id
|
| 863 |
+
dtype: string
|
| 864 |
+
- name: paraphrase
|
| 865 |
+
dtype: string
|
| 866 |
+
- name: lists
|
| 867 |
+
sequence: string
|
| 868 |
+
- name: tags
|
| 869 |
+
sequence: string
|
| 870 |
+
- name: language
|
| 871 |
+
dtype: string
|
| 872 |
+
splits:
|
| 873 |
+
- name: train
|
| 874 |
+
num_bytes: 132062
|
| 875 |
+
num_examples: 1641
|
| 876 |
+
download_size: 32213126
|
| 877 |
+
dataset_size: 132062
|
| 878 |
+
- config_name: it
|
| 879 |
+
features:
|
| 880 |
+
- name: paraphrase_set_id
|
| 881 |
+
dtype: string
|
| 882 |
+
- name: sentence_id
|
| 883 |
+
dtype: string
|
| 884 |
+
- name: paraphrase
|
| 885 |
+
dtype: string
|
| 886 |
+
- name: lists
|
| 887 |
+
sequence: string
|
| 888 |
+
- name: tags
|
| 889 |
+
sequence: string
|
| 890 |
+
- name: language
|
| 891 |
+
dtype: string
|
| 892 |
+
splits:
|
| 893 |
+
- name: train
|
| 894 |
+
num_bytes: 15073750
|
| 895 |
+
num_examples: 198919
|
| 896 |
+
download_size: 32213126
|
| 897 |
+
dataset_size: 15073750
|
| 898 |
+
- config_name: ja
|
| 899 |
+
features:
|
| 900 |
+
- name: paraphrase_set_id
|
| 901 |
+
dtype: string
|
| 902 |
+
- name: sentence_id
|
| 903 |
+
dtype: string
|
| 904 |
+
- name: paraphrase
|
| 905 |
+
dtype: string
|
| 906 |
+
- name: lists
|
| 907 |
+
sequence: string
|
| 908 |
+
- name: tags
|
| 909 |
+
sequence: string
|
| 910 |
+
- name: language
|
| 911 |
+
dtype: string
|
| 912 |
+
splits:
|
| 913 |
+
- name: train
|
| 914 |
+
num_bytes: 4314423
|
| 915 |
+
num_examples: 44267
|
| 916 |
+
download_size: 32213126
|
| 917 |
+
dataset_size: 4314423
|
| 918 |
+
- config_name: jbo
|
| 919 |
+
features:
|
| 920 |
+
- name: paraphrase_set_id
|
| 921 |
+
dtype: string
|
| 922 |
+
- name: sentence_id
|
| 923 |
+
dtype: string
|
| 924 |
+
- name: paraphrase
|
| 925 |
+
dtype: string
|
| 926 |
+
- name: lists
|
| 927 |
+
sequence: string
|
| 928 |
+
- name: tags
|
| 929 |
+
sequence: string
|
| 930 |
+
- name: language
|
| 931 |
+
dtype: string
|
| 932 |
+
splits:
|
| 933 |
+
- name: train
|
| 934 |
+
num_bytes: 201564
|
| 935 |
+
num_examples: 2704
|
| 936 |
+
download_size: 32213126
|
| 937 |
+
dataset_size: 201564
|
| 938 |
+
- config_name: kab
|
| 939 |
+
features:
|
| 940 |
+
- name: paraphrase_set_id
|
| 941 |
+
dtype: string
|
| 942 |
+
- name: sentence_id
|
| 943 |
+
dtype: string
|
| 944 |
+
- name: paraphrase
|
| 945 |
+
dtype: string
|
| 946 |
+
- name: lists
|
| 947 |
+
sequence: string
|
| 948 |
+
- name: tags
|
| 949 |
+
sequence: string
|
| 950 |
+
- name: language
|
| 951 |
+
dtype: string
|
| 952 |
+
splits:
|
| 953 |
+
- name: train
|
| 954 |
+
num_bytes: 1211051
|
| 955 |
+
num_examples: 15944
|
| 956 |
+
download_size: 32213126
|
| 957 |
+
dataset_size: 1211051
|
| 958 |
+
- config_name: ko
|
| 959 |
+
features:
|
| 960 |
+
- name: paraphrase_set_id
|
| 961 |
+
dtype: string
|
| 962 |
+
- name: sentence_id
|
| 963 |
+
dtype: string
|
| 964 |
+
- name: paraphrase
|
| 965 |
+
dtype: string
|
| 966 |
+
- name: lists
|
| 967 |
+
sequence: string
|
| 968 |
+
- name: tags
|
| 969 |
+
sequence: string
|
| 970 |
+
- name: language
|
| 971 |
+
dtype: string
|
| 972 |
+
splits:
|
| 973 |
+
- name: train
|
| 974 |
+
num_bytes: 40458
|
| 975 |
+
num_examples: 503
|
| 976 |
+
download_size: 32213126
|
| 977 |
+
dataset_size: 40458
|
| 978 |
+
- config_name: kw
|
| 979 |
+
features:
|
| 980 |
+
- name: paraphrase_set_id
|
| 981 |
+
dtype: string
|
| 982 |
+
- name: sentence_id
|
| 983 |
+
dtype: string
|
| 984 |
+
- name: paraphrase
|
| 985 |
+
dtype: string
|
| 986 |
+
- name: lists
|
| 987 |
+
sequence: string
|
| 988 |
+
- name: tags
|
| 989 |
+
sequence: string
|
| 990 |
+
- name: language
|
| 991 |
+
dtype: string
|
| 992 |
+
splits:
|
| 993 |
+
- name: train
|
| 994 |
+
num_bytes: 88577
|
| 995 |
+
num_examples: 1328
|
| 996 |
+
download_size: 32213126
|
| 997 |
+
dataset_size: 88577
|
| 998 |
+
- config_name: la
|
| 999 |
+
features:
|
| 1000 |
+
- name: paraphrase_set_id
|
| 1001 |
+
dtype: string
|
| 1002 |
+
- name: sentence_id
|
| 1003 |
+
dtype: string
|
| 1004 |
+
- name: paraphrase
|
| 1005 |
+
dtype: string
|
| 1006 |
+
- name: lists
|
| 1007 |
+
sequence: string
|
| 1008 |
+
- name: tags
|
| 1009 |
+
sequence: string
|
| 1010 |
+
- name: language
|
| 1011 |
+
