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06eb33548c674fb415dca8a2c116b8818205dcd1 |
> # Deprecation Notice!
> [This dataset has been superseded by v2](https://huggingface.co/datasets/hearmeneigh/e621-rising-v2-raw). Use v2 instead of this dataset.
**Warning: THIS dataset is NOT suitable for use by minors. The dataset contains X-rated/NFSW content.**
# E621 Rising: Raw Image Dataset v1
**2,905,671** images (~1.1TB) downloaded from `e621.net` with [tags](https://huggingface.co/datasets/hearmeneigh/e621-rising-v1-raw/raw/main/meta/tag-counts.json).
This is a raw, uncurated, and largely unprocessed dataset. You likely want to use the curated version, [available here](https://huggingface.co/datasets/hearmeneigh/e621-rising-v1-curated). This dataset contains all kinds of NFSW material. You have been warned.
## Image Processing
* Only `jpg` and `png` images were considered
* Image width and height have been clamped to `(0, 4096]px`; larger images have been resized to meet the limit
* Alpha channels have been removed
* All images have been converted to `jpg` format
* All images have been converted to TrueColor `RGB`
* All images have been verified to load with `Pillow`
* Metadata from E621 is [available here](https://huggingface.co/datasets/hearmeneigh/e621-rising-v1-raw/tree/main/meta).
## Tags
For a comprehensive list of tags and counts, [see here](https://huggingface.co/datasets/hearmeneigh/e621-rising-v1-raw/raw/main/meta/tag-counts.json).
### Changes From E621
* Symbols have been prefixed with `symbol:`, e.g. `symbol:<3`
* Aspect ratio has been prefixed with `aspect_ratio:`, e.g. `aspect_ratio:16_9`
* All categories except `general` have been prefixed with the category name, e.g. `artist:somename`. The categories are:
* `artist`
* `copyright`
* `character`
* `species`
* `invalid`
* `meta`
* `lore`
### Additional Tags
* Image rating
* `rating:explicit`
* `rating:questionable`
* `rating:safe`
* Image score
* `score:above_250`
* `score:above_500`
* `score:above_1000`
* `score:above_1500`
* `score:above_2000`
* `score:below_250`
* `score:below_100`
* `score:below_50`
* `score:below_25`
* `score:below_0`
* Image favorites
* `favorites:above_4000`
* `favorites:above_3000`
* `favorites:above_2000`
* `favorites:above_1000`
* `favorites:below_1000`
* `favorites:below_500`
* `favorites:below_250`
* `favorites:below_100`
* `favorites:below_50`
* `favorites:below_25` | hearmeneigh/e621-rising-v1-raw | [
"size_categories:1M<n<10M",
"not-for-all-audiences",
"region:us"
]
| 2023-01-11T08:52:10+00:00 | {"size_categories": ["1M<n<10M"], "pretty_name": "E621 Rising: Raw Image Dataset v1", "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1192534908282.634, "num_examples": 2905671}], "download_size": 210413447679, "dataset_size": 1192534908282.634}, "viewer": false, "tags": ["not-for-all-audiences"]} | 2023-05-12T15:35:09+00:00 |
bdac0343d333c72ea8c64f17298accaae424136c | # Dataset Card for "vul_lines"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | EddieChen372/Vudenc_with_norm_vul_lines | [
"region:us"
]
| 2023-01-11T09:17:49+00:00 | {"dataset_info": {"features": [{"name": "lines", "sequence": "string"}, {"name": "raw_lines", "sequence": "string"}, {"name": "label", "sequence": "int64"}, {"name": "type", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 14476057, "num_examples": 12672}, {"name": "test", "num_bytes": 3485317, "num_examples": 3169}], "download_size": 7020615, "dataset_size": 17961374}} | 2023-04-01T00:24:45+00:00 |
7747df46ae4efef9a33af5c877bc9dc9d4c1ba95 | See https://github.com/PaulLerner/ViQuAE | PaulLerner/wit_for_mict | [
"region:us"
]
| 2023-01-11T09:47:56+00:00 | {} | 2023-01-11T10:01:40+00:00 |
a4045dfafc8f653eff42ae42ecea9167dc6fdb46 | AviationQA is introduced in the paper titled- There is No Big Brother or Small Brother: Knowledge Infusion in Language Models for Link Prediction and Question Answering
https://aclanthology.org/2022.icon-main.26/
The paper is accepted in the main conference of ICON 2022.
We create a synthetic dataset, AviationQA, a set of 1 million factoid QA pairs from 12,000 National Transportation Safety Board (NTSB) reports using templates. These QA pairs contain questions such that answers to them are entities occurring in the AviationKG (Agarwal et al., 2022). AviationQA will be helpful to researchers in finding insights into aircraft accidents and their prevention.
Examples from dataset:
What was the Aircraft Damage of the accident no. ERA22LA162? Answer: Substantial
Where was the Destination of the accident no. ERA22LA162?, Answer: Naples, GA (APH) | sakharamg/AviationQA | [
"task_categories:question-answering",
"language:en",
"license:cc-by-4.0",
"Question Answering",
"Aviation",
"Knowledge Graphs",
"region:us"
]
| 2023-01-11T09:52:39+00:00 | {"language": ["en"], "license": "cc-by-4.0", "task_categories": ["question-answering"], "pretty_name": "AviationQA", "tags": ["Question Answering", "Aviation", "Knowledge Graphs"]} | 2023-04-06T18:08:21+00:00 |
758679ce409e6cc3a496654ebd0198c2fdcda19e | # Dataset Card for "npsc_dataset_tmp"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | perisolb/npsc_dataset_tmp | [
"region:us"
]
| 2023-01-11T10:32:08+00:00 | {"dataset_info": {"features": [{"name": "speaker_id", "dtype": "string"}, {"name": "gender", "dtype": "string"}, {"name": "utterance_id", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "raw_text", "dtype": "string"}, {"name": "full_audio_file", "dtype": "string"}, {"name": "original_data_split", "dtype": "string"}, {"name": "region", "dtype": "string"}, {"name": "duration", "dtype": "float64"}, {"name": "start", "dtype": "float64"}, {"name": "end", "dtype": "float64"}, {"name": "utterance_audio_file", "dtype": "audio"}, {"name": "standardized_text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 9050653.0, "num_examples": 50}, {"name": "test", "num_bytes": 1225074.0, "num_examples": 10}, {"name": "validation", "num_bytes": 1225074.0, "num_examples": 10}], "download_size": 11505743, "dataset_size": 11500801.0}} | 2023-01-11T10:32:34+00:00 |
934499fd731de47ad3411f24e4659439bc837da5 | anytp/conflicto | [
"license:apache-2.0",
"region:us"
]
| 2023-01-11T11:34:46+00:00 | {"license": "apache-2.0"} | 2023-02-01T18:06:45+00:00 |
|
8a51973c55ce233490fe8095b5d3eac5ad1b9438 | # Colection of models trained on axis cameras | evo4np/testing | [
"region:us"
]
| 2023-01-11T12:12:56+00:00 | {} | 2023-01-12T08:40:59+00:00 |
9a1d9de442ec35d7058dca8b6f4ee18d1434a1f8 | # AutoTrain Dataset for project: hannah-jpg-test
## Dataset Description
This dataset has been automatically processed by AutoTrain for project hannah-jpg-test.
### 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
[
{
"image": "<256x256 RGB PIL image>",
"target": 0
},
{
"image": "<256x256 RGB PIL image>",
"target": 0
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"image": "Image(decode=True, id=None)",
"target": "ClassLabel(names=['hannah'], 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 | 7 |
| valid | 7 |
| slushily/autotrain-data-hannah-jpg-test | [
"task_categories:image-classification",
"region:us"
]
| 2023-01-11T12:27:58+00:00 | {"task_categories": ["image-classification"]} | 2023-01-11T12:30:06+00:00 |
d3224f7e8b83f7578302ac641aec6150b670843d | BauyrjanQ/qazkorp | [
"region:us"
]
| 2023-01-11T12:54:20+00:00 | {} | 2023-01-16T06:49:00+00:00 |
|
8441d85d9ba03ada1f27d1a593d8defcd22f6a42 | Eddiefloat/paot | [
"license:other",
"region:us"
]
| 2023-01-11T13:05:58+00:00 | {"license": "other"} | 2023-03-23T16:27:41+00:00 |
|
516e91c0c9c165288a6c2b3883b454e263f8414e | cxumol/temp-data | [
"license:mit",
"region:us"
]
| 2023-01-11T13:08:25+00:00 | {"license": "mit"} | 2023-01-11T13:08:44+00:00 |
|
e17505aed638ae4195205e002f821bbecfe685cd | # Dataset Card for "dreambooth-hackathon-images"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | GeorgeBredis/dreambooth-hackathon-images | [
"region:us"
]
| 2023-01-11T13:28:22+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 3118843.0, "num_examples": 42}], "download_size": 3118955, "dataset_size": 3118843.0}} | 2023-01-11T13:28:35+00:00 |
57d3e08648dc62129998d73b579e918759f956b4 | Dilgam/exploit | [
"language:en",
"license:openrail",
"region:us"
]
| 2023-01-11T13:49:47+00:00 | {"language": ["en"], "license": "openrail"} | 2023-01-11T13:50:14+00:00 |
|
e8518d4ed2edff28e85e8a710e4b76659ffa1fe0 | rcky/stck | [
"license:afl-3.0",
"region:us"
]
| 2023-01-11T14:05:10+00:00 | {"license": "afl-3.0"} | 2023-01-11T14:05:51+00:00 |
|
675a67ad0d2050cd042714c3713130def1ec02af | oxrider/CHAIRS | [
"license:gpl-3.0",
"region:us"
]
| 2023-01-11T15:08:11+00:00 | {"license": "gpl-3.0"} | 2023-01-11T15:08:11+00:00 |
|
fd17def157bfb3f1dcb4a372dc0d49489c5c00e1 |
# Dataset Card for E3C
## Dataset Description
- **Homepage:** https://github.com/hltfbk/E3C-Corpus
- **PubMed** False
- **Public:** True
- **Tasks:** NER,RE
The European Clinical Case Corpus (E3C) project aims at collecting and \
annotating a large corpus of clinical documents in five European languages (Spanish, \
Basque, English, French and Italian), which will be freely distributed. Annotations \
include temporal information, to allow temporal reasoning on chronologies, and \
information about clinical entities based on medical taxonomies, to be used for semantic reasoning.
## Citation Information
```
@report{Magnini2021,
author = {Bernardo Magnini and Begoña Altuna and Alberto Lavelli and Manuela Speranza
and Roberto Zanoli and Fondazione Bruno Kessler},
keywords = {Clinical data,clinical enti-ties,corpus,multilingual,temporal information},
title = {The E3C Project:
European Clinical Case Corpus El proyecto E3C: European Clinical Case Corpus},
url = {https://uts.nlm.nih.gov/uts/umls/home},
year = {2021},
}
```
| bio-datasets/e3c | [
"region:us"
]
| 2023-01-11T15:13:39+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "document_id", "dtype": "int32"}, {"name": "text", "dtype": "string"}, {"name": "passages", "list": [{"name": "id", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "offsets", "list": "int32"}]}, {"name": "entities", "list": [{"name": "id", "dtype": "string"}, {"name": "type", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "offsets", "list": "int32"}, {"name": "semantic_type_id", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "relations", "list": [{"name": "id", "dtype": "string"}, {"name": "type", "dtype": "string"}, {"name": "contextualAspect", "dtype": "string"}, {"name": "contextualModality", "dtype": "string"}, {"name": "degree", "dtype": "string"}, {"name": "docTimeRel", "dtype": "string"}, {"name": "eventType", "dtype": "string"}, {"name": "permanence", "dtype": "string"}, {"name": "polarity", "dtype": "string"}, {"name": "functionInDocument", "dtype": "string"}, {"name": "timex3Class", "dtype": "string"}, {"name": "value", "dtype": "string"}, {"name": "concept_1", "dtype": "string"}, {"name": "concept_2", "dtype": "string"}]}], "config_name": "e3c_source", "splits": [{"name": "en.layer1", "num_bytes": 1645819, "num_examples": 84}, {"name": "en.layer2", "num_bytes": 881290, "num_examples": 171}, {"name": "en.layer2.validation", "num_bytes": 101379, "num_examples": 19}, {"name": "en.layer3", "num_bytes": 7672589, "num_examples": 9779}, {"name": "es.layer1", "num_bytes": 1398186, "num_examples": 81}, {"name": "es.layer2", "num_bytes": 907515, "num_examples": 162}, {"name": "es.layer2.validation", "num_bytes": 103936, "num_examples": 18}, {"name": "es.layer3", "num_bytes": 6656630, "num_examples": 1876}, {"name": "eu.layer1", "num_bytes": 2217479, "num_examples": 90}, {"name": "eu.layer2", "num_bytes": 306291, "num_examples": 111}, {"name": "eu.layer2.validation", "num_bytes": 95276, "num_examples": 10}, {"name": "eu.layer3", "num_bytes": 4656179, "num_examples": 1232}, {"name": "fr.layer1", "num_bytes": 1474138, "num_examples": 81}, {"name": "fr.layer2", "num_bytes": 905084, "num_examples": 168}, {"name": "fr.layer2.validation", "num_bytes": 101701, "num_examples": 18}, {"name": "fr.layer3", "num_bytes": 457927491, "num_examples": 25740}, {"name": "it.layer1", "num_bytes": 1036560, "num_examples": 86}, {"name": "it.layer2", "num_bytes": 888138, "num_examples": 174}, {"name": "it.layer2.validation", "num_bytes": 99549, "num_examples": 18}, {"name": "it.layer3", "num_bytes": 86243680, "num_examples": 10213}], "download_size": 230213492, "dataset_size": 575318910}} | 2023-08-16T07:56:50+00:00 |
6656a30197e5f9bc52d2636dae3346c42aa781bc | Zabin/SDL1.5xxEmbeddings | [
"license:other",
"region:us"
]
| 2023-01-11T15:18:40+00:00 | {"license": "other"} | 2023-01-21T16:28:49+00:00 |
|
6f7fdea52534b2bc1c83435305ca705f67aed30b | from datasets import load_dataset
dataset = load_dataset("argilla/banking_sentiment_zs_gpt3") | Mohamed-Ibrahim/Banking | [
"region:us"
]
| 2023-01-11T15:22:13+00:00 | {} | 2023-01-11T15:22:53+00:00 |
4f227c1cc47897cebad5b3e8a2bf9629dd49ba7d | havli/kitana | [
"license:afl-3.0",
"region:us"
]
| 2023-01-11T15:22:18+00:00 | {"license": "afl-3.0"} | 2023-01-11T15:22:18+00:00 |
|
db6213b37e27be48479ec72d0909328a7c3f515b | # Dataset Card for "mystery_box"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | bakhuisdennis/mystery_box | [
"region:us"
]
| 2023-01-11T16:30:19+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 454701.0, "num_examples": 249}], "download_size": 253139, "dataset_size": 454701.0}} | 2023-01-11T16:30:32+00:00 |
d2a17fba6e401fcf957a28a7706a63e3df6e4806 | # AutoTrain Dataset for project: improved-pidgin-model
## Dataset Description
This dataset has been automatically processed by AutoTrain for project improved-pidgin-model.
### 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
[
{
"source": "My people, good evening!",
"target": "My people, good evening o!"
},
{
"source": "Uh... my name is Kabiru Sule.",
"target": "Ehm my name be Kabiru Sule."
