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4987a51f6118b2b3b9b766addf28fde26e1f1dbc
|
# Dataset Card for "squad3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Mel-Iza0/squad3
|
[
"region:us"
] |
2023-03-31T11:52:13+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "input_ids", "dtype": "string"}, {"name": "answers", "struct": [{"name": "answer_start", "sequence": "int64"}, {"name": "text", "sequence": "string"}]}], "splits": [{"name": "train", "num_bytes": 3833665, "num_examples": 5928}, {"name": "validation", "num_bytes": 982887, "num_examples": 1482}], "download_size": 2412739, "dataset_size": 4816552}}
|
2023-03-31T15:14:58+00:00
|
14b93c6f0dda0ecd2046c4fdc50ae7fb819085cd
|
# Dataset Card for "vehicle_segmentation"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Onegafer/vehicle_segmentation
|
[
"region:us"
] |
2023-03-31T11:52:57+00:00
|
{"dataset_info": {"features": [{"name": "pixel_values", "dtype": "image"}, {"name": "label", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 160555965.0, "num_examples": 320}], "download_size": 0, "dataset_size": 160555965.0}}
|
2023-04-25T12:52:06+00:00
|
3894eb0ba23390dfe7c5fcacc9cd8f390fa14247
|
moizsajid/dreambooth-markhor
|
[
"keras-dreambooth",
"nature",
"diffusers",
"text-to-image",
"region:us"
] |
2023-03-31T12:13:59+00:00
|
{"title": "Markhor images from iStock", "tags": ["keras-dreambooth", "nature", "diffusers", "text-to-image"]}
|
2023-04-02T00:04:32+00:00
|
|
2a0c9c3ea6fd68733f9507fab98efe58fa989ad0
|
<div align="center">
<img width="640" alt="nflechas/recycling_app" src="https://huggingface.co/datasets/nflechas/recycling_app/resolve/main/thumbnail.jpg">
</div>
### Dataset Labels
```
['biodegradable', 'cardboard', 'glass', 'metal', 'paper', 'plastic']
```
### Number of Images
```json
{'valid': 2098, 'test': 1042, 'train': 7324}
```
### How to Use
- Install [datasets](https://pypi.org/project/datasets/):
```bash
pip install datasets
```
- Load the dataset:
```python
from datasets import load_dataset
ds = load_dataset("nflechas/recycling_app", name="full")
example = ds['train'][0]
```
### Roboflow Dataset Page
[https://universe.roboflow.com/material-identification/garbage-classification-3/dataset/2](https://universe.roboflow.com/material-identification/garbage-classification-3/dataset/2?ref=roboflow2huggingface)
### Citation
```
@misc{ garbage-classification-3_dataset,
title = { GARBAGE CLASSIFICATION 3 Dataset },
type = { Open Source Dataset },
author = { Material Identification },
howpublished = { \\url{ https://universe.roboflow.com/material-identification/garbage-classification-3 } },
url = { https://universe.roboflow.com/material-identification/garbage-classification-3 },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { mar },
note = { visited on 2023-03-31 },
}
```
### License
CC BY 4.0
### Dataset Summary
This dataset was exported via roboflow.com on July 27, 2022 at 5:44 AM GMT
Roboflow is an end-to-end computer vision platform that helps you
* collaborate with your team on computer vision projects
* collect & organize images
* understand unstructured image data
* annotate, and create datasets
* export, train, and deploy computer vision models
* use active learning to improve your dataset over time
It includes 10464 images.
GARBAGE-GARBAGE-CLASSIFICATION are annotated in COCO format.
The following pre-processing was applied to each image:
* Auto-orientation of pixel data (with EXIF-orientation stripping)
* Resize to 416x416 (Stretch)
The following augmentation was applied to create 1 versions of each source image:
* 50% probability of horizontal flip
* 50% probability of vertical flip
* Equal probability of one of the following 90-degree rotations: none, clockwise, counter-clockwise, upside-down
|
nflechas/recycling_app
|
[
"task_categories:object-detection",
"roboflow",
"roboflow2huggingface",
"Manufacturing",
"region:us"
] |
2023-03-31T12:26:20+00:00
|
{"task_categories": ["object-detection"], "tags": ["roboflow", "roboflow2huggingface", "Manufacturing"]}
|
2023-03-31T12:26:33+00:00
|
72ae206f21f416f3e7981183537e9ab071aff006
|
# Covertype
Classification of pixels into 7 forest cover types based on attributes such as elevation, aspect, slope, hillshade, soil-type, and more.
The [Covertype dataset](https://archive-beta.ics.uci.edu/dataset/31/covertype) from the [UCI ML repository](https://archive-beta.ics.uci.edu).
| **Configuration** | **Task** | **Description** |
|-------------------|---------------------------|-----------------------------------------------------------------|
| covertype | Multiclass classification | Classify the area as one of 7 cover classes. |
# Usage
```python
from datasets import load_dataset
dataset = load_dataset("mstz/covertype")["train"]
```
|
mstz/covertype
|
[
"task_categories:tabular-classification",
"size_categories:100K<n<1M",
"language:en",
"license:cc",
"biology",
"UCI",
"binary_classification",
"multiclass_classification",
"region:us"
] |
2023-03-31T12:33:53+00:00
|
{"language": ["en"], "license": "cc", "size_categories": ["100K<n<1M"], "task_categories": ["tabular-classification"], "pretty_name": "Covertype", "tags": ["biology", "UCI", "binary_classification", "multiclass_classification"]}
|
2023-05-29T09:09:11+00:00
|
f4de14db58cd402f76f6224b585563d3b93a8954
|
</b>Testing purpose only. Do not redistribute. </b>
Original contents: [url] https://huggingface.co/datasets/tatsu-lab/alpaca
Ko-alpaca: [url] https://github.com/Beomi/KoAlpaca/blob/main/ko_alpaca_data.json
|
royboy0416/ko-alpaca
|
[
"task_categories:text-generation",
"language:ko",
"license:cc-by-4.0",
"region:us"
] |
2023-03-31T13:16:10+00:00
|
{"language": ["ko"], "license": "cc-by-4.0", "task_categories": ["text-generation"]}
|
2023-03-31T20:14:40+00:00
|
c9e69154e45534b41951ef9beeaf1d54be5eb9b6
|
# AutoTrain Dataset for project: severe-js100-sentiment
## Dataset Description
This dataset has been automatically processed by AutoTrain for project severe-js100-sentiment.
### Languages
The BCP-47 code for the dataset's language is unk.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"text": "00:58 #\u0e2d\u0e38\u0e1a\u0e31\u0e15\u0e34\u0e40\u0e2b\u0e15\u0e38 #\u0e16\u0e19\u0e19\u0e1a\u0e32\u0e07\u0e1a\u0e2d\u0e193 \u0e0a\u0e48\u0e27\u0e07\u0e42\u0e23\u0e07\u0e40\u0e23\u0e35\u0e22\u0e19\u0e2a\u0e32\u0e23\u0e2a\u0e32\u0e2a\u0e19\u0e4c\u0e27\u0e34\u0e40\u0e17\u0e28\u0e1a\u0e32\u0e07\u0e1a\u0e2d\u0e19 >\u0e42\u0e23\u0e07\u0e40\u0e23\u0e35\u0e22\u0e19\u0e01\u0e23\u0e1e\u0e34\u0e17\u0e31\u0e01\u0e29\u0e4c\u0e28\u0e36\u0e01\u0e29\u0e32 \u0e1b\u0e32\u0e01\u0e0b\u0e2d\u0e22\u0e1a\u0e32\u0e07\u0e1a\u0e2d\u0e193\u0e0b\u0e2d\u0e225 \u0e23\u0e16\u0e08\u0e31\u0e01\u0e23\u0e22\u0e32\u0e19\u0e22\u0e19\u0e15\u0e4c\u0e40\u0e2a\u0e35\u0e22\u0e2b\u0e25\u0e31\u0e01\u0e25\u0e49\u0e21 \u0e02\u0e27\u0e32\u0e07\u0e0a\u0e48\u0e2d\u0e07\u0e17\u0e32\u0e07\u0e0b\u0e49\u0e32\u0e22",
"target": 2
},
{
"text": "03:22 #\u0e2d\u0e38\u0e1a\u0e31\u0e15\u0e34\u0e40\u0e2b\u0e15\u0e38 #\u0e16\u0e19\u0e19\u0e01\u0e32\u0e0d\u0e08\u0e19\u0e32\u0e20\u0e34\u0e40\u0e29\u0e01 \u0e0a\u0e48\u0e27\u0e07\u0e2a\u0e30\u0e1e\u0e32\u0e19\u0e02\u0e49\u0e32\u0e21\u0e04\u0e25\u0e2d\u0e07\u0e20\u0e32\u0e29\u0e35\u0e40\u0e08\u0e23\u0e34\u0e0d >\u0e41\u0e22\u0e01\u0e1a\u0e32\u0e07\u0e41\u0e27\u0e01 \u0e1a\u0e19\u0e15\u0e48\u0e32\u0e07\u0e23\u0e30\u0e14\u0e31\u0e1a\u0e40\u0e1e\u0e0a\u0e23\u0e40\u0e01\u0e29\u0e21 \u0e23\u0e16\u0e1b\u0e34\u0e04\u0e2d\u0e31\u0e1e\u0e40\u0e2a\u0e35\u0e22\u0e2b\u0e25\u0e31\u0e01\u0e0a\u0e19\u0e02\u0e2d\u0e1a\u0e17\u0e32\u0e07 \u0e02\u0e27\u0e32\u0e07\u0e0a\u0e48\u0e2d\u0e07\u0e17\u0e32\u0e07\u0e0b\u0e49\u0e32\u0e22",
"target": 2
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"text": "Value(dtype='string', id=None)",
"target": "ClassLabel(names=['\u0e23\u0e38\u0e19\u0e41\u0e23\u0e07', '\u0e23\u0e38\u0e19\u0e41\u0e23\u0e07\u0e21\u0e32\u0e01', '\u0e44\u0e21\u0e48\u0e23\u0e38\u0e19\u0e41\u0e23\u0e07'], 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 | 5348 |
| valid | 1339 |
|
Garfieldgx/DataSet_for_thesis
|
[
"task_categories:text-classification",
"region:us"
] |
2023-03-31T13:28:27+00:00
|
{"task_categories": ["text-classification"]}
|
2023-03-31T13:33:01+00:00
|
9afa55076fe28de3c6ca9fa0c1f5cf02897fed49
|
# Sonar
The [Sonar dataset](https://archive-beta.ics.uci.edu/dataset/151/connectionist+bench+sonar+mines+vs+rocks) from the [UCI ML repository](https://archive.ics.uci.edu/ml/datasets).
