author stringlengths 2 29 ⌀ | cardData null | citation stringlengths 0 9.58k ⌀ | description stringlengths 0 5.93k ⌀ | disabled bool 1 class | downloads float64 1 1M ⌀ | gated bool 2 classes | id stringlengths 2 108 | lastModified stringlengths 24 24 | paperswithcode_id stringlengths 2 45 ⌀ | private bool 2 classes | sha stringlengths 40 40 | siblings list | tags list | readme_url stringlengths 57 163 | readme stringlengths 0 977k |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
gere | null | null | null | false | 2 | false | gere/Dataset | 2022-08-05T15:03:20.000Z | null | false | f8190b3b6133c6a3bcc36ac328c4639038bde8d8 | [] | [] | https://huggingface.co/datasets/gere/Dataset/resolve/main/README.md | |
simonduerr | null | null | null | false | 2 | false | simonduerr/inversefolding | 2022-08-05T23:28:08.000Z | null | false | 5a44bca6c3dea67e08711345f5187835f8dbda6e | [] | [
"license:odc-by"
] | https://huggingface.co/datasets/simonduerr/inversefolding/resolve/main/README.md | ---
license: odc-by
---
|
quantity | null | null | null | false | 2 | false | quantity/mydataset1 | 2022-08-06T00:06:21.000Z | null | false | 505b42d138140786fc9632bfea619eb6ebb9ea87 | [] | [
"license:apache-2.0"
] | https://huggingface.co/datasets/quantity/mydataset1/resolve/main/README.md | ---
license: apache-2.0
---
|
quantity | null | null | null | false | 3 | false | quantity/model7 | 2022-08-06T00:06:50.000Z | null | false | 937b4e764f7988566909e6f68fd8bbe0c4359514 | [] | [
"license:apache-2.0"
] | https://huggingface.co/datasets/quantity/model7/resolve/main/README.md | ---
license: apache-2.0
---
|
salim-ingram | null | null | null | false | 3 | false | salim-ingram/philosophy_quotes | 2022-08-06T01:42:59.000Z | null | false | 73747883223e6886f5b304e180e04254fb2d4f41 | [] | [
"license:wtfpl"
] | https://huggingface.co/datasets/salim-ingram/philosophy_quotes/resolve/main/README.md | ---
license: wtfpl
---
|
jakartaresearch | null | null | This dataset is built as a playground for beginner to make a use case for creating sentiment analysis model. | false | 10 | false | jakartaresearch/google-play-review | 2022-08-06T16:24:49.000Z | null | false | 4030949b0360722d8853eb01d407393de0b40bad | [] | [
"annotations_creators:found",
"language:id",
"language_creators:found",
"license:cc-by-4.0",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"tags:sentiment",
"tags:google-play",
"tags:indonesian",
"task_categories:text-classification",
"task_ids:sentimen... | https://huggingface.co/datasets/jakartaresearch/google-play-review/resolve/main/README.md | ---
annotations_creators:
- found
language:
- id
language_creators:
- found
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: Indonesian Google Play Review
size_categories:
- 1K<n<10K
source_datasets:
- original
tags:
- sentiment
- google-play
- indonesian
task_categories:
- text-classification
task_ids:
- sentiment-classification
---
# Dataset Card for Indonesian Google Play Review
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
Scrapped from e-commerce app on Google Play.
### Supported Tasks and Leaderboards
Sentiment Analysis
### Languages
Indonesian
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@andreaschandra](https://github.com/andreaschandra) for adding this dataset. |
autoevaluate | null | null | null | false | 3 | false | autoevaluate/autoeval-staging-eval-project-Blaise-g__SumPubmed-24db4c9a-12575677 | 2022-08-06T12:52:18.000Z | null | false | e758e7c5ea70be1fcfd0287c8a798ff91ff6e3d4 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:Blaise-g/SumPubmed"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-Blaise-g__SumPubmed-24db4c9a-12575677/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- Blaise-g/SumPubmed
eval_info:
task: summarization
model: L-macc/autotrain-Biomedical_sc_summ-1217846148
metrics: []
dataset_name: Blaise-g/SumPubmed
dataset_config: Blaise-g--SumPubmed
dataset_split: test
col_mapping:
text: text
target: abstract
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: L-macc/autotrain-Biomedical_sc_summ-1217846148
* Dataset: Blaise-g/SumPubmed
* Config: Blaise-g--SumPubmed
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@L-macc](https://huggingface.co/L-macc) for evaluating this model. |
autoevaluate | null | null | null | false | 2 | false | autoevaluate/autoeval-staging-eval-project-Blaise-g__SumPubmed-24db4c9a-12575678 | 2022-08-06T13:16:16.000Z | null | false | 53ad23a7638e94f869adadb1bad94c93d6de0854 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:Blaise-g/SumPubmed"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-Blaise-g__SumPubmed-24db4c9a-12575678/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- Blaise-g/SumPubmed
eval_info:
task: summarization
model: L-macc/autotrain-Biomedical_sc_summ-1217846144
metrics: []
dataset_name: Blaise-g/SumPubmed
dataset_config: Blaise-g--SumPubmed
dataset_split: test
col_mapping:
text: text
target: abstract
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: L-macc/autotrain-Biomedical_sc_summ-1217846144
* Dataset: Blaise-g/SumPubmed
* Config: Blaise-g--SumPubmed
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@L-macc](https://huggingface.co/L-macc) for evaluating this model. |
autoevaluate | null | null | null | false | 2 | false | autoevaluate/autoeval-staging-eval-project-Blaise-g__SumPubmed-24db4c9a-12575679 | 2022-08-06T13:52:49.000Z | null | false | 3ea2191ea55e1d81f858bec4b51fb42cda713184 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:Blaise-g/SumPubmed"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-Blaise-g__SumPubmed-24db4c9a-12575679/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- Blaise-g/SumPubmed
eval_info:
task: summarization
model: L-macc/autotrain-Biomedical_sc_summ-1217846142
metrics: []
dataset_name: Blaise-g/SumPubmed
dataset_config: Blaise-g--SumPubmed
dataset_split: test
col_mapping:
text: text
target: abstract
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: L-macc/autotrain-Biomedical_sc_summ-1217846142
* Dataset: Blaise-g/SumPubmed
* Config: Blaise-g--SumPubmed
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@L-macc](https://huggingface.co/L-macc) for evaluating this model. |
autoevaluate | null | null | null | false | 3 | false | autoevaluate/autoeval-staging-eval-project-Blaise-g__SumPubmed-c887ce73-12585680 | 2022-08-06T16:29:37.000Z | null | false | 2f911a890c1c1b9220100b4c83cfec52bc6cfe96 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:Blaise-g/SumPubmed"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-Blaise-g__SumPubmed-c887ce73-12585680/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- Blaise-g/SumPubmed
eval_info:
task: summarization
model: Blaise-g/long_t5_global_large_pubmed_wip2
metrics: []
dataset_name: Blaise-g/SumPubmed
dataset_config: Blaise-g--SumPubmed
dataset_split: test
col_mapping:
text: text
target: abstract
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: Blaise-g/long_t5_global_large_pubmed_wip2
* Dataset: Blaise-g/SumPubmed
* Config: Blaise-g--SumPubmed
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@Blaise-g](https://huggingface.co/Blaise-g) for evaluating this model. |
dali-does | null | @misc{https://doi.org/10.48550/arxiv.2208.05358,
doi = {10.48550/ARXIV.2208.05358},
url = {https://arxiv.org/abs/2208.05358},
author = {Lindström, Adam Dahlgren and Abraham, Savitha Sam},
keywords = {Machine Learning (cs.LG), Computation and Language (cs.CL), Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and information sciences, FOS: Computer and information sciences, I.2.7; I.2.10; I.2.6; I.4.8; I.1.4},
title = {CLEVR-Math: A Dataset for Compositional Language, Visual, and Mathematical Reasoning},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution Share Alike 4.0 International}
} | CLEVR-Math is a dataset for compositional language, visual and mathematical reasoning. CLEVR-Math poses questions about mathematical operations on visual scenes using subtraction and addition, such as "Remove all large red cylinders. How many objects are left?". There are also adversarial (e.g. "Remove all blue cubes. How many cylinders are left?") and multihop questions (e.g. "Remove all blue cubes. Remove all small purple spheres. How many objects are left?"). | false | 446 | false | dali-does/clevr-math | 2022-10-31T11:28:31.000Z | null | false | 6a30110f887edd7edbad033275aa853ddd8c4a26 | [] | [
"arxiv:2208.05358",
"annotations_creators:machine-generated",
"language:en",
"language_creators:machine-generated",
"license:cc-by-4.0",
"multilinguality:monolingual",
"source_datasets:clevr",
"tags:reasoning",
"tags:neuro-symbolic",
"tags:multimodal",
"task_categories:visual-question-answering"... | https://huggingface.co/datasets/dali-does/clevr-math/resolve/main/README.md | ---
annotations_creators:
- machine-generated
language:
- en
language_creators:
- machine-generated
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: CLEVR-Math - Compositional language, visual, and mathematical reasoning
size_categories:
#- 100K<n<1M
source_datasets: [clevr]
tags:
- reasoning
- neuro-symbolic
- multimodal
task_categories:
- visual-question-answering
task_ids:
- visual-question-answering
---
# Dataset Card for CLEVR-Math
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:*https://github.com/dali-does/clevr-math*
- **Paper:*https://arxiv.org/abs/2208.05358*
- **Leaderboard:**
- **Point of Contact:*dali@cs.umu.se*
### Dataset Summary
Dataset for compositional multimodal mathematical reasoning based on CLEVR.
#### Loading the data, preprocessing text with CLIP
```
from transformers import CLIPPreprocessor
from datasets import load_dataset, DownloadConfig
dl_config = DownloadConfig(resume_download=True,
num_proc=8,
force_download=True)
# Load 'general' instance of dataset
dataset = load_dataset('dali-does/clevr-math', download_config=dl_config)
# Load version with only multihop in test data
dataset_multihop = load_dataset('dali-does/clevr-math', 'multihop',
download_config=dl_config)
model_path = "openai/clip-vit-base-patch32"
extractor = CLIPProcessor.from_pretrained(model_path)
def transform_tokenize(e):
e['image'] = [image.convert('RGB') for image in e['image']]
return extractor(text=e['question'],
images=e['image'],
padding=True)
dataset = dataset.map(transform_tokenize,
batched=True,
num_proc=8,
padding='max_length')
dataset_subtraction = dataset.filter(lambda e:
e['template'].startswith('subtraction'), num_proc=4)
```
### Supported Tasks and Leaderboards
Leaderboard will be announced at a later date.
### Languages
The dataset is currently only available in English. To extend the dataset to other languages, the CLEVR templates must be rewritten in the target language.
## Dataset Structure
### Data Instances
* `general` containing the default version with multihop questions in train and test
* `multihop` containing multihop questions only in test data to test generalisation of reasoning
### Data Fields
```
features = datasets.Features(
{
"template": datasets.Value("string"),
"id": datasets.Value("string"),
"question": datasets.Value("string"),
"image": datasets.Image(),
"label": datasets.Value("int64")
}
)
```
### Data Splits
train/val/test
## Dataset Creation
Data is generated using code provided with the CLEVR-dataset, using blender and templates constructed by the dataset curators.
## Considerations for Using the Data
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
Adam Dahlgren Lindström - dali@cs.umu.se
### Licensing Information
Licensed under Creative Commons Attribution Share Alike 4.0 International (CC-by 4.0).
### Citation Information
[More Information Needed]
```
@misc{https://doi.org/10.48550/arxiv.2208.05358,
doi = {10.48550/ARXIV.2208.05358},
url = {https://arxiv.org/abs/2208.05358},
author = {Lindström, Adam Dahlgren and Abraham, Savitha Sam},
keywords = {Machine Learning (cs.LG), Computation and Language (cs.CL), Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and information sciences, FOS: Computer and information sciences, I.2.7; I.2.10; I.2.6; I.4.8; I.1.4},
title = {CLEVR-Math: A Dataset for Compositional Language, Visual, and Mathematical Reasoning},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution Share Alike 4.0 International}
}
```
### Contributions
Thanks to [@dali-does](https://github.com/dali-does) for adding this dataset.
|
chinoll | null | null | null | false | 1 | false | chinoll/ACGVoice | 2022-08-06T14:35:21.000Z | null | false | d05b50253b2cc0a1742dbc0f5cc0f76de4c1a301 | [] | [
"license:cc-by-nc-sa-4.0"
] | https://huggingface.co/datasets/chinoll/ACGVoice/resolve/main/README.md | ---
license: cc-by-nc-sa-4.0
---
|
pcuenq | null | null | null | false | 52 | false | pcuenq/oxford-pets | 2022-08-06T16:01:34.000Z | null | false | 2c628097f293a86bdba429379dbb91c0952415eb | [] | [
"tags:pets",
"tags:oxford",
"license:cc-by-sa-4.0",
"source_datasets:https://www.robots.ox.ac.uk/~vgg/data/pets/",
"task_categories:image-classification"
] | https://huggingface.co/datasets/pcuenq/oxford-pets/resolve/main/README.md | ---
tags:
- pets
- oxford
license: cc-by-sa-4.0
license_details: https://www.robots.ox.ac.uk/~vgg/data/pets/
pretty_name: Oxford-IIIT Pet Dataset (no annotations)
source_datasets: https://www.robots.ox.ac.uk/~vgg/data/pets/
task_categories:
- image-classification
---
# Oxford-IIIT Pet Dataset
Images from [The Oxford-IIIT Pet Dataset](https://www.robots.ox.ac.uk/~vgg/data/pets/). Only images and labels have been pushed, segmentation annotations were ignored.
- **Homepage:** https://www.robots.ox.ac.uk/~vgg/data/pets/
License:
Same as the original dataset.
|
Qilex | null | null | null | false | 2 | false | Qilex/EN-MEspecialChars | 2022-08-06T21:38:43.000Z | null | false | d598048f46b2f7796dcf3f29f969dd53114d13af | [] | [
"language:en",
"language:me",
"license:afl-3.0",
"multilinguality:translation",
"size_categories:10K<n<100K",
"tags:middle english",
"task_categories:translation"
] | https://huggingface.co/datasets/Qilex/EN-MEspecialChars/resolve/main/README.md | ---
annotations_creators: []
language:
- en
- me
language_creators: []
license:
- afl-3.0
multilinguality:
- translation
pretty_name: EN-MEspecialChars
size_categories:
- 10K<n<100K
source_datasets: []
tags:
- middle english
task_categories:
- translation
task_ids: []
---
EN-ME Special Chars is a dataset of roughly 58000 aligned sentence pairs in English and Middle English, collected from the works of Geoffrey Chaucer, John Lydgate, John Wycliffe, and the Gawain Poet.
It includes special characters such as þ.
There is mild standardization, but this dataset reflects the spelling inconsistencies characteristic of Middle English.
|
VanessaSchenkel | null | null | null | false | 6 | false | VanessaSchenkel/handmade-dataset | 2022-08-06T22:11:34.000Z | null | false | b839c6ac6fc3fbf9ed2c3926433196b35f72afb9 | [] | [
"annotations_creators:found",
"language:en",
"language:pt",
"language_creators:found",
"license:afl-3.0",
"multilinguality:translation",
"size_categories:n<1K",
"source_datasets:original",
"task_categories:translation"
] | https://huggingface.co/datasets/VanessaSchenkel/handmade-dataset/resolve/main/README.md | ---
annotations_creators:
- found
language:
- en
- pt
language_creators:
- found
license:
- afl-3.0
multilinguality:
- translation
pretty_name: VanessaSchenkel/handmade-dataset
size_categories:
- n<1K
source_datasets:
- original
tags: []
task_categories:
- translation
task_ids: []
---
Dataset with sentences regarding professions, half of the translations are to feminine and half for masculine sentences.