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"source": "Value(dtype='string', id=None)",
"target": "Value(dtype='string', id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 8591 |
| valid | 648 |
| jamm55/freePidginDataset | [
"task_categories:translation",
"region:us"
]
| 2023-01-11T17:39:46+00:00 | {"task_categories": ["translation"]} | 2023-01-11T17:42:05+00:00 |
60aa725fcfd6d2200fd5994320b42dea49403b86 | minathor/132 | [
"license:openrail",
"region:us"
]
| 2023-01-11T17:49:07+00:00 | {"license": "openrail"} | 2023-01-11T17:49:07+00:00 |
|
b74af53b1f04a68c4422b479600ab02e90b279f6 | minathor/456 | [
"license:openrail",
"region:us"
]
| 2023-01-11T17:55:39+00:00 | {"license": "openrail"} | 2023-01-11T17:55:39+00:00 |
|
ec938f1d8b91fb044140c5383dfdc22179c575e8 | cloneofsimo/GeneratedImageOfCelebs | [
"license:bigscience-openrail-m",
"region:us"
]
| 2023-01-11T20:01:40+00:00 | {"license": "bigscience-openrail-m"} | 2023-01-11T20:01:40+00:00 |
|
0d5cbee5220a13b3c0701d3a165d70d006a35339 | https://github.com/Alicia-Parrish/ling_in_loop/
```bib
@inproceedings{parrish-etal-2021-putting-linguist,
title = "Does Putting a Linguist in the Loop Improve {NLU} Data Collection?",
author = "Parrish, Alicia and
Huang, William and
Agha, Omar and
Lee, Soo-Hwan and
Nangia, Nikita and
Warstadt, Alexia and
Aggarwal, Karmanya and
Allaway, Emily and
Linzen, Tal and
Bowman, Samuel R.",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021",
month = nov,
year = "2021",
address = "Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.findings-emnlp.421",
doi = "10.18653/v1/2021.findings-emnlp.421",
pages = "4886--4901",
}
``` | tasksource/lingnli | [
"task_categories:text-classification",
"language:en",
"license:unknown",
"region:us"
]
| 2023-01-11T20:59:56+00:00 | {"language": ["en"], "license": "unknown", "task_categories": ["text-classification"]} | 2023-05-31T07:40:53+00:00 |
cefdd2300894bf2329428d0262f60e2dd9e59a25 |
# Dataset Card for NeuCLIR1
## Dataset Description
- **Website:** https://neuclir.github.io/
- **Repository:** https://github.com/NeuCLIR/download-collection
### Dataset Summary
This is the dataset created for TREC 2022 NeuCLIR Track. The collection designed to be similar to HC4 and a large portion of documents from HC4 are ported to this collection.
The documents are Web pages from Common Crawl in Chinese, Persian, and Russian.
### Languages
- Chinese
- Persian
- Russian
## Dataset Structure
### Data Instances
| Split | Documents |
|-----------------|----------:|
| `fas` (Persian) | 2.2M |
| `rus` (Russian) | 4.6M |
| `zho` (Chinese) | 3.2M |
### Data Fields
- `id`: unique identifier for this document
- `cc_file`: source file from connon crawl
- `time`: extracted date/time from article
- `title`: title extracted from article
- `text`: extracted article body
- `url`: source URL
## Dataset Usage
Using 🤗 Datasets:
```python
from datasets import load_dataset
dataset = load_dataset('neuclir/neuclir1')
dataset['fas'] # Persian documents
dataset['rus'] # Russian documents
dataset['zho'] # Chinese documents
```
| neuclir/neuclir1 | [
"task_categories:text-retrieval",
"task_ids:document-retrieval",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:1M<n<10M",
"source_datasets:extended|c4",
"language:fa",
"language:ru",
"language:zh",
"license:odc-by",
"region:us"
]
| 2023-01-11T21:08:24+00:00 | {"annotations_creators": ["no-annotation"], "language_creators": ["found"], "language": ["fa", "ru", "zh"], "license": ["odc-by"], "multilinguality": ["multilingual"], "size_categories": ["1M<n<10M"], "source_datasets": ["extended|c4"], "task_categories": ["text-retrieval"], "task_ids": ["document-retrieval"], "pretty_name": "NeuCLIR1", "tags": []} | 2023-01-12T18:43:52+00:00 |
6e7c86a2ae58a6ee84be59c71ef6ddf30904d95b |
# Dataset Card for HC4
## Dataset Description
- **Repository:** https://github.com/hltcoe/HC4
- **Paper:** https://arxiv.org/abs/2201.09992
### Dataset Summary
HC4 is a suite of test collections for ad hoc Cross-Language Information Retrieval (CLIR), with Common Crawl News documents in Chinese, Persian, and Russian. The documents
are Web pages from Common Crawl in Chinese, Persian, and Russian.
### Languages
- Chinese
- Persian
- Russian
## Dataset Structure
### Data Instances
| Split | Documents |
|-----------------|----------:|
| `fas` (Persian) | 486K |
| `rus` (Russian) | 4.7M |
| `zho` (Chinese) | 646K |
### Data Fields
- `id`: unique identifier for this document
- `cc_file`: source file from connon crawl
- `time`: extracted date/time from article
- `title`: title extracted from article
- `text`: extracted article body
- `url`: source URL
## Dataset Usage
Using 🤗 Datasets:
```python
from datasets import load_dataset
dataset = load_dataset('neuclir/hc4')
dataset['fas'] # Persian documents
dataset['rus'] # Russian documents
dataset['zho'] # Chinese documents
```
## Citation Information
```
@article{Lawrie2022HC4,
author = {Dawn Lawrie and James Mayfield and Douglas W. Oard and Eugene Yang},
title = {HC4: A New Suite of Test Collections for Ad Hoc CLIR},
booktitle = {{Advances in Information Retrieval. 44th European Conference on IR Research (ECIR 2022)},
year = {2022},
month = apr,
publisher = {Springer},
series = {Lecture Notes in Computer Science},
site = {Stavanger, Norway},
url = {https://arxiv.org/abs/2201.09992}
}
```
| neuclir/hc4 | [
"task_categories:text-retrieval",
"task_ids:document-retrieval",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:1M<n<10M",
"source_datasets:extended|c4",
"language:fa",
"language:ru",
"language:zh",
"license:odc-by",
"arxiv:2201.09992",
"region:us"
]
| 2023-01-11T21:10:06+00:00 | {"annotations_creators": ["no-annotation"], "language_creators": ["found"], "language": ["fa", "ru", "zh"], "license": ["odc-by"], "multilinguality": ["multilingual"], "size_categories": ["1M<n<10M"], "source_datasets": ["extended|c4"], "task_categories": ["text-retrieval"], "task_ids": ["document-retrieval"], "pretty_name": "HC4", "tags": []} | 2023-01-17T09:38:31+00:00 |
1d97f2ef58fef5d412f78d6e43e303f1ab4da4f3 | # AutoTrain Dataset for project: yempp
## Dataset Description
This dataset has been automatically processed by AutoTrain for project yempp.
### 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
[
{
"context": "kaputt nord stream 2 spezialschiff schaden untersuch bnn badische neueste nachrichten laut kreml-herrscher wladimir putin r\u00f6hre pipeline nord stream 2 explosionan intakt geblieben bundesregierung sieht spezialschiff betreiberfirma sache grund gehen nutzung angebot gelten agb widerrufsbelehrung information ze verarbeitung personenbezogener daten find unserer datenschutzerkl\u00e4rung \u00e4hnliche artikelkurzfristige maqnahm ze losung europ\u00e4ischen energiekrise solarify energie zukunft solarify ver\u00f6ffentlicht am1 november 20221 november 2022autorgh gerard reidangst blackout st\u00e4dte kreis g\u00fctersloh bereiten neue westf\u00e4lische stadt verl investiert 500.000 euro 72-st\u00fcndigen stromausfall grundversorg aufrechthalten knnne schlo\u00df holte-stukenbrock schafft w\u00e4rmeinseln sowie zufluchtsorte kauft feldbetten weiterlesen jahr rund h\u00e4lfte sparensusi partner geht partnerschaft sastech energate messenger schweiz bereits eina zugang einloggengasspeicher fast voll woher importierte erdgas gerade kommt stern.de deutschland hochgradig abh\u00e4ngig rund 94 prozent de hierzulande ben\u00f6tigten gas stammt importen forschungszentrum j\u00fclich faktenblatt auff\u00fchrt no kleiner teil stammt inlandsf\u00f6rderung fast verschwindend kleiner wiederum bioga mehr 53 prozent stammte gro\u00dfteil erdgasimporte j\u00fcngeren vergangenheit russland nochstgr\u00f6ceren lieferanten waran norwegen rund 38 prozent niederlande knapp neun prozent gasspeicherf\u00fcllst\u00e4nde deutschland europa bestellt zeigen beiden unten stehenden infografiken laut gesetz sollen speicher hierzulande be 1 november 90 prozent gef\u00fcllt per verordnung wurde gert ende juli 95 prozent angehoben ziel wurde \u00fcbertroffen allerdings gibt immer gro\u00dfe unterschiede einzeln anlagen hauptgasverbraucher hierzulande vergangenan jahren industrie knapp h\u00e4lfte de gas ben\u00f6tigte gefolgt haushalten mehr 30 prozent rest entf\u00e4llt fernw\u00e4rme stromversorgung stetiger gasnachschub dringend gebraucht land be laufen warm halten gerade herkommt zeigen untenstehend grafikenausblick 2023 gaspreis b\u00f6rse gefallan wirkt ewe-kunden nwzonline r\u00fcdiger klampen gashandelspl\u00e4tzen waran preise vergangen woch teil stark r\u00fcckl\u00e4ufig bedeutet schon trendwende endverbraucher wirl fragten eweenergiekrise gaspreisbremse vorgezogen strompreis 40 cent gedeckelt handelsblatt bundesregierung strompreisbremse jahresanfang angek\u00fcndigt bisher offen gelassen preisen bremse greift berlin gaspreisbremse eina monat fr\u00fcher bisher geplant greif geht beschlussvorschlag de kanzleramt ministerpr\u00e4sidentenkonferenz be mittwoch hervorenergiekrise richtig sparen b\u00f6rse anlegen sharedeal deenergiekrise richtig sparen b\u00f6rse anlegen sharedeal deneuheit fuel rettung verbrenner motorzeit de fuel pkw immer diskussion verbrennungsmotor bewahren weiterhin unwahrscheinlich verbrennungsmotor steht europa",
"question": "Wird es einen Anstieg der Energiekosten in der n\u00e4heren Zukunft geben?",
"answers.text": [
"['unwahrscheinlich']",
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{
"context": "kaputt nord stream 2 spezialschiff schaden untersuch bnn badische neueste nachrichten laut kreml-herrscher wladimir putin r\u00f6hre pipeline nord stream 2 explosionan intakt geblieben bundesregierung sieht spezialschiff betreiberfirma sache grund gehen nutzung angebot gelten agb widerrufsbelehrung information ze verarbeitung personenbezogener daten find unserer datenschutzerkl\u00e4rung \u00e4hnliche artikelkurzfristige maqnahm ze losung europ\u00e4ischen energiekrise solarify energie zukunft solarify ver\u00f6ffentlicht am1 november 20221 november 2022autorgh gerard reidangst blackout st\u00e4dte kreis g\u00fctersloh bereiten neue westf\u00e4lische stadt verl investiert 500.000 euro 72-st\u00fcndigen stromausfall grundversorg aufrechthalten knnne schlo\u00df holte-stukenbrock schafft w\u00e4rmeinseln sowie zufluchtsorte kauft feldbetten weiterlesen jahr rund h\u00e4lfte sparensusi partner geht partnerschaft sastech energate messenger schweiz bereits eina zugang einloggengasspeicher fast voll woher importierte erdgas gerade kommt stern.de deutschland hochgradig abh\u00e4ngig rund 94 prozent de hierzulande ben\u00f6tigten gas stammt importen forschungszentrum j\u00fclich faktenblatt auff\u00fchrt no kleiner teil stammt inlandsf\u00f6rderung fast verschwindend kleiner wiederum bioga mehr 53 prozent stammte gro\u00dfteil erdgasimporte j\u00fcngeren vergangenheit russland nochstgr\u00f6ceren lieferanten waran norwegen rund 38 prozent niederlande knapp neun prozent gasspeicherf\u00fcllst\u00e4nde deutschland europa bestellt zeigen beiden unten stehenden infografiken laut gesetz sollen speicher hierzulande be 1 november 90 prozent gef\u00fcllt per verordnung wurde gert ende juli 95 prozent angehoben ziel wurde \u00fcbertroffen allerdings gibt immer gro\u00dfe unterschiede einzeln anlagen hauptgasverbraucher hierzulande vergangenan jahren industrie knapp h\u00e4lfte de gas ben\u00f6tigte gefolgt haushalten mehr 30 prozent rest entf\u00e4llt fernw\u00e4rme stromversorgung stetiger gasnachschub dringend gebraucht land be laufen warm halten gerade herkommt zeigen untenstehend grafikenausblick 2023 gaspreis b\u00f6rse gefallan wirkt ewe-kunden nwzonline r\u00fcdiger klampen gashandelspl\u00e4tzen waran preise vergangen woch teil stark r\u00fcckl\u00e4ufig bedeutet schon trendwende endverbraucher wirl fragten eweenergiekrise gaspreisbremse vorgezogen strompreis 40 cent gedeckelt handelsblatt bundesregierung strompreisbremse jahresanfang angek\u00fcndigt bisher offen gelassen preisen bremse greift berlin gaspreisbremse eina monat fr\u00fcher bisher geplant greif geht beschlussvorschlag de kanzleramt ministerpr\u00e4sidentenkonferenz be mittwoch hervorenergiekrise richtig sparen b\u00f6rse anlegen sharedeal deenergiekrise richtig sparen b\u00f6rse anlegen sharedeal deneuheit fuel rettung verbrenner motorzeit de fuel pkw immer diskussion verbrennungsmotor bewahren weiterhin unwahrscheinlich verbrennungsmotor steht europa",
"question": "Wird es einen Anstieg der Energiekosten in der n\u00e4heren Zukunft geben?",
"answers.text": [
"['unwahrscheinlich']",
"['unwahrscheinlich']",
"['unwahrscheinlich']",
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}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"context": "Value(dtype='string', id=None)",
"question": "Value(dtype='string', id=None)",
"answers.text": "Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)",
"answers.answer_start": "Sequence(feature=Value(dtype='int32', id=None), length=-1, 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 | 2 |
| valid | 1 |
| Prajvi/autotrain-data-yempp | [
"region:us"
]
| 2023-01-11T22:00:50+00:00 | {} | 2023-01-11T22:05:44+00:00 |
0facac34ab0eb4947aab5777f48873b1fb57a9f4 | mehul7/captioned_military_aircraft | [
"license:mit",
"region:us"
]
| 2023-01-11T22:54:08+00:00 | {"license": "mit", "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 5806592710.697, "num_examples": 8341}], "download_size": 6709513141, "dataset_size": 5806592710.697}} | 2023-01-11T23:35:22+00:00 |
|
06c7ddb159057137587aad998a926121c4b8d299 | thisisgonz/falopita | [
"license:openrail",
"region:us"
]
| 2023-01-12T00:48:44+00:00 | {"license": "openrail"} | 2023-01-12T00:48:44+00:00 |
|
665744d561f00355bd4b88b52bcce98deeba99ef | # Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Zero-Shot Text Classification
* Model: Aalaa/opt-125m-wikitext2
* Dataset: mathemakitten/winobias_antistereotype_dev
* Config: mathemakitten--winobias_antistereotype_dev
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@gmcather](https://huggingface.co/gmcather) for evaluating this model. | autoevaluate/autoeval-eval-mathemakitten__winobias_antistereotype_dev-mathemakitte-c87316-2844283322 | [
"autotrain",
"evaluation",
"region:us"
]
| 2023-01-12T01:07:38+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["mathemakitten/winobias_antistereotype_dev"], "eval_info": {"task": "text_zero_shot_classification", "model": "Aalaa/opt-125m-wikitext2", "metrics": [], "dataset_name": "mathemakitten/winobias_antistereotype_dev", "dataset_config": "mathemakitten--winobias_antistereotype_dev", "dataset_split": "validation", "col_mapping": {"text": "text", "classes": "classes", "target": "target"}}} | 2023-01-12T01:08:25+00:00 |
0e78936d0863db202c267520f8ce9a535df59240 | # Dataset Card for "bookcorpus_compact_1024_shard0_meta"
132 hours to finish
num_examples: 61605
size: 1.5GB
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | saibo/bookcorpus_compact_1024_shard0_of_10_meta | [
"region:us"
]
| 2023-01-12T01:45:55+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "concept_with_offset", "dtype": "string"}, {"name": "cid_arrangement", "sequence": "int32"}, {"name": "schema_lengths", "sequence": "int64"}, {"name": "topic_entity_mask", "sequence": "int64"}, {"name": "text_lengths", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 7429184891, "num_examples": 61605}], "download_size": 1631318898, "dataset_size": 7429184891}} | 2023-01-12T20:59:01+00:00 |
301e7fbe3b936cd25a20c9bff8d2933ff1373d05 | suwitlam/whisper-sun-system | [
"license:cc0-1.0",
"region:us"
]
| 2023-01-12T02:54:38+00:00 | {"license": "cc0-1.0"} | 2023-01-12T02:54:38+00:00 |
|
acda1b00d384f4696bbe4518a1c4cf6b1d5d3a68 | suwitlam/whisper-sun-system-dataset | [
"license:cc0-1.0",
"region:us"
]
| 2023-01-12T02:55:37+00:00 | {"license": "cc0-1.0"} | 2023-01-12T07:02:20+00:00 |
|
3fb7212c389d7818b8e6179e2cdac762f2e081d9 |
# Dataset Card for WRIME
[](https://github.com/shunk031/huggingface-datasets_wrime/actions/workflows/ci.yaml)
## 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://github.com/ids-cv/wrime
- Repository: https://github.com/shunk031/huggingface-datasets_wrime
- Paper: https://aclanthology.org/2021.naacl-main.169/
### Dataset Summary
In this study, we introduce a new dataset, WRIME, for emotional intensity estimation. We collect both the subjective emotional intensity ofthe writers themselves and the objective one annotated by the readers, and explore the differences between them. In our data collection, we hired 50 participants via crowdsourcing service. They annotated their own past posts on a social networking service (SNS) with the subjective emotional intensity. We also hired 3 annotators, who annotated allposts with the objective emotional intensity. Consequently, our Japanese emotion analysis datasetconsists of 17,000 posts with both subjective andobjective emotional intensities for Plutchik’s eightemotions ([Plutchik, 1980](https://www.sciencedirect.com/science/article/pii/B9780125587013500077)), which are given in afour-point scale (no, weak, medium, and strong).