Dataset to discriminate between sonar signals bounced off a metal cylinder and those bounced off a roughly cylindrical rock.
# Configurations and tasks
| **Configuration** | **Task** | **Description** |
|-------------------|---------------------------|-----------------------------------------------------------------|
| sonar | Binary classification | Is the sonar detecting a rock? |
# Usage
```python
from datasets import load_dataset
dataset = load_dataset("mstz/sonar")["train"]
```
|
mstz/sonar
|
[
"task_categories:tabular-classification",
"size_categories:n<1K",
"language:en",
"license:cc",
"adult",
"tabular_classification",
"binary_classification",
"UCI",
"region:us"
] |
2023-03-31T13:43:15+00:00
|
{"language": ["en"], "license": "cc", "size_categories": ["n<1K"], "task_categories": ["tabular-classification"], "pretty_name": "Sonar", "tags": ["adult", "tabular_classification", "binary_classification", "UCI"], "configs": ["sonar"]}
|
2023-04-16T17:02:16+00:00
|
46ac8bc5eac1461e5a1f7f212f09044fc06623a8
|
# Adult
The [Toxicity dataset](https://archive-beta.ics.uci.edu/dataset/728/toxicity) from the [UCI ML repository](https://archive.ics.uci.edu/ml/datasets).
The dataset includes 171 molecules designed for functional domains of a core clock protein, CRY1, responsible for generating circadian rhythm.
# Configurations and tasks
| **Configuration** | **Task** | **Description** |
|-------------------|---------------------------|-----------------------------------------------------------------|
| toxicity | Binary classification | Is the molecule toxic? |
# Usage
```python
from datasets import load_dataset
dataset = load_dataset("mstz/toxicity")["train"]
```
|
mstz/toxicity
|
[
"task_categories:tabular-classification",
"size_categories:n<1K",
"language:en",
"license:cc",
"toxicity",
"tabular_classification",
"binary_classification",
"multiclass_classification",
"UCI",
"region:us"
] |
2023-03-31T13:59:54+00:00
|
{"language": ["en"], "license": "cc", "size_categories": ["n<1K"], "task_categories": ["tabular-classification"], "pretty_name": "Toxicity", "tags": ["toxicity", "tabular_classification", "binary_classification", "multiclass_classification", "UCI"], "configs": ["encoding", "income", "income-no race", "race"]}
|
2023-04-16T17:03:37+00:00
|
1ffb2b525d281093c631cd4092bbdf7460250e92
|
# Dataset Card for "ner_naamapadam"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
livinNector/ner_naamapadam
|
[
"region:us"
] |
2023-03-31T14:06:29+00:00
|
{"dataset_info": {"features": [{"name": "tokens", "sequence": "string"}, {"name": "ner_tags", "sequence": {"class_label": {"names": {"0": "O", "1": "B-PER", "2": "I-PER", "3": "B-ORG", "4": "I-ORG", "5": "B-LOC", "6": "I-LOC"}}}}], "splits": [{"name": "train", "num_bytes": 186464659, "num_examples": 497882}, {"name": "test", "num_bytes": 319747, "num_examples": 758}, {"name": "validation", "num_bytes": 1046343, "num_examples": 2795}], "download_size": 48623255, "dataset_size": 187830749}}
|
2023-04-04T06:09:02+00:00
|
581ef135892badd87c59f2c8f4ad09c4f555968a
|
# Dataset Card for "ner_wikiann"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
livinNector/ner_wikiann
|
[
"region:us"
] |
2023-03-31T14:06:36+00:00
|
{"dataset_info": {"features": [{"name": "tokens", "sequence": "string"}, {"name": "ner_tags", "sequence": {"class_label": {"names": {"0": "O", "1": "B-PER", "2": "I-PER", "3": "B-ORG", "4": "I-ORG", "5": "B-LOC", "6": "I-LOC"}}}}], "splits": [{"name": "validation", "num_bytes": 229847, "num_examples": 1000}, {"name": "test", "num_bytes": 230672, "num_examples": 1000}, {"name": "train", "num_bytes": 3413046, "num_examples": 15000}], "download_size": 1020943, "dataset_size": 3873565}}
|
2023-04-04T06:09:04+00:00
|
616e5b00cefb18166abcad33b7dc2b2431b4b964
|
# Dataset Card for "ner_xtreme"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
livinNector/ner_xtreme
|
[
"region:us"
] |
2023-03-31T14:06:40+00:00
|
{"dataset_info": {"features": [{"name": "tokens", "sequence": "string"}, {"name": "ner_tags", "sequence": {"class_label": {"names": {"0": "O", "1": "B-PER", "2": "I-PER", "3": "B-ORG", "4": "I-ORG", "5": "B-LOC", "6": "I-LOC"}}}}], "splits": [{"name": "train", "num_bytes": 3413046, "num_examples": 15000}, {"name": "validation", "num_bytes": 229847, "num_examples": 1000}, {"name": "test", "num_bytes": 230672, "num_examples": 1000}], "download_size": 1020943, "dataset_size": 3873565}}
|
2023-04-04T06:09:07+00:00
|
4b704ccb2df29dcd654e2f37640e03c89fc7f931
|
# Dataset Card for "ner_indic_glue"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
livinNector/ner_indic_glue
|
[
"region:us"
] |
2023-03-31T14:06:43+00:00
|
{"dataset_info": {"features": [{"name": "tokens", "sequence": "string"}, {"name": "ner_tags", "sequence": {"class_label": {"names": {"0": "O", "1": "B-PER", "2": "I-PER", "3": "B-ORG", "4": "I-ORG", "5": "B-LOC", "6": "I-LOC"}}}}], "splits": [{"name": "train", "num_bytes": 4687618, "num_examples": 20466}, {"name": "validation", "num_bytes": 591516, "num_examples": 2586}, {"name": "test", "num_bytes": 585925, "num_examples": 2611}], "download_size": 1381725, "dataset_size": 5865059}}
|
2023-04-04T06:09:11+00:00
|
861b51c0ad56beba65c0d47d75105681cf7915b3
|
# Dataset Card for "cyber-threat-intelligence_v2"
updated version of mrmoor/cyber-threat-intelligence
RE and NER Dataset for Cyber Threat Intelegence (CTI)
T5 Model trained on NYT and this dataset: Olec/cyber_rebel
This dataset only contains sentences with realtions.
Full dataset is available at mrmoor/cyber-threat-intelligence.