dtype: string
|
| 1012 |
+
splits:
|
| 1013 |
+
- name: train
|
| 1014 |
+
num_bytes: 485749
|
| 1015 |
+
num_examples: 6889
|
| 1016 |
+
download_size: 32213126
|
| 1017 |
+
dataset_size: 485749
|
| 1018 |
+
- config_name: lfn
|
| 1019 |
+
features:
|
| 1020 |
+
- name: paraphrase_set_id
|
| 1021 |
+
dtype: string
|
| 1022 |
+
- name: sentence_id
|
| 1023 |
+
dtype: string
|
| 1024 |
+
- name: paraphrase
|
| 1025 |
+
dtype: string
|
| 1026 |
+
- name: lists
|
| 1027 |
+
sequence: string
|
| 1028 |
+
- name: tags
|
| 1029 |
+
sequence: string
|
| 1030 |
+
- name: language
|
| 1031 |
+
dtype: string
|
| 1032 |
+
splits:
|
| 1033 |
+
- name: train
|
| 1034 |
+
num_bytes: 203383
|
| 1035 |
+
num_examples: 2313
|
| 1036 |
+
download_size: 32213126
|
| 1037 |
+
dataset_size: 203383
|
| 1038 |
+
- config_name: lt
|
| 1039 |
+
features:
|
| 1040 |
+
- name: paraphrase_set_id
|
| 1041 |
+
dtype: string
|
| 1042 |
+
- name: sentence_id
|
| 1043 |
+
dtype: string
|
| 1044 |
+
- name: paraphrase
|
| 1045 |
+
dtype: string
|
| 1046 |
+
- name: lists
|
| 1047 |
+
sequence: string
|
| 1048 |
+
- name: tags
|
| 1049 |
+
sequence: string
|
| 1050 |
+
- name: language
|
| 1051 |
+
dtype: string
|
| 1052 |
+
splits:
|
| 1053 |
+
- name: train
|
| 1054 |
+
num_bytes: 599166
|
| 1055 |
+
num_examples: 8042
|
| 1056 |
+
download_size: 32213126
|
| 1057 |
+
dataset_size: 599166
|
| 1058 |
+
- config_name: mk
|
| 1059 |
+
features:
|
| 1060 |
+
- name: paraphrase_set_id
|
| 1061 |
+
dtype: string
|
| 1062 |
+
- name: sentence_id
|
| 1063 |
+
dtype: string
|
| 1064 |
+
- name: paraphrase
|
| 1065 |
+
dtype: string
|
| 1066 |
+
- name: lists
|
| 1067 |
+
sequence: string
|
| 1068 |
+
- name: tags
|
| 1069 |
+
sequence: string
|
| 1070 |
+
- name: language
|
| 1071 |
+
dtype: string
|
| 1072 |
+
splits:
|
| 1073 |
+
- name: train
|
| 1074 |
+
num_bytes: 1240185
|
| 1075 |
+
num_examples: 14678
|
| 1076 |
+
download_size: 32213126
|
| 1077 |
+
dataset_size: 1240185
|
| 1078 |
+
- config_name: mr
|
| 1079 |
+
features:
|
| 1080 |
+
- name: paraphrase_set_id
|
| 1081 |
+
dtype: string
|
| 1082 |
+
- name: sentence_id
|
| 1083 |
+
dtype: string
|
| 1084 |
+
- name: paraphrase
|
| 1085 |
+
dtype: string
|
| 1086 |
+
- name: lists
|
| 1087 |
+
sequence: string
|
| 1088 |
+
- name: tags
|
| 1089 |
+
sequence: string
|
| 1090 |
+
- name: language
|
| 1091 |
+
dtype: string
|
| 1092 |
+
splits:
|
| 1093 |
+
- name: train
|
| 1094 |
+
num_bytes: 1838921
|
| 1095 |
+
num_examples: 16413
|
| 1096 |
+
download_size: 32213126
|
| 1097 |
+
dataset_size: 1838921
|
| 1098 |
+
- config_name: nb
|
| 1099 |
+
features:
|
| 1100 |
+
- name: paraphrase_set_id
|
| 1101 |
+
dtype: string
|
| 1102 |
+
- name: sentence_id
|
| 1103 |
+
dtype: string
|
| 1104 |
+
- name: paraphrase
|
| 1105 |
+
dtype: string
|
| 1106 |
+
- name: lists
|
| 1107 |
+
sequence: string
|
| 1108 |
+
- name: tags
|
| 1109 |
+
sequence: string
|
| 1110 |
+
- name: language
|
| 1111 |
+
dtype: string
|
| 1112 |
+
splits:
|
| 1113 |
+
- name: train
|
| 1114 |
+
num_bytes: 85371
|
| 1115 |
+
num_examples: 1094
|
| 1116 |
+
download_size: 32213126
|
| 1117 |
+
dataset_size: 85371
|
| 1118 |
+
- config_name: nds
|
| 1119 |
+
features:
|
| 1120 |
+
- name: paraphrase_set_id
|
| 1121 |
+
dtype: string
|
| 1122 |
+
- name: sentence_id
|
| 1123 |
+
dtype: string
|
| 1124 |
+
- name: paraphrase
|
| 1125 |
+
dtype: string
|
| 1126 |
+
- name: lists
|
| 1127 |
+
sequence: string
|
| 1128 |
+
- name: tags
|
| 1129 |
+
sequence: string
|
| 1130 |
+
- name: language
|
| 1131 |
+
dtype: string
|
| 1132 |
+
splits:
|
| 1133 |
+
- name: train
|
| 1134 |
+
num_bytes: 195021
|
| 1135 |
+
num_examples: 2633
|
| 1136 |
+
download_size: 32213126
|
| 1137 |
+
dataset_size: 195021
|
| 1138 |
+
- config_name: nl
|
| 1139 |
+
features:
|
| 1140 |
+
- name: paraphrase_set_id
|
| 1141 |
+
dtype: string
|
| 1142 |
+
- name: sentence_id
|
| 1143 |
+
dtype: string
|
| 1144 |
+
- name: paraphrase
|
| 1145 |
+
dtype: string
|
| 1146 |
+
- name: lists
|
| 1147 |
+
sequence: string
|
| 1148 |
+
- name: tags
|
| 1149 |
+
sequence: string
|
| 1150 |
+
- name: language
|
| 1151 |
+
dtype: string
|
| 1152 |
+
splits:
|
| 1153 |
+
- name: train
|
| 1154 |
+
num_bytes: 1790975
|
| 1155 |
+
num_examples: 23561