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
- Japanese
## Dataset Structure
### Data Instances
When loading a specific configuration, users has to append a version dependent suffix:
```python
from datasets import load_dataset
dataset = load_dataset("shunk031/wrime", name="ver1")
print(dataset)
# DatasetDict({
# train: Dataset({
# features: ['sentence', 'user_id', 'datetime', 'writer', 'reader1', 'reader2', 'reader3', 'avg_readers'],
# num_rows: 40000
# })
# validation: Dataset({
# features: ['sentence', 'user_id', 'datetime', 'writer', 'reader1', 'reader2', 'reader3', 'avg_readers'],
# num_rows: 1200
# })
# test: Dataset({
# features: ['sentence', 'user_id', 'datetime', 'writer', 'reader1', 'reader2', 'reader3', 'avg_readers'],
# num_rows: 2000
# })
# })
```
#### Ver. 1
An example of looks as follows:
```json
{
"sentence": "ぼけっとしてたらこんな時間｡チャリあるから食べにでたいのに…",
"user_id": "1",
"datetime": "2012/07/31 23:48",
"writer": {
"joy": 0,
"sadness": 1,
"anticipation": 2,
"surprise": 1,
"anger": 1,
"fear": 0,
"disgust": 0,
"trust": 1
},
"reader1": {
"joy": 0,
"sadness": 2,
"anticipation": 0,
"surprise": 0,
"anger": 0,
"fear": 0,
"disgust": 0,
"trust": 0
},
"reader2": {
"joy": 0,
"sadness": 2,
"anticipation": 0,
"surprise": 1,
"anger": 0,
"fear": 0,
"disgust": 0,
"trust": 0
},
"reader3": {
"joy": 0,
"sadness": 2,
"anticipation": 0,
"surprise": 0,
"anger": 0,
"fear": 1,
"disgust": 1,
"trust": 0
},
"avg_readers": {
"joy": 0,
"sadness": 2,
"anticipation": 0,
"surprise": 0,
"anger": 0,
"fear": 0,
"disgust": 0,
"trust": 0
}
}
```
#### Ver. 1
An example of looks as follows:
```json
{
"sentence": "ぼけっとしてたらこんな時間。チャリあるから食べにでたいのに…",
"user_id": "1",
"datetime": "2012/7/31 23:48",
"writer": {
"joy": 0,
"sadness": 1,
"anticipation": 2,
"surprise": 1,
"anger": 1,
"fear": 0,
"disgust": 0,
"trust": 1,
"sentiment": 0
},
"reader1": {
"joy": 0,
"sadness": 2,
"anticipation": 0,
"surprise": 0,
"anger": 0,
"fear": 0,
"disgust": 0,
"trust": 0,
"sentiment": -2
},
"reader2": {
"joy": 0,
"sadness": 2,
"anticipation": 0,
"surprise": 0,
"anger": 0,
"fear": 1,
"disgust": 1,
"trust": 0,
"sentiment": -1
},
"reader3": {
"joy": 0,
"sadness": 2,
"anticipation": 0,
"surprise": 1,
"anger": 0,
"fear": 0,
"disgust": 0,
"trust": 0,
"sentiment": -1
},
"avg_readers": {
"joy": 0,
"sadness": 2,
"anticipation": 0,
"surprise": 0,
"anger": 0,
"fear": 0,
"disgust": 0,
"trust": 0,
"sentiment": -1
}
}
```
### Data Fields
#### Ver. 1
- `sentence`: 投稿テキスト
- `user_id`: ユーザー ID
- `datetime`: 投稿日時
- `writer`: 主観 （書き手）
- `joy`: 主観の喜びの感情
- `sadness`: 主観の悲しみの感情
- `anticipation`: 主観の期待の感情
- `surprise`: 主観の驚きの感情
- `anger`: 主観の怒りの感情
- `fear`: 主観の恐れの感情
- `disgust`: 主観の嫌悪の感情
- `trust`: 主観の信頼の感情
- `reader1`: 客観 A （読み手 A)
- `joy`: 客観 A の喜びの感情
- `sadness`: 客観 A の悲しみの感情
- `anticipation`: 客観 A の期待の感情
- `surprise`: 客観 A の驚きの感情
- `anger`: 客観 A の怒りの感情
- `fear`: 客観 A の恐れの感情
- `disgust`: 客観 A の嫌悪の感情
- `trust`: 客観 A の信頼の感情
- `reader2`: 客観 B （読み手 B)
- `joy`: 客観 B の喜びの感情
- `sadness`: 客観 B の悲しみの感情
- `anticipation`: 客観 B の期待の感情
- `surprise`: 客観 B の驚きの感情
- `anger`: 客観 B の怒りの感情
- `fear`: 客観 B の恐れの感情
- `disgust`: 客観 B の嫌悪の感情
- `trust`: 客観 B の信頼の感情
- `reader3`: 客観 C （読み手 C)
- `joy`: 客観 C の喜びの感情
- `sadness`: 客観 C の悲しみの感情
- `anticipation`: 客観 C の期待の感情
- `surprise`: 客観 C の驚きの感情
- `anger`: 客観 C の怒りの感情
- `fear`: 客観 C の恐れの感情
- `disgust`: 客観 C の嫌悪の感情
- `trust`: 客観 C の信頼の感情
- `avg_readers`
- `joy`: 客観 A, B, C 平均の喜びの感情
- `sadness`: 客観 A, B, C 平均の悲しみの感情
- `anticipation`: 客観 A, B, C 平均の期待の感情
- `surprise`: 客観 A, B, C 平均の驚きの感情
- `anger`: 客観 A, B, C 平均の怒りの感情
- `fear`: 客観 A, B, C 平均の恐れの感情
- `disgust`: 客観 A, B, C 平均の嫌悪の感情
- `trust`: 客観 A, B, C 平均の信頼の感情
#### Ver. 2
- `sentence`: 投稿テキスト
- `user_id`: ユーザー ID
- `datetime`: 投稿日時
- `writer`: 主観 （書き手）
- `joy`: 主観の喜びの感情
- `sadness`: 主観の悲しみの感情
- `anticipation`: 主観の期待の感情
- `surprise`: 主観の驚きの感情
- `anger`: 主観の怒りの感情
- `fear`: 主観の恐れの感情
- `disgust`: 主観の嫌悪の感情
- `trust`: 主観の信頼の感情
- `sentiment`: 主観の感情極性
- `reader1`: 客観 A （読み手 A)
- `joy`: 客観 A の喜びの感情
- `sadness`: 客観 A の悲しみの感情
- `anticipation`: 客観 A の期待の感情
- `surprise`: 客観 A の驚きの感情
- `anger`: 客観 A の怒りの感情
- `fear`: 客観 A の恐れの感情
- `disgust`: 客観 A の嫌悪の感情
- `trust`: 客観 A の信頼の感情
- `sentiment`: 客観 A の感情極性
- `reader2`: 客観 B （読み手 B)
- `joy`: 客観 B の喜びの感情
- `sadness`: 客観 B の悲しみの感情
- `anticipation`: 客観 B の期待の感情
- `surprise`: 客観 B の驚きの感情
- `anger`: 客観 B の怒りの感情
- `fear`: 客観 B の恐れの感情
- `disgust`: 客観 B の嫌悪の感情
- `trust`: 客観 B の信頼の感情
- `sentiment`: 客観 B の感情極性
- `reader3`: 客観 C （読み手 C)
- `joy`: 客観 C の喜びの感情
- `sadness`: 客観 C の悲しみの感情
- `anticipation`: 客観 C の期待の感情
- `surprise`: 客観 C の驚きの感情
- `anger`: 客観 C の怒りの感情
- `fear`: 客観 C の恐れの感情
- `disgust`: 客観 C の嫌悪の感情
- `trust`: 客観 C の信頼の感情
- `sentiment`: 客観 C の感情極性
- `avg_readers`
- `joy`: 客観 A, B, C 平均の喜びの感情
- `sadness`: 客観 A, B, C 平均の悲しみの感情
- `anticipation`: 客観 A, B, C 平均の期待の感情
- `surprise`: 客観 A, B, C 平均の驚きの感情
- `anger`: 客観 A, B, C 平均の怒りの感情
- `fear`: 客観 A, B, C 平均の恐れの感情
- `disgust`: 客観 A, B, C 平均の嫌悪の感情
- `trust`: 客観 A, B, C 平均の信頼の感情
- `sentiment`: 客観 A, B, C 平均の感情極性
### Data Splits
| name | train | validation | test |
|------|-------:|-----------:|------:|
| ver1 | 40,000 | 1,200 | 2,000 |
| ver2 | 30,000 | 2,500 | 2,500 |
## 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
From [the README](https://github.com/ids-cv/wrime/blob/master/README.en.md#licence) of the GitHub:
- The dataset is available for research purposes only.
- Redistribution of the dataset is prohibited.
### Citation Information
```bibtex
@inproceedings{kajiwara-etal-2021-wrime,
title = "{WRIME}: A New Dataset for Emotional Intensity Estimation with Subjective and Objective Annotations",
author = "Kajiwara, Tomoyuki and
Chu, Chenhui and
Takemura, Noriko and
Nakashima, Yuta and
Nagahara, Hajime",
booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.naacl-main.169",
doi = "10.18653/v1/2021.naacl-main.169",
pages = "2095--2104",
abstract = "We annotate 17,000 SNS posts with both the writer{'}s subjective emotional intensity and the reader{'}s objective one to construct a Japanese emotion analysis dataset. In this study, we explore the difference between the emotional intensity of the writer and that of the readers with this dataset. We found that the reader cannot fully detect the emotions of the writer, especially anger and trust. In addition, experimental results in estimating the emotional intensity show that it is more difficult to estimate the writer{'}s subjective labels than the readers{'}. The large gap between the subjective and objective emotions imply the complexity of the mapping from a post to the subjective emotion intensities, which also leads to a lower performance with machine learning models.",
}
```
```bibtex
@inproceedings{suzuki-etal-2022-japanese,
title = "A {J}apanese Dataset for Subjective and Objective Sentiment Polarity Classification in Micro Blog Domain",
author = "Suzuki, Haruya and
Miyauchi, Yuto and
Akiyama, Kazuki and
Kajiwara, Tomoyuki and
Ninomiya, Takashi and
Takemura, Noriko and
Nakashima, Yuta and
Nagahara, Hajime",
booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference",
month = jun,
year = "2022",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2022.lrec-1.759",
pages = "7022--7028",
abstract = "We annotate 35,000 SNS posts with both the writer{'}s subjective sentiment polarity labels and the reader{'}s objective ones to construct a Japanese sentiment analysis dataset. Our dataset includes intensity labels (\textit{none}, \textit{weak}, \textit{medium}, and \textit{strong}) for each of the eight basic emotions by Plutchik (\textit{joy}, \textit{sadness}, \textit{anticipation}, \textit{surprise}, \textit{anger}, \textit{fear}, \textit{disgust}, and \textit{trust}) as well as sentiment polarity labels (\textit{strong positive}, \textit{positive}, \textit{neutral}, \textit{negative}, and \textit{strong negative}). Previous studies on emotion analysis have studied the analysis of basic emotions and sentiment polarity independently. In other words, there are few corpora that are annotated with both basic emotions and sentiment polarity. Our dataset is the first large-scale corpus to annotate both of these emotion labels, and from both the writer{'}s and reader{'}s perspectives. In this paper, we analyze the relationship between basic emotion intensity and sentiment polarity on our dataset and report the results of benchmarking sentiment polarity classification.",
}
```
### Contributions
Thanks to [@moguranosenshi](https://github.com/moguranosenshi) for creating this dataset.
| shunk031/wrime | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"language:ja",
"license:unknown",
"sentiment-analysis",
"wrime",
"region:us"
]
| 2023-01-12T03:04:20+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["crowdsourced"], "language": ["ja"], "license": ["unknown"], "multilinguality": ["monolingual"], "task_categories": ["text-classification"], "task_ids": ["sentiment-classification"], "pretty_name": "wrime", "tags": ["sentiment-analysis", "wrime"], "datasets": ["ver1", "ver2"], "metrics": ["accuracy"]} | 2023-01-15T03:39:01+00:00 |
afbd39edb1a160731fb6449bf3e2fb27b26f537b | # Dataset Card for "bookcorpus_compact_1024_shard8"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | saibo/bookcorpus_compact_1024_shard8_of_10 | [
"region:us"
]
| 2023-01-12T03:24:19+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "concept_with_offset", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 755424246, "num_examples": 61605}], "download_size": 380882733, "dataset_size": 755424246}} | 2023-01-12T03:27:31+00:00 |
3c19b0488d794d30c36f73d132d8a22e64f42f2e | # Dataset Card for LogiQA
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
LogiQA is constructed from the logical comprehension problems from publically available questions of the National Civil Servants Examination of China, which are designed to test the civil servant candidates’ critical thinking and problem solving. This dataset includes the English versions only; the Chinese versions are available via the homepage/original source.