|
Olec/cyber-threat-intelligence_v2
|
[
"region:us"
] |
2023-03-31T14:08:08+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "text", "dtype": "string"}, {"name": "entities", "list": [{"name": "end_offset", "dtype": "int64"}, {"name": "id", "dtype": "int64"}, {"name": "label", "dtype": "string"}, {"name": "start_offset", "dtype": "int64"}]}, {"name": "relations", "list": [{"name": "from_id", "dtype": "int64"}, {"name": "id", "dtype": "int64"}, {"name": "to_id", "dtype": "int64"}, {"name": "type", "dtype": "string"}]}], "splits": [{"name": "test", "num_bytes": 29518, "num_examples": 72}, {"name": "train", "num_bytes": 147723, "num_examples": 332}, {"name": "validation", "num_bytes": 36580, "num_examples": 76}], "download_size": 119557, "dataset_size": 213821}}
|
2023-04-15T10:00:18+00:00
|
b7ffb10b683e59e365b39caf6955150ca46d478f
|
martingrzzler/concreteness_phrase_ratings
|
[
"task_categories:text-classification",
"size_categories:10K<n<100K",
"language:en",
"psycholinguistics",
"concreteness",
"region:us"
] |
2023-03-31T14:22:46+00:00
|
{"language": ["en"], "size_categories": ["10K<n<100K"], "task_categories": ["text-classification"], "tags": ["psycholinguistics", "concreteness"]}
|
2023-03-31T14:23:57+00:00
|
|
3d3d0b9d89272f18e2d405f16a951733d1a70825
|
datacrunch/finnish_alpaca
|
[
"license:mit",
"region:us"
] |
2023-03-31T14:23:53+00:00
|
{"license": "mit", "dataset_info": {"features": [{"name": "instruction", "dtype": "string"}, {"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 20402896, "num_examples": 51715}], "download_size": 13168174, "dataset_size": 20402896}}
|
2023-07-20T13:13:12+00:00
|
|
0263ae881810b1a40f5dbce699b7265dd5679b0b
|
# Dataset Card for "ner_ta4ner"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
livinNector/ner_ta4ner
|
[
"region:us"
] |
2023-03-31T14:29:07+00:00
|
{"dataset_info": {"features": [{"name": "tokens", "sequence": "string"}, {"name": "ner_tags", "sequence": {"class_label": {"names": {"0": "O", "1": "B-PER", "2": "I-PER", "3": "B-ORG", "4": "I-ORG", "5": "B-LOC", "6": "I-LOC"}}}}], "splits": [{"name": "train", "num_bytes": 197978369, "num_examples": 548348}, {"name": "test", "num_bytes": 1367016, "num_examples": 5369}, {"name": "validation", "num_bytes": 2097553, "num_examples": 7381}], "download_size": 52034852, "dataset_size": 201442938}}
|
2023-03-31T14:29:14+00:00
|
b7aa43fc09ab2ebb230e48ed0c8fdc5f7947dc62
|
NickKolok/regs-sunshinemix
|
[
"license:agpl-3.0",
"region:us"
] |
2023-03-31T14:46:18+00:00
|
{"license": "agpl-3.0"}
|
2023-03-31T14:47:55+00:00
|
|
ce276ff5adeb9f3d99483b0ffe0cfdda27b07373
|
iiranna/BUI
|
[
"region:us"
] |
2023-03-31T15:39:42+00:00
|
{}
|
2023-05-10T05:48:12+00:00
|
|
d97350382d23020b3a6081ac9279588c49575788
|
haiyan1/image
|
[
"license:apache-2.0",
"region:us"
] |
2023-03-31T15:48:12+00:00
|
{"license": "apache-2.0"}
|
2023-03-31T15:48:12+00:00
|
|
6243af4425a17ae2f3a8377948a7b29a21fee195
|
# Dataset Card for Mtg Pauper BLIP captions
Dataset inspired by [Pokemon Blip Captions](https://huggingface.co/datasets/lambdalabs/pokemon-blip-captions/tree/main)
BLIP generated captions for Magic: the gathering pauper cards artworks from _BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation (Salesforce Research)_
For each row the dataset contains `image` and `caption` keys. `image` is a varying size PIL jpeg, and `caption` is the accompanying text caption. Only a train split is provided.
## Examples
> a drawing of a small blue man holding a yellow toy
For more in-depth informations in how this dataset was created, check the [Google Colab](https://colab.research.google.com/drive/14Hxsm8o25rGOeYozqPmy6dBNMTudkw_H?usp=sharing)
## Citation
If you use this dataset, please cite it as:
```
@misc{mazzola2022pauper,
author = {Matteo Mazzola},
title = {Magic:the Gatering pauper BLIP captions},
year={2023},
howpublished= {\url{https://huggingface.co/datasets/vedalken/mtg-pauper-blip-captions}}
}
```
### Acknowledgments:
This dataset was made possible by [Scryfall](https://scryfall.com/).
Portions of artwork on Scryfall are unofficial Fan Content permitted under the Wizards of the Coast Fan Content Policy. The literal and graphical information presented on this site about Magic: The Gathering, including card images, mana symbols, and Oracle text, is copyright Wizards of the Coast, LLC, a subsidiary of Hasbro, This dataset is not produced by or endorsed by Wizards of the Coast.
|
vedalken/mtg-pauper-blip-captions
|
[
"region:us"
] |
2023-03-31T16:51:14+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "caption", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 253832724.0, "num_examples": 3000}], "download_size": 0, "dataset_size": 253832724.0}}
|
2023-04-06T11:50:14+00:00
|
819c6ecbccbe0da7a5c70593c2dbe02aac56fb96
|
# Dataset Card for "alpaca-gigo-detector"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
argilla/alpaca-gigo-detector
|
[
"task_categories:text-classification",
"language:en",
"region:us"
] |
2023-03-31T16:52:24+00:00
|
{"language": ["en"], "task_categories": ["text-classification"], "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "output", "dtype": "string"}, {"name": "input", "dtype": "string"}, {"name": "_instruction", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "ALL GOOD", "1": "BAD INSTRUCTION"}}}}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 545007, "num_examples": 697}, {"name": "test", "num_bytes": 58515, "num_examples": 78}], "download_size": 364798, "dataset_size": 603522}}
|
2023-04-02T18:40:38+00:00
|
976019b896a111193e5fb4ddeb7df0b717737574
|
# Dataset Card for "cracks2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
GarayMC/cracks2
|
[
"region:us"
] |
2023-03-31T16:53:02+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 3434976.0, "num_examples": 55}, {"name": "validation", "num_bytes": 691668.0, "num_examples": 10}], "download_size": 0, "dataset_size": 4126644.0}}
|
2023-03-31T19:00:45+00:00
|
8201958666658a69d9a952cd7a46842fd9ea1ea3
|
# Dataset Card for "digit_mask_augmented_raw"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mazkooleg/digit_mask_augmented_raw
|
[
"region:us"
] |
2023-03-31T16:56:51+00:00
|
{"dataset_info": {"features": [{"name": "audio", "dtype": "audio"}, {"name": "label", "dtype": "bool"}], "splits": [{"name": "train", "num_bytes": 58513564703.2, "num_examples": 1825800}, {"name": "test", "num_bytes": 195044953.756, "num_examples": 6086}, {"name": "validation", "num_bytes": 169086020.324, "num_examples": 5276}], "download_size": 54506700314, "dataset_size": 58877695677.27999}}
|
2023-04-01T00:03:28+00:00
|
0a2e9c8e7ee7b5c70af4e685f34f7338a4692eb1
|
tayamaken/myDtataset
|
[
"license:other",
"region:us"
] |
2023-03-31T17:05:55+00:00
|
{"license": "other"}
|
2023-06-12T18:38:50+00:00
|
|
6bc7a2902d99b1815a50cae2e69544c52562e096
|
# Dataset Card for "translated_german_alpaca"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
LEL-A/translated_german_alpaca
|
[
"region:us"
] |
2023-03-31T17:08:19+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "inputs", "struct": [{"name": "_instruction", "dtype": "string"}, {"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}]}, {"name": "prediction", "list": [{"name": "label", "dtype": "string"}, {"name": "score", "dtype": "float64"}]}, {"name": "prediction_agent", "dtype": "null"}, {"name": "annotation", "dtype": "null"}, {"name": "annotation_agent", "dtype": "null"}, {"name": "vectors", "struct": [{"name": "input", "sequence": "float64"}, {"name": "instruction", "sequence": "float64"}, {"name": "output", "sequence": "float64"}]}, {"name": "multi_label", "dtype": "bool"}, {"name": "explanation", "dtype": "null"}, {"name": "id", "dtype": "string"}, {"name": "metadata", "struct": [{"name": "original_id", "dtype": "int64"}, {"name": "translation_model", "dtype": "string"}]}, {"name": "status", "dtype": "string"}, {"name": "event_timestamp", "dtype": "timestamp[us]"}, {"name": "metrics", "dtype": "null"}], "splits": [{"name": "train", "num_bytes": 1004916509, "num_examples": 51759}], "download_size": 690637366, "dataset_size": 1004916509}}
|
2023-04-10T08:32:34+00:00
|
fce2d4f779ff68956e187f5d85619ca0d37d6ed0
|
Adeeb-qu/New-grooul
|
[
"license:openrail",
"region:us"
] |
2023-03-31T17:51:41+00:00
|
{"license": "openrail"}
|
2023-03-31T17:51:41+00:00
|
|
44a6b95864bcb51df8f2ffe032049e399b0fce28
|
# Dataset Card for "soda_input_output-clean"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
AlekseyKorshuk/soda_input_output-clean
|
[
"region:us"
] |
2023-03-31T19:05:40+00:00
|
{"dataset_info": {"features": [{"name": "input_text", "dtype": "string"}, {"name": "output_text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 842581512.2664871, "num_examples": 940754}], "download_size": 495782858, "dataset_size": 842581512.2664871}}
|
2023-03-31T19:31:56+00:00
|
86be64b30075120dfa60f829972e06d936a0909f
|
# AutoTrain Dataset for project: pegasus-subreddit-comments-summarizer
## Dataset Description
This dataset has been automatically processed by AutoTrain for project pegasus-subreddit-comments-summarizer.
### Languages
The BCP-47 code for the dataset's language is en.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"text": "I go through this every single year. We have an Ironman competition that is 2 miles from my hotel, and I sell out for that weekend almost a year in advance. Without fail I will have some nitwit who will come up on their checkout day and ask to extend, when I tell them I can't they lose their mind at me. It's their room, they paid for it, they're already in there how can I just give it away. People do not understand how reservations work.",
"target": "The commenter experiences this every year - they sell out their hotel almost a year in advance for an Ironman competition nearby. Despite this, some customers still ask to extend their stay at checkout and get angry when told it's not possible because they don't understand how reservations work."
},
{
"text": "Can i just say .. thanks for going back to make sure you hadn't overreacted. Im sure that made things so much easier on all the staff, with it being their first days back, being understaffed, I'm sure, and trying to get back into the swing of things. I think you handled that really well :)",
"target": "The commenter appreciates the poster's effort in going back to verify if they had overreacted. The commenter believes this action might have made things easier for the understaffed team during their first days back. The commenter commends the poster for handling the situation well."