How to use it:
```
from datasets import load_dataset
remote_dataset = load_dataset("VanessaSchenkel/handmade-dataset", field="data")
remote_dataset
```
Output:
```
DatasetDict({
train: Dataset({
features: ['id', 'translation'],
num_rows: 388
})
})
```
Exemple:
```
remote_dataset["train"][5]
```
Output:
```
{'id': '5',
'translation': {'english': 'the postman finished her work .',
'portuguese': 'A carteira terminou seu trabalho .'}}
``` |
VanessaSchenkel | null | null | null | false | 6 | false | VanessaSchenkel/opus_books_en_pt | 2022-08-06T22:46:10.000Z | null | false | 7dd7ea5bc04520e2d01b963a15830ebff6e5db4b | [] | [
"annotations_creators:found",
"language:en",
"language:pt",
"language_creators:found",
"license:afl-3.0",
"multilinguality:translation",
"size_categories:1K<n<10K",
"source_datasets:extended|opus_books",
"task_categories:translation"
] | https://huggingface.co/datasets/VanessaSchenkel/opus_books_en_pt/resolve/main/README.md | ---
annotations_creators:
- found
language:
- en
- pt
language_creators:
- found
license:
- afl-3.0
multilinguality:
- translation
pretty_name: VanessaSchenkel/opus_books_en_pt
size_categories:
- 1K<n<10K
source_datasets:
- extended|opus_books
tags: []
task_categories:
- translation
task_ids: []
---
How to use it:
```
from datasets import load_dataset
remote_dataset = load_dataset("VanessaSchenkel/opus_books_en_pt", field="data")
remote_dataset
```
Output:
```
DatasetDict({
train: Dataset({
features: ['id', 'translation'],
num_rows: 1404
})
})
```
Exemple:
```
remote_dataset["train"][5]
```
Output:
```
{'id': '5',
'translation': {'en': "There was nothing so very remarkable in that; nor did Alice think it so very much out of the way to hear the Rabbit say to itself, 'Oh dear!",
'pt': 'Não havia nada de tão extraordinário nisso; nem Alice achou assim tão fora do normal ouvir o Coelho dizer para si mesmo: —"Oh, céus!'}}
``` |
jakartaresearch | null | null | This dataset is built as a playground for beginner to make a use case for creating sentiment analysis model. | false | 3 | false | jakartaresearch/indonews | 2022-08-07T04:27:54.000Z | null | false | d628ab354f86c439b1eb1db39b3dc6cde6497346 | [] | [
"annotations_creators:found",
"language:id",
"language_creators:found",
"license:cc-by-4.0",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"tags:news",
"tags:news-classifcation",
"tags:indonesia",
"task_categories:text-classification",
"task_ids:multi-c... | https://huggingface.co/datasets/jakartaresearch/indonews/resolve/main/README.md | ---
annotations_creators:
- found
language:
- id
language_creators:
- found
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: Indonews
size_categories:
- 1K<n<10K
source_datasets:
- original
tags:
- news
- news-classifcation
- indonesia
task_categories:
- text-classification
task_ids:
- multi-class-classification
---
# Indonesian News Categorization
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
Indonews: Multiclass News Categorization scrapped popular news portals in Indonesia.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@andreaschandra](https://github.com/andreaschandra) for adding this dataset. |
jakartaresearch | null | null | This dataset is built for text generation task in context of poem tweets in Bahasa. | false | 8 | false | jakartaresearch/poem-tweets | 2022-08-07T08:54:18.000Z | null | false | c73fd7730502cf3694ce5072b899b6ee6ac2bebf | [] | [
"annotations_creators:no-annotation",
"language:id",
"language_creators:found",
"license:cc-by-4.0",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"tags:poem",
"tags:tweets",
"tags:twitter",
"tags:indonesian",
"task_categories:text-generation",
"tas... | https://huggingface.co/datasets/jakartaresearch/poem-tweets/resolve/main/README.md | ---
annotations_creators:
- no-annotation
language:
- id
language_creators:
- found
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: poem_tweets
size_categories:
- 10K<n<100K
source_datasets:
- original
tags:
- poem
- tweets
- twitter
- indonesian
task_categories:
- text-generation
task_ids:
- language-modeling
---
# Dataset Card for Poem Tweets
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
The data are from Twitter. The purpose of this data is to create text generation model for short text and make sure they are all coherence and rhythmic
### Supported Tasks and Leaderboards
- Text Generation
- Language Model
### Languages
Indonesian
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@andreaschandra](https://github.com/andreaschandra) for adding this dataset. |
n6L3 | null | null | null | false | 1 | false | n6L3/tabular | 2022-08-07T10:11:27.000Z | null | false | 7a5acc07f9db8c57080a713c40a3e24484d865c0 | [] | [
"license:cc-by-sa-3.0"
] | https://huggingface.co/datasets/n6L3/tabular/resolve/main/README.md | ---
license: cc-by-sa-3.0
---
|
munggok | null | null | null | false | 3 | false | munggok/KoPI-CC | 2022-09-30T00:10:56.000Z | oscar | false | db57f7c6f265ccf59c03b8c9fb2b32e9a9ca90f5 | [] | [
"arxiv:2201.06642",
"license:cc",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"language:id",
"source_datasets:original",
"task_ids:language-modeling"
] | https://huggingface.co/datasets/munggok/KoPI-CC/resolve/main/README.md |
---
license: cc
annotations_creators:
- no-annotation
language_creators:
- found
multilinguality:
- monolingual
language:
- id
source_datasets:
- original
task_categories:
- sequence-modeling
task_ids:
- language-modeling
paperswithcode_id: oscar
---
### Dataset Summary
KoPI-CC (Korpus Perayapan Indonesia)-CC is Indonesian only extract from Common Crawl snapshots using [ungoliant](https://github.com/oscar-corpus/ungoliant), each snapshot also filtered using some some deduplicate technique such as exact hash(md5) dedup technique and minhash LSH neardup
### Preprocessing
Each folder name inside snapshots folder denoted preprocessing technique that has been applied .
- **Raw**
- this processed directly from cc snapshot using ungoliant without any addition filter ,you can read it in their paper (citation below)
- use same "raw cc snapshot" for `2021_10` and `2021_49` which can be found in oscar dataset ([2109](https://huggingface.co/datasets/oscar-corpus/OSCAR-2109/tree/main/packaged_nondedup/id) and [2201](https://huggingface.co/datasets/oscar-corpus/OSCAR-2201/tree/main/compressed/id_meta))
- **Dedup**
- use data from raw folder
- apply cleaning techniques for every text in documents such as
- fix html
- remove noisy unicode
- fix news tag
- remove control char
- filter by removing short text (20 words)
- filter by character ratio occurred inside text such as
- min_alphabet_ratio (0.75)
- max_upper_ratio (0.10)
- max_number_ratio(0.05)
- filter by exact dedup technique
- hash all text with md5 hashlib
- remove non-unique hash
- full code about dedup step adapted from [here](https://huggingface.co/datasets/Finnish-NLP/mc4_fi_cleaned/tree/main)
- **Neardup**
- use data from dedup folder
- create index cluster using neardup [Minhash and LSH](http://ekzhu.com/datasketch/lsh.html) with following config :
- use 128 permuation
- 6 n-grams size
- use word tokenization (split sentence by space)
- use 0.8 as similarity score
- fillter by removing all index from cluster
- full code about neardup step adapted from [here](https://github.com/ChenghaoMou/text-dedup)
- **Neardup_clean**
- use data from neardup folder
- Removing documents containing words from a selection of the [Indonesian Bad Words](https://github.com/acul3/c4_id_processed/blob/67e10c086d43152788549ef05b7f09060e769993/clean/badwords_ennl.py#L64).
- Removing sentences containing:
- Less than 3 words.
- A word longer than 1000 characters.
- An end symbol not matching end-of-sentence punctuation.
- Strings associated to javascript code (e.g. `{`), lorem ipsum, policy information in indonesia
- Removing documents (after sentence filtering):
- Containing less than 5 sentences.
- Containing less than 500 or more than 50'000 characters.
- full code about neardup_clean step adapted from [here](https://gitlab.com/yhavinga/c4nlpreproc)
## Dataset Structure
### Data Instances
An example from the dataset:
```
{'text': 'Panitia Kerja (Panja) pembahasan RUU Cipta Kerja (Ciptaker) DPR RI memastikan naskah UU Ciptaker sudah final, tapi masih dalam penyisiran. Penyisiran dilakukan agar isi UU Ciptaker sesuai dengan kesepakatan dalam pembahasan dan tidak ada salah pengetikan (typo).\n"Kan memang sudah diumumkan, naskah final itu sudah. Cuma kita sekarang … DPR itu kan punya waktu 7 hari sebelum naskah resminya kita kirim ke pemerintah. Nah, sekarang itu kita sisir, jangan sampai ada yang salah pengetikan, tapi tidak mengubah substansi," kata Ketua Panja RUU Ciptaker Supratman Andi Agtas saat berbincang dengan detikcom, Jumat (9/10/2020) pukul 10.56 WIB.\nSupratman mengungkapkan Panja RUU Ciptaker menggelar rapat hari ini untuk melakukan penyisiran terhadap naskah UU Ciptaker. Panja, sebut dia, bekerja sama dengan pemerintah dan ahli bahasa untuk melakukan penyisiran naskah.\n"Sebentar, siang saya undang seluruh poksi-poksi (kelompok fraksi) Baleg (Badan Legislasi DPR), anggota Panja itu datang ke Baleg untuk melihat satu per satu, jangan sampai …. Karena kan sekarang ini tim dapur pemerintah dan DPR lagi bekerja bersama dengan ahli bahasa melihat jangan sampai ada yang typo, redundant," terangnya.\nSupratman membenarkan bahwa naskah UU Ciptaker yang final itu sudah beredar. Ketua Baleg DPR itu memastikan penyisiran yang dilakukan tidak mengubah substansi setiap pasal yang telah melalui proses pembahasan.\n"Itu yang sudah dibagikan. Tapi kan itu substansinya yang tidak mungkin akan berubah. Nah, kita pastikan nih dari sisi drafting-nya yang jadi kita pastikan," tutur Supratman.\nLebih lanjut Supratman menjelaskan DPR memiliki waktu 7 hari untuk melakukan penyisiran. Anggota DPR dari Fraksi Gerindra itu memastikan paling lambat Selasa (13/10) pekan depan, naskah UU Ciptaker sudah bisa diakses oleh masyarakat melalui situs DPR.\n"Kita itu, DPR, punya waktu sampai 7 hari kerja. Jadi harusnya hari Selasa sudah final semua, paling lambat. Tapi saya usahakan hari ini bisa final. Kalau sudah final, semua itu langsung bisa diakses di web DPR," terang Supratman.\nDiberitakan sebelumnya, Wakil Ketua Baleg DPR Achmad Baidowi mengakui naskah UU Ciptaker yang telah disahkan di paripurna DPR masih dalam proses pengecekan untuk menghindari kesalahan pengetikan. Anggota Komisi VI DPR itu menyinggung soal salah ketik dalam revisi UU KPK yang disahkan pada 2019.\n"Mengoreksi yang typo itu boleh, asalkan tidak mengubah substansi. Jangan sampai seperti tahun lalu, ada UU salah ketik soal umur \'50 (empat puluh)\', sehingga pemerintah harus mengonfirmasi lagi ke DPR," ucap Baidowi, Kamis (8/10).',
'url': 'https://news.detik.com/berita/d-5206925/baleg-dpr-naskah-final-uu-ciptaker-sedang-diperbaiki-tanpa-ubah-substansi?tag_from=wp_cb_mostPopular_list&_ga=2.71339034.848625040.1602222726-629985507.1602222726',
'timestamp': '2021-10-22T04:09:47Z',
'meta': '{"warc_headers": {"content-length": "2747", "content-type": "text/plain", "warc-date": "2021-10-22T04:09:47Z", "warc-record-id": "<urn:uuid:a5b2cc09-bd2b-4d0e-9e5b-2fcc5fce47cb>", "warc-identified-content-language": "ind,eng", "warc-target-uri": "https://news.detik.com/berita/d-5206925/baleg-dpr-naskah-final-uu-ciptaker-sedang-diperbaiki-tanpa-ubah-substansi?tag_from=wp_cb_mostPopular_list&_ga=2.71339034.848625040.1602222726-629985507.1602222726", "warc-block-digest": "sha1:65AWBDBLS74AGDCGDBNDHBHADOKSXCKV", "warc-type": "conversion", "warc-refers-to": "<urn:uuid:b7ceadba-7120-4e38-927c-a50db21f0d4f>"}, "identification": {"label": "id", "prob": 0.6240405}, "annotations": null, "line_identifications": [null, {"label": "id", "prob": 0.9043896}, null, null, {"label": "id", "prob": 0.87111086}, {"label": "id", "prob": 0.9095224}, {"label": "id", "prob": 0.8579232}, {"label": "id", "prob": 0.81366056}, {"label": "id", "prob": 0.9286813}, {"label": "id", "prob": 0.8435194}, {"label": "id", "prob": 0.8387821}, null]}'}
```
### Data Fields
The data contains the following fields:
- `url`: url of the source as a string
- `text`: text content as a string
- `timestamp`: timestamp of extraction as a string
- `meta` : json representation of the original from ungoliant tools,can be found [here](https://oscar-corpus.com/post/oscar-v22-01/) (warc_heder)
## Additional Information
### Dataset Curators
For inquiries or requests regarding the KoPI-CC contained in this repository, please contact me at [samsulrahmadani@gmail.com](mailto:samsulrahmadani@gmail.com)
### Licensing Information
These data are released under this licensing scheme
I do not own any of the text from which these data has been extracted.
the license actual packaging of these data under the Creative Commons CC0 license ("no rights reserved") http://creativecommons.org/publicdomain/zero/1.0/
Should you consider that data contains material that is owned by you and should therefore not be reproduced here, please:
* Clearly identify yourself, with detailed contact data such as an address, telephone number or email address at which you can be contacted.
* Clearly identify the copyrighted work claimed to be infringed.
* Clearly identify the material that is claimed to be infringing and information reasonably sufficient to allow us to locate the material.
I will comply to legitimate requests by removing the affected sources from the next release of the corpus.