## Dataset Structure
### Data Instances
An example from `train` looks as follows:
```
{'context': 'Continuous exposure to indoor fluorescent lights is beneficial to the health of hamsters with heart disease. One group of hamsters exposed to continuous exposure to fluorescent lights has an average lifespan that is 2.5% longer than another one of the same species but living in a black wall.',
'query': 'Which of the following questions was the initial motivation for conducting the above experiment?',
'options': ['Can hospital light therapy be proved to promote patient recovery?',
'Which one lives longer, the hamster living under the light or the hamster living in the dark?',
'What kind of illness does the hamster have?',
'Do some hamsters need a period of darkness?'],
'correct_option': 0}
```
### Data Fields
- `context`: a `string` feature.
- `query`: a `string` feature.
- `answers`: a `list` feature containing `string` features.
- `correct_option`: a `string` feature.
### Data Splits
|train|validation|test|
|----:|---------:|---:|
| 7376| 651| 651|
## Additional Information
### Dataset Curators
The original LogiQA was produced by Jian Liu, Leyang Cui , Hanmeng Liu, Dandan Huang, Yile Wang, and Yue Zhang.
### Licensing Information
[More Information Needed]
### Citation Information
```
@article{liu2020logiqa,
title={Logiqa: A challenge dataset for machine reading comprehension with logical reasoning},
author={Liu, Jian and Cui, Leyang and Liu, Hanmeng and Huang, Dandan and Wang, Yile and Zhang, Yue},
journal={arXiv preprint arXiv:2007.08124},
year={2020}
}
```
### Contributions
[@lucasmccabe](https://github.com/lucasmccabe) added this dataset. | lucasmccabe/logiqa | [
"task_categories:question-answering",
"size_categories:1K<n<10K",
"language:en",
"region:us"
]
| 2023-01-12T04:14:53+00:00 | {"language": ["en"], "size_categories": ["1K<n<10K"], "task_categories": ["question-answering"], "paperswithcode_id": "logiqa", "pretty_name": "LogiQA", "dataset_info": {"features": [{"name": "context", "dtype": "string"}, {"name": "query", "dtype": "string"}, {"name": "options", "sequence": {"dtype": "string"}}, {"name": "correct_option", "dtype": "string"}], "splits": [{"name": "train", "num_examples": 7376}, {"name": "validation", "num_examples": 651}, {"name": "test", "num_examples": 651}]}} | 2023-02-08T01:51:31+00:00 |
e7bab7c323d5794913b6a4ec1f75ebac70a4d3c6 | # Dataset Card for "bookcorpus_compact_1024_shard2_meta"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | saibo/bookcorpus_compact_1024_shard2_of_10_meta | [
"region:us"
]
| 2023-01-12T04:44:35+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "concept_with_offset", "dtype": "string"}, {"name": "cid_arrangement", "sequence": "int32"}, {"name": "schema_lengths", "sequence": "int64"}, {"name": "topic_entity_mask", "sequence": "int64"}, {"name": "text_lengths", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 7742678868, "num_examples": 61605}], "download_size": 1715122126, "dataset_size": 7742678868}} | 2023-01-12T04:49:01+00:00 |
c2bf86c6d0c1331a6aa950b61b2520dcface8532 |
# Dataset Card for aeroBERT-classification
## Dataset Description
- **Paper:** aeroBERT-Classifier: Classification of Aerospace Requirements using BERT
- **Point of Contact:** [email protected]
### Dataset Summary
This dataset contains requirements from the aerospace domain. The requirements are tagged based on the "type"/category of requirement they belong to.
The creation of this dataset is aimed at - <br>
(1) Making available an **open-source** dataset for aerospace requirements which are often proprietary <br>
(2) Fine-tuning language models for **requirements classification** specific to the aerospace domain <br>
This dataset can be used for training or fine-tuning language models for the identification of the following types of requirements - <br>
<br>
**Design Requirement** - Dictates "how" a system should be designed given certain technical standards and specifications;
**Example:** Trim control systems must be designed to prevent creeping in flight.<br>
<br>
**Functional Requirement** - Defines the functions that need to be performed by a system in order to accomplish the desired system functionality;
**Example:** Each cockpit voice recorder shall record the voice communications of flight crew members on the flight deck.<br>
<br>
**Performance Requirement** - Defines "how well" a system needs to perform a certain function;
**Example:** The airplane must be free from flutter, control reversal, and divergence for any configuration and condition of operation.<br>
## Dataset Structure
The tagging scheme followed: <br>
(1) Design requirements: 0 (Count = 149) <br>
(2) Functional requirements: 1 (Count = 99) <br>
(3) Performance requirements: 2 (Count = 62) <br>
<br>
The dataset is of the format: ``requirements | label`` <br>
| requirements | label |
| :----: | :----: |
| Each cockpit voice recorder shall record voice communications transmitted from or received in the airplane by radio.| 1 |
| Each recorder container must be either bright orange or bright yellow.| 0 |
| Single-engine airplanes, not certified for aerobatics, must not have a tendency to inadvertently depart controlled flight. | 2|
| Each part of the airplane must have adequate provisions for ventilation and drainage. | 0 |
| Each baggage and cargo compartment must have a means to prevent the contents of the compartment from becoming a hazard by impacting occupants or shifting. | 1 |
## Dataset Creation
### Source Data
A total of 325 aerospace requirements were collected from Parts 23 and 25 of Title 14 of the Code of Federal Regulations (CFRs) and annotated (refer to the paper for more details). <br>
### Importing dataset into Python environment
Use the following code chunk to import the dataset into Python environment as a DataFrame.
```
from datasets import load_dataset
import pandas as pd
dataset = load_dataset("archanatikayatray/aeroBERT-classification")
#Converting the dataset into a pandas DataFrame
dataset = pd.DataFrame(dataset["train"]["text"])
dataset = dataset[0].str.split('*', expand = True)
#Getting the headers from the first row
header = dataset.iloc[0]
#Excluding the first row since it contains the headers
dataset = dataset[1:]
#Assigning the header to the DataFrame
dataset.columns = header
#Viewing the last 10 rows of the annotated dataset
dataset.tail(10)
```
### Annotations
#### Annotation process
A Subject Matter Expert (SME) was consulted for deciding on the annotation categories for the requirements.
The final classification dataset had 149 Design requirements, 99 Functional requirements, and 62 Performance requirements.
Lastly, the 'labels' attached to the requirements (design requirement, functional requirement, and performance requirement) were converted into numeric values: 0, 1, and 2 respectively.
### Limitations
(1)The dataset is an imbalanced dataset (more Design requirements as compared to the other types). Hence, using ``Accuracy`` as a metric for the model performance is
NOT a good idea. The use of Precision, Recall, and F1 scores are suggested for model performance evaluation.
(2)This dataset does not contain a test set. Hence, it is suggested that the user split the dataset into training/validation/testing after importing the data into a Python environment.
Please refer to the Appendix of the paper for information on the test set.
### Citation Information
```
@Article{aeroBERT-Classifier,
AUTHOR = {Tikayat Ray, Archana and Cole, Bjorn F. and Pinon Fischer, Olivia J. and White, Ryan T. and Mavris, Dimitri N.},
TITLE = {aeroBERT-Classifier: Classification of Aerospace Requirements Using BERT},
JOURNAL = {Aerospace},
VOLUME = {10},
YEAR = {2023},
NUMBER = {3},
ARTICLE-NUMBER = {279},
URL = {https://www.mdpi.com/2226-4310/10/3/279},
ISSN = {2226-4310},
DOI = {10.3390/aerospace10030279}
}
@phdthesis{tikayatray_thesis,
author = {Tikayat Ray, Archana},
title = {Standardization of Engineering Requirements Using Large Language Models},
school = {Georgia Institute of Technology},
year = {2023},
doi = {10.13140/RG.2.2.17792.40961},
URL = {https://repository.gatech.edu/items/964c73e3-f0a8-487d-a3fa-a0988c840d04}
}
``` | archanatikayatray/aeroBERT-classification | [
"task_categories:text-classification",
"size_categories:n<1K",
"language:en",
"license:apache-2.0",
"sentence classification",
"aerospace requirements",
"design",
"functional",
"performance",
"requirements",
"NLP4RE",
"doi:10.57967/hf/0433",
"region:us"
]
| 2023-01-12T05:00:31+00:00 | {"language": ["en"], "license": "apache-2.0", "size_categories": ["n<1K"], "task_categories": ["text-classification"], "pretty_name": "requirements_classification_dataset.txt", "tags": ["sentence classification", "aerospace requirements", "design", "functional", "performance", "requirements", "NLP4RE"]} | 2023-05-20T21:40:37+00:00 |
b718f8840ee0773ef9b96369007b38653085719b |
# Dataset Card for "Emoji_Dataset-Openmoji"
All data is 618*618 size *.png + text(4083 couple).
All emojis designed by OpenMoji(https://openmoji.org/) - the open-source emoji and icon project.
License: CC BY-SA 4.0
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | soypablo/Emoji_Dataset-Openmoji | [
"size_categories:1K<n<10K",
"language:en",
"license:cc-by-sa-4.0",
"region:us"
]
| 2023-01-12T06:28:53+00:00 | {"language": ["en"], "license": "cc-by-sa-4.0", "size_categories": ["1K<n<10K"], "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 85090151.546, "num_examples": 4083}], "download_size": 101470798, "dataset_size": 85090151.546}} | 2023-01-25T10:04:04+00:00 |
91ebca14baafcf9dd528c2a9b444a61663b754dc |
A database of Wikipedia pages summarizes certain Natural Launage Processing Model applications. | SinonTM/Wiki-Scraper | [
"task_categories:summarization",
"size_categories:10K<n<100K",
"language:en",
"license:openrail",
"region:us"
]
| 2023-01-12T06:34:35+00:00 | {"language": ["en"], "license": "openrail", "size_categories": ["10K<n<100K"], "task_categories": ["summarization"], "pretty_name": "Wiki Scraper"} | 2023-01-12T22:51:50+00:00 |
7fdf6211323d9578965652e717d3250883a15e30 |
# Dataset Card for "NER Model Tune"
## 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:** None
- **Repository:** https://huggingface.co/datasets/ayuhamaro/nlp-model-tune
- **Paper:** [More Information Needed]
- **Leaderboard:** [If the dataset supports an active leaderboard, add link here]()
- **Point of Contact:** [More Information Needed]
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
[More Information Needed]
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
[More Information Needed]
#### 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 | ayuhamaro/ner-model-tune | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:zh",
"license:unknown",
"region:us"
]
| 2023-01-12T06:35:26+00:00 | {"annotations_creators": ["expert-generated"], "language_creators": ["found"], "language": ["zh"], "license": ["unknown"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["token-classification"], "task_ids": ["named-entity-recognition"], "paperswithcode_id": "nlp-model-tune", "pretty_name": "NER Model Tune", "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "tokens", "sequence": "string"}, {"name": "ner_tags", "sequence": {"class_label": {"names": {"0": "O,", "1": "B-CARDINAL,", "2": "B-DATE,", "3": "B-EVENT,", "4": "B-FAC,", "5": "B-GPE,", "6": "B-LANGUAGE,", "7": "B-LAW,", "8": "B-LOC,", "9": "B-MONEY,", "10": "B-NORP,", "11": "B-ORDINAL,", "12": "B-ORG,", "13": "B-PERCENT,", "14": "B-PERSON,", "15": "B-PRODUCT,", "16": "B-QUANTITY,", "17": "B-TIME,", "18": "B-WORK_OF_ART,", "19": "I-CARDINAL,", "20": "I-DATE,", "21": "I-EVENT,", "22": "I-FAC,", "23": "I-GPE,", "24": "I-LANGUAGE,", "25": "I-LAW,", "26": "I-LOC,", "27": "I-MONEY,", "28": "I-NORP,", "29": "I-ORDINAL,", "30": "I-ORG,", "31": "I-PERCENT,", "32": "I-PERSON,", "33": "I-PRODUCT,", "34": "I-QUANTITY,", "35": "I-TIME,", "36": "I-WORK_OF_ART,", "37": "E-CARDINAL,", "38": "E-DATE,", "39": "E-EVENT,", "40": "E-FAC,", "41": "E-GPE,", "42": "E-LANGUAGE,", "43": "E-LAW,", "44": "E-LOC,", "45": "E-MONEY,", "46": "E-NORP,", "47": "E-ORDINAL,", "48": "E-ORG,", "49": "E-PERCENT,", "50": "E-PERSON,", "51": "E-PRODUCT,", "52": "E-QUANTITY,", "53": "E-TIME,", "54": "E-WORK_OF_ART,", "55": "S-CARDINAL,", "56": "S-DATE,", "57": "S-EVENT,", "58": "S-FAC,", "59": "S-GPE,", "60": "S-LANGUAGE,", "61": "S-LAW,", "62": "S-LOC,", "63": "S-MONEY,", "64": "S-NORP,", "65": "S-ORDINAL,", "66": "S-ORG,", "67": "S-PERCENT,", "68": "S-PERSON,", "69": "S-PRODUCT,", "70": "S-QUANTITY,", "71": "S-TIME,", "72": "S-WORK_OF_ART"}}}}], "splits": [{"name": "train", "num_bytes": 568, "num_examples": 1}], "download_size": 568, "dataset_size": 568}, "train-eval-index": [{"config": "default", "task": "token-classification", "task_id": "entity_extraction", "splits": {"train_split": "train", "eval_split": "test"}, "col_mapping": {"tokens": "tokens", "ner_tags": "tags"}, "metrics": [{"type": "seqeval", "name": "seqeval"}]}]} | 2023-01-13T07:53:28+00:00 |
8214795c511cdc0da792e11058c3bb23fdba8687 | # Dataset Card for "dreambooth-hackathon-images"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | akshaypt7/dreambooth-hackathon-images | [
"region:us"
]
| 2023-01-12T07:00:10+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 936008.0, "num_examples": 30}], "download_size": 0, "dataset_size": 936008.0}} | 2023-01-12T15:41:07+00:00 |
597be4f71e9286864b3188e550275b6cf2da889b | Glac1er/hereby | [
"license:unknown",
"region:us"
]
| 2023-01-12T07:32:12+00:00 | {"license": "unknown"} | 2023-01-12T08:19:29+00:00 |
|
d605e677fe05830c53f2491a18c17687e09786f8 | suwitlam/whisper-sun-system-dataset-sentence | [
"license:cc0-1.0",
"region:us"
]
| 2023-01-12T07:38:22+00:00 | {"license": "cc0-1.0"} | 2023-01-12T07:38:22+00:00 |
|
2728cd590cbac25cf7231203035c88b2f5e8b5ff | # Dataset Card for "free_marco"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | bhavnicksm/free_marco | [
"region:us"
]
| 2023-01-12T08:07:55+00:00 | {"dataset_info": {"features": [{"name": "query", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "answer", "dtype": "string"}], "splits": [{"name": "validation", "num_bytes": 25790184.920231506, "num_examples": 55578}, {"name": "train", "num_bytes": 238011027.40998867, "num_examples": 502939}, {"name": "test"}], "download_size": 175593615, "dataset_size": 263801212.33022016}} | 2023-01-16T08:38:36+00:00 |
c027d794614fc649338d0946cec304d4317e0c45 | yaodi/shijing | [
"license:mit",
"region:us"
]
| 2023-01-12T08:17:26+00:00 | {"license": "mit"} | 2023-01-12T08:18:49+00:00 |
|
0516be374425db7240d4a8d7275b4236a9601d5c | hkgkjg111/crack | [
"region:us"
]
| 2023-01-12T09:10:35+00:00 | {} | 2023-01-12T09:10:58+00:00 |
|
d47c1f0110dc50b716f3c61e2110dbfcd73b1788 |
<h1>This dataset is used to train AI how to use python.</h1> | derchr/py | [
"license:bigscience-openrail-m",
"region:us"
]
| 2023-01-12T09:15:53+00:00 | {"license": "bigscience-openrail-m"} | 2023-01-12T09:21:37+00:00 |
2f06f7ce1fdca18a088e689c64a3eac5c3788a78 | # Dataset Card for "analysed-diff-metadata"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | mamiksik/analysed-diff-metadata | [
"region:us"
]
| 2023-01-12T09:58:34+00:00 | {"dataset_info": {"features": [{"name": "sha", "dtype": "string"}, {"name": "author", "dtype": "string"}, {"name": "committer", "dtype": "string"}, {"name": "message", "dtype": "string"}, {"name": "subject", "dtype": "string"}, {"name": "subject_length", "dtype": "float64"}, {"name": "is_chore", "dtype": "bool"}, {"name": "is_bot", "dtype": "bool"}, {"name": "subject_word_count", "dtype": "float64"}, {"name": "verb_object_spacy", "dtype": "bool"}, {"name": "verb_object_stanza", "dtype": "bool"}, {"name": "fits_requirements", "dtype": "bool"}, {"name": "owner", "dtype": "string"}, {"name": "repo", "dtype": "string"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 237352522, "num_examples": 742125}], "download_size": 114567812, "dataset_size": 237352522}} | 2023-01-17T14:31:35+00:00 |