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"text": "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 | 7177 |
| valid | 1796 |
|
stevied67/autotrain-data-pegasus-subreddit-comments-summarizer
|
[
"task_categories:summarization",
"language:en",
"region:us"
] |
2023-03-31T19:09:49+00:00
|
{"language": ["en"], "task_categories": ["summarization"]}
|
2023-03-31T19:10:36+00:00
|
ac493c22f1e9e1da2ea35511ad8dfb216cd7632f
|
# Dataset Card for "turkishReviews-ds-mini"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
sebinbusra/turkishReviews-ds-mini
|
[
"region:us"
] |
2023-03-31T20:04:14+00:00
|
{"dataset_info": {"features": [{"name": "review", "dtype": "string"}, {"name": "review_length", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 1252876.2642514652, "num_examples": 3378}, {"name": "validation", "num_bytes": 139455.7357485349, "num_examples": 376}], "download_size": 896649, "dataset_size": 1392332.0}}
|
2023-03-31T20:04:31+00:00
|
45bd2671a067b851549201eb07d2421d0c47ae4a
|
# Dataset Card for Criticality Prediction
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
Legal Criticality Prediction (LCP) is a multilingual, diachronic dataset of 139K Swiss Federal Supreme Court (FSCS) cases annotated with two criticality labels. The bge_label i a binary label (critical, non-critical), while the citation label has 5 classes (critical-1, critical-2, critical-3, critical-4, non-critical). Critical classes of the citation_label are distinct subsets of the critical class of the bge_label. This dataset creates a challenging text classification task. We also provide additional metadata as the publication year, the law area and the canton of origin per case, to promote robustness and fairness studies on the critical area of legal NLP.
### Supported Tasks and Leaderboards
LCP can be used as text classification task
### Languages
Switzerland has four official languages with three languages German, French and Italian being represenated. The decisions are written by the judges and clerks in the language of the proceedings.
German (91k), French (33k), Italian (15k)
## Dataset Structure
```
{
"decision_id": "008d8a52-f0ea-4820-a18c-d06066dbb407",
"language": "fr",
"year": "2018",
"chamber": "CH_BGer_004",
"region": "Federation",
"origin_chamber": "338.0",
"origin_court": "127.0",
"origin_canton": "24.0",
"law_area": "civil_law",
"law_sub_area": ,
"bge_label": "critical",
"citation_label": "critical-1",
"facts": "Faits : A. A.a. Le 17 août 2007, C.X._, née le 14 février 1944 et domiciliée...",
"considerations": "Considérant en droit : 1. Interjeté en temps utile (art. 100 al. 1 LTF) par les défendeurs qui ont succombé dans leurs conclusions (art. 76 LTF) contre une décision...",
"rulings": "Par ces motifs, le Tribunal fédéral prononce : 1. Le recours est rejeté. 2. Les frais judiciaires, arrêtés à 10'000 fr., sont mis solidairement à la charge des recourants...",
}
```
### Data Fields
```
decision_id: (str) a unique identifier of the for the document
language: (str) one of (de, fr, it)
year: (int) the publication year
chamber: (str) the chamber of the case
region: (str) the region of the case
origin_chamber: (str) the chamber of the origin case
origin_court: (str) the court of the origin case
origin_canton: (str) the canton of the origin case
law_area: (str) the law area of the case
law_sub_area:(str) the law sub area of the case
bge_label: (str) critical or non-critical
citation_label: (str) critical-1, critical-2, critical-3, critical-4, non-critical
facts: (str) the facts of the case
considerations: (str) the considerations of the case
rulings: (str) the rulings of the case
```
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
The dataset was split date-stratisfied
- Train: 2002-2015
- Validation: 2016-2017
- Test: 2018-2022
| Language | Subset | Number of Documents (Training/Validation/Test) |
|------------|------------|--------------------------------------------|
| German | **de** | 81'264 (56592 / 19601 / 5071) |
| French | **fr** | 49'354 (29263 / 11117 / 8974) |
| Italian | **it** | 7913 (5220 / 1901 / 792) |
## Dataset Creation
### Curation Rationale
The dataset was created by Stern (2023).
### Source Data
#### Initial Data Collection and Normalization
The original data are published from the Swiss Federal Supreme Court (https://www.bger.ch) in unprocessed formats (HTML). The documents were downloaded from the Entscheidsuche portal (https://entscheidsuche.ch) in HTML.
#### Who are the source language producers?
The decisions are written by the judges and clerks in the language of the proceedings.
### Annotations
#### Annotation process
bge_label:
1. all bger_references in the bge header were extracted (for bge see rcds/swiss_rulings).
2. bger file_names are compared with the found references
citation_label:
1. count all citations for all bger cases and weight citations
2. divide cited cases in four different classes, depending on amount of citations
#### Who are the annotators?
Stern processed data and introduced bge and citation-label
Metadata is published by the Swiss Federal Supreme Court (https://www.bger.ch).
### Personal and Sensitive Information
The dataset contains publicly available court decisions from the Swiss Federal Supreme Court. Personal or sensitive information has been anonymized by the court before publication according to the following guidelines: https://www.bger.ch/home/juridiction/anonymisierungsregeln.html.
## 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
We release the data under CC-BY-4.0 which complies with the court licensing (https://www.bger.ch/files/live/sites/bger/files/pdf/de/urteilsveroeffentlichung_d.pdf)
© Swiss Federal Supreme Court, 2002-2022
The copyright for the editorial content of this website and the consolidated texts, which is owned by the Swiss Federal Supreme Court, is licensed under the Creative Commons Attribution 4.0 International licence. This means that you can re-use the content provided you acknowledge the source and indicate any changes you have made.
Source: https://www.bger.ch/files/live/sites/bger/files/pdf/de/urteilsveroeffentlichung_d.pdf
### Citation Information
Please cite our [ArXiv-Preprint](https://arxiv.org/abs/2306.09237)
```
@misc{rasiah2023scale,
title={SCALE: Scaling up the Complexity for Advanced Language Model Evaluation},
author={Vishvaksenan Rasiah and Ronja Stern and Veton Matoshi and Matthias Stürmer and Ilias Chalkidis and Daniel E. Ho and Joel Niklaus},
year={2023},
eprint={2306.09237},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### Contributions
Thanks to [@Stern5497](https://github.com/stern5497) for adding this dataset.
|
rcds/swiss_criticality_prediction
|
[
"task_categories:text-classification",
"annotations_creators:machine-generated",
"language_creators:expert-generated",
"multilinguality:multilingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:de",
"language:fr",
"language:it",
"license:cc-by-sa-4.0",
"arxiv:2306.09237",
"region:us"
] |
2023-03-31T20:21:30+00:00
|
{"annotations_creators": ["machine-generated"], "language_creators": ["expert-generated"], "language": ["de", "fr", "it"], "license": ["cc-by-sa-4.0"], "multilinguality": ["multilingual"], "size_categories": ["100K<n<1M"], "source_datasets": ["original"], "task_categories": ["text-classification"], "pretty_name": "Legal Criticality Prediction", "tags": []}
|
2023-07-20T06:39:07+00:00
|
fe0ee68f0e3083ece02c3229a4e1fac9f06daca8
|
# Dataset Card for "my_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
bbooth/my_dataset
|
[
"region:us"
] |
2023-03-31T20:22:15+00:00
|
{"dataset_info": {"features": [{"name": "data", "dtype": "float64"}], "splits": [{"name": "train", "num_bytes": 80000, "num_examples": 10000}], "download_size": 96280, "dataset_size": 80000}}
|
2023-04-05T05:24:52+00:00
|
3f763ce6e65dd3c17a913f24145d448fe9078d98
|
Neeraj8180/BrainTumor
|
[
"license:apache-2.0",
"region:us"
] |
2023-03-31T21:14:24+00:00
|
{"license": "apache-2.0"}
|
2023-03-31T21:14:24+00:00
|
|
a5123bb354d480dac3db82d2e9004d876a66a222
|
# Dataset Card for "reklamation24_unterhaltung-kultur-freizeit-full"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
fathyshalab/reklamation24_unterhaltung-kultur-freizeit-full
|
[
"region:us"
] |
2023-03-31T21:21:11+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "inputs", "struct": [{"name": "text", "dtype": "string"}]}, {"name": "prediction", "list": [{"name": "label", "dtype": "string"}, {"name": "score", "dtype": "float64"}]}, {"name": "prediction_agent", "dtype": "string"}, {"name": "annotation", "dtype": "string"}, {"name": "annotation_agent", "dtype": "string"}, {"name": "vectors", "struct": [{"name": "mini-lm-sentence-transformers", "sequence": "float64"}]}, {"name": "multi_label", "dtype": "bool"}, {"name": "explanation", "dtype": "null"}, {"name": "id", "dtype": "string"}, {"name": "metadata", "dtype": "null"}, {"name": "status", "dtype": "string"}, {"name": "event_timestamp", "dtype": "timestamp[us]"}, {"name": "metrics", "struct": [{"name": "text_length", "dtype": "int64"}]}], "splits": [{"name": "train", "num_bytes": 25643117, "num_examples": 4699}], "download_size": 0, "dataset_size": 25643117}}