### Citation Information
```
@ARTICLE{2022arXiv220106642A,
author = {{Abadji}, Julien and {Ortiz Suarez}, Pedro and {Romary}, Laurent and {Sagot}, Beno{\^\i}t},
title = "{Towards a Cleaner Document-Oriented Multilingual Crawled Corpus}",
journal = {arXiv e-prints},
keywords = {Computer Science - Computation and Language},
year = 2022,
month = jan,
eid = {arXiv:2201.06642},
pages = {arXiv:2201.06642},
archivePrefix = {arXiv},
eprint = {2201.06642},
primaryClass = {cs.CL},
adsurl = {https://ui.adsabs.harvard.edu/abs/2022arXiv220106642A},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
@inproceedings{AbadjiOrtizSuarezRomaryetal.2021,
author = {Julien Abadji and Pedro Javier Ortiz Su{\'a}rez and Laurent Romary and Beno{\^i}t Sagot},
title = {Ungoliant: An optimized pipeline for the generation of a very large-scale multilingual web corpus},
series = {Proceedings of the Workshop on Challenges in the Management of Large Corpora (CMLC-9) 2021. Limerick, 12 July 2021 (Online-Event)},
editor = {Harald L{\"u}ngen and Marc Kupietz and Piotr Bański and Adrien Barbaresi and Simon Clematide and Ines Pisetta},
publisher = {Leibniz-Institut f{\"u}r Deutsche Sprache},
address = {Mannheim},
doi = {10.14618/ids-pub-10468},
url = {https://nbn-resolving.org/urn:nbn:de:bsz:mh39-104688},
pages = {1 -- 9},
year = {2021},
abstract = {Since the introduction of large language models in Natural Language Processing, large raw corpora have played a crucial role in Computational Linguistics. However, most of these large raw corpora are either available only for English or not available to the general public due to copyright issues. Nevertheless, there are some examples of freely available multilingual corpora for training Deep Learning NLP models, such as the OSCAR and Paracrawl corpora. However, they have quality issues, especially for low-resource languages. Moreover, recreating or updating these corpora is very complex. In this work, we try to reproduce and improve the goclassy pipeline used to create the OSCAR corpus. We propose a new pipeline that is faster, modular, parameterizable, and well documented. We use it to create a corpus similar to OSCAR but larger and based on recent data. Also, unlike OSCAR, the metadata information is at the document level. We release our pipeline under an open source license and publish the corpus under a research-only license.},
language = {en}
}
``` |
biglam | null | null | null | false | 2 | false | biglam/bnl_ground_truth_newspapers_before_1878 | 2022-08-07T13:16:10.000Z | null | false | 64c7044457dd130ae6db88a0a5c386a1c1a6249e | [] | [
"license:cc0-1.0"
] | https://huggingface.co/datasets/biglam/bnl_ground_truth_newspapers_before_1878/resolve/main/README.md | ---
license: cc0-1.0
---
### Dataset description
33.000 transcribed text lines from historical newspapers (before 1878) along with the cropped images of the original scans
Text line based OCR
19.000 text lines in Antiqua
14.000 text lines in Fraktur
Transcribed using double-keying (99.95% accuracy)
Public Domain, CC0 (See copyright notice)
Best for training an OCR engine
The newspapers used are:
- Le Gratis luxembourgeois (1857-1858)
- Luxemburger Volks-Freund (1869-1876)
- L'Arlequin (1848-1848)
- Courrier du Grand-Duché de Luxembourg (1844-1868)
- L'Avenir (1868-1871)
- Der Wächter an der Sauer (1849-1869)
- Luxemburger Zeitung (1844-1845)
- Luxemburger Zeitung = Journal de Luxembourg (1858-1859)
- Der Volksfreund (1848-1849)
- Cäcilia (1862-1871)
- Kirchlicher Anzeiger für die Diözese Luxemburg (1871-1878)
- L'Indépendance luxembourgeoise (1871-1878)
- Luxemburger Anzeiger (1856)
- L'Union (1860-1871)
- Diekircher Wochenblatt (1837-1848)
- Das Vaterland (1869-1870)
- D'Wäschfra (1868-1878)
- Luxemburger Bauernzeitung (1857)
- Luxemburger Wort (1848-1878)
### URL for this dataset
https://data.bnl.lu/data/historical-newspapers/
### Dataset format
Two JSONL files (antiqua.jsonl.gz and fraktur.jsonl.gz) with the follwing fields:
- `font` is either antiqua or fraktur
- `img` is the filename of the associated image for the text
- `text` is the handcorrected double-keyed text transcribed from the image
Sample:
```json
{
"font": "fraktur",
"img": "fraktur-000011.png",
"text": "Vidal die Vollmacht für Paris an. Auch"
}
```
In addition there are two `.zip` files with the associated images
### Dataset modality
Text and associated Images from Scans
### Dataset licence
Creative Commons Public Domain Dedication and Certification
### size of dataset
500MB-2GB
### Contact details for data custodian
opendata@bnl.etat.lu
|
luigisaetta | null | null | null | false | 10 | false | luigisaetta/atco2 | 2022-08-29T07:36:28.000Z | null | false | 2f37090fe26d8da9b59f8403426fa17c69a9f157 | [] | [] | https://huggingface.co/datasets/luigisaetta/atco2/resolve/main/README.md | This dataset contains ATC communication.
It can be used to fine tune an **ASR** model, specialised for Air Traffic Control Communications (ATC)
Its data have been taken from the [ATCO2 site](https://www.atco2.org/data) |
Truthful | null | null | null | false | 2 | false | Truthful/autotrain-data-provision_classification | 2022-08-08T05:29:45.000Z | null | false | 704867178079f256151dc7d561bb241083f3c0de | [] | [
"task_categories:text-classification"
] | https://huggingface.co/datasets/Truthful/autotrain-data-provision_classification/resolve/main/README.md | ---
task_categories:
- text-classification
---
# AutoTrain Dataset for project: provision_classification
## Dataset Descritpion
This dataset has been automatically processed by AutoTrain for project provision_classification.
### Languages
The BCP-47 code for the dataset's language is unk.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"text": "Each Partner hereby represents and warrants to the Partnership and each other Partner that (a)\u00a0if such Partner is a corporation, it is duly organized, validly existing, and in good standing under the laws of the jurisdiction of its incorporation and is duly qualified and in good standing as a foreign corporation in the jurisdiction of its principal place of business (if not incorporated therein), (b) if such Partner is a trust, estate or other entity, it is duly formed, validly existing, and (if applicable) in good standing under the laws of the jurisdiction of its formation, and if required by law is duly qualified to do business and (if applicable) in good standing in the jurisdiction of its principal place of business (if not formed therein), (c) such Partner has full corporate, trust, or other applicable right, power and authority to enter into this Agreement and to perform its obligations hereunder and all necessary actions by the board of directors, trustees, beneficiaries, or other Persons necessary for the due authorization, execution, delivery, and performance of this Agreement by such Partner have been duly taken, and such authorization, execution, delivery, and performance do not conflict with any other agreement or arrangement to which such Partner is a party or by which it is bound, and (d)\u00a0such Partner is acquiring its interest in the Partnership for investment purposes and not with a view to distribution thereof.",
"target": 13
},
{
"text": "This Letter Agreement is binding upon and inures to the benefit of the parties and their respective heirs, executors, administrators, personal representatives, successors, and permitted assigns. This Letter Agreement is personal to you and the availability of you to perform services and the covenants provided by you hereunder have been a material consideration for the Company to enter into this Letter Agreement. Accordingly, you may not assign any of your rights or delegate any of your duties under this Letter Agreement, either voluntarily or by operation of law, without the prior written consent of the Company, which may be given or withheld by the Company in its sole and absolute discretion.",
"target": 0
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"text": "Value(dtype='string', id=None)",
"target": "ClassLabel(num_classes=19, names=['Assignment', 'Attorney Fees', 'Bankruptcy', 'Change of Control', 'Compliance with Laws', 'Confidentiality', 'Entire Agreement', 'General Definition', 'Governing Law', 'Indemnification', 'Injunctive Relief', 'Jurisdiction and Venue', 'Liens', 'No Warranties', 'Other', 'Permitted Disclosure', 'Survival', 'Term', 'Termination for Convenience'], id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 119023 |
| valid | 13225 |
|
DeveloperOats | null | null | null | false | 60 | false | DeveloperOats/DBPedia_Classes | 2022-08-08T14:54:42.000Z | null | false | 4d0aa96069f24063697e4df63b95be78d3f7fb7d | [] | [
"language:en",
"license:cc0-1.0",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"task_categories:text-classification",
"task_ids:topic-classification"
] | https://huggingface.co/datasets/DeveloperOats/DBPedia_Classes/resolve/main/README.md | ---
annotations_creators: []
language:
- en
language_creators: []
license:
- cc0-1.0
multilinguality:
- monolingual
pretty_name: 'DBpedia'
size_categories:
- 1M<n<10M
source_datasets: []
tags: []
task_categories:
- text-classification
task_ids:
- topic-classification
---
About Dataset
DBpedia (from "DB" for "database") is a project aiming to extract structured content from the information created in Wikipedia.
This is an extract of the data (after cleaning, kernel included) that provides taxonomic, hierarchical categories ("classes") for 342,782 wikipedia articles. There are 3 levels, with 9, 70 and 219 classes respectively.
A version of this dataset is a popular baseline for NLP/text classification tasks. This version of the dataset is much tougher, especially if the L2/L3 levels are used as the targets.
This is an excellent benchmark for hierarchical multiclass/multilabel text classification.
Some example approaches are included as code snippets.
Content
DBPedia dataset with multiple levels of hierarchy/classes, as a multiclass dataset.
Original DBPedia ontology (triplets data): https://wiki.dbpedia.org/develop/datasets
Listing of the class tree/taxonomy: http://mappings.dbpedia.org/server/ontology/classes/
Acknowledgements
Thanks to the Wikimedia foundation for creating Wikipedia, DBPedia and associated open-data goodness!
Thanks to my colleagues at Sparkbeyond (https://www.sparkbeyond.com) for pointing me towards the taxonomical version of this dataset (as opposed to the classic 14 class version)
Inspiration
Try different NLP models.
See also https://www.kaggle.com/datasets/danofer/dbpedia-classes
Compare to the SOTA in Text Classification on DBpedia - https://paperswithcode.com/sota/text-classification-on-dbpedia |
DeveloperOats | null | null | null | false | 4 | false | DeveloperOats/Million_News_Headlines | 2022-08-08T14:56:01.000Z | null | false | bc91c8c8dbea6a44069e0a955b6ed8dd54fb7fe3 | [] | [
"language:en",
"license:cc0-1.0",
"multilinguality:monolingual",
"size_categories:1M<n<10M"
] | https://huggingface.co/datasets/DeveloperOats/Million_News_Headlines/resolve/main/README.md | ---
annotations_creators: []
language:
- en
language_creators: []
license:
- cc0-1.0
multilinguality:
- monolingual
pretty_name: million news headline
size_categories:
- 1M<n<10M
source_datasets: []
tags: []
task_categories: []
task_ids: []
---
About Dataset
Context
This contains data of news headlines published over a period of nineteen years.
Sourced from the reputable Australian news source ABC (Australian Broadcasting Corporation)
Agency Site: (http://www.abc.net.au)
Content
Format: CSV ; Single File
publish_date: Date of publishing for the article in yyyyMMdd format
headline_text: Text of the headline in Ascii , English , lowercase
Start Date: 2003-02-19 ; End Date: 2021-12-31
Inspiration
I look at this news dataset as a summarised historical record of noteworthy events in the globe from early-2003 to end-2021 with a more granular focus on Australia.
This includes the entire corpus of articles published by the abcnews website in the given date range.
With a volume of two hundred articles per day and a good focus on international news, we can be fairly certain that every event of significance has been captured here.
Digging into the keywords, one can see all the important episodes shaping the last decade and how they evolved over time.
Ex: afghanistan war, financial crisis, multiple elections, ecological disasters, terrorism, famous people, criminal activity et cetera.
Similar Work
Similar news datasets exploring other attributes, countries and topics can be seen on my profile.
Most kernals can be reused with minimal changes across these news datasets.
Prepared by Rohit Kulkarni
Taken from https://www.kaggle.com/datasets/therohk/million-headlines |
jakartaresearch | null | null | This dataset is built as a playground for beginner to make a use case for creating sentiment analysis model. | false | 2 | false | jakartaresearch/cerpen-corpus | 2022-08-08T14:35:40.000Z | null | false | 46112e07762195b01e3c3b53e22cfd69e88e61c3 | [] | [
"annotations_creators:no-annotation",
"language:id",
"language_creators:found",
"license:cc-by-4.0",
"multilinguality:monolingual",
"size_categories:n<1K",
"size_categories:10K<n<100K",
"source_datasets:original",
"tags:cerpen",
"tags:short-story",
"task_categories:text-generation",
"task_ids:... | https://huggingface.co/datasets/jakartaresearch/cerpen-corpus/resolve/main/README.md | ---
annotations_creators:
- no-annotation
language:
- id
language_creators:
- found
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: Small Indonesian Short Story Corpus
size_categories:
- n<1K
- 10K<n<100K
source_datasets:
- original
tags:
- cerpen
- short-story
task_categories:
- text-generation
task_ids:
- language-modeling
---
# Dataset Card for Cerpen Corpus
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This is a small size for Indonesian short story gathered from the internet.
We keep the large size for internal research. if you are interested, please join to [our discord server](https://discord.gg/6v28dq8dRE)
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@andreaschandra](https://github.com/andreaschandra) for adding this dataset. |
hkunlp | null | null | null | false | 1 | false | hkunlp/ds_codegen | 2022-08-08T14:51:05.000Z | null | false | 177611ed22e1d0e361fcf4b455a677b9ec9a5921 | [] | [
"license:apache-2.0"
] | https://huggingface.co/datasets/hkunlp/ds_codegen/resolve/main/README.md | ---
license: apache-2.0
---
|
lslattery | null | null | null | false | 2 | false | lslattery/wafer-defect-detection | 2022-08-14T19:53:45.000Z | null | false | 903fe28851d02a976db7a3a4bc12b6cfa2f5443c | [] | [] | https://huggingface.co/datasets/lslattery/wafer-defect-detection/resolve/main/README.md | Dataset used by the paper:
Wu, Ming-Ju, Jyh-Shing R. Jang, and Jui-Long Chen. “Wafer Map Failure Pattern Recognition and Similarity Ranking for Large-Scale Data Sets.” IEEE Transactions on Semiconductor Manufacturing 28, no. 1 (February 2015): 1–12. |
scikit-learn | null | null | null | false | 31 | false | scikit-learn/churn-prediction | 2022-08-08T17:56:29.000Z | null | false | aa09900373d90780ee70d27571775aff0e51569c | [] | [
"license:cc-by-4.0"
] | https://huggingface.co/datasets/scikit-learn/churn-prediction/resolve/main/README.md | ---
license: cc-by-4.0
---
Customer churn prediction dataset of a fictional telecommunication company made by IBM Sample Datasets.
Context
Predict behavior to retain customers. You can analyze all relevant customer data and develop focused customer retention programs.
Content
Each row represents a customer, each column contains customer’s attributes described on the column metadata.