e4cd8ce4cf25ff25443e7b64657665a5735e5eb7 | # Dataset Card for "squad_id"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | rahmanfadhil/squad_v2_id | [
"region:us"
]
| 2023-01-12T11:01:07+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "struct": [{"name": "answer_start", "sequence": "int32"}, {"name": "text", "sequence": "string"}]}], "splits": [{"name": "train", "num_bytes": 121632833, "num_examples": 130318}, {"name": "validation", "num_bytes": 12218827, "num_examples": 11858}], "download_size": 0, "dataset_size": 133851660}} | 2023-01-12T11:14:51+00:00 |
4459a61365cbfc407774c2093b966bfdc12a1f06 | # Dataset Card for "chicago_early_childhood_education_centers"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | dmargutierrez/chicago_early_childhood_education_centers | [
"region:us"
]
| 2023-01-12T11:04:04+00:00 | {"dataset_info": {"features": [{"name": "Id", "dtype": "int64"}, {"name": "Site name", "dtype": "string"}, {"name": "Address", "dtype": "string"}, {"name": "Zip", "dtype": "float64"}, {"name": "Phone", "dtype": "float64"}, {"name": "Program Name", "dtype": "string"}, {"name": "Length of Day", "dtype": "string"}, {"name": "Neighborhood", "dtype": "string"}, {"name": "Funded Enrollment", "dtype": "string"}, {"name": "Program Option", "dtype": "string"}, {"name": "Eearly Head Start Fund", "dtype": "string"}, {"name": "CC fund", "dtype": "string"}, {"name": "Progmod", "dtype": "string"}, {"name": "Website", "dtype": "string"}, {"name": "Center Director", "dtype": "string"}, {"name": "ECE Available Programs", "dtype": "string"}, {"name": "NAEYC Valid Until", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "0", "1": "1", "2": "2", "3": "3", "4": "4", "5": "5", "6": "6", "7": "7", "8": "8", "9": "9", "10": "10", "11": "11", "12": 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"1147": "1147", "1148": "1148", "1149": "1149", "1150": "1150", "1151": "1151", "1152": "1152", "1153": "1153", "1154": "1154", "1155": "1155", "1156": "1156", "1157": "1157", "1158": "1158", "1159": "1159", "1160": "1160", "1161": "1161"}}}}, {"name": "tisix_row_index", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 662438, "num_examples": 3337}], "download_size": 247923, "dataset_size": 662438}} | 2023-01-12T11:04:12+00:00 |
b9c1e53e23999d2cd6ff4edc2441cc0c5b224c37 | # Dataset Card for "c_corpus_br_finetuning_language_model_bert"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | rosimeirecosta/c_corpus_br_finetuning_language_model_bert | [
"region:us"
]
| 2023-01-12T13:40:03+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 36065567, "num_examples": 228736}, {"name": "validation", "num_bytes": 9012563, "num_examples": 57184}], "download_size": 0, "dataset_size": 45078130}} | 2023-01-12T14:37:37+00:00 |
3c830737fc5e984a6415d45923fa575c711338c7 | # Dataset Card for "danbooru_small"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | leemeng/danbooru_small | [
"region:us"
]
| 2023-01-12T13:40:20+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 215463820.99, "num_examples": 1953}], "download_size": 207744589, "dataset_size": 215463820.99}} | 2023-01-12T13:40:43+00:00 |
e0c036e775088b536d2294738611520bc5d01cce | oghali/innovation | [
"license:openrail",
"region:us"
]
| 2023-01-12T14:01:40+00:00 | {"license": "openrail"} | 2023-01-12T14:01:40+00:00 |
|
c4a4cdc67b77fce148b45484a067957bf75ec4c3 | # Flickr30k (1K test set)
Original paper: [From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions](https://aclanthology.org/Q14-1006)
Homepage: https://shannon.cs.illinois.edu/DenotationGraph/
1K test set split from: http://cs.stanford.edu/people/karpathy/deepimagesent/caption_datasets.zip
Bibtex:
```
@article{young2014image,
title={From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions},
author={Young, Peter and Lai, Alice and Hodosh, Micah and Hockenmaier, Julia},
journal={Transactions of the Association for Computational Linguistics},
volume={2},
pages={67--78},
year={2014},
publisher={MIT Press}
}
``` | nlphuji/flickr_1k_test_image_text_retrieval | [
"region:us"
]
| 2023-01-12T14:36:57+00:00 | {} | 2023-01-14T19:54:08+00:00 |
551c4f7667f06fa82b4ef0a07617bfc4cf324ac3 | # MSCOCO (5K test set)
Original paper: [Microsoft COCO: Common Objects in Context
](https://arxiv.org/abs/1405.0312)
Homepage: https://cocodataset.org/#home
5K test set split from: http://cs.stanford.edu/people/karpathy/deepimagesent/caption_datasets.zip
Bibtex:
```
@inproceedings{lin2014microsoft,
title={Microsoft coco: Common objects in context},
author={Lin, Tsung-Yi and Maire, Michael and Belongie, Serge and Hays, James and Perona, Pietro and Ramanan, Deva and Doll{\'a}r, Piotr and Zitnick, C Lawrence},
booktitle={European conference on computer vision},
pages={740--755},
year={2014},
organization={Springer}
}
``` | nlphuji/mscoco_2014_5k_test_image_text_retrieval | [
"arxiv:1405.0312",
"region:us"
]
| 2023-01-12T14:37:24+00:00 | {} | 2023-01-18T00:08:42+00:00 |
87ffdba057529dad93e5fa285d098cf37192fe77 | peter2000/ecoicop_online_product | [
"task_categories:text-classification",
"size_categories:10K<n<100K",
"language:de",
"language:fr",
"language:it",
"license:cc",
"region:us"
]
| 2023-01-12T15:23:44+00:00 | {"language": ["de", "fr", "it"], "license": "cc", "size_categories": ["10K<n<100K"], "task_categories": ["text-classification"]} | 2023-01-12T15:27:42+00:00 |
|
0dcf8311cae5a45dee0ded3fea676a1551c1cd68 |
# Dataset Card for HumSet
## 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)
## Dataset Description
- **Homepage:** [http://blog.thedeep.io/humset/](http://blog.thedeep.io/humset/)
- **Repository:** [https://github.com/the-deep/humset](https://github.com/the-deep/humset)
- **Paper:** [EMNLP Findings 2022](https://aclanthology.org/2022.findings-emnlp.321)
- **Leaderboard:**
- **Point of Contact:**[the DEEP NLP team](mailto:[email protected])
### Dataset Summary
HumSet is a novel and rich multilingual dataset of humanitarian response documents annotated by experts in the humanitarian response community. HumSet is curated by humanitarian analysts and covers various disasters around the globe that occurred from 2018 to 2021 in 46 humanitarian response projects. The dataset consists of approximately 17K annotated documents in three languages of English, French, and Spanish, originally taken from publicly-available resources. For each document, analysts have identified informative snippets (entries) in respect to common humanitarian frameworks, and assigned one or many classes to each entry. See the our paper for details.
### Supported Tasks and Leaderboards
This dataset is intended for multi-label classification
### Languages
This dataset is in English, French and Spanish
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
- **entry_id**: unique identification number for a given entry. (string)
- **lead_id**: unique identification number for the document to which the corrisponding entry belongs. (string)
- **project_id** unique identification number for the project to which the corrisponding entry belongs. (string)
- **sectors**, **pillars_1d**, **pillars_2d**, **subpillars_1d**, **subpillars_2d**: labels assigned to the corresponding entry. Since this is a multi-label dataset (each entry may have several annotations belonging to the same category), they are reported as arrays of strings. See the paper for a detailed description of these categories. (list)
- **lang**: language. (str)
- **n_tokens**: number of tokens (tokenized using NLTK v3.7 library). (int64)
- **project_title**: the name of the project where the corresponding annotation was created. (str)
- **created_at**: date and time of creation of the annotation in stardard ISO 8601 format. (str)
- **document**: document URL source of the excerpt. (str)
- **excerpt**: excerpt text. (str)
### Data Splits
The dataset includes a set of train/validation/test splits, with 117435, 16039 and 15147 examples respectively.
## Dataset Creation
The collection originated from a multi-organizational platform called <em>the Data Entry and Exploration Platform (DEEP)</em> developed and maintained by Data Friendly Space (DFS). The platform facilitates classifying primarily qualitative information with respect to analysis frameworks and allows for collaborative classification and annotation of secondary data.
### Curation Rationale
[More Information Needed]
### Source Data
Documents are selected from different sources, ranging from official reports by humanitarian organizations to international and national media articles. See the paper for more informations.
#### Initial Data Collection and Normalization
#### Who are the source language producers?
[More Information Needed]
#### Annotation process
HumSet is curated by humanitarian analysts and covers various disasters around the globe that occurred from 2018 to 2021 in 46 humanitarian response projects. The dataset consists of approximately 17K annotated documents in three
languages of English, French, and Spanish, originally taken from publicly-available resources. For
each document, analysts have identified informative snippets (entries, or excerpt in the imported dataset) with respect to common <em>humanitarian frameworks</em> and assigned one or many classes to each entry.
#### 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
NLP team at [Data Friendly Space](https://datafriendlyspace.org/)
### Licensing Information
The GitHub repository which houses this dataset has an Apache License 2.0.
### Citation Information
```
@inproceedings{fekih-etal-2022-humset,
title = "{H}um{S}et: Dataset of Multilingual Information Extraction and Classification for Humanitarian Crises Response",
author = "Fekih, Selim and
Tamagnone, Nicolo{'} and
Minixhofer, Benjamin and
Shrestha, Ranjan and
Contla, Ximena and
Oglethorpe, Ewan and
Rekabsaz, Navid",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-emnlp.321",
pages = "4379--4389",
}
```
| nlp-thedeep/humset | [
"task_categories:text-classification",
"task_categories:text-retrieval",
"task_categories:token-classification",
"task_ids:multi-label-classification",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:multilingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"language:fr",
"language:es",
"license:apache-2.0",
"humanitarian",
"research",
"analytical-framework",
"multilabel",
"humset",
"humbert",
"region:us"
]
| 2023-01-12T16:00:58+00:00 | {"annotations_creators": ["expert-generated"], "language_creators": ["expert-generated"], "language": ["en", "fr", "es"], "license": ["apache-2.0"], "multilinguality": ["multilingual"], "size_categories": ["100K<n<1M"], "source_datasets": ["original"], "task_categories": ["text-classification", "text-retrieval", "token-classification"], "task_ids": ["multi-label-classification"], "pretty_name": "HumSet", "tags": ["humanitarian", "research", "analytical-framework", "multilabel", "humset", "humbert"], "dataset_info": {"features": [{"name": "entry_id", "dtype": "string"}, {"name": "lead_id", "dtype": "string"}, {"name": "project_id", "dtype": "string"}, {"name": "lang", "dtype": "string"}, {"name": "n_tokens", "dtype": "int64"}, {"name": "project_title", "dtype": "string"}, {"name": "created_at", "dtype": "string"}, {"name": "document", "dtype": "string"}, {"name": "excerpt", "dtype": "string"}, {"name": "sectors", "sequence": {"class_label": {"names": {"0": "Agriculture", "1": "Cross", "2": "Education", "3": "Food Security", "4": "Health", "5": "Livelihoods", "6": "Logistics", "7": "Nutrition", "8": "Protection", "9": "Shelter", "10": "WASH"}}}}, {"name": "pillars_1d", "sequence": {"class_label": {"names": {"0": "Casualties", "1": "Context", "2": "Covid-19", "3": "Displacement", "4": "Humanitarian Access", "5": "Information And Communication", "6": "Shock/Event"}}}}, {"name": "pillars_2d", "sequence": {"class_label": {"names": {"0": "At Risk", "1": "Capacities & Response", "2": "Humanitarian Conditions", "3": "Impact", "4": "Priority Interventions", "5": "Priority Needs"}}}}, {"name": "subpillars_1d", "sequence": {"class_label": {"names": {"0": "Casualties->Dead", "1": "Casualties->Injured", "2": "Casualties->Missing", "3": "Context->Demography", "4": "Context->Economy", "5": "Context->Environment", "6": "Context->Legal & Policy", "7": "Context->Politics", "8": "Context->Security & Stability", "9": "Context->Socio Cultural", "10": "Covid-19->Cases", "11": "Covid-19->Contact Tracing", "12": "Covid-19->Deaths", "13": "Covid-19->Hospitalization & Care", "14": "Covid-19->Restriction Measures", "15": "Covid-19->Testing", "16": "Covid-19->Vaccination", "17": "Displacement->Intentions", "18": "Displacement->Local Integration", "19": "Displacement->Pull Factors", "20": "Displacement->Push Factors", "21": "Displacement->Type/Numbers/Movements", "22": "Humanitarian Access->Number Of People Facing Humanitarian Access Constraints/Humanitarian Access Gaps", "23": "Humanitarian Access->Physical Constraints", "24": "Humanitarian Access->Population To Relief", "25": "Humanitarian Access->Relief To Population", "26": "Information And Communication->Communication Means And Preferences", "27": "Information And Communication->Information Challenges And Barriers", "28": "Information And Communication->Knowledge And Info Gaps (Hum)", "29": "Information And Communication->Knowledge And Info Gaps (Pop)", "30": "Shock/Event->Hazard & Threats", "31": "Shock/Event->Type And Characteristics", "32": "Shock/Event->Underlying/Aggravating Factors"}}}}, {"name": "subpillars_2d", "sequence": {"class_label": {"names": {"0": "At Risk->Number Of People At Risk", "1": "At Risk->Risk And Vulnerabilities", "2": "Capacities & Response->International Response", "3": "Capacities & Response->Local Response", "4": "Capacities & Response->National Response", "5": "Capacities & Response->Number Of People Reached/Response Gaps", "6": "Humanitarian Conditions->Coping Mechanisms", "7": "Humanitarian Conditions->Living Standards", "8": "Humanitarian Conditions->Number Of People In Need", "9": "Humanitarian Conditions->Physical And Mental Well Being", "10": "Impact->Driver/Aggravating Factors", "11": "Impact->Impact On People", "12": "Impact->Impact On Systems, Services And Networks", "13": "Impact->Number Of People Affected", "14": "Priority Interventions->Expressed By Humanitarian Staff", "15": "Priority Interventions->Expressed By Population", "16": "Priority Needs->Expressed By Humanitarian Staff", "17": "Priority Needs->Expressed By Population"}}}}], "splits": [{"name": "train", "num_examples": 117435}, {"name": "validation", "num_examples": 16039}, {"name": "test", "num_examples": 15147}]}} | 2023-05-25T16:14:31+00:00 |
4f279c19e5019d79b6e16018dd618dd00e8e8b56 | oz117/xinyan117 | [
"license:openrail",
"region:us"
]
| 2023-01-12T18:53:39+00:00 | {"license": "openrail"} | 2023-01-12T19:06:21+00:00 |
|
f573fc09166c8a89d17893edb74a4d9b3c6932f5 | # Dataset Card for "dreambooth-hackathon-images"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | hedronstone/dreambooth-hackathon-images | [
"region:us"
]
| 2023-01-12T19:34:44+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 1396946.0, "num_examples": 5}], "download_size": 1323697, "dataset_size": 1396946.0}} | 2023-01-12T19:34:50+00:00 |
5f8d6c9f2c7177c778bcc04792ea1d16f3c74b7f | RuudVelo/my_awesome_new_bike | [
"license:apache-2.0",
"region:us"
]
| 2023-01-12T19:58:16+00:00 | {"license": "apache-2.0", "dataset_info": {"features": [{"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 29796713.0, "num_examples": 10}], "download_size": 26771158, "dataset_size": 29796713.0}} | 2023-01-12T20:56:47+00:00 |
|
5015082451567355c73261dcf2c9594b09501e41 | # Dataset Card for "Scored-Summarization-datasets"
A collection of Text summarization datasets geared towards training a multi-purpose text summarizer.