|
2023-04-25T13:16:56+00:00
|
f8198fa687d790ad5828e2f3ded5dd56668477d4
|
# Dataset Card for "reklamation24_mode-schmuck-zubehoer-full"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
fathyshalab/reklamation24_mode-schmuck-zubehoer-full
|
[
"region:us"
] |
2023-03-31T21:23:51+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "inputs", "struct": [{"name": "text", "dtype": "string"}]}, {"name": "prediction", "list": [{"name": "label", "dtype": "string"}, {"name": "score", "dtype": "float64"}]}, {"name": "prediction_agent", "dtype": "string"}, {"name": "annotation", "dtype": "string"}, {"name": "annotation_agent", "dtype": "string"}, {"name": "vectors", "struct": [{"name": "mini-lm-sentence-transformers", "sequence": "float64"}]}, {"name": "multi_label", "dtype": "bool"}, {"name": "explanation", "dtype": "null"}, {"name": "id", "dtype": "string"}, {"name": "metadata", "dtype": "null"}, {"name": "status", "dtype": "string"}, {"name": "event_timestamp", "dtype": "timestamp[us]"}, {"name": "metrics", "struct": [{"name": "text_length", "dtype": "int64"}]}], "splits": [{"name": "train", "num_bytes": 68271429, "num_examples": 12923}], "download_size": 0, "dataset_size": 68271429}}
|
2023-04-25T13:01:08+00:00
|
d1f06516d89246d3d187217bbf42e572baf14c03
|
# Dataset Card for "reklamation24_finanzen-full"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
fathyshalab/reklamation24_finanzen-full
|
[
"region:us"
] |
2023-03-31T21:25:02+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "inputs", "struct": [{"name": "text", "dtype": "string"}]}, {"name": "prediction", "list": [{"name": "label", "dtype": "string"}, {"name": "score", "dtype": "float64"}]}, {"name": "prediction_agent", "dtype": "string"}, {"name": "annotation", "dtype": "string"}, {"name": "annotation_agent", "dtype": "string"}, {"name": "vectors", "struct": [{"name": "mini-lm-sentence-transformers", "sequence": "float64"}]}, {"name": "multi_label", "dtype": "bool"}, {"name": "explanation", "dtype": "null"}, {"name": "id", "dtype": "string"}, {"name": "metadata", "dtype": "null"}, {"name": "status", "dtype": "string"}, {"name": "event_timestamp", "dtype": "timestamp[us]"}, {"name": "metrics", "struct": [{"name": "text_length", "dtype": "int64"}]}], "splits": [{"name": "train", "num_bytes": 24578022, "num_examples": 4334}], "download_size": 0, "dataset_size": 24578022}}
|
2023-04-25T13:04:08+00:00
|
3e5689d2fd4e6dec06df10b2cfec5fde484783aa
|
# Dataset Card for "reklamation24_moebel-einrichtungshaeuser-full"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
fathyshalab/reklamation24_moebel-einrichtungshaeuser-full
|
[
"region:us"
] |
2023-03-31T21:26:52+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "inputs", "struct": [{"name": "text", "dtype": "string"}]}, {"name": "prediction", "list": [{"name": "label", "dtype": "string"}, {"name": "score", "dtype": "float64"}]}, {"name": "prediction_agent", "dtype": "string"}, {"name": "annotation", "dtype": "string"}, {"name": "annotation_agent", "dtype": "string"}, {"name": "vectors", "struct": [{"name": "mini-lm-sentence-transformers", "sequence": "float64"}]}, {"name": "multi_label", "dtype": "bool"}, {"name": "explanation", "dtype": "null"}, {"name": "id", "dtype": "string"}, {"name": "metadata", "dtype": "null"}, {"name": "status", "dtype": "string"}, {"name": "event_timestamp", "dtype": "timestamp[us]"}, {"name": "metrics", "struct": [{"name": "text_length", "dtype": "int64"}]}], "splits": [{"name": "train", "num_bytes": 48126857, "num_examples": 8452}], "download_size": 0, "dataset_size": 48126857}}
|
2023-04-25T13:03:08+00:00
|
1a91a050d010189f40d680f179936035f5540814
|
# Dataset Card for "reklamation24_supermaerkte-drogerien-full"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
fathyshalab/reklamation24_supermaerkte-drogerien-full
|
[
"region:us"
] |
2023-03-31T21:30:10+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "inputs", "struct": [{"name": "text", "dtype": "string"}]}, {"name": "prediction", "list": [{"name": "label", "dtype": "string"}, {"name": "score", "dtype": "float64"}]}, {"name": "prediction_agent", "dtype": "string"}, {"name": "annotation", "dtype": "string"}, {"name": "annotation_agent", "dtype": "string"}, {"name": "vectors", "struct": [{"name": "mini-lm-sentence-transformers", "sequence": "float64"}]}, {"name": "multi_label", "dtype": "bool"}, {"name": "explanation", "dtype": "null"}, {"name": "id", "dtype": "string"}, {"name": "metadata", "dtype": "null"}, {"name": "status", "dtype": "string"}, {"name": "event_timestamp", "dtype": "timestamp[us]"}, {"name": "metrics", "struct": [{"name": "text_length", "dtype": "int64"}]}], "splits": [{"name": "train", "num_bytes": 84943744, "num_examples": 15366}], "download_size": 0, "dataset_size": 84943744}}
|
2023-04-25T12:57:44+00:00
|
cbf963e6b721b7e5e3d87fc23ad97a93bd27a6f2
|
# Dataset Card for "wikipedia_stage2_coverage_20230331"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
MartinKu/wikipedia_stage2_coverage_20230331
|
[
"region:us"
] |
2023-03-31T22:05:15+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "S_V_position", "sequence": "int64"}, {"name": "O_C_position", "sequence": "int64"}, {"name": "start_point_list", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 60121841137, "num_examples": 1089628}], "download_size": 18558792841, "dataset_size": 60121841137}}
|
2023-04-02T13:44:49+00:00
|
4103166dd2dad407e8b385c1468b3c5690b4233d
|
jeffwan/BELLE-Alpaca-CN
|
[
"license:gpl-3.0",
"region:us"
] |
2023-03-31T22:20:20+00:00
|
{"license": "gpl-3.0"}
|
2023-03-31T22:20:20+00:00
|
|
82c70da478a4f7604256ed2b2805957b1a0f5240
|
# Dataset Card for "wikipedia_stage1_SV_20230331"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
MartinKu/wikipedia_stage1_SV_20230331
|
[
"region:us"
] |
2023-03-31T22:32:10+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3712575761, "num_examples": 124985104}], "download_size": 2439087910, "dataset_size": 3712575761}}
|
2023-04-02T18:32:54+00:00
|
854cc2d12d9b8e5b06fa121e709d2e4eab0797be
|
# Dataset Card for "wikipedia_stage1_OC_20230331"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
MartinKu/wikipedia_stage1_OC_20230331
|
[
"region:us"
] |
2023-03-31T22:32:48+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 10763367877, "num_examples": 152512467}], "download_size": 6671458208, "dataset_size": 10763367877}}
|
2023-04-02T18:41:19+00:00
|
266b2f170c42b55a693ab45943aefb53656a8831
|
# Dataset Card for "AICE_dataset"
[AICE project on Kaggle](https://www.kaggle.com/datasets/capsuleyolo/kyucapsule)
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
zxcej/AICE_dataset
|
[
"region:us"
] |
2023-03-31T23:30:18+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "SMT", "1": "angiodysplasia", "2": "bleeding", "3": "diverticulum", "4": "erosion", "5": "erythema", "6": "foreign body", "7": "lymph follicle", "8": "lymphangiectasia", "9": "no_class", "10": "polyp-like", "11": "stenosis"}}}}], "splits": [{"name": "train", "num_bytes": 993869095.1352087, "num_examples": 14784}, {"name": "test", "num_bytes": 247932424.8427913, "num_examples": 3697}], "download_size": 1242057657, "dataset_size": 1241801519.978}}
|
2023-04-04T20:17:59+00:00
|
660971b7d5e5336aab0b59288734b6ed9a8eb3d8
|
# Dataset Card for "little_dataset-combined"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
jlbaker361/little_dataset-combined
|
[
"region:us"
] |
2023-03-31T23:47:26+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "src", "dtype": "string"}, {"name": "split", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "name", "dtype": "string"}, {"name": "caption", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3528300.0, "num_examples": 10}], "download_size": 355277, "dataset_size": 3528300.0}}
|
2023-03-31T23:47:28+00:00
|
a0225c44fb2d96a53fcf340391a04cc8d7e5de0a
|
[
{ "question": "я", "answer": "I" },
{ "question": "не", "answer": "not" },
{ "question": "в", "answer": "in" },
{ "question": "що", "answer": "what" },
{ "question": "на", "answer": "on" },
{ "question": "з", "answer": "from" },
{ "question": "як", "answer": "how" },
{ "question": "ти", "answer": "you" },
{ "question": "він", "answer": "he" },
{ "question": "вона", "answer": "she" },
{ "question": "це", "answer": "this" },
{ "question": "але", "answer": "but" },
{ "question": "за", "answer": "for" },
{ "question": "його", "answer": "his" },
{ "question": "її", "answer": "her" },
{ "question": "їх", "answer": "their" },
{ "question": "так", "answer": "yes" },
{ "question": "або", "answer": "or" },
{ "question": "щоб", "answer": "to" },
{ "question": "неї", "answer": "hers" },
{ "question": "той", "answer": "that" },
{ "question": "зі", "answer": "with" },
{ "question": "де", "answer": "where" },
{ "question": "йому", "answer": "him" },