The data set includes information about:
- Customers who left within the last month: the column is called Churn
- Services that each customer has signed up for: phone, multiple lines, internet, online security, online backup, device protection, tech support, and streaming TV and movies
- Customer account information: how long they’ve been a customer, contract, payment method, paperless billing, monthly charges, and total charges
- Demographic info about customers: gender, age range, and if they have partners and dependents
Credits for the dataset and the card:
- [Kaggle](https://www.kaggle.com/datasets/blastchar/telco-customer-churn)
- [Latest version of the dataset by IBM Samples team](https://community.ibm.com/community/user/businessanalytics/blogs/steven-macko/2019/07/11/telco-customer-churn-1113)
|
asaxena1990 | null | null | null | false | 2 | false | asaxena1990/Dummy_dataset | 2022-09-05T01:29:27.000Z | null | false | 5cde9ecee39de419b1a7c5838e86248a8a51ceef | [] | [
"license:cc-by-sa-4.0"
] | https://huggingface.co/datasets/asaxena1990/Dummy_dataset/resolve/main/README.md | ---
license: cc-by-sa-4.0
---
annotations_creators:
- no-annotation
language:
- en
language_creators:
- expert-generated
license:
- cc-by-nc-sa-4.0
multilinguality:
- monolingual
paperswithcode_id: acronym-identification
pretty_name: Massive E-commerce Dataset for Retail and Insurance domain.
size_categories:
- n<1K
source_datasets:
- original
tags:
- chatbots
- e-commerce
- retail
- insurance
- consumer
- consumer goods
task_categories:
- question-answering
- text-retrieval
- text2text-generation
- other
- translation
- conversational
task_ids:
- extractive-qa
- closed-domain-qa
- utterance-retrieval
- document-retrieval
- closed-domain-qa
- open-book-qa
- closed-book-qa
train-eval-index:
- col_mapping:
labels: tags
tokens: tokens
config: default
splits:
eval_split: test
task: token-classification
task_id: entity_extraction |
Chr0my | null | null | null | false | 1 | false | Chr0my/public_flickr_photos_license_1 | 2022-08-08T20:39:40.000Z | null | false | ae7ffce08599695beb1a5fe3ba6736ec686abdd6 | [] | [
"license:cc-by-nc-sa-3.0"
] | https://huggingface.co/datasets/Chr0my/public_flickr_photos_license_1/resolve/main/README.md | ---
license: cc-by-nc-sa-3.0
---
119893266 photos from flickr (https://www.flickr.com/creativecommons/by-nc-sa-2.0/)
---
all photos are under license id = 1 name=Attribution-NonCommercial-ShareAlike License url=https://creativecommons.org/licenses/by-nc-sa/2.0/ |
hoskinson-center | null | @InProceedings{huggingface:dataset,
title = {A great new dataset},
author={huggingface, Inc.
},
year={2020}
} | This new dataset is designed to solve this great NLP task and is crafted with a lot of care. | false | 36 | false | hoskinson-center/proof-pile | 2022-10-20T17:14:11.000Z | null | false | d2ed9e500d8df9db25a6e5c86139a196d700a22e | [] | [
"annotations_creators:no-annotation",
"language:en",
"language_creators:found",
"multilinguality:monolingual",
"tags:math",
"tags:mathematics",
"tags:formal-mathematics",
"task_categories:text-generation",
"task_ids:language-modeling"
] | https://huggingface.co/datasets/hoskinson-center/proof-pile/resolve/main/README.md | ---
annotations_creators:
- no-annotation
language:
- en
language_creators:
- found
license: []
multilinguality:
- monolingual
pretty_name: proof-pile
size_categories: []
source_datasets: []
tags:
- math
- mathematics
- formal-mathematics
task_categories:
- text-generation
task_ids:
- language-modeling
---
# Dataset Description
The `proof-pile` is a 40GB pre-training dataset of mathematical text that comprises roughly 15 billion tokens. The dataset is composed of diverse sources of both informal and formal mathematics, namely
- ArXiv.math (37GB)
- Open-source math textbooks (50MB)
- Formal mathematics libraries (500MB)
- Lean mathlib and other Lean repositories
- Isabelle AFP
- Coq mathematical components and other Coq repositories
- HOL Light
- set.mm
- Mizar Mathematical Library
- Math Overflow and Math Stack Exchange (500MB)
- Wiki-style sources (50MB)
- ProofWiki
- Wikipedia math articles
- MATH dataset (6MB)
# Supported Tasks
This dataset is intended to be used for pre-training language models. We envision models pre-trained on the `proof-pile` will have many downstream applications, including informal quantitative reasoning, formal theorem proving, semantic search for formal mathematics, and autoformalization.
# Languages
All informal mathematics in the `proof-pile` is written in English and LaTeX (arXiv articles in other languages are filtered out using [languagedetect](https://github.com/shuyo/language-detection/blob/wiki/ProjectHome.md)). Formal theorem proving languages represented in this dataset are Lean 3, Isabelle, Coq, HOL Light, Metamath, and Mizar.
# Configurations
The data is sorted into `"arxiv", "books", "formal", "stack-exchange", "wiki",` and `"math-dataset"` configurations. This is so that it is easy to upsample particular configurations during pre-training with the `datasets.interleave_datasets()` function.
# Evaluation
The version of `set.mm` in this dataset has 10% of proofs replaced with the `?` character in order to preserve a validation and test set for Metamath provers pre-trained on the `proof-pile`. The precise split can be found here: [validation](https://github.com/zhangir-azerbayev/mm-extract/blob/main/valid_decls.json) and [test](https://github.com/zhangir-azerbayev/mm-extract/blob/main/test_decls.json).
The Lean mathlib commit used in this dataset is `6313863`. Theorems created in subsequent commits can be used for evaluating Lean theorem provers.
This dataset contains only the training set of the [MATH dataset](https://github.com/hendrycks/math). However, because this dataset contains ProofWiki, the Stacks Project, Trench's Analysis, and Stein's Number Theory, models trained on it cannot be evaluated on the [NaturalProofs dataset](https://github.com/wellecks/naturalproofs).
## Contributions
Authors: Zhangir Azerbayev, Edward Ayers, Bartosz Piotrowski.
We would like to thank Jeremy Avigad, Albert Jiang, and Wenda Li for their invaluable guidance, and the Hoskinson Center for Formal Mathematics for its support.
|
arunreddy | null | null | null | false | 2 | false | arunreddy/Invictus | 2022-08-09T02:26:40.000Z | null | false | f6d54c0f3822be12d65526ea4563372048576c1f | [] | [
"license:cc-by-nc-4.0"
] | https://huggingface.co/datasets/arunreddy/Invictus/resolve/main/README.md | ---
license: cc-by-nc-4.0
---
|
ahadda5 | null | null | null | false | 1 | false | ahadda5/sanad | 2022-08-10T07:08:32.000Z | null | false | f656fde795f465d3a4ffae1f78575f4f98f684c9 | [] | [
"license:apache-2.0"
] | https://huggingface.co/datasets/ahadda5/sanad/resolve/main/README.md | ---
license: apache-2.0
---
|
deepklarity | null | null | null | false | 2 | false | deepklarity/top-flutter-packages | 2022-08-09T09:05:39.000Z | null | false | c219bc69317de924709cd566a027284ffc79953f | [] | [
"license:cc"
] | https://huggingface.co/datasets/deepklarity/top-flutter-packages/resolve/main/README.md | ---
license: cc
---
**Top Flutter Packages Dataset**
Flutter is an open source framework by Google for building beautiful, natively compiled, multi-platform applications from a single codebase. It is gaining quite a bit of popularity because of ability to code in a single language and have it running on Android/iOS and web as well.
This dataset contains a snapshot of Top 5000+ flutter/dart packages hosted on [Flutter package repository](https://pub.dev/)
The dataset was scraped in `July-2022`.
We aim to use this dataset to perform analysis and identify trends and get a bird's eye view of the rapidly evolving flutter ecosystem.
#### Mantainers:
- [Kondrolla Dinesh Reddy](https://twitter.com/KondrollaR)
- [Keshaw Soni](https://twitter.com/SoniKeshaw)
- [Somya Gautam](http://linkedin.in/in/somya-gautam)
|
deepklarity | null | null | null | false | 2 | false | deepklarity/top-npm-packages | 2022-08-09T09:13:13.000Z | null | false | eb6de1b8c90f77ec0a8cadc297268308367de753 | [] | [
"license:cc"
] | https://huggingface.co/datasets/deepklarity/top-npm-packages/resolve/main/README.md | ---
license: cc
---
**Top NPM Packages Dataset**
This dataset contains a snapshot of Top 3000 popular node packages hosted on [Node Package Manager](https://www.npmjs.com/)
The dataset was scraped in `July-2022`. This includes a combination of data gathered by [Libraries.io](https://libraries.io/) and [npm](https://www.npmjs.com/)
We aim to use this dataset to perform analysis and identify trends and get a bird's eye view of nodejs ecosystem.
#### Mantainers:
- [Keshaw Soni](https://twitter.com/SoniKeshaw)
- [Somya Gautam](http://linkedin.in/in/somya-gautam)
- [Kondrolla Dinesh Reddy](https://twitter.com/KondrollaR)
|
deepklarity | null | null | null | false | 2 | false | deepklarity/indian-premier-league | 2022-08-09T09:47:29.000Z | null | false | 1555264e93350b2cb253e4dd2ca7596b030cc143 | [] | [
"license:cc"
] | https://huggingface.co/datasets/deepklarity/indian-premier-league/resolve/main/README.md | ---
license: cc
---
**Indian Premier League Dataset**
This dataset contains info on all of the [IPL(Indian Premier League)](https://www.iplt20.com/) cricket matches.
Ball-by-Ball level info and scorecard info to be added soon.
The dataset was scraped in `July-2022`.
#### Mantainers:
- [Somya Gautam](http://linkedin.in/in/somya-gautam)
- [Kondrolla Dinesh Reddy](https://twitter.com/KondrollaR)
- [Keshaw Soni](https://twitter.com/SoniKeshaw)
|
NitishKarra | null | null | null | false | 1 | false | NitishKarra/Extractoin | 2022-08-09T11:20:53.000Z | null | false | 8c1b7854c3bcdca5c2346fa285cbdda798d7d5ff | [] | [] | https://huggingface.co/datasets/NitishKarra/Extractoin/resolve/main/README.md | |
zchflyer | null | null | null | false | 1 | false | zchflyer/testData | 2022-08-09T14:14:07.000Z | null | false | 2343fdc383e5333d6f214e452b2801d6602e54ea | [] | [
"license:mit"
] | https://huggingface.co/datasets/zchflyer/testData/resolve/main/README.md | ---
license: mit
---
|
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-Blaise-g__scitldr-89735e41-12705693 | 2022-08-09T16:51:14.000Z | null | false | c310f2a990aa87b7119122cbbf6b4664c8c5b5b7 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:Blaise-g/scitldr"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-Blaise-g__scitldr-89735e41-12705693/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- Blaise-g/scitldr
eval_info:
task: summarization
model: Blaise-g/longt5_tglobal_large_scitldr
metrics: ['bertscore']
dataset_name: Blaise-g/scitldr
dataset_config: Blaise-g--scitldr
dataset_split: test
col_mapping:
text: source
target: target
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: Blaise-g/longt5_tglobal_large_scitldr
* Dataset: Blaise-g/scitldr
* Config: Blaise-g--scitldr
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@Blaise-g](https://huggingface.co/Blaise-g) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-Blaise-g__scitldr-89735e41-12705694 | 2022-08-09T16:02:45.000Z | null | false | 506292df69a01a71aa75ff0fcdd162eba2120920 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:Blaise-g/scitldr"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-Blaise-g__scitldr-89735e41-12705694/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- Blaise-g/scitldr
eval_info:
task: summarization
model: Blaise-g/longt5_tglobal_large_explanatory_baseline_scitldr
metrics: ['bertscore']
dataset_name: Blaise-g/scitldr
dataset_config: Blaise-g--scitldr
dataset_split: test
col_mapping:
text: source
target: target
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: Blaise-g/longt5_tglobal_large_explanatory_baseline_scitldr
* Dataset: Blaise-g/scitldr
* Config: Blaise-g--scitldr
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@Blaise-g](https://huggingface.co/Blaise-g) for evaluating this model. |
autoevaluate | null | null | null | false | 2 | false | autoevaluate/autoeval-staging-eval-project-cnn_dailymail-1bafd1c4-12715695 | 2022-08-09T16:13:13.000Z | null | false | f48e06e7e27a3e222fe5923a930ccdf2d3fd9eee | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:cnn_dailymail"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-cnn_dailymail-1bafd1c4-12715695/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- cnn_dailymail
eval_info:
task: summarization
model: sshleifer/distilbart-xsum-12-6
metrics: []
dataset_name: cnn_dailymail
dataset_config: 3.0.0
dataset_split: test
col_mapping:
text: article
target: highlights
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: sshleifer/distilbart-xsum-12-6
* Dataset: cnn_dailymail
* Config: 3.0.0
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@grappler](https://huggingface.co/grappler) for evaluating this model. |
NX2411 | null | null | null | false | 4 | false | NX2411/AIhub-korean-speech-data-large-no-lm | 2022-08-09T17:25:15.000Z | null | false | dad96f0a811f89a30ac40d27161ef0ed609f3c49 | [] | [
"license:apache-2.0"
] | https://huggingface.co/datasets/NX2411/AIhub-korean-speech-data-large-no-lm/resolve/main/README.md | ---
license: apache-2.0
---
|
BlueAquilae | null | null | null | false | 1 | false | BlueAquilae/agro | 2022-08-09T20:43:51.000Z | null | false | 0825b7991240aa91ef61186ca7dab49f9df91c49 | [] | [
"license:lgpl-3.0"
] | https://huggingface.co/datasets/BlueAquilae/agro/resolve/main/README.md | ---
license: lgpl-3.0
---
|
benjaminaw93 | null | null | null | false | 2 | false | benjaminaw93/test | 2022-08-10T01:12:14.000Z | null | false | 73073e5fa4f784efc4a6568dbf2b088cdf27277c | [] | [
"license:apache-2.0"
] | https://huggingface.co/datasets/benjaminaw93/test/resolve/main/README.md | ---
license: apache-2.0
---
|
nateraw | null | null | null | false | 1 | false | nateraw/soundcamp-snares | 2022-08-10T03:49:07.000Z | null | false | 2c2c43f7ba7358e95de10283386e1e5670076214 | [] | [
"license:unknown"
] | https://huggingface.co/datasets/nateraw/soundcamp-snares/resolve/main/README.md | ---
license: unknown
---
|
SLPL | null | null | null | false | 1 | false | SLPL/syntran-fa | 2022-11-03T06:34:17.000Z | null | false | 1cc485463d5c2c6c6e3ef239239eb9857e6bebb2 | [] | [
"language:fa",
"license:mit",
"multilinguality:monolingual",
"size_categories:30k<n<50k",
"task_categories:question-answering",
"task_categories:text2text-generation",
"task_categories:text-generation",
"tags:conditional-text-generation",
"tags:conversational-question-answering"
] | https://huggingface.co/datasets/SLPL/syntran-fa/resolve/main/README.md | ---
language:
- fa
license: mit
multilinguality:
- monolingual
size_categories:
- 30k<n<50k
task_categories:
- question-answering
- text2text-generation
- text-generation
task_ids: []
pretty_name: SynTranFa
tags:
- conditional-text-generation
- conversational-question-answering
---
# SynTran-fa
Syntactic Transformed Version of Farsi QA datasets to make fluent responses from questions and short answers. You can use this dataset by the code below:
```python
import datasets
data = datasets.load_dataset('SLPL/syntran-fa', split="train")
```
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Dataset Creation](#dataset-creation)
- [Source Data](#source-data)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [Sharif-SLPL](https://github.com/Sharif-SLPL)
- **Repository:** [SynTran-fa](https://github.com/agp-internship/syntran-fa)
- **Point of Contact:** [Sadra Sabouri](mailto:sabouri.sadra@gmail.com)
### Dataset Summary
Generating fluent responses has always been challenging for the question-answering task, especially in low-resource languages like Farsi. In recent years there were some efforts for enhancing the size of datasets in Farsi. Syntran-fa is a question-answering dataset that accumulates the former Farsi QA dataset's short answers and proposes a complete fluent answer for each pair of (question, short_answer).
This dataset contains nearly 50,000 indices of questions and answers. The dataset that has been used as our sources are in [Source Data section](#source-data).
The main idea for this dataset comes from [Fluent Response Generation for Conversational Question Answering](https://aclanthology.org/2020.acl-main.19.pdf) where they used a "parser + syntactic rules" module to make different fluent answers from a pair of question and a short answer using a parser and some syntactic rules. In this project, we used [stanza](https://stanfordnlp.github.io/stanza/) as our parser to parse the question and generate a response according to it using the short (sentences without verbs - up to ~4 words) answers. One can continue this project by generating different permutations of the sentence's parts (and thus providing more than one sentence for an answer) or training a seq2seq model which does what we do with our rule-based system (by defining a new text-to-text task).