Each dataset is a parquet file with the following features.
#### default
- `text`: a `string` feature. The `source` document
- `summary`: a `string` feature. The summary of the document
- `provenance`: a `string` feature. Information about the sub dataset.
- `t5_text_token_count`: a `int64` feature. The number of tokens the text is encoded in.
- `t5_summary_token_count `: a `int64` feature. The number of tokens the summary is encoded in.
- `contriever_cos`: a `float64` feature. The Cosine Similarity of the Contriever text embedding and Contriever summary embedding.
### Sub-datasets
- billsum
- cnn_dailymail/3.0.0
- multixscience
- newsroom
- samsum
- scitldr/AIC
- tldr-challenge
- wikihow
- xsum
Information about the Contriever model can be found here: https://github.com/facebookresearch/contriever.
| jordiclive/scored_summarization_datasets | [
"region:us"
]
| 2023-01-12T20:05:45+00:00 | {} | 2023-02-05T16:14:10+00:00 |
5f9e666e90d0ddfd6413089f074019da08cdad52 |
## Dataset Description
- **Repository:** https://github.com/tscheepers/Wikipedia-Summary-Dataset
### Dataset Summary
This is a dataset that can be used for research into machine learning and natural language processing. It contains all titles and summaries (or introductions) of English Wikipedia articles, extracted in September of 2017.
The dataset is different from the regular Wikipedia dump and different from the datasets that can be created by gensim because ours contains the extracted summaries and not the entire unprocessed page body. This could be useful if one wants to use the smaller, more concise, and more definitional summaries in their research. Or if one just wants to use a smaller but still diverse dataset for efficient training with resource constraints.
A summary or introduction of an article is everything starting from the page title up to the content outline.
### Citation Information
```
@mastersthesis{scheepers2017compositionality,
author = {Scheepers, Thijs},
title = {Improving the Compositionality of Word Embeddings},
school = {Universiteit van Amsterdam},
year = {2017},
month = {11},
address = {Science Park 904, Amsterdam, Netherlands}
}
``` | jordiclive/wikipedia-summary-dataset | [
"region:us"
]
| 2023-01-12T20:53:47+00:00 | {} | 2023-02-05T16:15:04+00:00 |
57eb2a126dfb7d6637b724d9e4930873af99463b | # Dataset Card for "bookcorpus_compact_1024_shard6"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | saibo/bookcorpus_compact_1024_shard6_of_10 | [
"region:us"
]
| 2023-01-12T21:22:28+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "concept_with_offset", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 769286180, "num_examples": 61605}], "download_size": 387348752, "dataset_size": 769286180}} | 2023-01-12T21:23:04+00:00 |
c7137fe79252a24b615f1f0cb7c16ebb257d7a42 | sauradip/FAKE | [
"license:apache-2.0",
"region:us"
]
| 2023-01-12T21:45:50+00:00 | {"license": "apache-2.0"} | 2023-01-12T22:06:36+00:00 |
|
fca20ace55abb932b5694363a0ed316823bf819d | nczarli/cherry_images_1 | [
"license:apache-2.0",
"region:us"
]
| 2023-01-12T21:54:01+00:00 | {"license": "apache-2.0"} | 2023-01-12T21:54:01+00:00 |
|
d8d4d9c0515278f4847e1081ea6571b4ec8ae317 | # Dataset Card for "magic_cards"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | andrewljohnson/magic_cards | [
"region:us"
]
| 2023-01-12T21:54:54+00:00 | {"dataset_info": {"features": [{"name": "pixel_values", "dtype": "image"}, {"name": "label", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 137488238.0, "num_examples": 102}], "download_size": 133768507, "dataset_size": 137488238.0}} | 2023-01-17T23:00:18+00:00 |
0cdd1f11e9ffad8c9b3df10bdbfc4a8342a272b2 | yousefim/1 | [
"license:cc-by-2.0",
"region:us"
]
| 2023-01-12T22:04:52+00:00 | {"license": "cc-by-2.0"} | 2023-01-12T22:06:04+00:00 |
|
0f163d5662816ecef645ebc251a309ff8e1b79f5 | # Dataset Card for "banking77_MiniLM_embeddings"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | argilla/banking77_MiniLM_embeddings | [
"region:us"
]
| 2023-01-12T22:54:57+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "activate_my_card", "1": "age_limit", "2": "apple_pay_or_google_pay", "3": "atm_support", "4": "automatic_top_up", "5": "balance_not_updated_after_bank_transfer", "6": "balance_not_updated_after_cheque_or_cash_deposit", "7": "beneficiary_not_allowed", "8": "cancel_transfer", "9": "card_about_to_expire", "10": "card_acceptance", "11": "card_arrival", "12": "card_delivery_estimate", "13": "card_linking", "14": "card_not_working", "15": "card_payment_fee_charged", "16": "card_payment_not_recognised", "17": "card_payment_wrong_exchange_rate", "18": "card_swallowed", "19": "cash_withdrawal_charge", "20": "cash_withdrawal_not_recognised", "21": "change_pin", "22": "compromised_card", "23": "contactless_not_working", "24": "country_support", "25": "declined_card_payment", "26": "declined_cash_withdrawal", "27": "declined_transfer", "28": "direct_debit_payment_not_recognised", "29": "disposable_card_limits", "30": "edit_personal_details", "31": "exchange_charge", "32": "exchange_rate", "33": "exchange_via_app", "34": "extra_charge_on_statement", "35": "failed_transfer", "36": "fiat_currency_support", "37": "get_disposable_virtual_card", "38": "get_physical_card", "39": "getting_spare_card", "40": "getting_virtual_card", "41": "lost_or_stolen_card", "42": "lost_or_stolen_phone", "43": "order_physical_card", "44": "passcode_forgotten", "45": "pending_card_payment", "46": "pending_cash_withdrawal", "47": "pending_top_up", "48": "pending_transfer", "49": "pin_blocked", "50": "receiving_money", "51": "Refund_not_showing_up", "52": "request_refund", "53": "reverted_card_payment?", "54": "supported_cards_and_currencies", "55": "terminate_account", "56": "top_up_by_bank_transfer_charge", "57": "top_up_by_card_charge", "58": "top_up_by_cash_or_cheque", "59": "top_up_failed", "60": "top_up_limits", "61": "top_up_reverted", "62": "topping_up_by_card", "63": "transaction_charged_twice", "64": "transfer_fee_charged", "65": "transfer_into_account", "66": "transfer_not_received_by_recipient", "67": "transfer_timing", "68": "unable_to_verify_identity", "69": "verify_my_identity", "70": "verify_source_of_funds", "71": "verify_top_up", "72": "virtual_card_not_working", "73": "visa_or_mastercard", "74": "why_verify_identity", "75": "wrong_amount_of_cash_received", "76": "wrong_exchange_rate_for_cash_withdrawal"}}}}, {"name": "vectors", "struct": [{"name": "mini-lm-sentence-transformers", "sequence": "float64"}]}], "splits": [{"name": "test", "num_bytes": 9678090, "num_examples": 3080}], "download_size": 8319885, "dataset_size": 9678090}} | 2023-01-12T22:55:15+00:00 |
3af6cf2597934f8cf5d798e7a473b69ba454e18d | # Dataset Card for "Jan2023Abstracts"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Corran/Jan2023Abstracts | [
"region:us"
]
| 2023-01-13T01:45:31+00:00 | {"dataset_info": {"features": [{"name": "corpusid", "dtype": "int64"}, {"name": "openaccessinfo", "struct": [{"name": "externalids", "struct": [{"name": "ACL", "dtype": "string"}, {"name": "ArXiv", "dtype": "string"}, {"name": "DOI", "dtype": "string"}, {"name": "MAG", "dtype": "string"}, {"name": "PubMedCentral", "dtype": "string"}]}, {"name": "license", "dtype": "string"}, {"name": "status", "dtype": "string"}, {"name": "url", "dtype": "string"}]}, {"name": "abstract", "dtype": "string"}, {"name": "updated", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 72173232090, "num_examples": 55324451}], "download_size": 43689807417, "dataset_size": 72173232090}} | 2023-01-13T02:11:23+00:00 |
4b5332a8771b3f6388f8ecc51c2b8ade4be31c73 | SMS Spam Multilingual Collection Dataset
Collection of Multilingual SMS messages tagged as spam or legitimate
About Dataset
Context
The SMS Spam Collection is a set of SMS-tagged messages that have been collected for SMS Spam research. It originally contained one set of SMS messages in English of 5,574 messages, tagged according to being ham (legitimate) or spam and later Machine Translated into Hindi, German and French.
The text has been further translated into Spanish, Chinese, Arabic, Bengali, Russian, Portuguese, Indonesian, Urdu, Japanese, Punjabi, Javanese, Turkish, Korean, Marathi, Ukrainian, Swedish, and Norwegian using M2M100_418M a multilingual encoder-decoder (seq-to-seq) model trained for Many-to-Many multilingual translation created by Facebook AI.
Content
The augmented Dataset contains multilingual text and corresponding labels.
ham- non-spam text
spam- spam text
Acknowledgments
The original English text was taken from- https://www.kaggle.com/uciml/sms-spam-collection-dataset
Hindi, German and French taken from - https://www.kaggle.com/datasets/rajnathpatel/multilingual-spam-data | dbarbedillo/SMS_Spam_Multilingual_Collection_Dataset | [
"task_categories:text-classification",
"size_categories:1K<n<10K",
"language:en",
"language:zh",
"language:es",
"language:hi",
"language:fr",
"language:de",
"language:ar",
"language:bn",
"language:ru",
"language:pt",
"language:id",
"language:ur",
"language:ja",
"language:pa",
"language:jv",
"language:tr",
"language:ko",
"language:mr",
"language:uk",
"language:sv",
"language:no",
"license:gpl",
"region:us"
]
| 2023-01-13T02:13:03+00:00 | {"language": ["en", "zh", "es", "hi", "fr", "de", "ar", "bn", "ru", "pt", "id", "ur", "ja", "pa", "jv", "tr", "ko", "mr", "uk", "sv", "no"], "license": "gpl", "size_categories": ["1K<n<10K"], "task_categories": ["text-classification"]} | 2023-01-13T03:07:17+00:00 |
dabc787146a866488b1df9d0493bbff2169875d7 |
# Dataset Card for Wikipedia
This repo is a fork of the original Hugging Face Wikipedia repo [here](https://huggingface.co/datasets/wikipedia).
The difference is that this fork does away with the need for `apache-beam`, and this fork is very fast if you have a lot of CPUs on your machine.
It will use all CPUs available to create a clean Wikipedia pretraining dataset. It takes less than an hour to process all of English wikipedia on a GCP n1-standard-96.
This fork is also used in the [OLM Project](https://github.com/huggingface/olm-datasets) to pull and process up-to-date wikipedia snapshots.
## 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://dumps.wikimedia.org](https://dumps.wikimedia.org)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Dataset Summary
Wikipedia dataset containing cleaned articles of all languages.
The datasets are built from the Wikipedia dump
(https://dumps.wikimedia.org/) with one split per language. Each example
contains the content of one full Wikipedia article with cleaning to strip
markdown and unwanted sections (references, etc.).
The articles are parsed using the ``mwparserfromhell`` tool, and we use ``multiprocess`` for parallelization.
To load this dataset you need to install these first:
```
pip install mwparserfromhell==0.6.4 multiprocess==0.70.13
```
Then, you can load any subset of Wikipedia per language and per date this way:
```python
from datasets import load_dataset
load_dataset("olm/wikipedia", language="en", date="20220920")
```
You can find the full list of languages and dates [here](https://dumps.wikimedia.org/backup-index.html).
### Supported Tasks and Leaderboards
The dataset is generally used for Language Modeling.
### Languages
You can find the list of languages [here](https://meta.wikimedia.org/wiki/List_of_Wikipedias).
## Dataset Structure
### Data Instances
An example looks as follows:
```
{'id': '1',
'url': 'https://simple.wikipedia.org/wiki/April',
'title': 'April',
'text': 'April is the fourth month...'
}
```
### Data Fields
The data fields are the same among all configurations:
- `id` (`str`): ID of the article.
- `url` (`str`): URL of the article.
- `title` (`str`): Title of the article.
- `text` (`str`): Text content of the article.
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
Most of Wikipedia's text and many of its images are co-licensed under the
[Creative Commons Attribution-ShareAlike 3.0 Unported License](https://en.wikipedia.org/wiki/Wikipedia:Text_of_Creative_Commons_Attribution-ShareAlike_3.0_Unported_License)
(CC BY-SA) and the [GNU Free Documentation License](https://en.wikipedia.org/wiki/Wikipedia:Text_of_the_GNU_Free_Documentation_License)
(GFDL) (unversioned, with no invariant sections, front-cover texts, or back-cover texts).
Some text has been imported only under CC BY-SA and CC BY-SA-compatible license and cannot be reused under GFDL; such
text will be identified on the page footer, in the page history, or on the discussion page of the article that utilizes
the text.