{ "question": "їм", "answer": "them" },
{ "question": "вже", "answer": "already" },
{ "question": "ні", "answer": "no" },
{ "question": "та", "answer": "and" },
{ "question": "є", "answer": "is" },
{ "question": "все", "answer": "all" },
{ "question": "до", "answer": "to" },
{ "question": "ще", "answer": "still" },
{ "question": "тут", "answer": "here" },
{ "question": "мій", "answer": "my" },
{"question": "Привіт", "answer": "Hello"},
{"question": "Як справи?", "answer": "How are you?"},
{"question": "Добре", "answer": "Good"},
{"question": "Погано", "answer": "Bad"},
{"question": "Я не розумію", "answer": "I don't understand"},
{"question": "Будь ласка", "answer": "Please"},
{"question": "Дякую", "answer": "Thank you"},
{"question": "Без проблем", "answer": "No problem"},
{"question": "До побачення", "answer": "Goodbye"},
{"question": "Я згоден", "answer": "I agree"},
{"question": "Я не згоден", "answer": "I disagree"},
{"question": "Це можливо?", "answer": "Is it possible?"},
{"question": "Допоможіть мені, будь ласка", "answer": "Help me, please"},
{"question": "Як вас звати?", "answer": "What's your name?"},
{"question": "Мене звуть...", "answer": "My name is..."},
{"question": "Скільки це коштує?", "answer": "How much does it cost?"},
{"question": "Як довго це займає?", "answer": "How long does it take?"},
{"question": "Я не впевнений", "answer": "I'm not sure"},
{"question": "Де знаходиться туалет?", "answer": "Where is the bathroom?"},
{"question": "Де знаходиться готель?", "answer": "Where is the hotel?"},
{"question": "Де знаходиться ресторан?", "answer": "Where is the restaurant?"},
{"question": "Де знаходиться банк?", "answer": "Where is the bank?"},
{"question": "Де знаходиться аеропорт?", "answer": "Where is the airport?"},
{"question": "Де знаходиться вокзал?", "answer": "Where is the train station?"},
{"question": "Я не знаю", "answer": "I don't know"},
{"question": "Як тебе справи?", "answer": "How are you doing?"},
{"question": "Дуже добре, дякую", "answer": "Very well, thank you"},
{"question": "Чим ти займаєшся?", "answer": "What do you do?"},
{"question": "Я студент", "answer": "I am a student"},
{"question": "Я працюю в IT", "answer": "I work in IT"}
]
|
sherkoo/language
|
[
"region:us"
] |
2023-04-01T00:00:28+00:00
|
{}
|
2023-04-01T00:01:40+00:00
|
0251eed5a7b6f5bbc6c7418292e29430a99f0f2d
|
# Dataset Card for "CVEFixes_Python_with_norm_vul_lines"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
EddieChen372/CVEFixes_Python_with_norm_vul_lines
|
[
"region:us"
] |
2023-04-01T00:25:11+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": 5568381, "num_examples": 4584}, {"name": "test", "num_bytes": 1334879, "num_examples": 1146}], "download_size": 2782844, "dataset_size": 6903260}}
|
2023-04-01T00:25:33+00:00
|
49c311d7167b89f33475da4da963f663d01fd7d9
|
Amani96/BUI_ViT
|
[
"license:openrail",
"region:us"
] |
2023-04-01T00:28:28+00:00
|
{"license": "openrail"}
|
2023-04-01T01:01:30+00:00
|
|
b22ac8e0f48998f4671b88b69554f1145c5e6e20
|
Hyperspace-Technologies/scp-wiki-text
|
[
"size_categories:100M<n<1B",
"language:en",
"license:cc-by-4.0",
"scp",
"region:us"
] |
2023-04-01T00:40:27+00:00
|
{"language": ["en"], "license": "cc-by-4.0", "size_categories": ["100M<n<1B"], "tags": ["scp"], "dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 24497718.02277939, "num_examples": 314294}, {"name": "test", "num_bytes": 2722003.3115220205, "num_examples": 34922}], "download_size": 72410093, "dataset_size": 27219721.334301412}}
|
2023-04-01T01:44:11+00:00
|
|
c89a3c5c755d45a083685d3a0c1ca99ff85af5db
|
# Dataset Card for "AICE_binary_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
zxcej/AICE_binary_dataset
|
[
"region:us"
] |
2023-04-01T01:53:36+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "Disease", "1": "Healthy"}}}}], "splits": [{"name": "train", "num_bytes": 996171511.8039621, "num_examples": 14784}, {"name": "test", "num_bytes": 247564754.71103784, "num_examples": 3697}], "download_size": 1242379757, "dataset_size": 1243736266.5149999}}
|
2023-04-01T01:58:46+00:00
|
7a1fc31c5242497edd26fea455e7894deb946027
|
# Dataset Card for "korra-lite_captioned-augmented"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
jlbaker361/korra-lite_captioned-augmented
|
[
"region:us"
] |
2023-04-01T02:27:13+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "src", "dtype": "string"}, {"name": "split", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "caption", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 254281033.375, "num_examples": 1173}], "download_size": 254182437, "dataset_size": 254281033.375}}
|
2023-04-01T02:27:29+00:00
|
44f7a7919e5eebac84c9d47e3fa4a478d7891c71
|
Scraped conversations of the LIHKG forum. Content scraped by Ayaka: https://github.com/ayaka14732/lihkg-scraper
|
AlienKevin/LIHKG
|
[
"size_categories:1M<n<10M",
"language:yue",
"license:mit",
"region:us"
] |
2023-04-01T02:36:12+00:00
|
{"language": ["yue"], "license": "mit", "size_categories": ["1M<n<10M"], "pretty_name": "\u9023\u767b"}
|
2023-05-08T13:38:36+00:00
|
08eb3409e2ad117d0eec8efb0ac9ff2ca4d6a790
|
# Dataset Card for "ask2democracy-cqa-salud"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
jorge-henao/ask2democracy-cqa-salud
|
[
"region:us"
] |
2023-04-01T02:41:20+00:00
|
{"dataset_info": {"features": [{"name": "instruction", "dtype": "string"}, {"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}, {"name": "topics", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 849585, "num_examples": 445}], "download_size": 132480, "dataset_size": 849585}}
|
2023-04-01T02:41:25+00:00
|
866b2a89ab700c377a1598853c2643542be477ec
|
# Dataset Card for "movie-love-actually_captioned-augmented"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
jlbaker361/movie-love-actually_captioned-augmented
|
[
"region:us"
] |
2023-04-01T02:55:41+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "src", "dtype": "string"}, {"name": "split", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "caption", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 228506362.0, "num_examples": 732}], "download_size": 228474429, "dataset_size": 228506362.0}}
|
2023-04-01T02:55:50+00:00
|
d3ca6b1a49c0cf8716315a96c2fc5f884ec97cf3
|
satwikapaul/retinaldisease
|
[
"license:unknown",
"region:us"
] |
2023-04-01T03:15:15+00:00
|
{"license": "unknown"}
|
2023-04-01T03:15:53+00:00
|
|
4e8f12d8eaa2f26c6719b1cfc97137842e1434e5
|
Rico777/hgfyuc
|
[
"license:unknown",
"region:us"
] |
2023-04-01T04:00:04+00:00
|
{"license": "unknown"}
|
2023-04-01T04:00:04+00:00
|
|
9aebbaf2949f6d71ea97f6b147dab0e1235493fd
|
# Arabic Translated Conceptual Captions Dataset
## Overview
This dataset consists of conceptual captions translated into Arabic using the Google Translate API. It serves as a resource for researchers and developers interested in exploring the vision-language tasks and biases introduced during the translation process.
## Dataset Information
- **Source Dataset**: Conceptual Captions
- **Translation Tool**: Google Translate API
- **Translation Language**: English to Arabic
## Important Notes
1. **Translation Quality**: The translations are machine-generated and may contain errors, inaccuracies, or cultural nuances that were not appropriately captured. Researchers are encouraged to verify translations for accuracy.
2. **Biases**: The dataset is prone to various types of biases, including but not limited to gender bias. Google Translate API, like any other machine translation tool, may inadvertently introduce biases present in its training data.
3. **Usage Guidelines**: Please refer to the original Conceptual Captions dataset usage guidelines, as they apply to this translated version. Respect copyright and licensing agreements associated with the source dataset.
## Description
the dataset contains two file train and validation and each has:
- **`arabic_caption`**: This column includes the machine-translated captions in Arabic, generated using the Google Translate API.
- **`caption`**: This column contains the original captions in English sourced from the Conceptual Captions dataset.
- **`link`**: This column contains links to images corresponding to the captions in the dataset.
|
LinaAlhuri/ArabicConceptualCaptions3M
|
[
"task_categories:image-to-text",
"size_categories:1M<n<10M",
"language:ar",
"region:us"
] |
2023-04-01T04:20:06+00:00
|
{"language": ["ar"], "size_categories": ["1M<n<10M"], "task_categories": ["image-to-text"], "pretty_name": "ArabicConceptualCaptions3M"}
|
2023-11-15T09:24:55+00:00
|
f552854050c1626fb34efb07a9d8157f5558d32f
|
# Arabic Text-Image Dataset from Wikipedia
## Overview
This repository contains a dataset created by scraping images and their captions from Wikipedia, focusing on content that represents the Arab world. The goal is to provide a diverse and representative collection of images with corresponding Arabic captions to support research and development in natural language processing (NLP), computer vision, and cross-modal applications.