### Supported Tasks and Leaderboards
This dataset can be used for the question-answering task, especially when you are going to generate fluent responses. You can train a seq2seq model with this dataset to generate fluent responses - as done by [Fluent Response Generation for Conversational Question Answering](https://aclanthology.org/2020.acl-main.19.pdf).
### Languages
+ Persian (fa)
## Dataset Structure
Each row of the dataset will look like something like the below:
```json
{
'id': 0,
'question': 'باشگاه هاکی ساوتهمپتون چه نام دارد؟',
'short_answer': 'باشگاه هاکی ساوتهمپتون',
'fluent_answer': 'باشگاه هاکی ساوتهمپتون باشگاه هاکی ساوتهمپتون نام دارد.',
'bert_loss': 1.110097069682014
}
```
+ `id` : the entry id in dataset
+ `question` : the question
+ `short_answer` : the short answer corresponding to the `question` (the primary answer)
+ `fluent_answer` : fluent (long) answer generated from both `question` and the `short_answer` (the secondary answer)
+ `bert_loss` : the loss that [pars-bert](https://huggingface.co/HooshvareLab/bert-base-parsbert-uncased) gives when inputting the `fluent_answer` to it. As it increases the sentence is more likely to be influent.
Note: the dataset is sorted increasingly by the `bert_loss`, so first sentences are more likely to be fluent.
### Data Splits
Currently, the dataset just provided the `train` split. There would be a `test` split soon.
## Dataset Creation
### Source Data
The source datasets that we used are as follows:
+ [PersianQA](https://github.com/sajjjadayobi/PersianQA)
+ [PersianQuAD](https://ieeexplore.ieee.org/document/9729745)
#### Initial Data Collection and Normalization
We extract all short answer (sentences without verbs - up to ~4 words) entries of all open source QA datasets in Farsi and used some rules featuring the question parse tree to make long (fluent) answers.
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
The dataset is completely a subset of open source known datasets so all information in it is already there on the internet as a open-source dataset. By the way, we do not take responsibility for any of that.
## Additional Information
### Dataset Curators
The dataset is gathered together completely in the Asr Gooyesh Pardaz company's summer internship under the supervision of Soroush Gooran, Prof. Hossein Sameti, and the mentorship of Sadra Sabouri. This project was Farhan Farsi's first internship project.
### Licensing Information
MIT
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@farhaaaaa](https://github.com/farhaaaaa) and [@sadrasabouri](https://github.com/sadrasabouri) for adding this dataset. |
UCL-DARK | null | TBC | TODO | false | 619 | false | UCL-DARK/ludwig | 2022-08-11T15:51:56.000Z | null | false | 8bea8b3d7a39664cd7827474342599b6ab016991 | [] | [
"annotations_creators:expert-generated",
"language:en",
"language_creators:expert-generated",
"license:cc-by-4.0",
"multilinguality:monolingual",
"size_categories:n<1K",
"source_datasets:original",
"tags:implicature",
"tags:pragmatics",
"tags:language",
"tags:llm",
"tags:conversation",
"tags... | https://huggingface.co/datasets/UCL-DARK/ludwig/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language:
- en
language_creators:
- expert-generated
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: ludwig
size_categories:
- n<1K
source_datasets:
- original
tags:
- implicature
- pragmatics
- language
- llm
- conversation
- dialogue
task_categories:
- text-generation
- fill-mask
task_ids:
- language-modeling
- masked-language-modeling
---
# Dataset Card for LUDWIG
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository: https://github.com/ucl-dark/ludwig**
- **Paper: TODO**
- **Leaderboard: TODO**
- **Point of Contact: Laura Ruis**
### Dataset Summary
LUDWIG (**L**anguage **U**nderstanding **W**ith **I**mplied meanin**G**) is a dataset containing English conversational implicatures.
Implicature is the act of meaning or implying one thing by saying something else.
There's different types of implicatures, from simple ones like "Some guests came to the party"
(implying not all guests came) to more complicated implicatures that depend on context like
"A: Are you going to the party this Friday? B: There's a global pandemic.", implying no. Implicatures serve a wide range of
goals in communication: efficiency, style, navigating social interactions, and more. We cannot fully
understand utterances without understanding their implications.
The implicatures in this dataset are conversational because they come in utterance-response tuples.
Each tuple has an implicature associated with it,
which is the implied meaning of the response. For example:
Utterance: Are you going to the party this Friday?
Response: There's a global pandemic.
Implicature: No.
This dataset can be used to evaluate language models on their pragmatic language understanding.
### Supported Tasks and Leaderboards
- ```text-generation```: The dataset can be used to evaluate a models ability to generate the correct next token, i.e. "yes" or "no", depending on the implicature. For example, if you pass the model an example wrapped in a template like "Esther asked 'Are you coming to the party this Friday' and Juan responded 'There's a global pandemic', which means" the correct completion would be "no". Success in this task can be determined by the ability to generate the correct answer or by the ability to give the right token a higher likelihood than the wrong token, e.g. p("no") > p("yes").
- ```fill-mask```: The dataset can be used to evaluate a models ability to fill the correct token, i.e. "yes" or "no", depending on the implicature. For example, if you pass the model an example wrapped in a template like "Esther asked 'Are you coming to the party this Friday' and Juan responded 'There's a global pandemic', which means [mask]" the correct mask-fill would be "no". Success in this task can be determined by the ability to fill the correct answer or by the ability to give the right token a higher likelihood than the wrong token, e.g. p("no") > p("yes").
### Languages
English
## Dataset Structure
### Data Instances
Find below an example of a 1-shot example instance (1-shot because there's 1 prompt example).
```
{
"id": 1,
"utterance": "Are you going to the party this Friday?",
"response": "There's a global pandemic.",
"implicature": "No.",
"incoherent_implicature": "Yes".
"prompts": [
{
"utterance": "Was that hot?",
"response": "The sun was scorching.",
"implicature": "Yes.",
"incoherent_implicature": "No.".
}
]
}
```
### Data Fields
```
{
"id": int, # unique identifier of data points
"utterance": str, # the utterance in this example
"response": str, # the response in this example
"implicature": str, # the implied meaning of the response, e.g. 'yes'
"incoherent_implicature": str, # the wrong implied meaning, e.g. 'no'
"prompts": [ # optional: prompt examples from the validation set
{
"utterance": str,
"response": str,
"implicature": str,
"incoherent_implicature": str,
}
]
}
```
### Data Splits
**Validation**: 118 instances that can be used for finetuning or few-shot learning
**Test**: 600 instances that can be used for evaluating models.
NB: the splits weren't originally part of the paper that presents this dataset. The same goes for the k-shot prompts. Added
by @LauraRuis.
## Dataset Creation
### Curation Rationale
Pragmatic language understanding is a crucial aspect of human communication, and implicatures are the primary object of study in this field.
We want computational models of language to understand all the speakers implications.
### Source Data
#### Initial Data Collection and Normalization
"Conversational implicatures in English dialogue: Annotated dataset", Elizabeth Jasmi George and Radhika Mamidi 2020.
[Link to paper](https://doi.org/10.1016/j.procs.2020.04.251)
#### Who are the source language producers?
These written representations of the utterances are collected manually by scraping and transcribing from relevant sources from August, 2019 to August, 2020. The source of dialogues in the data include TOEFL listening comprehension short conversations, movie dialogues from IMSDb and websites explaining idioms, similes, metaphors and hyperboles. The implicatures are annotated manually.
### Annotations
#### Annotation process
Manually annotated by dataset collectors.
#### Who are the annotators?
Authors of the original paper.
### Personal and Sensitive Information
All the data is public and not sensitive.
## Considerations for Using the Data
### Social Impact of Dataset
Any application that requires communicating with humans requires pragmatic language understanding.
### Discussion of Biases
Implicatures can be biased to specific cultures. For example, whether the Pope is Catholic (a common used response implicature to indicate "yes") might not be common knowledge for everyone.
Implicatures are also language-specific, the way people use pragmatic language depends on the language. This dataset only focuses on the English language.
### Other Known Limitations
None yet.
## Additional Information
### Dataset Curators
Elizabeth Jasmi George and Radhika Mamidi
### Licensing Information
[license](https://creativecommons.org/licenses/by/4.0/)
### Citation Information
```
@article{George:Mamidi:2020,
author = {George, Elizabeth Jasmi and Mamidi, Radhika},
doi = {10.1016/j.procs.2020.04.251},
journal = {Procedia Computer Science},
keywords = {},
note = {https://doi.org/10.1016/j.procs.2020.04.251},
number = {},
pages = {2316-2323},
title = {Conversational implicatures in English dialogue: Annotated dataset},
url = {https://app.dimensions.ai/details/publication/pub.1128198497},
volume = {171},
year = {2020}
}
```
### Contributions
Thanks to [@LauraRuis](https://github.com/LauraRuis) for adding this dataset. |
yuan1729 | null | null | null | false | 2 | false | yuan1729/YuAN-001 | 2022-08-10T09:54:37.000Z | null | false | ec949ded2c09f0eb3a75779088bba0b445d1edf8 | [] | [
"license:mit"
] | https://huggingface.co/datasets/yuan1729/YuAN-001/resolve/main/README.md | ---
license: mit
---
|
valurank | null | null | null | false | 4 | false | valurank/News_headlines | 2022-08-17T08:19:18.000Z | null | false | fe832a80cc04621645f721f68baa80783bf88486 | [] | [
"license:other"
] | https://huggingface.co/datasets/valurank/News_headlines/resolve/main/README.md | ---
license: other
---
|
ChristophSchuhmann | null | null | null | false | 2 | false | ChristophSchuhmann/test-files | 2022-08-10T10:34:27.000Z | null | false | aea0b7d088a9d02b09262cdb55c9d1208efb48b3 | [] | [
"license:apache-2.0"
] | https://huggingface.co/datasets/ChristophSchuhmann/test-files/resolve/main/README.md | ---
license: apache-2.0
---
|
mariosasko | null | null | null | false | 1 | false | mariosasko/sql | 2022-08-17T17:13:22.000Z | null | false | d8dc8dc5ba9e7f44b4974590c26f62f345a47f56 | [] | [
"license:apache-2.0"
] | https://huggingface.co/datasets/mariosasko/sql/resolve/main/README.md | ---
license: apache-2.0
viewer: false
---
### Usage
```python
from datasets import load_dataset
# Load everything into "train" set
## Using a query
dset = load_dataset("mariosasko/sql", sql="SELECT * FROM my_dataset LIMIT 10", con="sqlite:///my_db.db")
## Referencing a table
dset = load_dataset("mariosasko/sql", sql="data_table", con="postgres:///db_name")
# Load multiple splits
dset = load_dataset(
"mariosasko/sql",
sql={
"train": "SELECT * FROM my_dataset LIMIT 10",
"test": "SELECT * FROM my_dataset LIMIT 10 OFFSET 10",
},
con="sqlite:///my_db.db"
)
```
`sql` and `con` can only be specified as strings to work with `datasets`' caching mechanism. `pd.read_sql` is used internally for query processing, so refer to its [doc](https://pandas.pydata.org/docs/reference/api/pandas.read_sql.html) for the complete list of supported parameters. |
USC-MOLA-Lab | null | null | null | false | 68 | false | USC-MOLA-Lab/MFRC | 2022-08-26T00:36:03.000Z | null | false | 7f5939deef9875465d3ff70ab0102ef957f4f352 | [] | [
"arxiv:2208.05545"
] | https://huggingface.co/datasets/USC-MOLA-Lab/MFRC/resolve/main/README.md | # Dataset Card for MFRC
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
Reddit posts annotated for moral foundations
### Supported Tasks and Leaderboards
### Languages
English
## Dataset Structure
### Data Instances
### Data Fields
- text
- subreddit
- bucket
- annotator
- annotation
- confidence
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
cc-by-4.0
### Citation Information
```bibtex
@misc{trager2022moral,
title={The Moral Foundations Reddit Corpus},
author={Jackson Trager and Alireza S. Ziabari and Aida Mostafazadeh Davani and Preni Golazazian and Farzan Karimi-Malekabadi and Ali Omrani and Zhihe Li and Brendan Kennedy and Nils Karl Reimer and Melissa Reyes and Kelsey Cheng and Mellow Wei and Christina Merrifield and Arta Khosravi and Evans Alvarez and Morteza Dehghani},
year={2022},
eprint={2208.05545},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### Contributions
|
CShorten | null | null | null | false | 2 | false | CShorten/CORD-19-prototype | 2022-08-11T20:49:11.000Z | null | false | 2328dd311177458120231b103ac782bae1844c0a | [] | [] | https://huggingface.co/datasets/CShorten/CORD-19-prototype/resolve/main/README.md | Subset of CORD-19 for rapid prototyping of ideas in vector encodings and Weaviate. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-multi_news-416d7689-12805701 | 2022-08-10T19:11:52.000Z | null | false | ffcbba9a8234249d2f89c0e828415cbc81d52428 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:multi_news"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-multi_news-416d7689-12805701/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- multi_news
eval_info:
task: summarization
model: datien228/distilbart-cnn-12-6-ftn-multi_news
metrics: []
dataset_name: multi_news
dataset_config: default
dataset_split: test
col_mapping:
text: document
target: summary
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: datien228/distilbart-cnn-12-6-ftn-multi_news
* Dataset: multi_news
* Config: default
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@ccdv](https://huggingface.co/ccdv) for evaluating this model. |
Meowren | null | null | null | false | 1 | false | Meowren/CapBot | 2022-08-10T18:58:02.000Z | null | false | 306b7c093b3d71163e9e7d0a6f4bd572fefca7ae | [] | [] | https://huggingface.co/datasets/Meowren/CapBot/resolve/main/README.md | 'Conversational bot' |
google | null | @inproceedings{jia2022cvss,
title={{CVSS} Corpus and Massively Multilingual Speech-to-Speech Translation},
author={Jia, Ye and Tadmor Ramanovich, Michelle and Wang, Quan and Zen, Heiga},
booktitle={Proceedings of Language Resources and Evaluation Conference (LREC)},
pages={6691--6703},
year={2022}
} | CVSS is a massively multilingual-to-English speech-to-speech translation corpus,
covering sentence-level parallel speech-to-speech translation pairs from 21
languages into English. | false | 6 | false | google/cvss | 2022-08-27T23:19:14.000Z | null | false | 206b001828fb8532e569701519dac19d048fbf09 | [] | [
"arxiv:2201.03713",
"license:cc-by-4.0"
] | https://huggingface.co/datasets/google/cvss/resolve/main/README.md | ---
license: cc-by-4.0
---
# CVSS: A Massively Multilingual Speech-to-Speech Translation Corpus
*CVSS* is a massively multilingual-to-English speech-to-speech translation corpus, covering sentence-level parallel speech-to-speech translation pairs from 21 languages into English. CVSS is derived from the [Common Voice](https://commonvoice.mozilla.org/) speech corpus and the [CoVoST 2](https://github.com/facebookresearch/covost) speech-to-text translation corpus. The translation speech in CVSS is synthesized with two state-of-the-art TTS models trained on the [LibriTTS](http://www.openslr.org/60/) corpus.