|
| 1156 |
+
download_size: 32213126
|
| 1157 |
+
dataset_size: 1790975
|
| 1158 |
+
- config_name: orv
|
| 1159 |
+
features:
|
| 1160 |
+
- name: paraphrase_set_id
|
| 1161 |
+
dtype: string
|
| 1162 |
+
- name: sentence_id
|
| 1163 |
+
dtype: string
|
| 1164 |
+
- name: paraphrase
|
| 1165 |
+
dtype: string
|
| 1166 |
+
- name: lists
|
| 1167 |
+
sequence: string
|
| 1168 |
+
- name: tags
|
| 1169 |
+
sequence: string
|
| 1170 |
+
- name: language
|
| 1171 |
+
dtype: string
|
| 1172 |
+
splits:
|
| 1173 |
+
- name: train
|
| 1174 |
+
num_bytes: 40484
|
| 1175 |
+
num_examples: 471
|
| 1176 |
+
download_size: 32213126
|
| 1177 |
+
dataset_size: 40484
|
| 1178 |
+
- config_name: ota
|
| 1179 |
+
features:
|
| 1180 |
+
- name: paraphrase_set_id
|
| 1181 |
+
dtype: string
|
| 1182 |
+
- name: sentence_id
|
| 1183 |
+
dtype: string
|
| 1184 |
+
- name: paraphrase
|
| 1185 |
+
dtype: string
|
| 1186 |
+
- name: lists
|
| 1187 |
+
sequence: string
|
| 1188 |
+
- name: tags
|
| 1189 |
+
sequence: string
|
| 1190 |
+
- name: language
|
| 1191 |
+
dtype: string
|
| 1192 |
+
splits:
|
| 1193 |
+
- name: train
|
| 1194 |
+
num_bytes: 44996
|
| 1195 |
+
num_examples: 486
|
| 1196 |
+
download_size: 32213126
|
| 1197 |
+
dataset_size: 44996
|
| 1198 |
+
- config_name: pes
|
| 1199 |
+
features:
|
| 1200 |
+
- name: paraphrase_set_id
|
| 1201 |
+
dtype: string
|
| 1202 |
+
- name: sentence_id
|
| 1203 |
+
dtype: string
|
| 1204 |
+
- name: paraphrase
|
| 1205 |
+
dtype: string
|
| 1206 |
+
- name: lists
|
| 1207 |
+
sequence: string
|
| 1208 |
+
- name: tags
|
| 1209 |
+
sequence: string
|
| 1210 |
+
- name: language
|
| 1211 |
+
dtype: string
|
| 1212 |
+
splits:
|
| 1213 |
+
- name: train
|
| 1214 |
+
num_bytes: 433406
|
| 1215 |
+
num_examples: 4285
|
| 1216 |
+
download_size: 32213126
|
| 1217 |
+
dataset_size: 433406
|
| 1218 |
+
- config_name: pl
|
| 1219 |
+
features:
|
| 1220 |
+
- name: paraphrase_set_id
|
| 1221 |
+
dtype: string
|
| 1222 |
+
- name: sentence_id
|
| 1223 |
+
dtype: string
|
| 1224 |
+
- name: paraphrase
|
| 1225 |
+
dtype: string
|
| 1226 |
+
- name: lists
|
| 1227 |
+
sequence: string
|
| 1228 |
+
- name: tags
|
| 1229 |
+
sequence: string
|
| 1230 |
+
- name: language
|
| 1231 |
+
dtype: string
|
| 1232 |
+
splits:
|
| 1233 |
+
- name: train
|
| 1234 |
+
num_bytes: 1722188
|
| 1235 |
+
num_examples: 22391
|
| 1236 |
+
download_size: 32213126
|
| 1237 |
+
dataset_size: 1722188
|
| 1238 |
+
- config_name: pt
|
| 1239 |
+
features:
|
| 1240 |
+
- name: paraphrase_set_id
|
| 1241 |
+
dtype: string
|
| 1242 |
+
- name: sentence_id
|
| 1243 |
+
dtype: string
|
| 1244 |
+
- name: paraphrase
|
| 1245 |
+
dtype: string
|
| 1246 |
+
- name: lists
|
| 1247 |
+
sequence: string
|
| 1248 |
+
- name: tags
|
| 1249 |
+
sequence: string
|
| 1250 |
+
- name: language
|
| 1251 |
+
dtype: string
|
| 1252 |
+
splits:
|
| 1253 |
+
- name: train
|
| 1254 |
+
num_bytes: 6141178
|
| 1255 |
+
num_examples: 78430
|
| 1256 |
+
download_size: 32213126
|
| 1257 |
+
dataset_size: 6141178
|
| 1258 |
+
- config_name: rn
|
| 1259 |
+
features:
|
| 1260 |
+
- name: paraphrase_set_id
|
| 1261 |
+
dtype: string
|
| 1262 |
+
- name: sentence_id
|
| 1263 |
+
dtype: string
|
| 1264 |
+
- name: paraphrase
|
| 1265 |
+
dtype: string
|
| 1266 |
+
- name: lists
|
| 1267 |
+
sequence: string
|
| 1268 |
+
- name: tags
|
| 1269 |
+
sequence: string
|
| 1270 |
+
- name: language
|
| 1271 |
+
dtype: string
|
| 1272 |
+
splits:
|
| 1273 |
+
- name: train
|
| 1274 |
+
num_bytes: 47387
|
| 1275 |
+
num_examples: 648
|
| 1276 |
+
download_size: 32213126
|
| 1277 |
+
dataset_size: 47387
|
| 1278 |
+
- config_name: ro
|
| 1279 |
+
features:
|
| 1280 |
+
- name: paraphrase_set_id
|
| 1281 |
+
dtype: string
|
| 1282 |
+
- name: sentence_id
|
| 1283 |
+
dtype: string
|
| 1284 |
+
- name: paraphrase
|
| 1285 |
+
dtype: string
|
| 1286 |
+
- name: lists
|
| 1287 |
+
sequence: string
|
| 1288 |
+
- name: tags
|
| 1289 |
+
sequence: string
|
| 1290 |
+
- name: language
|
| 1291 |
+
dtype: string
|
| 1292 |
+
splits:
|
| 1293 |
+
- name: train
|
| 1294 |
+
num_bytes: 162955
|
| 1295 |
+
num_examples: 2092
|
| 1296 |
+
download_size: 32213126
|
| 1297 |
+
dataset_size: 162955
|
| 1298 |
+
- config_name: ru
|
| 1299 |
+