## Dataset Structure
The dataset contains four columns:
1. **source:** Indicates the origin of the image-text pair.
2. **link:** Provides the link to the Wikipedia image.
3. **caption:** Arabic captions corresponding to each image.
4. **extension:** Specifies the file extension of each image.
## Recommended Data Preprocessing
It is recommended to incorporate various filtering techniques inspired by established practices:
1. **Text-Based Filtering:**
- Discarded images with captions containing fewer than three tokens.
- Maintained digits in captions to increase dataset complexity.
- Preserved Latin words to minimize information loss for foreign entities or scientific terms.
2. **Diacritics Removal:**
- Employed the Araby library in Python for diacritics removal in captions.
3. **Speech Tagger Utilization:**
- Utilized the CAMeL-Lab/bert-base-arabic-camelbert-msa-pos-msa tagger to eliminate captions that are mostly proper nouns to generate higher-quality captions.
- Aimed to prioritize learning more generic concepts over fine details.
5. **Image-Based Filtering:**
- Removed images with fewer than one hundred pixels to supply our model with detail-rich images.
- Included only JPEG images in the final dataset due to potential information loss in other formats.
6. **Handling Non-JPEG Images:**
- Excluded images of other extensions as they usually contain maps, logos or non-rich information content.
7. **Color System Standardization:**
- Converted images into RGB using Python OpenCV to ensure a standardized color space.
**Note:** The dataset lacks protection for individual personal images due to the absence of Arabic graph tools that substitute specific concepts for more generic ones. This presents an opportunity for future research and improvement in image privacy and protection.
## Usage
Researchers and developers are encouraged to use this preprocessed dataset for tasks such as image captioning, cross-modal learning, and other NLP and computer vision applications. Please adhere to ethical standards and ensure that the usage of this dataset aligns with Wikipedia's terms of service and licensing.
|
LinaAlhuri/WikipediaArabicPairs
|
[
"task_categories:image-to-text",
"size_categories:100K<n<1M",
"language:ar",
"region:us"
] |
2023-04-01T04:21:52+00:00
|
{"language": ["ar"], "size_categories": ["100K<n<1M"], "task_categories": ["image-to-text"], "pretty_name": "WAP"}
|
2023-11-15T10:03:44+00:00
|
0497f6cf2607d82d612692fbe77214f195d43f7c
|
inkcoo/111
|
[
"license:other",
"region:us"
] |
2023-04-01T05:03:43+00:00
|
{"license": "other"}
|
2023-04-01T05:03:43+00:00
|
|
b3c7428796e86c3eaaaa1c760db6d88bdd30ad2f
|
# Dataset Card for "coyo1k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
YiYiXu/coyo1k
|
[
"region:us"
] |
2023-04-01T05:22:49+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "conditioning_image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 766412778.0, "num_examples": 1000}], "download_size": 766320726, "dataset_size": 766412778.0}}
|
2023-04-01T05:23:31+00:00
|
da3b0d669a54d4a47cf13ef761aa9ae62c0c7341
|
# Arabic Translated ImageNet Dataset
---
## Overview
Welcome to the Arabic Translated ImageNet Dataset! This dataset is a version of the ImageNet dataset, translated into Arabic. ImageNet is a widely used benchmark for image classification tasks, and this translation aims to facilitate research and development in the Arabic language.
## Contents
1. **Arabic_query_short:** Arabic translations of the original ImageNet class labels.
2. **Arabic_query_long:** Arabic translations of the original ImageNet class labels within a simple template.
## Usage
- **Image Classification:** Use this dataset for training and evaluating models in the domain of image classification with a focus on the Arabic language.
## Dataset Translation and Considerations
This dataset has been translated using the Google Translation API. It's important to note that automated translation methods, including machine translation, may introduce biases and inaccuracies. The translations are generated algorithmically, and some categories may not have accurate or complete Arabic translations. Researchers and users are advised to be mindful of these limitations and consider the implications of incomplete or potentially inaccurate translations in their analyses.
|
LinaAlhuri/ArabicImageNet
|
[
"task_categories:zero-shot-classification",
"task_categories:image-classification",
"size_categories:n<1K",
"language:ar",
"region:us"
] |
2023-04-01T05:43:01+00:00
|
{"language": ["ar"], "size_categories": ["n<1K"], "task_categories": ["zero-shot-classification", "image-classification"], "pretty_name": "ArabicImageNet"}
|
2023-11-15T08:46:51+00:00
|
e30820e54a281f01adebec48d1b11cfd1576e17c
|
magicgh/alpaca-cleaned-random-50
|
[
"task_categories:text-generation",
"language:en",
"license:cc-by-4.0",
"instruction-finetuning",
"region:us"
] |
2023-04-01T05:53:48+00:00
|
{"language": ["en"], "license": "cc-by-4.0", "task_categories": ["text-generation"], "pretty_name": "Alpaca-Cleaned-Random-50", "tags": ["instruction-finetuning"]}
|
2023-04-01T06:25:31+00:00
|
|
e5299633d423d5bae1bf272849f8ae1b5973beb0
|
magicgh/alpaca-cleaned-random-25
|
[
"task_categories:text-generation",
"language:en",
"license:cc-by-4.0",
"instruction-finetuning",
"region:us"
] |
2023-04-01T05:54:02+00:00
|
{"language": ["en"], "license": "cc-by-4.0", "task_categories": ["text-generation"], "pretty_name": "Alpaca-Cleaned-Random-25", "tags": ["instruction-finetuning"]}
|
2023-04-01T06:23:31+00:00
|
|
692882804282f2e2eba83cfb92331b15968df198
|
calistacxy/imda-dataset
|
[
"region:us"
] |
2023-04-01T06:02:29+00:00
|
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"allFMALAY", "features": [{"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}, {"name": "transcript", "dtype": "string"}, {"name": "mic", "dtype": "string"}, {"name": "audio_name", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 315419763, "num_examples": 2046}, {"name": "test", "num_bytes": 309929082, "num_examples": 2079}], "download_size": 0, "dataset_size": 625348845}, {"config_name": "allFOTHERS", "features": [{"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}, {"name": "transcript", "dtype": "string"}, {"name": "mic", "dtype": "string"}, {"name": "audio_name", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 315419763, "num_examples": 2046}, {"name": "test", "num_bytes": 309929082, "num_examples": 2079}], "download_size": 0, "dataset_size": 625348845}, {"config_name": "allFall", "features": [{"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}, {"name": "transcript", "dtype": "string"}, {"name": "mic", "dtype": "string"}, {"name": "audio_name", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 315419763, "num_examples": 2046}, {"name": "test", "num_bytes": 309929082, "num_examples": 2079}], "download_size": 0, "dataset_size": 625348845}, {"config_name": "allMCHINESE", "features": [{"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}, {"name": "transcript", "dtype": "string"}, {"name": "mic", "dtype": "string"}, {"name": "audio_name", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 315419763, "num_examples": 2046}, {"name": "test", "num_bytes": 309929082, "num_examples": 2079}], "download_size": 0, "dataset_size": 625348845}, {"config_name": "allMINDIAN", "features": [{"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}, {"name": "transcript", "dtype": "string"}, {"name": "mic", "dtype": "string"}, {"name": "audio_name", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 315419763, "num_examples": 2046}, {"name": "test", "num_bytes": 309929082, "num_examples": 2079}], "download_size": 0, "dataset_size": 625348845}, {"config_name": "allMMALAY", "features": [{"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}, {"name": "transcript", "dtype": "string"}, {"name": "mic", "dtype": "string"}, {"name": "audio_name", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 315419763, "num_examples": 2046}, {"name": "test", "num_bytes": 309929082, "num_examples": 2079}], "download_size": 0, "dataset_size": 625348845}, {"config_name": "allMOTHERS", "features": [{"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}, {"name": "transcript", "dtype": "string"}, {"name": "mic", "dtype": "string"}, {"name": "audio_name", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 315419763, "num_examples": 2046}, {"name": "test", "num_bytes": 309929082, "num_examples": 2079}], "download_size": 0, "dataset_size": 625348845}, {"config_name": "allMall", "features": [{"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}, {"name": "transcript", "dtype": "string"}, {"name": "mic", "dtype": "string"}, {"name": "audio_name", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 315419763, "num_examples": 2046}, {"name": "test", "num_bytes": 309929082, "num_examples": 2079}], "download_size": 0, "dataset_size": 625348845}, {"config_name": "allallCHINESE", "features": [{"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}, {"name": "transcript", "dtype": "string"}, {"name": "mic", "dtype": "string"}, {"name": "audio_name", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 315419763, "num_examples": 2046}, {"name": "test", "num_bytes": 309929082, "num_examples": 2079}], "download_size": 0, "dataset_size": 625348845}, {"config_name": "allallINDIAN", "features": [{"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}, {"name": "transcript", "dtype": "string"}, {"name": "mic", "dtype": "string"}, {"name": "audio_name", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 315419763, "num_examples": 2046}, {"name": "test", "num_bytes": 309929082, "num_examples": 2079}], "download_size": 0, "dataset_size": 625348845}, {"config_name": "allallMALAY", "features": [{"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}, {"name": "transcript", "dtype": "string"}, {"name": "mic", "dtype": "string"}, {"name": "audio_name", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 315419763, "num_examples": 2046}, {"name": "test", "num_bytes": 309929082, "num_examples": 2079}], "download_size": 0, "dataset_size": 625348845}, {"config_name": "allallOTHERS", "features": [{"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}, {"name": "transcript", "dtype": "string"}, {"name": "mic", "dtype": "string"}, {"name": "audio_name", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 315419763, "num_examples": 2046}, {"name": "test", "num_bytes": 309929082, "num_examples": 2079}], "download_size": 0, "dataset_size": 625348845}, {"config_name": "allallall", "features": [{"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}, {"name": "transcript", "dtype": "string"}, {"name": "mic", "dtype": "string"}, {"name": "audio_name", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 315419763, "num_examples": 2046}, {"name": "test", "num_bytes": 309929082, "num_examples": 2079}], "download_size": 0, "dataset_size": 625348845}]}
|
2023-05-17T11:10:33+00:00
|
|
a5d5fa7ae6db9b1ad6ca576da36aa79c7e467029
|
# Arabic Translated COCO Validation Dataset
---
## Overview
Welcome to the Arabic Translated COCO Validation Dataset! This dataset is a version of the Common Objects in Context (COCO) dataset, specifically translated into Arabic. The COCO dataset is a widely used benchmark for image captioning and object detection tasks, and this translation aims to facilitate research and development in the Arabic language.