CVSS includes two versions of spoken translation for all the 21 x-en language pairs from CoVoST 2, with each version providing unique values:
- *CVSS-C*: All the translation speeches are in a single canonical speaker's voice. Despite being synthetic, these speeches are of very high naturalness and cleanness, as well as having a consistent speaking style. These properties ease the modeling of the target speech and enable models to produce high quality translation speech suitable for user-facing applications.
- *CVSS-T*: The translation speeches are in voices transferred from the corresponding source speeches. Each translation pair has similar voices on the two sides despite being in different languages, making this dataset suitable for building models that preserve speakers' voices when translating speech into different languages.
Together with the source speeches originated from Common Voice, they make two multilingual speech-to-speech translation datasets each with about 1,900 hours of speech.
In addition to translation speech, CVSS also provides normalized translation text matching the pronunciation in the translation speech (e.g. on numbers, currencies, acronyms, etc.), which can be used for both model training as well as standardizing evaluation.
Please check out [our paper](https://arxiv.org/abs/2201.03713) for the detailed description of this corpus, as well as the baseline models we trained on both datasets.
# Load the data
The following example loads the translation speech (i.e. target speech) and the normalized translation text (i.e. target text) released in CVSS corpus. You'll need to load the source speech and optionally the source text from [Common Voice v4.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_4_0) separately, and join them by the file names.
```py
from datasets import load_dataset
# Load only ar-en and ja-en language pairs. Omitting the `languages` argument
# would load all the language pairs.
cvss_c = load_dataset('google/cvss', 'cvss_c', languages=['ar', 'ja'])
# Print the structure of the dataset.
print(cvss_c)
```
# License
CVSS is released under the very permissive [Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/) license.
## Citation
Please cite this paper when referencing the CVSS corpus:
```
@inproceedings{jia2022cvss,
title={{CVSS} Corpus and Massively Multilingual Speech-to-Speech Translation},
author={Jia, Ye and Tadmor Ramanovich, Michelle and Wang, Quan and Zen, Heiga},
booktitle={Proceedings of Language Resources and Evaluation Conference (LREC)},
pages={6691--6703},
year={2022}
}
```
|
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-cnn_dailymail-00961196-12825703 | 2022-08-11T16:19:41.000Z | null | false | 5e3c3e47fc4b7946b1475ac53a45f83fc6430ba7 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:cnn_dailymail"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-cnn_dailymail-00961196-12825703/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- cnn_dailymail
eval_info:
task: summarization
model: sysresearch101/t5-large-finetuned-xsum-cnn
metrics: []
dataset_name: cnn_dailymail
dataset_config: 3.0.0
dataset_split: train
col_mapping:
text: article
target: highlights
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: sysresearch101/t5-large-finetuned-xsum-cnn
* Dataset: cnn_dailymail
* Config: 3.0.0
* Split: train
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@sysresearch101](https://huggingface.co/sysresearch101) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-cnn_dailymail-2bf8ffdd-12835704 | 2022-08-11T16:34:44.000Z | null | false | 5c0489317b6d18b9e69c837e2940f2033b7fd0d7 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:cnn_dailymail"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-cnn_dailymail-2bf8ffdd-12835704/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- cnn_dailymail
eval_info:
task: summarization
model: sysresearch101/t5-large-finetuned-xsum
metrics: []
dataset_name: cnn_dailymail
dataset_config: 3.0.0
dataset_split: train
col_mapping:
text: article
target: highlights
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: sysresearch101/t5-large-finetuned-xsum
* Dataset: cnn_dailymail
* Config: 3.0.0
* Split: train
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@sysresearch101](https://huggingface.co/sysresearch101) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-cnn_dailymail-2bf8ffdd-12835705 | 2022-08-11T16:24:08.000Z | null | false | 973997ff4b661d0de5320aef3345d5b4b66ad482 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:cnn_dailymail"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-cnn_dailymail-2bf8ffdd-12835705/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- cnn_dailymail
eval_info:
task: summarization
model: t5-large
metrics: []
dataset_name: cnn_dailymail
dataset_config: 3.0.0
dataset_split: train
col_mapping:
text: article
target: highlights
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: t5-large
* Dataset: cnn_dailymail
* Config: 3.0.0
* Split: train
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@sysresearch101](https://huggingface.co/sysresearch101) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-cnn_dailymail-2bf8ffdd-12835706 | 2022-08-11T06:07:30.000Z | null | false | b1b725d70e20a37d1d94aa41d0c22a0fe4c3245a | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:cnn_dailymail"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-cnn_dailymail-2bf8ffdd-12835706/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- cnn_dailymail
eval_info:
task: summarization
model: t5-base
metrics: []
dataset_name: cnn_dailymail
dataset_config: 3.0.0
dataset_split: train
col_mapping:
text: article
target: highlights
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: t5-base
* Dataset: cnn_dailymail
* Config: 3.0.0
* Split: train
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@sysresearch101](https://huggingface.co/sysresearch101) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-xsum-d7ddcd7b-12845708 | 2022-08-11T03:07:02.000Z | null | false | 07a8b5711578956e3962668341e696c23b4afba8 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:xsum"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-xsum-d7ddcd7b-12845708/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- xsum
eval_info:
task: summarization
model: sysresearch101/t5-large-finetuned-xsum
metrics: []
dataset_name: xsum
dataset_config: default
dataset_split: test
col_mapping:
text: document
target: summary
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: sysresearch101/t5-large-finetuned-xsum
* Dataset: xsum
* Config: default
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@sysresearch101](https://huggingface.co/sysresearch101) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-xsum-d7ddcd7b-12845709 | 2022-08-11T03:16:03.000Z | null | false | 8b3718ab8d417b60b0841465810b4e9cc062d710 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:xsum"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-xsum-d7ddcd7b-12845709/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- xsum
eval_info:
task: summarization
model: facebook/bart-large-cnn
metrics: []
dataset_name: xsum
dataset_config: default
dataset_split: test
col_mapping:
text: document
target: summary
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: facebook/bart-large-cnn
* Dataset: xsum
* Config: default
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@sysresearch101](https://huggingface.co/sysresearch101) for evaluating this model. |
autoevaluate | null | null | null | false | 12 | false | autoevaluate/autoeval-staging-eval-project-xsum-d7ddcd7b-12845710 | 2022-08-11T03:07:06.000Z | null | false | 97af091b1c1eeae4c0f48d669716625ccd78c2c6 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:xsum"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-xsum-d7ddcd7b-12845710/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- xsum
eval_info:
task: summarization
model: sysresearch101/t5-large-finetuned-xsum-cnn
metrics: []
dataset_name: xsum
dataset_config: default
dataset_split: test
col_mapping:
text: document
target: summary
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: sysresearch101/t5-large-finetuned-xsum-cnn
* Dataset: xsum
* Config: default
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@sysresearch101](https://huggingface.co/sysresearch101) for evaluating this model. |
planhanasan | null | null | null | false | 1 | false | planhanasan/github-issues | 2022-08-11T04:22:30.000Z | null | false | 4b84d943bd01791746753c43d65d04d4bd72c098 | [] | [
"arxiv:2005.00614"
] | https://huggingface.co/datasets/planhanasan/github-issues/resolve/main/README.md | # Dataset Card for GitHub Issues
## Dataset Description
- **Point of Contact:** [Lewis Tunstall](lewis@huggingface.co)
### Dataset Summary
GitHub Issues is a dataset consisting of GitHub issues and pull requests associated with the 🤗 Datasets [repository](https://github.com/huggingface/datasets). It is intended for educational purposes and can be used for semantic search or multilabel text classification. The contents of each GitHub issue are in English and concern the domain of datasets for NLP, computer vision, and beyond.
### Supported Tasks and Leaderboards
For each of the tasks tagged for this dataset, give a brief description of the tag, metrics, and suggested models (with a link to their HuggingFace implementation if available). Give a similar description of tasks that were not covered by the structured tag set (repace the `task-category-tag` with an appropriate `other:other-task-name`).
- `task-category-tag`: The dataset can be used to train a model for [TASK NAME], which consists in [TASK DESCRIPTION]. Success on this task is typically measured by achieving a *high/low* [metric name](https://huggingface.co/metrics/metric_name). The ([model name](https://huggingface.co/model_name) or [model class](https://huggingface.co/transformers/model_doc/model_class.html)) model currently achieves the following score. *[IF A LEADERBOARD IS AVAILABLE]:* This task has an active leaderboard which can be found at [leaderboard url]() and ranks models based on [metric name](https://huggingface.co/metrics/metric_name) while also reporting [other metric name](https://huggingface.co/metrics/other_metric_name).
### Languages
Provide a brief overview of the languages represented in the dataset. Describe relevant details about specifics of the language such as whether it is social media text, African American English,...
When relevant, please provide [BCP-47 codes](https://tools.ietf.org/html/bcp47), which consist of a [primary language subtag](https://tools.ietf.org/html/bcp47#section-2.2.1), with a [script subtag](https://tools.ietf.org/html/bcp47#section-2.2.3) and/or [region subtag](https://tools.ietf.org/html/bcp47#section-2.2.4) if available.
## Dataset Structure
### Data Instances
Provide an JSON-formatted example and brief description of a typical instance in the dataset. If available, provide a link to further examples.
```
{
'example_field': ...,
...
}
```
Provide any additional information that is not covered in the other sections about the data here. In particular describe any relationships between data points and if these relationships are made explicit.
### Data Fields
List and describe the fields present in the dataset. Mention their data type, and whether they are used as input or output in any of the tasks the dataset currently supports. If the data has span indices, describe their attributes, such as whether they are at the character level or word level, whether they are contiguous or not, etc. If the datasets contains example IDs, state whether they have an inherent meaning, such as a mapping to other datasets or pointing to relationships between data points.
- `example_field`: description of `example_field`
Note that the descriptions can be initialized with the **Show Markdown Data Fields** output of the [tagging app](https://github.com/huggingface/datasets-tagging), you will then only need to refine the generated descriptions.
### Data Splits
Describe and name the splits in the dataset if there are more than one.
Describe any criteria for splitting the data, if used. If their are differences between the splits (e.g. if the training annotations are machine-generated and the dev and test ones are created by humans, or if different numbers of annotators contributed to each example), describe them here.
Provide the sizes of each split. As appropriate, provide any descriptive statistics for the features, such as average length. For example:
| | Tain | Valid | Test |
| ----- | ------ | ----- | ---- |
| Input Sentences | | | |
| Average Sentence Length | | | |
## Dataset Creation
### Curation Rationale
What need motivated the creation of this dataset? What are some of the reasons underlying the major choices involved in putting it together?
### Source Data
This section describes the source data (e.g. news text and headlines, social media posts, translated sentences,...)
#### Initial Data Collection and Normalization
Describe the data collection process. Describe any criteria for data selection or filtering. List any key words or search terms used. If possible, include runtime information for the collection process.
If data was collected from other pre-existing datasets, link to source here and to their [Hugging Face version](https://huggingface.co/datasets/dataset_name).
If the data was modified or normalized after being collected (e.g. if the data is word-tokenized), describe the process and the tools used.
#### Who are the source language producers?
State whether the data was produced by humans or machine generated. Describe the people or systems who originally created the data.
If available, include self-reported demographic or identity information for the source data creators, but avoid inferring this information. Instead state that this information is unknown. See [Larson 2017](https://www.aclweb.org/anthology/W17-1601.pdf) for using identity categories as a variables, particularly gender.
Describe the conditions under which the data was created (for example, if the producers were crowdworkers, state what platform was used, or if the data was found, what website the data was found on). If compensation was provided, include that information here.
Describe other people represented or mentioned in the data. Where possible, link to references for the information.
### Annotations
If the dataset contains annotations which are not part of the initial data collection, describe them in the following paragraphs.
#### Annotation process
If applicable, describe the annotation process and any tools used, or state otherwise. Describe the amount of data annotated, if not all. Describe or reference annotation guidelines provided to the annotators. If available, provide interannotator statistics. Describe any annotation validation processes.
#### Who are the annotators?
If annotations were collected for the source data (such as class labels or syntactic parses), state whether the annotations were produced by humans or machine generated.
Describe the people or systems who originally created the annotations and their selection criteria if applicable.
If available, include self-reported demographic or identity information for the annotators, but avoid inferring this information. Instead state that this information is unknown. See [Larson 2017](https://www.aclweb.org/anthology/W17-1601.pdf) for using identity categories as a variables, particularly gender.
Describe the conditions under which the data was annotated (for example, if the annotators were crowdworkers, state what platform was used, or if the data was found, what website the data was found on). If compensation was provided, include that information here.
### Personal and Sensitive Information
State whether the dataset uses identity categories and, if so, how the information is used. Describe where this information comes from (i.e. self-reporting, collecting from profiles, inferring, etc.). See [Larson 2017](https://www.aclweb.org/anthology/W17-1601.pdf) for using identity categories as a variables, particularly gender. State whether the data is linked to individuals and whether those individuals can be identified in the dataset, either directly or indirectly (i.e., in combination with other data).
State whether the dataset contains other data that might be considered sensitive (e.g., data that reveals racial or ethnic origins, sexual orientations, religious beliefs, political opinions or union memberships, or locations; financial or health data; biometric or genetic data; forms of government identification, such as social security numbers; criminal history).
If efforts were made to anonymize the data, describe the anonymization process.
## Considerations for Using the Data
### Social Impact of Dataset
Please discuss some of the ways you believe the use of this dataset will impact society.
The statement should include both positive outlooks, such as outlining how technologies developed through its use may improve people's lives, and discuss the accompanying risks. These risks may range from making important decisions more opaque to people who are affected by the technology, to reinforcing existing harmful biases (whose specifics should be discussed in the next section), among other considerations.
Also describe in this section if the proposed dataset contains a low-resource or under-represented language. If this is the case or if this task has any impact on underserved communities, please elaborate here.
### Discussion of Biases
Provide descriptions of specific biases that are likely to be reflected in the data, and state whether any steps were taken to reduce their impact.
For Wikipedia text, see for example [Dinan et al 2020 on biases in Wikipedia (esp. Table 1)](https://arxiv.org/abs/2005.00614), or [Blodgett et al 2020](https://www.aclweb.org/anthology/2020.acl-main.485/) for a more general discussion of the topic.
If analyses have been run quantifying these biases, please add brief summaries and links to the studies here.
### Other Known Limitations
If studies of the datasets have outlined other limitations of the dataset, such as annotation artifacts, please outline and cite them here.
## Additional Information
### Dataset Curators
List the people involved in collecting the dataset and their affiliation(s). If funding information is known, include it here.
### Licensing Information
Provide the license and link to the license webpage if available.
### Citation Information
Provide the [BibTex](http://www.bibtex.org/)-formatted reference for the dataset. For example:
```
@article{article_id,
author = {Author List},
title = {Dataset Paper Title},
journal = {Publication Venue},
year = {2525}
}
```
If the dataset has a [DOI](https://www.doi.org/), please provide it here.