features:
|
| 1300 |
+
- name: paraphrase_set_id
|
| 1301 |
+
dtype: string
|
| 1302 |
+
- name: sentence_id
|
| 1303 |
+
dtype: string
|
| 1304 |
+
- name: paraphrase
|
| 1305 |
+
dtype: string
|
| 1306 |
+
- name: lists
|
| 1307 |
+
sequence: string
|
| 1308 |
+
- name: tags
|
| 1309 |
+
sequence: string
|
| 1310 |
+
- name: language
|
| 1311 |
+
dtype: string
|
| 1312 |
+
splits:
|
| 1313 |
+
- name: train
|
| 1314 |
+
num_bytes: 24540667
|
| 1315 |
+
num_examples: 251263
|
| 1316 |
+
download_size: 32213126
|
| 1317 |
+
dataset_size: 24540667
|
| 1318 |
+
- config_name: sl
|
| 1319 |
+
features:
|
| 1320 |
+
- name: paraphrase_set_id
|
| 1321 |
+
dtype: string
|
| 1322 |
+
- name: sentence_id
|
| 1323 |
+
dtype: string
|
| 1324 |
+
- name: paraphrase
|
| 1325 |
+
dtype: string
|
| 1326 |
+
- name: lists
|
| 1327 |
+
sequence: string
|
| 1328 |
+
- name: tags
|
| 1329 |
+
sequence: string
|
| 1330 |
+
- name: language
|
| 1331 |
+
dtype: string
|
| 1332 |
+
splits:
|
| 1333 |
+
- name: train
|
| 1334 |
+
num_bytes: 49610
|
| 1335 |
+
num_examples: 706
|
| 1336 |
+
download_size: 32213126
|
| 1337 |
+
dataset_size: 49610
|
| 1338 |
+
- config_name: sr
|
| 1339 |
+
features:
|
| 1340 |
+
- name: paraphrase_set_id
|
| 1341 |
+
dtype: string
|
| 1342 |
+
- name: sentence_id
|
| 1343 |
+
dtype: string
|
| 1344 |
+
- name: paraphrase
|
| 1345 |
+
dtype: string
|
| 1346 |
+
- name: lists
|
| 1347 |
+
sequence: string
|
| 1348 |
+
- name: tags
|
| 1349 |
+
sequence: string
|
| 1350 |
+
- name: language
|
| 1351 |
+
dtype: string
|
| 1352 |
+
splits:
|
| 1353 |
+
- name: train
|
| 1354 |
+
num_bytes: 667308
|
| 1355 |
+
num_examples: 8175
|
| 1356 |
+
download_size: 32213126
|
| 1357 |
+
dataset_size: 667308
|
| 1358 |
+
- config_name: sv
|
| 1359 |
+
features:
|
| 1360 |
+
- name: paraphrase_set_id
|
| 1361 |
+
dtype: string
|
| 1362 |
+
- name: sentence_id
|
| 1363 |
+
dtype: string
|
| 1364 |
+
- name: paraphrase
|
| 1365 |
+
dtype: string
|
| 1366 |
+
- name: lists
|
| 1367 |
+
sequence: string
|
| 1368 |
+
- name: tags
|
| 1369 |
+
sequence: string
|
| 1370 |
+
- name: language
|
| 1371 |
+
dtype: string
|
| 1372 |
+
splits:
|
| 1373 |
+
- name: train
|
| 1374 |
+
num_bytes: 509884
|
| 1375 |
+
num_examples: 7005
|
| 1376 |
+
download_size: 32213126
|
| 1377 |
+
dataset_size: 509884
|
| 1378 |
+
- config_name: tk
|
| 1379 |
+
features:
|
| 1380 |
+
- name: paraphrase_set_id
|
| 1381 |
+
dtype: string
|
| 1382 |
+
- name: sentence_id
|
| 1383 |
+
dtype: string
|
| 1384 |
+
- name: paraphrase
|
| 1385 |
+
dtype: string
|
| 1386 |
+
- name: lists
|
| 1387 |
+
sequence: string
|
| 1388 |
+
- name: tags
|
| 1389 |
+
sequence: string
|
| 1390 |
+
- name: language
|
| 1391 |
+
dtype: string
|
| 1392 |
+
splits:
|
| 1393 |
+
- name: train
|
| 1394 |
+
num_bytes: 95047
|
| 1395 |
+
num_examples: 1165
|
| 1396 |
+
download_size: 32213126
|
| 1397 |
+
dataset_size: 95047
|
| 1398 |
+
- config_name: tl
|
| 1399 |
+
features:
|
| 1400 |
+
- name: paraphrase_set_id
|
| 1401 |
+
dtype: string
|
| 1402 |
+
- name: sentence_id
|
| 1403 |
+
dtype: string
|
| 1404 |
+
- name: paraphrase
|
| 1405 |
+
dtype: string
|
| 1406 |
+
- name: lists
|
| 1407 |
+
sequence: string
|
| 1408 |
+
- name: tags
|
| 1409 |
+
sequence: string
|
| 1410 |
+
- name: language
|
| 1411 |
+
dtype: string
|
| 1412 |
+
splits:
|
| 1413 |
+
- name: train
|
| 1414 |
+
num_bytes: 76059
|
| 1415 |
+
num_examples: 1017
|
| 1416 |
+
download_size: 32213126
|
| 1417 |
+
dataset_size: 76059
|
| 1418 |
+
- config_name: tlh
|
| 1419 |
+
features:
|
| 1420 |
+
- name: paraphrase_set_id
|
| 1421 |
+
dtype: string
|
| 1422 |
+
- name: sentence_id
|
| 1423 |
+
dtype: string
|
| 1424 |
+
- name: paraphrase
|
| 1425 |
+
dtype: string
|
| 1426 |
+
- name: lists
|
| 1427 |
+
sequence: string
|
| 1428 |
+
- name: tags
|
| 1429 |
+
sequence: string
|
| 1430 |
+
- name: language
|
| 1431 |
+
dtype: string
|
| 1432 |
+
splits:
|
| 1433 |
+
- name: train
|
| 1434 |
+
num_bytes: 185309
|
| 1435 |
+
num_examples: 2804
|
| 1436 |
+
download_size: 32213126
|
| 1437 |
+
dataset_size: 185309
|
| 1438 |
+
- config_name: toki
|
| 1439 |
+
features:
|
| 1440 |
+
- name: paraphrase_set_id
|
| 1441 |
+
dtype: string
|
| 1442 |
+
- name: sentence_id
|
| 1443 |