### Citation Information
```
@ONLINE{wikidump,
author = "Wikimedia Foundation",
title = "Wikimedia Downloads",
url = "https://dumps.wikimedia.org"
}
```
| reyoung/wikipedia | [
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]
| 2023-01-13T03:38:06+00:00 | {"annotations_creators": ["no-annotation"], "language_creators": ["crowdsourced"], "language": ["aa", "ab", "ace", "af", "ak", "als", "am", "an", "ang", "ar", "arc", "arz", "as", "ast", "atj", "av", "ay", "az", "azb", "ba", "bar", "bcl", "be", "bg", "bh", "bi", "bjn", "bm", "bn", "bo", "bpy", "br", "bs", "bug", "bxr", "ca", "cbk", "cdo", "ce", "ceb", "ch", "cho", "chr", "chy", "ckb", "co", "cr", "crh", "cs", "csb", "cu", "cv", "cy", "da", "de", "din", "diq", "dsb", "dty", "dv", "dz", "ee", "el", "eml", "en", "eo", "es", "et", "eu", "ext", "fa", "ff", "fi", "fj", "fo", "fr", "frp", "frr", "fur", "fy", "ga", "gag", "gan", "gd", "gl", "glk", "gn", "gom", "gor", "got", "gu", "gv", "ha", "hak", "haw", "he", "hi", "hif", "ho", "hr", "hsb", "ht", "hu", "hy", "ia", "id", "ie", "ig", "ii", "ik", "ilo", "inh", "io", "is", "it", "iu", "ja", "jam", "jbo", "jv", "ka", "kaa", "kab", "kbd", "kbp", "kg", "ki", "kj", "kk", "kl", "km", "kn", "ko", "koi", "krc", "ks", "ksh", 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"20220301.hr", "20220301.hsb", "20220301.ht", "20220301.hu", "20220301.hy", "20220301.ia", "20220301.id", "20220301.ie", "20220301.ig", "20220301.ii", "20220301.ik", "20220301.ilo", "20220301.inh", "20220301.io", "20220301.is", "20220301.it", "20220301.iu", "20220301.ja", "20220301.jam", "20220301.jbo", "20220301.jv", "20220301.ka", "20220301.kaa", "20220301.kab", "20220301.kbd", "20220301.kbp", "20220301.kg", "20220301.ki", "20220301.kj", "20220301.kk", "20220301.kl", "20220301.km", "20220301.kn", "20220301.ko", "20220301.koi", "20220301.krc", "20220301.ks", "20220301.ksh", "20220301.ku", "20220301.kv", "20220301.kw", "20220301.ky", "20220301.la", "20220301.lad", "20220301.lb", "20220301.lbe", "20220301.lez", "20220301.lfn", "20220301.lg", "20220301.li", "20220301.lij", "20220301.lmo", "20220301.ln", "20220301.lo", "20220301.lrc", "20220301.lt", "20220301.ltg", "20220301.lv", "20220301.mai", "20220301.map-bms", "20220301.mdf", "20220301.mg", "20220301.mh", "20220301.mhr", "20220301.mi", "20220301.min", "20220301.mk", "20220301.ml", "20220301.mn", "20220301.mr", "20220301.mrj", "20220301.ms", "20220301.mt", "20220301.mus", "20220301.mwl", "20220301.my", "20220301.myv", "20220301.mzn", "20220301.na", "20220301.nah", "20220301.nap", "20220301.nds", "20220301.nds-nl", "20220301.ne", "20220301.new", "20220301.ng", "20220301.nl", "20220301.nn", "20220301.no", "20220301.nov", "20220301.nrm", "20220301.nso", "20220301.nv", "20220301.ny", "20220301.oc", "20220301.olo", "20220301.om", "20220301.or", "20220301.os", "20220301.pa", "20220301.pag", "20220301.pam", "20220301.pap", "20220301.pcd", "20220301.pdc", "20220301.pfl", "20220301.pi", "20220301.pih", "20220301.pl", "20220301.pms", "20220301.pnb", "20220301.pnt", "20220301.ps", "20220301.pt", "20220301.qu", "20220301.rm", "20220301.rmy", "20220301.rn", "20220301.ro", "20220301.roa-rup", "20220301.roa-tara", "20220301.ru", "20220301.rue", "20220301.rw", "20220301.sa", "20220301.sah", "20220301.sat", "20220301.sc", "20220301.scn", "20220301.sco", "20220301.sd", "20220301.se", "20220301.sg", "20220301.sh", "20220301.si", "20220301.simple", "20220301.sk", "20220301.sl", "20220301.sm", "20220301.sn", "20220301.so", "20220301.sq", "20220301.sr", "20220301.srn", "20220301.ss", "20220301.st", "20220301.stq", "20220301.su", "20220301.sv", "20220301.sw", "20220301.szl", "20220301.ta", "20220301.tcy", "20220301.te", "20220301.tet", "20220301.tg", "20220301.th", "20220301.ti", "20220301.tk", "20220301.tl", "20220301.tn", "20220301.to", "20220301.tpi", "20220301.tr", "20220301.ts", "20220301.tt", "20220301.tum", "20220301.tw", "20220301.ty", "20220301.tyv", "20220301.udm", "20220301.ug", "20220301.uk", "20220301.ur", "20220301.uz", "20220301.ve", "20220301.vec", "20220301.vep", "20220301.vi", "20220301.vls", "20220301.vo", "20220301.wa", "20220301.war", "20220301.wo", "20220301.wuu", "20220301.xal", "20220301.xh", "20220301.xmf", "20220301.yi", "20220301.yo", "20220301.za", "20220301.zea", "20220301.zh", "20220301.zh-classical", "20220301.zh-min-nan", "20220301.zh-yue", "20220301.zu"]} | 2023-01-13T08:42:26+00:00 |
86a268eb92773dff444073a4cb8c6c3a2e8a8a95 | yaodi/poetrytxt | [
"license:mit",
"region:us"
]
| 2023-01-13T03:45:18+00:00 | {"license": "mit"} | 2023-01-13T03:46:10+00:00 |
|
01dcaf7253db20c2a39c65d785142509266e2d54 | yaodi/libai | [
"license:mit",
"region:us"
]
| 2023-01-13T03:50:38+00:00 | {"license": "mit"} | 2023-01-13T03:50:56+00:00 |
|
a0bd0040fd37862e01d1290349e14131a457fba7 |
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] | JunRyeol/jr_dataset | [
"region:us"
]
| 2023-01-13T03:59:01+00:00 | {} | 2023-03-03T06:01:42+00:00 |
4798a4d343e78409759b0eb869228e6715a6d612 | Sevenlee/ImageNet | [
"license:apache-2.0",
"region:us"
]
| 2023-01-13T06:11:30+00:00 | {"license": "apache-2.0"} | 2023-01-13T06:11:30+00:00 |
|
b04ef7c220aea95e783f65f26b8361a30bd38972 |
# Dataset Card for "WS POS Model Tune"
## 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:** None
- **Repository:** https://huggingface.co/datasets/ayuhamaro/nlp-model-tune
- **Paper:** [More Information Needed]
- **Leaderboard:** [If the dataset supports an active leaderboard, add link here]()
- **Point of Contact:** [More Information Needed]
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
[More Information Needed]
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
[More Information Needed]
#### 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 | ayuhamaro/ws-pos-model-tune | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:zh",
"license:unknown",
"region:us"
]
| 2023-01-13T06:23:33+00:00 | {"annotations_creators": ["expert-generated"], "language_creators": ["found"], "language": ["zh"], "license": ["unknown"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["token-classification"], "task_ids": ["named-entity-recognition"], "paperswithcode_id": "ws-pos-model-tune", "pretty_name": "WS POS Model Tune", "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "tokens", "sequence": "string"}, {"name": "ws_tags", "sequence": {"class_label": {"names": {"0": "B,", "1": "I"}}}}, {"name": "pos_tags", "sequence": {"class_label": {"names": {"0": "A,", "1": "Caa,", "2": "Cab,", "3": "Cba,", "4": "Cbb,", "5": "D,", "6": "Da,", "7": "Dfa,", "8": "Dfb,", "9": "Di,", "10": "Dk,", "11": "DM,", "12": "I,", "13": "Na,", "14": "Nb,", "15": "Nc,", "16": "Ncd,", "17": "Nd,", "18": "Nep,", "19": "Neqa,", "20": "Neqb,", "21": "Nes,", "22": "Neu,", "23": "Nf,", "24": "Ng,", "25": "Nh,", "26": "Nv,", "27": "P,", "28": "T,", "29": "VA,", "30": "VAC,", "31": "VB,", "32": "VC,", "33": "VCL,", "34": "VD,", "35": "VF,", "36": "VE,", "37": "VG,", "38": "VH,", "39": "VHC,", "40": "VI,", "41": "VJ,", "42": "VK,", "43": "VL,", "44": "V_2,", "45": "DE,", "46": "SHI,", "47": "FW,", "48": "COLONCATEGORY,", "49": "COMMACATEGORY,", "50": "DASHCATEGORY,", "51": "DOTCATEGORY,", "52": "ETCCATEGORY,", "53": "EXCLAMATIONCATEGORY,", "54": "PARENTHESISCATEGORY,", "55": "PAUSECATEGORY,", "56": "PERIODCATEGORY,", "57": "QUESTIONCATEGORY,", "58": "SEMICOLONCATEGORY,", "59": "SPCHANGECATEGORY"}}}}], "splits": [{"name": "train", "num_bytes": 1024, "num_examples": 1}], "download_size": 1024, "dataset_size": 1024}, "train-eval-index": [{"config": "default", "task": "token-classification", "task_id": "entity_extraction", "splits": {"train_split": "train", "eval_split": "test"}, "col_mapping": {"tokens": "tokens", "ner_tags": "tags"}, "metrics": [{"type": "seqeval", "name": "seqeval"}]}]} | 2023-01-13T07:19:38+00:00 |
4dbb38cd1b5f093da4ff8f525f1a2b9d7931b864 | Achitha/simple_tamil | [
"task_categories:translation",
"size_categories:1K<n<10K",
"language:ta",
"language:en",
"license:mit",
"region:us"
]
| 2023-01-13T06:58:53+00:00 | {"language": ["ta", "en"], "license": "mit", "size_categories": ["1K<n<10K"], "task_categories": ["translation"], "pretty_name": "simple_tamil"} | 2023-01-13T11:27:13+00:00 |
|
09eaff0d01de538c89917d0a95520644187b582f | KETI-AIR/kowow | [
"license:cc-by-4.0",
"region:us"
]
| 2023-01-13T07:00:35+00:00 | {"license": "cc-by-4.0"} | 2023-01-13T08:22:07+00:00 |
|
a3f4c7a01ed45b0aa58a2c2682cc4e133f248514 | yaodi/tangshi | [
"license:mit",
"region:us"
]
| 2023-01-13T07:40:51+00:00 | {"license": "mit"} | 2023-01-13T07:41:39+00:00 |
|
d24f4673aa1a429c130617b53142b14156552502 | yaodi/tangdynastypoems | [
"license:mit",
"region:us"
]
| 2023-01-13T07:58:25+00:00 | {"license": "mit"} | 2023-01-13T08:04:09+00:00 |
|
8699ab44314d75aecd0607ecf83afe6d7f9d3dfb | skubis/laser | [
"license:gpl-3.0",
"region:us"
]
| 2023-01-13T08:56:28+00:00 | {"license": "gpl-3.0"} | 2023-01-13T08:57:12+00:00 |
|
7d66fa659fbc8c563200194c76f48ce287f947e0 | eengel7/sentiment_analysis_training | [
"license:apache-2.0",
"region:us"
]
| 2023-01-13T10:23:02+00:00 | {"license": "apache-2.0"} | 2023-01-14T19:37:30+00:00 |
|
baba86d542f804157a38ab23c2401b6a49920465 | eengel7/sentiment_analysis_batch | [
"license:apache-2.0",
"region:us"
]
| 2023-01-13T10:23:21+00:00 | {"license": "apache-2.0"} | 2023-01-15T11:35:50+00:00 |
|
fb93d76be56463cbd79290166c016934059cab50 | # Dataset Card for "markhor-images"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | ihanif/markhor-images | [
"region:us"
]
| 2023-01-13T11:38:26+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 1008453.0, "num_examples": 15}], "download_size": 1005068, "dataset_size": 1008453.0}} | 2023-01-13T11:38:39+00:00 |
45a356ecaa87f9eec5a844d0f7a3fc92cdee3ef8 | eengel7/sentiment_analysis_batch_predictions | [
"license:apache-2.0",
"region:us"
]
| 2023-01-13T11:41:21+00:00 | {"license": "apache-2.0"} | 2023-01-15T16:52:09+00:00 |
|
9f83e77824375ab1398975b8169488886839dcd6 | torileatherman/sentiment_analysis_batch_predictions | [
"license:apache-2.0",
"region:us"
]
| 2023-01-13T11:45:22+00:00 | {"license": "apache-2.0"} | 2023-01-15T12:04:48+00:00 |
|
88d756fe42b30317764ca8661c2c940dbb77b8ff |
# Dataset Card for [Dataset Name]
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://figshare.com/articles/dataset/Dataset_NER_Manufacturing_-_FabNER_Information_Extraction_from_Manufacturing_Process_Science_Domain_Literature_Using_Named_Entity_Recognition/14782407](https://figshare.com/articles/dataset/Dataset_NER_Manufacturing_-_FabNER_Information_Extraction_from_Manufacturing_Process_Science_Domain_Literature_Using_Named_Entity_Recognition/14782407)
- **Paper:** ["FabNER": information extraction from manufacturing process science domain literature using named entity recognition](https://par.nsf.gov/servlets/purl/10290810)
- **Size of downloaded dataset files:** 3.79 MB
- **Size of the generated dataset:** 6.27 MB
### Dataset Summary
FabNER is a manufacturing text corpus of 350,000+ words for Named Entity Recognition.
It is a collection of abstracts obtained from Web of Science through known journals available in manufacturing process
science research.
For every word, there were categories/entity labels defined namely Material (MATE), Manufacturing Process (MANP),
Machine/Equipment (MACEQ), Application (APPL), Features (FEAT), Mechanical Properties (PRO), Characterization (CHAR),
Parameters (PARA), Enabling Technology (ENAT), Concept/Principles (CONPRI), Manufacturing Standards (MANS) and
BioMedical (BIOP). Annotation was performed in all categories along with the output tag in 'BIOES' format:
B=Beginning, I-Intermediate, O=Outside, E=End, S=Single.
For details about the dataset, please refer to the paper: ["FabNER": information extraction from manufacturing process science domain literature using named entity recognition](https://par.nsf.gov/servlets/purl/10290810)
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
The language in the dataset is English.
## Dataset Structure
### Data Instances
- **Size of downloaded dataset files:** 3.79 MB
- **Size of the generated dataset:** 6.27 MB
An example of 'train' looks as follows:
```json
{
"id": "0",
"tokens": ["Revealed", "the", "location-specific", "flow", "patterns", "and", "quantified", "the", "speeds", "of", "various", "types", "of", "flow", "."],
"ner_tags": [0, 0, 0, 46, 49, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
}
```
### Data Fields
#### fabner
- `id`: the instance id of this sentence, a `string` feature.
- `tokens`: the list of tokens of this sentence, a `list` of `string` features.
- `ner_tags`: the list of entity tags, a `list` of classification labels.
```json
{"O": 0, "B-MATE": 1, "I-MATE": 2, "O-MATE": 3, "E-MATE": 4, "S-MATE": 5, "B-MANP": 6, "I-MANP": 7, "O-MANP": 8, "E-MANP": 9, "S-MANP": 10, "B-MACEQ": 11, "I-MACEQ": 12, "O-MACEQ": 13, "E-MACEQ": 14, "S-MACEQ": 15, "B-APPL": 16, "I-APPL": 17, "O-APPL": 18, "E-APPL": 19, "S-APPL": 20, "B-FEAT": 21, "I-FEAT": 22, "O-FEAT": 23, "E-FEAT": 24, "S-FEAT": 25, "B-PRO": 26, "I-PRO": 27, "O-PRO": 28, "E-PRO": 29, "S-PRO": 30, "B-CHAR": 31, "I-CHAR": 32, "O-CHAR": 33, "E-CHAR": 34, "S-CHAR": 35, "B-PARA": 36, "I-PARA": 37, "O-PARA": 38, "E-PARA": 39, "S-PARA": 40, "B-ENAT": 41, "I-ENAT": 42, "O-ENAT": 43, "E-ENAT": 44, "S-ENAT": 45, "B-CONPRI": 46, "I-CONPRI": 47, "O-CONPRI": 48, "E-CONPRI": 49, "S-CONPRI": 50, "B-MANS": 51, "I-MANS": 52, "O-MANS": 53, "E-MANS": 54, "S-MANS": 55, "B-BIOP": 56, "I-BIOP": 57, "O-BIOP": 58, "E-BIOP": 59, "S-BIOP": 60}
```
#### fabner_bio
- `id`: the instance id of this sentence, a `string` feature.
- `tokens`: the list of tokens of this sentence, a `list` of `string` features.
- `ner_tags`: the list of entity tags, a `list` of classification labels.