### Contributions
Thanks to [@lewtun](https://github.com/lewtun) for adding this dataset. |
autoevaluate | null | null | null | false | 6 | false | autoevaluate/autoeval-staging-eval-project-cnn_dailymail-3ca4a8a7-12855711 | 2022-08-11T19:55:34.000Z | null | false | e7d454b3ca32b66e7d270a2c766c42f5f5f70b46 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:cnn_dailymail"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-cnn_dailymail-3ca4a8a7-12855711/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- cnn_dailymail
eval_info:
task: summarization
model: sysresearch101/t5-large-finetuned-xsum
metrics: []
dataset_name: cnn_dailymail
dataset_config: 3.0.0
dataset_split: train
col_mapping:
text: article
target: highlights
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: sysresearch101/t5-large-finetuned-xsum
* Dataset: cnn_dailymail
* Config: 3.0.0
* Split: train
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@sysresearch101](https://huggingface.co/sysresearch101) for evaluating this model. |
autoevaluate | null | null | null | false | 6 | false | autoevaluate/autoeval-staging-eval-project-cnn_dailymail-3ca4a8a7-12855712 | 2022-08-11T20:04:47.000Z | null | false | 6f7358a3b383aea6d10788b8a63cd814e028f64b | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:cnn_dailymail"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-cnn_dailymail-3ca4a8a7-12855712/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- cnn_dailymail
eval_info:
task: summarization
model: sysresearch101/t5-large-finetuned-xsum-cnn
metrics: []
dataset_name: cnn_dailymail
dataset_config: 3.0.0
dataset_split: train
col_mapping:
text: article
target: highlights
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: sysresearch101/t5-large-finetuned-xsum-cnn
* Dataset: cnn_dailymail
* Config: 3.0.0
* Split: train
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@sysresearch101](https://huggingface.co/sysresearch101) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-cnn_dailymail-3ca4a8a7-12855713 | 2022-08-11T09:41:15.000Z | null | false | e404fa8894ce2092f89eae86da115760db88574f | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:cnn_dailymail"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-cnn_dailymail-3ca4a8a7-12855713/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- cnn_dailymail
eval_info:
task: summarization
model: t5-base
metrics: []
dataset_name: cnn_dailymail
dataset_config: 3.0.0
dataset_split: train
col_mapping:
text: article
target: highlights
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: t5-base
* Dataset: cnn_dailymail
* Config: 3.0.0
* Split: train
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@sysresearch101](https://huggingface.co/sysresearch101) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-cnn_dailymail-3ca4a8a7-12855714 | 2022-08-11T19:57:13.000Z | null | false | d6e0e001bba9b14661345a9575ca7f11609a3b59 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:cnn_dailymail"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-cnn_dailymail-3ca4a8a7-12855714/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- cnn_dailymail
eval_info:
task: summarization
model: t5-large
metrics: []
dataset_name: cnn_dailymail
dataset_config: 3.0.0
dataset_split: train
col_mapping:
text: article
target: highlights
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: t5-large
* Dataset: cnn_dailymail
* Config: 3.0.0
* Split: train
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@sysresearch101](https://huggingface.co/sysresearch101) for evaluating this model. |
BigBang | null | null | null | false | 17 | false | BigBang/rosetta_old | 2022-08-25T08:36:05.000Z | null | false | c9cf33cf2552490371e7694b1b8ffa8685cc7ba4 | [] | [
"license:cc-by-sa-4.0"
] | https://huggingface.co/datasets/BigBang/rosetta_old/resolve/main/README.md | ---
license:
- cc-by-sa-4.0
--- |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-cnn_dailymail-c1b20bff-12875715 | 2022-08-11T13:04:30.000Z | null | false | 44c960b81b39ddf04b08a9a23f451c23a30ea8b5 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:cnn_dailymail"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-cnn_dailymail-c1b20bff-12875715/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- cnn_dailymail
eval_info:
task: summarization
model: facebook/bart-large-cnn
metrics: []
dataset_name: cnn_dailymail
dataset_config: 3.0.0
dataset_split: test
col_mapping:
text: article
target: highlights
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: facebook/bart-large-cnn
* Dataset: cnn_dailymail
* Config: 3.0.0
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@grapplerulrich](https://huggingface.co/grapplerulrich) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-cnn_dailymail-c1b20bff-12875716 | 2022-08-11T12:35:14.000Z | null | false | 060d4151a9bed0e17f02cf8713bbb080109b6c2b | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:cnn_dailymail"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-cnn_dailymail-c1b20bff-12875716/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- cnn_dailymail
eval_info:
task: summarization
model: csebuetnlp/mT5_multilingual_XLSum
metrics: []
dataset_name: cnn_dailymail
dataset_config: 3.0.0
dataset_split: test
col_mapping:
text: article
target: highlights
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: csebuetnlp/mT5_multilingual_XLSum
* Dataset: cnn_dailymail
* Config: 3.0.0
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@grapplerulrich](https://huggingface.co/grapplerulrich) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-cnn_dailymail-c1b20bff-12875717 | 2022-08-11T13:46:48.000Z | null | false | 1312ec1d0f1935bb84c3e1471dbcac70b82944fd | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:cnn_dailymail"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-cnn_dailymail-c1b20bff-12875717/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- cnn_dailymail
eval_info:
task: summarization
model: google/pegasus-cnn_dailymail
metrics: []
dataset_name: cnn_dailymail
dataset_config: 3.0.0
dataset_split: test
col_mapping:
text: article
target: highlights
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: google/pegasus-cnn_dailymail
* Dataset: cnn_dailymail
* Config: 3.0.0
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@grapplerulrich](https://huggingface.co/grapplerulrich) for evaluating this model. |
ChristophSchuhmann | null | null | null | false | 1,160 | false | ChristophSchuhmann/improved_aesthetics_5plus | 2022-08-11T12:46:57.000Z | null | false | 3995ba969730f1dc7142a26a34d0b192763272a9 | [] | [
"license:apache-2.0"
] | https://huggingface.co/datasets/ChristophSchuhmann/improved_aesthetics_5plus/resolve/main/README.md | ---
license: apache-2.0
---
|
hf-internal-testing | null | null | null | false | 6 | false | hf-internal-testing/spaghetti-video-8-frames | 2022-08-25T16:00:38.000Z | null | false | 00e13174de84f6892fa7cdbcb030757504ee11d0 | [] | [] | https://huggingface.co/datasets/hf-internal-testing/spaghetti-video-8-frames/resolve/main/README.md | ---
---
This is the code that was used to generate this video:
```
from decord import VideoReader, cpu
from huggingface_hub import hf_hub_download
import numpy as np
np.random.seed(0)
def sample_frame_indices(clip_len, frame_sample_rate, seg_len):
converted_len = int(clip_len * frame_sample_rate)
end_idx = np.random.randint(converted_len, seg_len)
start_idx = end_idx - converted_len
indices = np.linspace(start_idx, end_idx, num=clip_len)
indices = np.clip(indices, start_idx, end_idx - 1).astype(np.int64)
return indices
file_path = hf_hub_download(
repo_id="nielsr/video-demo", filename="eating_spaghetti.mp4", repo_type="dataset"
)
vr = VideoReader(file_path, num_threads=1, ctx=cpu(0))
# sample 8 frames
vr.seek(0)
indices = sample_frame_indices(clip_len=8, frame_sample_rate=1, seg_len=len(vr))
buffer = vr.get_batch(indices).asnumpy()
# create a list of NumPy arrays
video = [buffer[i] for i in range(buffer.shape[0])]
video_numpy = np.array(video)
with open('spaghetti_video_8_frames.npy', 'wb') as f:
np.save(f, video_numpy)
``` |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-xsum-5cb1ece5-12895721 | 2022-08-11T15:29:15.000Z | null | false | 5ae360e13ed6372f2c5fe799bb2c4f0799b4ac50 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:xsum"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-xsum-5cb1ece5-12895721/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- xsum
eval_info:
task: summarization
model: google/bigbird-pegasus-large-arxiv
metrics: []
dataset_name: xsum
dataset_config: default
dataset_split: test
col_mapping:
text: document
target: summary
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: google/bigbird-pegasus-large-arxiv
* Dataset: xsum
* Config: default
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@grapplerulrich](https://huggingface.co/grapplerulrich) for evaluating this model. |
autoevaluate | null | null | null | false | 6 | false | autoevaluate/autoeval-staging-eval-project-xsum-4ce7da77-12905722 | 2022-08-11T15:31:22.000Z | null | false | 403c0e9b0f0c46a9cf2579124b06c47d3c08db61 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:xsum"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-xsum-4ce7da77-12905722/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- xsum
eval_info:
task: summarization
model: google/bigbird-pegasus-large-arxiv
metrics: []
dataset_name: xsum
dataset_config: default
dataset_split: test
col_mapping:
text: document
target: summary
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: google/bigbird-pegasus-large-arxiv
* Dataset: xsum
* Config: default
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@grapplerulrich](https://huggingface.co/grapplerulrich) for evaluating this model. |
autoevaluate | null | null | null | false | 2 | false | autoevaluate/autoeval-staging-eval-project-xsum-f0ba0c18-12915723 | 2022-08-11T13:28:11.000Z | null | false | bc903c85ac42397037b91bef89142243c7b4d7b6 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:xsum"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-xsum-f0ba0c18-12915723/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- xsum
eval_info:
task: summarization
model: facebook/bart-large-cnn
metrics: ['bleu']
dataset_name: xsum
dataset_config: default
dataset_split: test
col_mapping:
text: document
target: summary
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: facebook/bart-large-cnn
* Dataset: xsum
* Config: default
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@xarymast](https://huggingface.co/xarymast) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-xsum-f0ba0c18-12915724 | 2022-08-11T13:18:29.000Z | null | false | 3f3a3a357a6531c4e6127b8247aaa85fc8d26729 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:xsum"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-xsum-f0ba0c18-12915724/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- xsum
eval_info:
task: summarization
model: facebook/bart-large-xsum
metrics: ['bleu']
dataset_name: xsum
dataset_config: default
dataset_split: test
col_mapping:
text: document
target: summary
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: facebook/bart-large-xsum
* Dataset: xsum
* Config: default
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@xarymast](https://huggingface.co/xarymast) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-xsum-f0ba0c18-12915725 | 2022-08-11T13:20:52.000Z | null | false | d06a1f8d090c853b1122c540a6ff6d2b16c10d12 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:xsum"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-xsum-f0ba0c18-12915725/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- xsum
eval_info:
task: summarization
model: sshleifer/distilbart-cnn-12-6
metrics: ['bleu']
dataset_name: xsum
dataset_config: default
dataset_split: test
col_mapping:
text: document
target: summary
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: sshleifer/distilbart-cnn-12-6
* Dataset: xsum
* Config: default
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@xarymast](https://huggingface.co/xarymast) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-xsum-f0ba0c18-12915726 | 2022-08-11T13:10:53.000Z | null | false | 70986fc57830f32608141c7f2278093ebd811903 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:xsum"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-xsum-f0ba0c18-12915726/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- xsum
eval_info:
task: summarization
model: sshleifer/distilbart-xsum-12-6
metrics: ['bleu']
dataset_name: xsum
dataset_config: default
dataset_split: test
col_mapping:
text: document
target: summary
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: sshleifer/distilbart-xsum-12-6
* Dataset: xsum
* Config: default
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@xarymast](https://huggingface.co/xarymast) for evaluating this model. |
juletxara | null | @article{Liu2022VisualSR,
title={Visual Spatial Reasoning},
author={Fangyu Liu and Guy Edward Toh Emerson and Nigel Collier},
journal={ArXiv},
year={2022},
volume={abs/2205.00363}
} | The Visual Spatial Reasoning (VSR) corpus is a collection of caption-image pairs with true/false labels. Each caption describes the spatial relation of two individual objects in the image, and a vision-language model (VLM) needs to judge whether the caption is correctly describing the image (True) or not (False). | false | 1 | false | juletxara/visual-spatial-reasoning | 2022-08-11T20:11:21.000Z | null | false | a07bec7a6b1cbf4b5ca3a68bf744e854982b72bd | [] | [
"arxiv:2205.00363",
"arxiv:1908.03557",
"arxiv:1908.07490",
"arxiv:2102.03334",
"annotations_creators:crowdsourced",
"language:en",
"language_creators:machine-generated",
"license:apache-2.0",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_catego... | https://huggingface.co/datasets/juletxara/visual-spatial-reasoning/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
language:
- en
language_creators:
- machine-generated
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: Visual Spatial Reasoning
size_categories:
- 10K<n<100K
source_datasets:
- original
tags: []
task_categories:
- image-classification
task_ids: []
---
# Dataset Card for Visual Spatial Reasoning
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://ltl.mmll.cam.ac.uk/
- **Repository:** https://github.com/cambridgeltl/visual-spatial-reasoning
- **Paper:** https://arxiv.org/abs/2205.00363
- **Leaderboard:** https://paperswithcode.com/sota/visual-reasoning-on-vsr
- **Point of Contact:** https://ltl.mmll.cam.ac.uk/
### Dataset Summary
The Visual Spatial Reasoning (VSR) corpus is a collection of caption-image pairs with true/false labels. Each caption describes the spatial relation of two individual objects in the image, and a vision-language model (VLM) needs to judge whether the caption is correctly describing the image (True) or not (False).
### Supported Tasks and Leaderboards
We test three baselines, all supported in huggingface. They are VisualBERT [(Li et al. 2019)](https://arxiv.org/abs/1908.03557), LXMERT [(Tan and Bansal, 2019)](https://arxiv.org/abs/1908.07490) and ViLT [(Kim et al. 2021)](https://arxiv.org/abs/2102.03334). The leaderboard can be checked at [Papers With Code](https://paperswithcode.com/sota/visual-reasoning-on-vsr).
model | random split | zero-shot
:-------------|:-------------:|:-------------:
*human* | *95.4* | *95.4*
VisualBERT | 57.4 | 54.0
LXMERT | **72.5** | **63.2**
ViLT | 71.0 | 62.4
### Languages
The language in the dataset is English as spoken by the annotators. The BCP-47 code for English is en. [`meta_data.csv`](https://github.com/cambridgeltl/visual-spatial-reasoning/tree/master/data/data_files/meta_data.jsonl) contains meta data of annotators.
## Dataset Structure
### Data Instances
Each line is an individual data point. Each `jsonl` file is of the following format:
```json
{"image": "000000050403.jpg", "image_link": "http://images.cocodataset.org/train2017/000000050403.jpg", "caption": "The teddy bear is in front of the person.", "label": 1, "relation": "in front of", "annotator_id": 31, "vote_true_validator_id": [2, 6], "vote_false_validator_id": []}
{"image": "000000401552.jpg", "image_link": "http://images.cocodataset.org/train2017/000000401552.jpg", "caption": "The umbrella is far away from the motorcycle.", "label": 0, "relation": "far away from", "annotator_id": 2, "vote_true_validator_id": [], "vote_false_validator_id": [2, 9, 1]}
```
### Data Fields
`image` denotes name of the image in COCO and `image_link` points to the image on the COCO server (so you can also access directly). `caption` is self-explanatory. `label` being `0` and `1` corresponds to False and True respectively. `relation` records the spatial relation used. `annotator_id` points to the annotator who originally wrote the caption. `vote_true_validator_id` and `vote_false_validator_id` are annotators who voted True or False in the second phase validation.
### Data Splits
The VSR corpus, after validation, contains 10,119 data points with high agreement. On top of these, we create two splits (1) random split and (2) zero-shot split. For random split, we randomly split all data points into train, development, and test sets. Zero-shot split makes sure that train, development and test sets have no overlap of concepts (i.e., if *dog* is in test set, it is not used for training and development). Below are some basic statistics of the two splits.
split | train | dev | test | total
:------|:--------:|:--------:|:--------:|:--------:
random | 7,083 | 1,012 | 2,024 | 10,119
zero-shot | 5,440 | 259 | 731 | 6,430
Check out [`data/`](https://github.com/cambridgeltl/visual-spatial-reasoning/tree/master/data) for more details.
## Dataset Creation
### Curation Rationale
Understanding spatial relations is fundamental to achieve intelligence. Existing vision-language reasoning datasets are great but they compose multiple types of challenges and can thus conflate different sources of error.