+
dtype: string
|
| 1444 |
+
- name: paraphrase
|
| 1445 |
+
dtype: string
|
| 1446 |
+
- name: lists
|
| 1447 |
+
sequence: string
|
| 1448 |
+
- name: tags
|
| 1449 |
+
sequence: string
|
| 1450 |
+
- name: language
|
| 1451 |
+
dtype: string
|
| 1452 |
+
splits:
|
| 1453 |
+
- name: train
|
| 1454 |
+
num_bytes: 310864
|
| 1455 |
+
num_examples: 3738
|
| 1456 |
+
download_size: 32213126
|
| 1457 |
+
dataset_size: 310864
|
| 1458 |
+
- config_name: tr
|
| 1459 |
+
features:
|
| 1460 |
+
- name: paraphrase_set_id
|
| 1461 |
+
dtype: string
|
| 1462 |
+
- name: sentence_id
|
| 1463 |
+
dtype: string
|
| 1464 |
+
- name: paraphrase
|
| 1465 |
+
dtype: string
|
| 1466 |
+
- name: lists
|
| 1467 |
+
sequence: string
|
| 1468 |
+
- name: tags
|
| 1469 |
+
sequence: string
|
| 1470 |
+
- name: language
|
| 1471 |
+
dtype: string
|
| 1472 |
+
splits:
|
| 1473 |
+
- name: train
|
| 1474 |
+
num_bytes: 11271158
|
| 1475 |
+
num_examples: 142088
|
| 1476 |
+
download_size: 32213126
|
| 1477 |
+
dataset_size: 11271158
|
| 1478 |
+
- config_name: tt
|
| 1479 |
+
features:
|
| 1480 |
+
- name: paraphrase_set_id
|
| 1481 |
+
dtype: string
|
| 1482 |
+
- name: sentence_id
|
| 1483 |
+
dtype: string
|
| 1484 |
+
- name: paraphrase
|
| 1485 |
+
dtype: string
|
| 1486 |
+
- name: lists
|
| 1487 |
+
sequence: string
|
| 1488 |
+
- name: tags
|
| 1489 |
+
sequence: string
|
| 1490 |
+
- name: language
|
| 1491 |
+
dtype: string
|
| 1492 |
+
splits:
|
| 1493 |
+
- name: train
|
| 1494 |
+
num_bytes: 277269
|
| 1495 |
+
num_examples: 2398
|
| 1496 |
+
download_size: 32213126
|
| 1497 |
+
dataset_size: 277269
|
| 1498 |
+
- config_name: ug
|
| 1499 |
+
features:
|
| 1500 |
+
- name: paraphrase_set_id
|
| 1501 |
+
dtype: string
|
| 1502 |
+
- name: sentence_id
|
| 1503 |
+
dtype: string
|
| 1504 |
+
- name: paraphrase
|
| 1505 |
+
dtype: string
|
| 1506 |
+
- name: lists
|
| 1507 |
+
sequence: string
|
| 1508 |
+
- name: tags
|
| 1509 |
+
sequence: string
|
| 1510 |
+
- name: language
|
| 1511 |
+
dtype: string
|
| 1512 |
+
splits:
|
| 1513 |
+
- name: train
|
| 1514 |
+
num_bytes: 118474
|
| 1515 |
+
num_examples: 1183
|
| 1516 |
+
download_size: 32213126
|
| 1517 |
+
dataset_size: 118474
|
| 1518 |
+
- config_name: uk
|
| 1519 |
+
features:
|
| 1520 |
+
- name: paraphrase_set_id
|
| 1521 |
+
dtype: string
|
| 1522 |
+
- name: sentence_id
|
| 1523 |
+
dtype: string
|
| 1524 |
+
- name: paraphrase
|
| 1525 |
+
dtype: string
|
| 1526 |
+
- name: lists
|
| 1527 |
+
sequence: string
|
| 1528 |
+
- name: tags
|
| 1529 |
+
sequence: string
|
| 1530 |
+
- name: language
|
| 1531 |
+
dtype: string
|
| 1532 |
+
splits:
|
| 1533 |
+
- name: train
|
| 1534 |
+
num_bytes: 4885677
|
| 1535 |
+
num_examples: 54431
|
| 1536 |
+
download_size: 32213126
|
| 1537 |
+
dataset_size: 4885677
|
| 1538 |
+
- config_name: ur
|
| 1539 |
+
features:
|
| 1540 |
+
- name: paraphrase_set_id
|
| 1541 |
+
dtype: string
|
| 1542 |
+
- name: sentence_id
|
| 1543 |
+
dtype: string
|
| 1544 |
+
- name: paraphrase
|
| 1545 |
+
dtype: string
|
| 1546 |
+
- name: lists
|
| 1547 |
+
sequence: string
|
| 1548 |
+
- name: tags
|
| 1549 |
+
sequence: string
|
| 1550 |
+
- name: language
|
| 1551 |
+
dtype: string
|
| 1552 |
+
splits:
|
| 1553 |
+
- name: train
|
| 1554 |
+
num_bytes: 24075
|
| 1555 |
+
num_examples: 252
|
| 1556 |
+
download_size: 32213126
|
| 1557 |
+
dataset_size: 24075
|
| 1558 |
+
- config_name: vi
|
| 1559 |
+
features:
|
| 1560 |
+
- name: paraphrase_set_id
|
| 1561 |
+
dtype: string
|
| 1562 |
+
- name: sentence_id
|
| 1563 |
+
dtype: string
|
| 1564 |
+
- name: paraphrase
|
| 1565 |
+
dtype: string
|
| 1566 |
+
- name: lists
|
| 1567 |
+
sequence: string
|
| 1568 |
+
- name: tags
|
| 1569 |
+
sequence: string
|
| 1570 |
+
- name: language
|
| 1571 |
+
dtype: string
|
| 1572 |
+
splits:
|
| 1573 |
+
- name: train
|
| 1574 |
+
num_bytes: 84773
|
| 1575 |
+
num_examples: 962
|
| 1576 |
+
download_size: 32213126
|
| 1577 |
+
dataset_size: 84773
|
| 1578 |
+
- config_name: vo
|
| 1579 |
+
features:
|
| 1580 |
+
- name: paraphrase_set_id
|
| 1581 |
+
dtype: string
|
| 1582 |
+
- name: sentence_id
|
| 1583 |
+
dtype: string
|
| 1584 |
+
- name: paraphrase
|
| 1585 |
+
dtype: string
|
| 1586 |
+
- name: lists
|
| 1587 |
+