```json
{"O": 0, "B-MATE": 1, "I-MATE": 2, "B-MANP": 3, "I-MANP": 4, "B-MACEQ": 5, "I-MACEQ": 6, "B-APPL": 7, "I-APPL": 8, "B-FEAT": 9, "I-FEAT": 10, "B-PRO": 11, "I-PRO": 12, "B-CHAR": 13, "I-CHAR": 14, "B-PARA": 15, "I-PARA": 16, "B-ENAT": 17, "I-ENAT": 18, "B-CONPRI": 19, "I-CONPRI": 20, "B-MANS": 21, "I-MANS": 22, "B-BIOP": 23, "I-BIOP": 24}
```
#### fabner_simple
- `id`: the instance id of this sentence, a `string` feature.
- `tokens`: the list of tokens of this sentence, a `list` of `string` features.
- `ner_tags`: the list of entity tags, a `list` of classification labels.
```json
{"O": 0, "MATE": 1, "MANP": 2, "MACEQ": 3, "APPL": 4, "FEAT": 5, "PRO": 6, "CHAR": 7, "PARA": 8, "ENAT": 9, "CONPRI": 10, "MANS": 11, "BIOP": 12}
```
#### text2tech
- `id`: the instance id of this sentence, a `string` feature.
- `tokens`: the list of tokens of this sentence, a `list` of `string` features.
- `ner_tags`: the list of entity tags, a `list` of classification labels.
```json
{"O": 0, "Technological System": 1, "Method": 2, "Material": 3, "Technical Field": 4}
```
### Data Splits
| | Train | Dev | Test |
|--------|-------|------|------|
| fabner | 9435 | 2183 | 2064 |
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@article{DBLP:journals/jim/KumarS22,
author = {Aman Kumar and
Binil Starly},
title = {"FabNER": information extraction from manufacturing process science
domain literature using named entity recognition},
journal = {J. Intell. Manuf.},
volume = {33},
number = {8},
pages = {2393--2407},
year = {2022},
url = {https://doi.org/10.1007/s10845-021-01807-x},
doi = {10.1007/s10845-021-01807-x},
timestamp = {Sun, 13 Nov 2022 17:52:57 +0100},
biburl = {https://dblp.org/rec/journals/jim/KumarS22.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
### Contributions
Thanks to [@phucdev](https://github.com/phucdev) for adding this dataset. | DFKI-SLT/fabner | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"language:en",
"license:other",
"manufacturing",
"2000-2020",
"region:us"
]
| 2023-01-13T13:01:38+00:00 | {"annotations_creators": ["expert-generated"], "language_creators": ["found"], "language": ["en"], "license": ["other"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": [], "task_categories": ["token-classification"], "task_ids": ["named-entity-recognition"], "pretty_name": "FabNER is a manufacturing text dataset for Named Entity Recognition.", "tags": ["manufacturing", "2000-2020"], "dataset_info": [{"config_name": "fabner", "features": [{"name": "id", "dtype": "string"}, {"name": "tokens", "sequence": "string"}, {"name": "ner_tags", "sequence": {"class_label": {"names": {"0": "O", "1": "B-MATE", "2": "I-MATE", "3": "O-MATE", "4": "E-MATE", "5": "S-MATE", "6": "B-MANP", "7": "I-MANP", "8": "O-MANP", "9": "E-MANP", "10": "S-MANP", "11": "B-MACEQ", "12": "I-MACEQ", "13": "O-MACEQ", "14": "E-MACEQ", "15": "S-MACEQ", "16": "B-APPL", "17": "I-APPL", "18": "O-APPL", "19": "E-APPL", "20": "S-APPL", "21": "B-FEAT", "22": "I-FEAT", "23": "O-FEAT", "24": "E-FEAT", "25": "S-FEAT", "26": "B-PRO", "27": "I-PRO", "28": "O-PRO", "29": "E-PRO", "30": "S-PRO", "31": "B-CHAR", "32": "I-CHAR", "33": "O-CHAR", "34": "E-CHAR", "35": "S-CHAR", "36": "B-PARA", "37": "I-PARA", "38": "O-PARA", "39": "E-PARA", "40": "S-PARA", "41": "B-ENAT", "42": "I-ENAT", "43": "O-ENAT", "44": "E-ENAT", "45": "S-ENAT", "46": "B-CONPRI", "47": "I-CONPRI", "48": "O-CONPRI", "49": "E-CONPRI", "50": "S-CONPRI", "51": "B-MANS", "52": "I-MANS", "53": "O-MANS", "54": "E-MANS", "55": "S-MANS", "56": "B-BIOP", "57": "I-BIOP", "58": "O-BIOP", "59": "E-BIOP", "60": "S-BIOP"}}}}], "splits": [{"name": "train", "num_bytes": 4394010, "num_examples": 9435}, {"name": "validation", "num_bytes": 934347, "num_examples": 2183}, {"name": "test", "num_bytes": 940136, "num_examples": 2064}], "download_size": 3793613, "dataset_size": 6268493}, {"config_name": "fabner_bio", "features": [{"name": "id", "dtype": "string"}, {"name": "tokens", "sequence": "string"}, {"name": "ner_tags", "sequence": {"class_label": {"names": {"0": "O", "1": "B-MATE", "2": "I-MATE", "3": "B-MANP", "4": "I-MANP", "5": "B-MACEQ", "6": "I-MACEQ", "7": "B-APPL", "8": "I-APPL", "9": "B-FEAT", "10": "I-FEAT", "11": "B-PRO", "12": "I-PRO", "13": "B-CHAR", "14": "I-CHAR", "15": "B-PARA", "16": "I-PARA", "17": "B-ENAT", "18": "I-ENAT", "19": "B-CONPRI", "20": "I-CONPRI", "21": "B-MANS", "22": "I-MANS", "23": "B-BIOP", "24": "I-BIOP"}}}}], "splits": [{"name": "train", "num_bytes": 4394010, "num_examples": 9435}, {"name": "validation", "num_bytes": 934347, "num_examples": 2183}, {"name": "test", "num_bytes": 940136, "num_examples": 2064}], "download_size": 3793613, "dataset_size": 6268493}, {"config_name": "fabner_simple", "features": [{"name": "id", "dtype": "string"}, {"name": "tokens", "sequence": "string"}, {"name": "ner_tags", "sequence": {"class_label": {"names": {"0": "O", "1": "MATE", "2": "MANP", "3": "MACEQ", "4": "APPL", "5": "FEAT", "6": "PRO", "7": "CHAR", "8": "PARA", "9": "ENAT", "10": "CONPRI", "11": "MANS", "12": "BIOP"}}}}], "splits": [{"name": "train", "num_bytes": 4394010, "num_examples": 9435}, {"name": "validation", "num_bytes": 934347, "num_examples": 2183}, {"name": "test", "num_bytes": 940136, "num_examples": 2064}], "download_size": 3793613, "dataset_size": 6268493}, {"config_name": "text2tech", "features": [{"name": "id", "dtype": "string"}, {"name": "tokens", "sequence": "string"}, {"name": "ner_tags", "sequence": {"class_label": {"names": {"0": "O", "1": "Technological System", "2": "Method", "3": "Material", "4": "Technical Field"}}}}], "splits": [{"name": "train", "num_bytes": 4394010, "num_examples": 9435}, {"name": "validation", "num_bytes": 934347, "num_examples": 2183}, {"name": "test", "num_bytes": 940136, "num_examples": 2064}], "download_size": 3793613, "dataset_size": 6268493}]} | 2023-04-05T22:20:21+00:00 |
8be26569c0e056f6ceb5adb58dcfce8d5da975b1 | # Dataset Card for "subj"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | bstrai/subj | [
"region:us"
]
| 2023-01-13T13:18:15+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "objective", "1": "subjective"}}}}, {"name": "label_text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1231802, "num_examples": 8000}, {"name": "test", "num_bytes": 310282, "num_examples": 2000}], "download_size": 945221, "dataset_size": 1542084}} | 2023-01-13T13:19:25+00:00 |
3dd5e7171182e1c8fd1aa1e74c2201d4bc0b7784 | torileatherman/sentiment_analysis_training | [
"license:apache-2.0",
"region:us"
]
| 2023-01-13T13:43:15+00:00 | {"license": "apache-2.0", "dataset_info": {"features": [{"name": "Sentiment", "dtype": "int64"}, {"name": "Headline", "sequence": "int64"}, {"name": "Headline_string", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 6608592, "num_examples": 11143}], "download_size": 1012250, "dataset_size": 6608592}} | 2023-08-04T12:04:15+00:00 |
|
71f9aa7fb4a4f8108cc5e6281afac8d2e8c70292 | oz117/xinyan45 | [
"license:openrail",
"region:us"
]
| 2023-01-13T13:57:03+00:00 | {"license": "openrail"} | 2023-01-13T14:02:48+00:00 |
|
6d89b090e9c242901d919352a72f3e7008934f22 | # Dataset Card for "sentiment_analysis_batch"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | torileatherman/sentiment_analysis_batch | [
"region:us"
]
| 2023-01-13T13:58:49+00:00 | {"dataset_info": {"features": [{"name": "Headline", "sequence": "int64"}, {"name": "Url", "dtype": "string"}, {"name": "Headline_string", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 5984, "num_examples": 10}], "download_size": 3050, "dataset_size": 5984}} | 2023-01-14T09:46:23+00:00 |
c71253cb92ca07b1cd70aff448f87b390d766f84 | # Dataset Card for "SPC-v2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Spaiche/SPC | [
"region:us"
]
| 2023-01-13T14:36:29+00:00 | {"dataset_info": {"features": [{"name": "client_id", "dtype": "string"}, {"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}, {"name": "sentence", "dtype": "string"}, {"name": "up_votes", "dtype": "float64"}, {"name": "down_votes", "dtype": "float64"}, {"name": "age", "dtype": "float64"}, {"name": "gender", "dtype": "float64"}, {"name": "accent", "dtype": "float64"}, {"name": "iou_estimate", "dtype": "float64"}], "splits": [{"name": "test", "num_bytes": 831368132.0, "num_examples": 3332}, {"name": "train", "num_bytes": 23839499476.0, "num_examples": 90324}], "download_size": 24065048743, "dataset_size": 24670867608.0}} | 2023-01-13T14:57:20+00:00 |
989b7bd4d1fdb5da62c202a15b943d6b3471250d | Ismagopo/TradingZoo | [
"license:openrail",
"region:us"
]
| 2023-01-13T15:14:22+00:00 | {"license": "openrail"} | 2023-01-13T15:42:27+00:00 |
|
efbbbeeea272c81faad1b7aee44c5326cc4c8753 | nc33/shuffle_boolq | [
"license:mit",
"region:us"
]
| 2023-01-13T16:35:31+00:00 | {"license": "mit"} | 2023-01-13T16:48:23+00:00 |
|
15a453734c206af1bee0b21ae1beff894ed438b9 | team6/roast-history | [
"license:mit",
"region:us"
]
| 2023-01-13T16:49:08+00:00 | {"license": "mit"} | 2023-03-27T20:44:30+00:00 |
|
467c79529f58ac5e0d133111cf9dad0a7f94a113 | # Dataset Card for "bookcorpus_compact_1024_shard3_meta"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | saibo/bookcorpus_compact_1024_shard3_of_10_meta | [
"region:us"
]
| 2023-01-13T17:09:16+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "concept_with_offset", "dtype": "string"}, {"name": "cid_arrangement", "sequence": "int32"}, {"name": "schema_lengths", "sequence": "int64"}, {"name": "topic_entity_mask", "sequence": "int64"}, {"name": "text_lengths", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 7826091649, "num_examples": 61605}], "download_size": 1726433976, "dataset_size": 7826091649}} | 2023-01-13T17:13:08+00:00 |
33c49b76fcf3a18b0d521d8e760c88d49f3e47bc |
# textures-color-1k
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [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)
- [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 `textures-color-1k` dataset is an image dataset of 1000+ color image textures in 512x512 resolution with associated text descriptions.
The dataset was created for training/fine-tuning diffusion models on texture generation tasks.
It contains a combination of CC0 procedural and photoscanned PBR materials from [ambientCG](https://ambientcg.com/).
### Languages
The text descriptions are in English, and created by joining the tags of each material with a space character.
## Dataset Structure
### Data Instances
Each data point contains a 512x512 image and and additional `text` feature containing the description of the texture.
### Data Fields
* `image`: the color texture as a PIL image
* `text`: the associated text description created by merging the material's tags
### Data Splits
| | train |
| -- | ----- |
| ambientCG | 1426 |
## Dataset Creation
### Curation Rationale
`textures-color-1k` was created to provide an accesible source of data for automating 3D-asset creation workflows.
The [Dream Textures](https://github.com/carson-katri/dream-textures) add-on is one such tool providing AI automation in Blender.
By fine-tuning models such as Stable Diffusion on textures, this particular use-case can be more accurately automated.
### Source Data
#### Initial Data Collection and Normalization
The data was obtained from [ambientCG](https://ambientcg.com/)'s CC0 textures. Only the color maps were included in this dataset.
Text descriptions were synthesized by joining the tags associated with each material with a space.
## Additional Information
### Dataset Curators
The dataset was created by Carson Katri, with the images being provided by [ambientCG](https://ambientcg.com/).
### Licensing Information
All of the images used in this dataset are CC0.
### Citation Information
[N/A]
### Contributions
Thanks to [@carson-katri](https://github.com/carson-katri) for adding this dataset. | dream-textures/textures-color-1k | [
"task_categories:text-to-image",
"size_categories:1K<n<10K",
"language:en",
"license:cc0-1.0",
"region:us"
]
| 2023-01-13T17:27:40+00:00 | {"language": ["en"], "license": "cc0-1.0", "size_categories": ["1K<n<10K"], "task_categories": ["text-to-image"], "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 60933571.47, "num_examples": 1426}], "download_size": 58351352, "dataset_size": 60933571.47}} | 2023-01-13T17:54:04+00:00 |
a75ba66c91c2031dc20c977cf058897430c8b77c |
# textures-normal-1k
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [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)
- [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 `textures-normal-1k` dataset is an image dataset of 1000+ normal map textures in 512x512 resolution with associated text descriptions.
The dataset was created for training/fine-tuning models for text to image tasks.
It contains a combination of CC0 procedural and photoscanned PBR materials from [ambientCG](https://ambientcg.com/).
### Languages
The text descriptions are in English, and created by joining the tags of each material with a space character.
## Dataset Structure
### Data Instances
Each data point contains a 512x512 image and and additional `text` feature containing the description of the texture.
### Data Fields
* `image`: the normal map as a PIL image
* `text`: the associated text description created by merging the material's tags
### Data Splits
| | train |
| -- | ----- |
| ambientCG | 1447 |
## Dataset Creation
### Curation Rationale
`textures-normal-1k` was created to provide an accesible source of data for automating 3D-asset creation workflows.
The [Dream Textures](https://github.com/carson-katri/dream-textures) add-on is one such tool providing AI automation in Blender.
By fine-tuning models such as Stable Diffusion on textures, this particular use-case can be more accurately automated.
### Source Data
#### Initial Data Collection and Normalization
The data was obtained from [ambientCG](https://ambientcg.com/)'s CC0 textures. Only the normal maps were included in this dataset.
Text descriptions were synthesized by joining the tags associated with each material with a space.
## Additional Information
### Dataset Curators
The dataset was created by Carson Katri, with the images being provided by [ambientCG](https://ambientcg.com/).
### Licensing Information
All of the images used in this dataset are CC0.
### Citation Information
[N/A]
### Contributions
Thanks to [@carson-katri](https://github.com/carson-katri) for adding this dataset. | dream-textures/textures-normal-1k | [
"task_categories:text-to-image",
"size_categories:1K<n<10K",
"language:en",
"license:cc0-1.0",
"region:us"
]
| 2023-01-13T19:44:42+00:00 | {"language": ["en"], "license": "cc0-1.0", "size_categories": ["1K<n<10K"], "task_categories": ["text-to-image"], "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 59834059.794, "num_examples": 1447}], "download_size": 52173880, "dataset_size": 59834059.794}} | 2023-01-13T21:17:22+00:00 |
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