The VSR corpus focuses specifically on spatial relations so we can have accurate diagnosis and maximum interpretability.
### Source Data
#### Initial Data Collection and Normalization
**Image pair sampling.** MS COCO 2017 contains
123,287 images and has labelled the segmentation and classes of 886,284 instances (individual
objects). Leveraging the segmentation, we first
randomly select two concepts, then retrieve all images containing the two concepts in COCO 2017 (train and
validation sets). Then images that contain multiple instances of any of the concept are filtered
out to avoid referencing ambiguity. For the single-instance images, we also filter out any of the images with instance area size < 30, 000, to prevent extremely small instances. After these filtering steps,
we randomly sample a pair in the remaining images.
We repeat such process to obtain a large number of
individual image pairs for caption generation.
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
**Fill in the blank: template-based caption generation.** Given a pair of images, the annotator needs to come up with a valid caption that makes it correctly describing one image but incorrect for the other. In this way, the annotator could focus on the key difference of the two images (which should be spatial relation of the two objects of interest) and come up with challenging relation that differentiates the two. Similar paradigms are also used in the annotation of previous vision-language reasoning datasets such as NLVR2 (Suhr et al., 2017,
2019) and MaRVL (Liu et al., 2021). To regularise annotators from writing modifiers and differentiating the image pair with things beyond accurate spatial relations, we opt for a template-based classification task instead of free-form caption writing. Besides, the template-generated dataset can be easily categorised based on relations and their meta-categories.
The caption template has the format of “The
`OBJ1` (is) __ the `OBJ2`.”, and the annotators
are instructed to select a relation from a fixed set
to fill in the slot. The copula “is” can be omitted
for grammaticality. For example, for “contains”,
“consists of”, and “has as a part”, “is” should be
discarded in the template when extracting the final
caption.
The fixed set of spatial relations enable us to obtain the full control of the generation process. The
full list of used relations are listed in the table below. It
contains 71 spatial relations and is adapted from
the summarised relation table of Fagundes et al.
(2021). We made minor changes to filter out clearly
unusable relations, made relation names grammatical under our template, and reduced repeated relations.
In our final dataset, 65 out of the 71 available relations are actually included (the other 6 are
either not selected by annotators or are selected but
the captions did not pass the validation phase).
| Category | Spatial Relations |
|-------------|-------------------------------------------------------------------------------------------------------------------------------------------------|
| Adjacency | Adjacent to, alongside, at the side of, at the right side of, at the left side of, attached to, at the back of, ahead of, against, at the edge of |
| Directional | Off, past, toward, down, deep down*, up*, away from, along, around, from*, into, to*, across, across from, through*, down from |
| Orientation | Facing, facing away from, parallel to, perpendicular to |
| Projective | On top of, beneath, beside, behind, left of, right of, under, in front of, below, above, over, in the middle of |
| Proximity | By, close to, near, far from, far away from |
| Topological | Connected to, detached from, has as a part, part of, contains, within, at, on, in, with, surrounding, among, consists of, out of, between, inside, outside, touching |
| Unallocated | Beyond, next to, opposite to, after*, among, enclosed by |
**Second-round Human Validation.** Every annotated data point is reviewed by at least
two additional human annotators (validators). In
validation, given a data point (consists of an image
and a caption), the validator gives either a True or
False label. We exclude data points that have <
2/3 validators agreeing with the original label.
In the guideline, we communicated to the validators that, for relations such as “left”/“right”, “in
front of”/“behind”, they should tolerate different
reference frame: i.e., if the caption is true from either the object’s or the viewer’s reference, it should
be given a True label. Only
when the caption is incorrect under all reference
frames, a False label is assigned. This adds
difficulty to the models since they could not naively
rely on relative locations of the objects in the images but also need to correctly identify orientations of objects to make the best judgement.
#### Who are the annotators?
Annotators are hired from [prolific.co](https://prolific.co). We
require them (1) have at least a bachelor’s degree,
(2) are fluent in English or native speaker, and (3)
have a >99% historical approval rate on the platform. All annotators are paid with an hourly salary
of 12 GBP. Prolific takes an extra 33% of service
charge and 20% VAT on the service charge.
For caption generation, we release the task with
batches of 200 instances and the annotator is required to finish a batch in 80 minutes. An annotator
cannot take more than one batch per day. In this
way we have a diverse set of annotators and can
also prevent annotators from being fatigued. For
second round validation, we group 500 data points
in one batch and an annotator is asked to label each
batch in 90 minutes.
In total, 24 annotators participated in caption
generation and 26 participated in validation. The
annotators have diverse demographic background:
they were born in 13 different countries; live in 13
different couturiers; and have 14 different nationalities. 57.4% of the annotators identify themselves
as females and 42.6% as males.
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
This project is licensed under the [Apache-2.0 License](https://github.com/cambridgeltl/visual-spatial-reasoning/blob/master/LICENSE).
### Citation Information
```bibtex
@article{Liu2022VisualSR,
title={Visual Spatial Reasoning},
author={Fangyu Liu and Guy Edward Toh Emerson and Nigel Collier},
journal={ArXiv},
year={2022},
volume={abs/2205.00363}
}
```
### Contributions
Thanks to [@juletx](https://github.com/juletx) for adding this dataset. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-xsum-f0ba0c18-12915727 | 2022-08-11T16:01:35.000Z | null | false | c9ed41cbd1ee3f0275c4c4f0be802dc5864314b1 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:xsum"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-xsum-f0ba0c18-12915727/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- xsum
eval_info:
task: summarization
model: google/pegasus-large
metrics: ['bleu']
dataset_name: xsum
dataset_config: default
dataset_split: test
col_mapping:
text: document
target: summary
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: google/pegasus-large
* Dataset: xsum
* Config: default
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@xarymast](https://huggingface.co/xarymast) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-xsum-f0ba0c18-12915728 | 2022-08-11T13:45:26.000Z | null | false | d45ad40b7ef5fb1aabfc89408a6269ff6ecd9fbc | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:xsum"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-xsum-f0ba0c18-12915728/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- xsum
eval_info:
task: summarization
model: google/pegasus-xsum
metrics: ['bleu']
dataset_name: xsum
dataset_config: default
dataset_split: test
col_mapping:
text: document
target: summary
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: google/pegasus-xsum
* Dataset: xsum
* Config: default
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@xarymast](https://huggingface.co/xarymast) for evaluating this model. |
autoevaluate | null | null | null | false | 6 | false | autoevaluate/autoeval-staging-eval-project-xsum-f0ba0c18-12915729 | 2022-08-11T14:48:21.000Z | null | false | b137984a923a7f937710ac41d0a97f7d68eb0175 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:xsum"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-xsum-f0ba0c18-12915729/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- xsum
eval_info:
task: summarization
model: google/pegasus-cnn_dailymail
metrics: ['bleu']
dataset_name: xsum
dataset_config: default
dataset_split: test
col_mapping:
text: document
target: summary
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: google/pegasus-cnn_dailymail
* Dataset: xsum
* Config: default
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@xarymast](https://huggingface.co/xarymast) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-xsum-8dc1621c-12925730 | 2022-08-11T14:02:39.000Z | null | false | 3947e8559380f35ad1d92cad0266367c924c3888 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:xsum"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-xsum-8dc1621c-12925730/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- xsum
eval_info:
task: summarization
model: facebook/bart-large-cnn
metrics: ['bleu']
dataset_name: xsum
dataset_config: default
dataset_split: test
col_mapping:
text: document
target: summary
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: facebook/bart-large-cnn
* Dataset: xsum
* Config: default
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@xarymast](https://huggingface.co/xarymast) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-xsum-8dc1621c-12925731 | 2022-08-11T14:00:18.000Z | null | false | 544729e978e5120ece94dc40d9eba44bf865e748 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:xsum"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-xsum-8dc1621c-12925731/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- xsum
eval_info:
task: summarization
model: facebook/bart-large-xsum
metrics: ['bleu']
dataset_name: xsum
dataset_config: default
dataset_split: test
col_mapping:
text: document
target: summary
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: facebook/bart-large-xsum
* Dataset: xsum
* Config: default
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@xarymast](https://huggingface.co/xarymast) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-xsum-8dc1621c-12925732 | 2022-08-11T14:19:44.000Z | null | false | 5e3f25e9deec3aac79ff0edee782423f8dba814d | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:xsum"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-xsum-8dc1621c-12925732/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- xsum
eval_info:
task: summarization
model: sshleifer/distilbart-cnn-12-6
metrics: ['bleu']
dataset_name: xsum
dataset_config: default
dataset_split: test
col_mapping:
text: document
target: summary
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: sshleifer/distilbart-cnn-12-6
* Dataset: xsum
* Config: default
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@xarymast](https://huggingface.co/xarymast) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-xsum-8dc1621c-12925733 | 2022-08-11T14:11:20.000Z | null | false | 975a6926fa9fd2087ea7a397f74b579d6b22d723 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:xsum"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-xsum-8dc1621c-12925733/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- xsum
eval_info:
task: summarization
model: sshleifer/distilbart-xsum-12-6
metrics: ['bleu']
dataset_name: xsum
dataset_config: default
dataset_split: test
col_mapping:
text: document
target: summary
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: sshleifer/distilbart-xsum-12-6
* Dataset: xsum
* Config: default
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@xarymast](https://huggingface.co/xarymast) for evaluating this model. |
ChristophSchuhmann | null | null | null | false | 1 | false | ChristophSchuhmann/improved_aesthetics_4.75plus | 2022-08-13T18:16:44.000Z | null | false | e13a583aceced0b410e425156fef5f9387827936 | [] | [
"license:apache-2.0"
] | https://huggingface.co/datasets/ChristophSchuhmann/improved_aesthetics_4.75plus/resolve/main/README.md | ---
license: apache-2.0
---
|
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-xsum-8dc1621c-12925734 | 2022-08-11T16:59:36.000Z | null | false | 29784d9e5a9d2813d3a8df4b5da15a3a5b5a2f4c | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:xsum"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-xsum-8dc1621c-12925734/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- xsum
eval_info:
task: summarization
model: google/pegasus-large
metrics: ['bleu']
dataset_name: xsum
dataset_config: default
dataset_split: test
col_mapping:
text: document
target: summary
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: google/pegasus-large
* Dataset: xsum
* Config: default
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@xarymast](https://huggingface.co/xarymast) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-xsum-8dc1621c-12925735 | 2022-08-11T14:37:37.000Z | null | false | 4d6f83691af8dd7cea05a532a49d275462449670 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:xsum"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-xsum-8dc1621c-12925735/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- xsum
eval_info:
task: summarization
model: google/pegasus-xsum
metrics: ['bleu']
dataset_name: xsum
dataset_config: default
dataset_split: test
col_mapping:
text: document
target: summary
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: google/pegasus-xsum
* Dataset: xsum
* Config: default
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@xarymast](https://huggingface.co/xarymast) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-xsum-8dc1621c-12925736 | 2022-08-11T15:41:05.000Z | null | false | 48948a18fba7481186adc4ee477fe180bced55dc | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:xsum"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-xsum-8dc1621c-12925736/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- xsum
eval_info:
task: summarization
model: google/pegasus-cnn_dailymail
metrics: ['bleu']
dataset_name: xsum
dataset_config: default
dataset_split: test
col_mapping:
text: document
target: summary
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: google/pegasus-cnn_dailymail
* Dataset: xsum
* Config: default
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@xarymast](https://huggingface.co/xarymast) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-xsum-69daf1dd-12935737 | 2022-08-11T15:01:40.000Z | null | false | 3ebf510b9434206dfaaf35567ba531dcd70a4f99 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:xsum"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-xsum-69daf1dd-12935737/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- xsum
eval_info:
task: summarization
model: facebook/bart-large-cnn
metrics: ['bleu']
dataset_name: xsum
dataset_config: default
dataset_split: test
col_mapping:
text: document
target: summary
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: facebook/bart-large-cnn
* Dataset: xsum
* Config: default
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@xarymast](https://huggingface.co/xarymast) for evaluating this model. |
autoevaluate | null | null | null | false | 6 | false | autoevaluate/autoeval-staging-eval-project-xsum-69daf1dd-12935738 | 2022-08-11T15:09:17.000Z | null | false | 9dc58c7fae34f20dc3761b45eecfabd787f9f5dd | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:xsum"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-xsum-69daf1dd-12935738/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- xsum
eval_info:
task: summarization
model: facebook/bart-large-xsum
metrics: ['bleu']
dataset_name: xsum
dataset_config: default
dataset_split: test
col_mapping:
text: document
target: summary
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: facebook/bart-large-xsum
* Dataset: xsum
* Config: default
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@xarymast](https://huggingface.co/xarymast) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-xsum-69daf1dd-12935739 | 2022-08-11T15:23:03.000Z | null | false | 288023970a01b31e96633b3ed3c93edd1609f493 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:xsum"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-xsum-69daf1dd-12935739/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- xsum
eval_info:
task: summarization
model: sshleifer/distilbart-cnn-12-6
metrics: ['bleu']
dataset_name: xsum
dataset_config: default
dataset_split: test
col_mapping:
text: document
target: summary
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: sshleifer/distilbart-cnn-12-6
* Dataset: xsum
* Config: default
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@xarymast](https://huggingface.co/xarymast) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-xsum-69daf1dd-12935740 | 2022-08-11T15:26:20.000Z | null | false | 3705d8c1c5f58d29160f8e72eeb0cc27b3b15ac9 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:xsum"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-xsum-69daf1dd-12935740/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- xsum
eval_info:
task: summarization
model: sshleifer/distilbart-xsum-12-6
metrics: ['bleu']
dataset_name: xsum
dataset_config: default
dataset_split: test
col_mapping:
text: document
target: summary
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: sshleifer/distilbart-xsum-12-6
* Dataset: xsum
* Config: default
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@xarymast](https://huggingface.co/xarymast) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-xsum-69daf1dd-12935741 | 2022-08-11T18:08:26.000Z | null | false | 79be53a8ffd3f2b6062c431560cd95b332e6de0d | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:xsum"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-xsum-69daf1dd-12935741/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- xsum
eval_info:
task: summarization
model: google/pegasus-large
metrics: ['bleu']
dataset_name: xsum
dataset_config: default
dataset_split: test
col_mapping:
text: document
target: summary
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: google/pegasus-large
* Dataset: xsum
* Config: default
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@xarymast](https://huggingface.co/xarymast) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-xsum-69daf1dd-12935743 | 2022-08-11T16:55:47.000Z | null | false | 80853eab2ea846199ff76c3e6353951583bd6baf | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:xsum"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-xsum-69daf1dd-12935743/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- xsum
eval_info:
task: summarization
model: google/pegasus-cnn_dailymail
metrics: ['bleu']
dataset_name: xsum
dataset_config: default
dataset_split: test
col_mapping:
text: document
target: summary
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: google/pegasus-cnn_dailymail
* Dataset: xsum
* Config: default
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@xarymast](https://huggingface.co/xarymast) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-xsum-9818ea4b-12975766 | 2022-08-11T17:34:59.000Z | null | false | 00351121bd85b3ae5629274cabb72e73a17a782d | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:xsum"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-xsum-9818ea4b-12975766/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- xsum
eval_info:
task: summarization
model: sshleifer/distilbart-xsum-12-6
metrics: []
dataset_name: xsum
dataset_config: default
dataset_split: test
col_mapping:
text: document
target: summary
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: sshleifer/distilbart-xsum-12-6
* Dataset: xsum
* Config: default
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@grapplerulrich](https://huggingface.co/grapplerulrich) for evaluating this model. |
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