sequence: string
|
| 1588 |
+
- name: tags
|
| 1589 |
+
sequence: string
|
| 1590 |
+
- name: language
|
| 1591 |
+
dtype: string
|
| 1592 |
+
splits:
|
| 1593 |
+
- name: train
|
| 1594 |
+
num_bytes: 22164
|
| 1595 |
+
num_examples: 328
|
| 1596 |
+
download_size: 32213126
|
| 1597 |
+
dataset_size: 22164
|
| 1598 |
+
- config_name: war
|
| 1599 |
+
features:
|
| 1600 |
+
- name: paraphrase_set_id
|
| 1601 |
+
dtype: string
|
| 1602 |
+
- name: sentence_id
|
| 1603 |
+
dtype: string
|
| 1604 |
+
- name: paraphrase
|
| 1605 |
+
dtype: string
|
| 1606 |
+
- name: lists
|
| 1607 |
+
sequence: string
|
| 1608 |
+
- name: tags
|
| 1609 |
+
sequence: string
|
| 1610 |
+
- name: language
|
| 1611 |
+
dtype: string
|
| 1612 |
+
splits:
|
| 1613 |
+
- name: train
|
| 1614 |
+
num_bytes: 25759
|
| 1615 |
+
num_examples: 327
|
| 1616 |
+
download_size: 32213126
|
| 1617 |
+
dataset_size: 25759
|
| 1618 |
+
- config_name: wuu
|
| 1619 |
+
features:
|
| 1620 |
+
- name: paraphrase_set_id
|
| 1621 |
+
dtype: string
|
| 1622 |
+
- name: sentence_id
|
| 1623 |
+
dtype: string
|
| 1624 |
+
- name: paraphrase
|
| 1625 |
+
dtype: string
|
| 1626 |
+
- name: lists
|
| 1627 |
+
sequence: string
|
| 1628 |
+
- name: tags
|
| 1629 |
+
sequence: string
|
| 1630 |
+
- name: language
|
| 1631 |
+
dtype: string
|
| 1632 |
+
splits:
|
| 1633 |
+
- name: train
|
| 1634 |
+
num_bytes: 31640
|
| 1635 |
+
num_examples: 408
|
| 1636 |
+
download_size: 32213126
|
| 1637 |
+
dataset_size: 31640
|
| 1638 |
+
- config_name: yue
|
| 1639 |
+
features:
|
| 1640 |
+
- name: paraphrase_set_id
|
| 1641 |
+
dtype: string
|
| 1642 |
+
- name: sentence_id
|
| 1643 |
+
dtype: string
|
| 1644 |
+
- name: paraphrase
|
| 1645 |
+
dtype: string
|
| 1646 |
+
- name: lists
|
| 1647 |
+
sequence: string
|
| 1648 |
+
- name: tags
|
| 1649 |
+
sequence: string
|
| 1650 |
+
- name: language
|
| 1651 |
+
dtype: string
|
| 1652 |
+
splits:
|
| 1653 |
+
- name: train
|
| 1654 |
+
num_bytes: 42766
|
| 1655 |
+
num_examples: 561
|
| 1656 |
+
download_size: 32213126
|
| 1657 |
+
dataset_size: 42766
|
| 1658 |
+
---
|
| 1659 |
+
|
| 1660 |
+
# Dataset Card for TaPaCo Corpus
|
| 1661 |
+
|
| 1662 |
+
## Table of Contents
|
| 1663 |
+
- [Dataset Description](#dataset-description)
|
| 1664 |
+
- [Dataset Summary](#dataset-summary)
|
| 1665 |
+
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
|
| 1666 |
+
- [Languages](#languages)
|
| 1667 |
+
- [Dataset Structure](#dataset-structure)
|
| 1668 |
+
- [Data Instances](#data-instances)
|
| 1669 |
+
- [Data Fields](#data-fields)
|
| 1670 |
+
- [Data Splits](#data-splits)
|
| 1671 |
+
- [Dataset Creation](#dataset-creation)
|
| 1672 |
+
- [Curation Rationale](#curation-rationale)
|
| 1673 |
+
- [Source Data](#source-data)
|
| 1674 |
+
- [Annotations](#annotations)
|
| 1675 |
+
- [Personal and Sensitive Information](#personal-and-sensitive-information)
|
| 1676 |
+
- [Considerations for Using the Data](#considerations-for-using-the-data)
|
| 1677 |
+
- [Social Impact of Dataset](#social-impact-of-dataset)
|
| 1678 |
+
- [Discussion of Biases](#discussion-of-biases)
|
| 1679 |
+
- [Other Known Limitations](#other-known-limitations)
|
| 1680 |
+
- [Additional Information](#additional-information)
|
| 1681 |
+
- [Dataset Curators](#dataset-curators)
|
| 1682 |
+
- [Licensing Information](#licensing-information)
|
| 1683 |
+
- [Citation Information](#citation-information)
|
| 1684 |
+
- [Contributions](#contributions)
|
| 1685 |
+
|
| 1686 |
+
## Dataset Description
|
| 1687 |
+
|
| 1688 |
+
- **Homepage:** [TaPaCo: A Corpus of Sentential Paraphrases for 73 Languages](https://zenodo.org/record/3707949#.X9Dh0cYza3I)
|
| 1689 |
+
- **Paper:** [TaPaCo: A Corpus of Sentential Paraphrases for 73 Languages](https://www.aclweb.org/anthology/2020.lrec-1.848.pdf)
|
| 1690 |
+
- **Point of Contact:** [Yves Scherrer](https://blogs.helsinki.fi/yvesscherrer/)
|
| 1691 |
+
|
| 1692 |
+
### Dataset Summary
|
| 1693 |
+
A freely available paraphrase corpus for 73 languages extracted from the Tatoeba database.
|
| 1694 |
+
Tatoeba is a crowdsourcing project mainly geared towards language learners. Its aim is to provide example sentences
|
| 1695 |
+
and translations for particular linguistic constructions and words. The paraphrase corpus is created by populating a
|
| 1696 |
+
graph with Tatoeba sentences and equivalence links between sentences “meaning the same thing”. This graph is then
|
| 1697 |
+
traversed to extract sets of paraphrases. Several language-independent filters and pruning steps are applied to
|
| 1698 |
+
remove uninteresting sentences. A manual evaluation performed on three languages shows that between half and three
|
| 1699 |
+
quarters of inferred paraphrases are correct and that most remaining ones are either correct but trivial,
|
| 1700 |
+
or near-paraphrases that neutralize a morphological distinction. The corpus contains a total of 1.9 million
|
| 1701 |
+
sentences, with 200 – 250 000 sentences per language. It covers a range of languages for which, to our knowledge,
|
| 1702 |
+
no other paraphrase dataset exists.
|
| 1703 |
+
|
| 1704 |
+
### Supported Tasks and Leaderboards
|
| 1705 |
+
Paraphrase detection and generation have become popular tasks in NLP
|
| 1706 |
+
and are increasingly integrated into a wide variety of common downstream tasks such as machine translation
|
| 1707 |
+
, information retrieval, question answering, and semantic parsing. Most of the existing datasets
|
| 1708 |
+
cover only a single language – in most cases English – or a small number of languages. Furthermore, some paraphrase
|
| 1709 |
+
datasets focus on lexical and phrasal rather than sentential paraphrases, while others are created (semi
|
| 1710 |
+
-)automatically using machine translation.
|
| 1711 |
+
|
| 1712 |
+
The number of sentences per language ranges from 200 to 250 000, which makes the dataset
|
| 1713 |
+
more suitable for fine-tuning and evaluation purposes than
|
| 1714 |
+
for training. It is well-suited for multi-reference evaluation
|
| 1715 |
+
of paraphrase generation models, as there is generally not a
|
| 1716 |
+
single correct way of paraphrasing a given input sentence.
|
| 1717 |
+
|
| 1718 |
+
### Languages
|
| 1719 |
+
|
| 1720 |
+
The dataset contains paraphrases in Afrikaans, Arabic, Azerbaijani, Belarusian, Berber languages, Bulgarian, Bengali
|
| 1721 |
+
, Breton, Catalan; Valencian, Chavacano, Mandarin, Czech, Danish, German, Greek, Modern (1453-), English, Esperanto
|
| 1722 |
+
, Spanish; Castilian, Estonian, Basque, Finnish, French, Galician, Gronings, Hebrew, Hindi, Croatian, Hungarian
|
| 1723 |
+
, Armenian, Interlingua (International Auxiliary Language Association), Indonesian, Interlingue; Occidental, Ido
|
| 1724 |
+
, Icelandic, Italian, Japanese, Lojban, Kabyle, Korean, Cornish, Latin, Lingua Franca Nova\t, Lithuanian, Macedonian
|
| 1725 |
+
, Marathi, Bokmål, Norwegian; Norwegian Bokmål, Low German; Low Saxon; German, Low; Saxon, Low, Dutch; Flemish, ]Old
|
| 1726 |
+
Russian, Turkish, Ottoman (1500-1928), Iranian Persian, Polish, Portuguese, Rundi, Romanian; Moldavian; Moldovan,
|
| 1727 |
+
Russian, Slovenian, Serbian, Swedish, Turkmen, Tagalog, Klingon; tlhIngan-Hol, Toki Pona, Turkish, Tatar,
|
| 1728 |
+
Uighur; Uyghur, Ukrainian, Urdu, Vietnamese, Volapük, Waray, Wu Chinese and Yue Chinese
|
| 1729 |
+
|
| 1730 |
+
## Dataset Structure
|
| 1731 |
+
|
| 1732 |
+
### Data Instances
|
| 1733 |
+
Each data instance corresponds to a paraphrase, e.g.:
|
| 1734 |
+
```
|
| 1735 |
+
{
|
| 1736 |
+
'paraphrase_set_id': '1483',
|
| 1737 |
+
'sentence_id': '5778896',
|
| 1738 |
+
'paraphrase': 'Ɣremt adlis-a.',
|
| 1739 |
+
'lists': ['7546'],
|
| 1740 |
+
'tags': [''],
|
| 1741 |
+
'language': 'ber'
|
| 1742 |
+
}
|
| 1743 |
+
```
|
| 1744 |
+
|
| 1745 |
+
### Data Fields
|
| 1746 |
+
Each dialogue instance has the following fields:
|
| 1747 |
+
- `paraphrase_set_id`: a running number that groups together all sentences that are considered paraphrases of each
|
| 1748 |
+
other
|
| 1749 |
+
- `sentence_id`: OPUS sentence id
|
| 1750 |
+
- `paraphrase`: Sentential paraphrase in a given language for a given paraphrase_set_id
|
| 1751 |
+
- `lists`: Contributors can add sentences to list in order to specify the original source of the data
|
| 1752 |
+
- `tags`: Indicates morphological or phonological properties of the sentence when available
|
| 1753 |
+
- `language`: Language identifier, one of the 73 languages that belong to this dataset.
|
| 1754 |
+
|
| 1755 |
+
### Data Splits
|
| 1756 |
+
|
| 1757 |
+
The dataset is having a single `train` split, contains a total of 1.9 million sentences, with 200 – 250 000
|
| 1758 |
+
sentences per language
|
| 1759 |
+
|
| 1760 |
+
## Dataset Creation
|
| 1761 |
+
|
| 1762 |
+
### Curation Rationale
|
| 1763 |
+
|
| 1764 |
+
[More Information Needed]
|
| 1765 |
+
|
| 1766 |
+
### Source Data
|
| 1767 |
+
|
| 1768 |
+
#### Initial Data Collection and Normalization
|
| 1769 |
+
|
| 1770 |
+
[More Information Needed]
|
| 1771 |
+
|
| 1772 |
+
#### Who are the source language producers?
|
| 1773 |
+
|
| 1774 |
+
[More Information Needed]
|
| 1775 |
+
|
| 1776 |
+
### Annotations
|
| 1777 |
+
|
| 1778 |
+
#### Annotation process
|
| 1779 |
+
|
| 1780 |
+
[More Information Needed]
|
| 1781 |
+
|
| 1782 |
+
#### Who are the annotators?
|
| 1783 |
+
|
| 1784 |
+
[More Information Needed]
|
| 1785 |
+
|
| 1786 |
+
### Personal and Sensitive Information
|
| 1787 |
+
|
| 1788 |
+
[More Information Needed]
|
| 1789 |
+
|
| 1790 |
+
## Considerations for Using the Data
|
| 1791 |
+
|
| 1792 |
+
### Social Impact of Dataset
|
| 1793 |
+
|
| 1794 |
+
[More Information Needed]
|
| 1795 |
+
|
| 1796 |
+
### Discussion of Biases
|
| 1797 |
+
|
| 1798 |
+
[More Information Needed]
|
| 1799 |
+
|
| 1800 |
+
### Other Known Limitations
|
| 1801 |
+
|
| 1802 |
+
[More Information Needed]
|
| 1803 |
+
|
| 1804 |
+
## Additional Information
|
| 1805 |
+
|
| 1806 |
+
### Dataset Curators
|
| 1807 |
+
|
| 1808 |
+
[More Information Needed]
|
| 1809 |
+
|
| 1810 |
+
### Licensing Information
|
| 1811 |
+
|
| 1812 |
+
Creative Commons Attribution 2.0 Generic
|
| 1813 |
+
|
| 1814 |
+
### Citation Information
|
| 1815 |
+
|
| 1816 |
+
```
|
| 1817 |
+
@dataset{scherrer_yves_2020_3707949,
|
| 1818 |
+
author = {Scherrer, Yves},
|
| 1819 |
+
title = {{TaPaCo: A Corpus of Sentential Paraphrases for 73 Languages}},
|
| 1820 |
+
month = mar,
|
| 1821 |
+
year = 2020,
|
| 1822 |
+
publisher = {Zenodo},
|
| 1823 |
+
version = {1.0},
|
| 1824 |
+
doi = {10.5281/zenodo.3707949},
|
| 1825 |
+
url = {https://doi.org/10.5281/zenodo.3707949}
|
| 1826 |
+
}
|
| 1827 |
+
```
|
| 1828 |
+
|
| 1829 |
+
### Contributions
|
| 1830 |
+
|
| 1831 |
+
Thanks to [@pacman100](https://github.com/pacman100) for adding this dataset.
|
huggingface_dataset/Dataset_Card/thiemowa_empathyreviewcorpus.md
ADDED
|
@@ -0,0 +1,21 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
Empathy Annotated Student Peer Reviews Corpus (EASPRC) version 1.0
|
| 2 |
+
-----------------------------------------------------
|
| 3 |
+
|
| 4 |
+
Free and full access: https://github.com/thiemowa/empathy_annotated_peer_reviews
|
| 5 |
+
|
| 6 |
+
The corpus contains 500 student peer reviews about business model feedbacks annotated for their cognitive and emotional empathy levels based on three types of review components (strength, weakness and suggestions for improvement). The folder contains the following files:
|
| 7 |
+
|
| 8 |
+
1. guideline.pdf: the annotation guidelines used in this study
|
| 9 |
+
|
| 10 |
+
2. Corpus.zip: the corpus including the txt files and the ann (annotation) files for each student review
|
| 11 |
+
|
| 12 |
+
For annotating the texts, we used the tagtog annotation tool (https://www.tagtog.net/).
|
| 13 |
+
|
| 14 |
+
|
| 15 |
+
Citation
|
| 16 |
+
--------
|
| 17 |
+
|
| 18 |
+
If you use the data, cite the following publication
|
| 19 |
+
|
| 20 |
+
T. Wambsganss, C. Niklaus, M. Söllner, S. Handschuh and J. M. Leimeister,
|
| 21 |
+
“Supporting Cognitive and Emotional Empathic Writing of Students” In: _The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing_
|