## Contents
1. **coco_url:** This column includes images URL which makes a subset of the COCO validation images.
2. **arabic_caption:** Arabic translations of the original COCO annotations, providing detailed information about image captions.
## Usage
- **Research and Development:** Use this dataset for training and evaluating models in the domain of image captioning and object detection with a focus on the Arabic language.
- **Benchmarking:** Evaluate the performance of your algorithms on this translated COCO dataset to contribute to the advancement of Arabic-language computer vision research.
## Dataset Translation and Bias
This dataset has been translated using the Google Translation API. It's important to note that automated translation methods, including machine translation, may introduce biases and inaccuracies. The translations are generated algorithmically and might not capture the full context or cultural nuances or might contain gender bias, leading to potential biases in the dataset. Researchers and users are advised to be mindful of these limitations and consider the implications of bias in their analyses.
|
LinaAlhuri/Arabic-COCO2014-Validation
|
[
"task_categories:image-to-text",
"size_categories:100K<n<1M",
"language:ar",
"region:us"
] |
2023-04-01T06:09:45+00:00
|
{"language": ["ar"], "size_categories": ["100K<n<1M"], "task_categories": ["image-to-text"], "pretty_name": "Arabic COCO 2014 Validation"}
|
2023-11-15T08:38:07+00:00
|
3ee4f82be33fc80700c0b5e169f346b5a42c7589
|
# Dataset Card for "dtd"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
jxie/dtd
|
[
"region:us"
] |
2023-04-01T06:20:53+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "banded", "1": "blotchy", "2": "braided", "3": "bubbly", "4": "bumpy", "5": "chequered", "6": "cobwebbed", "7": "cracked", "8": "crosshatched", "9": "crystalline", "10": "dotted", "11": "fibrous", "12": "flecked", "13": "freckled", "14": "frilly", "15": "gauzy", "16": "grid", "17": "grooved", "18": "honeycombed", "19": "interlaced", "20": "knitted", "21": "lacelike", "22": "lined", "23": "marbled", "24": "matted", "25": "meshed", "26": "paisley", "27": "perforated", "28": "pitted", "29": "pleated", "30": "polka-dotted", "31": "porous", "32": "potholed", "33": "scaly", "34": "smeared", "35": "spiralled", "36": "sprinkled", "37": "stained", "38": "stratified", "39": "striped", "40": "studded", "41": "swirly", "42": "veined", "43": "waffled", "44": "woven", "45": "wrinkled", "46": "zigzagged"}}}}], "splits": [{"name": "train", "num_bytes": 226313270.04, "num_examples": 1880}, {"name": "test", "num_bytes": 172035822.0, "num_examples": 1880}, {"name": "validation", "num_bytes": 222278767.48, "num_examples": 1880}], "download_size": 629310459, "dataset_size": 620627859.52}}
|
2023-04-01T06:25:40+00:00
|
e434fe37cd95938344c4050a62e52734e0bbd706
|
UTF-8/piC1
|
[
"task_categories:question-answering",
"size_categories:100K<n<1M",
"language:en",
"language:ja",
"license:openrail",
"region:us"
] |
2023-04-01T06:31:01+00:00
|
{"language": ["en", "ja"], "license": "openrail", "size_categories": ["100K<n<1M"], "task_categories": ["question-answering"]}
|
2023-04-01T06:36:25+00:00
|
|
adc248ab67cd256066762340c776076fb71dabfc
|
# Dataset Card for "alpaca-german-validation"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
dvilasuero/alpaca-german-validation
|
[
"region:us"
] |
2023-04-01T06:43:23+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "null"}, {"name": "inputs", "struct": [{"name": "_instruction", "dtype": "string"}, {"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}]}, {"name": "prediction", "dtype": "null"}, {"name": "prediction_agent", "dtype": "null"}, {"name": "annotation", "dtype": "string"}, {"name": "annotation_agent", "dtype": "string"}, {"name": "vectors", "struct": [{"name": "input", "sequence": "float64"}, {"name": "instruction", "sequence": "float64"}, {"name": "output", "sequence": "float64"}]}, {"name": "multi_label", "dtype": "bool"}, {"name": "explanation", "dtype": "null"}, {"name": "id", "dtype": "string"}, {"name": "metadata", "struct": [{"name": "original_id", "dtype": "int64"}, {"name": "translation_model", "dtype": "string"}]}, {"name": "status", "dtype": "string"}, {"name": "event_timestamp", "dtype": "timestamp[us]"}, {"name": "metrics", "struct": [{"name": "text_length", "dtype": "int64"}]}], "splits": [{"name": "train", "num_bytes": 38104, "num_examples": 2}], "download_size": 0, "dataset_size": 38104}}
|
2023-04-01T07:23:23+00:00
|
ba53c8dc642c4ed50f8d978e41b4bd43409d178b
|
# Dataset Card for "translated_tasks_de_google_52k"
Copy of : https://github.com/thisserand/alpaca-lora-finetune-language/tree/main/data/translated
|
philschmid/translated_tasks_de_google_52k
|
[
"region:us"
] |
2023-04-01T08:05:24+00:00
|
{"dataset_info": {"features": [{"name": "instruction", "dtype": "string"}, {"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 22108071, "num_examples": 51664}], "download_size": 13686739, "dataset_size": 22108071}}
|
2023-04-01T08:06:05+00:00
|
8a37f5acf86117dbfd3a6cb269a6ac85c4ac5382
|
Quake24/sumPaypal
|
[
"license:apache-2.0",
"region:us"
] |
2023-04-01T08:14:09+00:00
|
{"license": "apache-2.0"}
|
2023-04-01T08:16:35+00:00
|
|
567359d2de171a4010f47c3dde018756ee7ab268
|
# Dataset Card for "qa_pairs_1st"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
tulip4attoo/qa_pairs_1st
|
[
"region:us"
] |
2023-04-01T08:23:44+00:00
|
{"dataset_info": {"features": [{"name": "instruction", "dtype": "string"}, {"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 969432, "num_examples": 2668}], "download_size": 612591, "dataset_size": 969432}}
|
2023-04-01T08:23:57+00:00
|
037986affbf70d6db12d52547a73e9bace086f56
|
djwright/mon-circular-bw
|
[
"license:cc0-1.0",
"region:us"
] |
2023-04-01T08:43:20+00:00
|
{"license": "cc0-1.0", "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 324687.0, "num_examples": 7}], "download_size": 0, "dataset_size": 324687.0}}
|
2023-04-02T02:37:40+00:00
|
|
e4723753929bbd42b1afa5be069aa907bb22bbe8
|
vsokolovskii/slue
|
[
"license:cc0-1.0",
"region:us"
] |
2023-04-01T08:53:04+00:00
|
{"license": "cc0-1.0"}
|
2023-04-01T12:59:35+00:00
|
|
fb096aa004e0c1199767f063a564a4509bf29cc5
|
WilliamWen/battery_by_shuhuang
|
[
"task_categories:token-classification",
"language:en",
"license:apache-2.0",
"region:us"
] |
2023-04-01T08:57:20+00:00
|
{"language": ["en"], "license": "apache-2.0", "task_categories": ["token-classification"]}
|
2023-04-01T09:16:32+00:00
|
|
4e75045d1ed8fae1902318e3b4d6ad061da0b5ba
|
rachel2999/lang_ident
|
[
"license:cc-by-4.0",
"region:us"
] |
2023-04-01T09:02:57+00:00
|
{"license": "cc-by-4.0"}
|
2023-04-01T09:03:42+00:00
|
|
b232c2e911bc91025986f4dfc72ab46a975e8d2b
|
# Dataset Card for "bayc"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
nielsgl/bayc
|
[
"region:us"
] |
2023-04-01T09:24:09+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 1467029950.0, "num_examples": 10000}], "download_size": 1463911871, "dataset_size": 1467029950.0}}
|
2023-04-01T09:44:50+00:00
|
2dfa3c21479fbc97424d136947b798770ae3c103
|
sirmews/all-my-bookmarks
|
[
"task_categories:text-classification",
"task_categories:token-classification",
"task_categories:summarization",
"not-for-all-audiences",
"region:us"
] |
2023-04-01T09:37:14+00:00
|
{"task_categories": ["text-classification", "token-classification", "summarization"], "tags": ["not-for-all-audiences"]}
|
2023-04-01T09:38:09+00:00
|
|
a97fe123e929327f404870cea8bbf09c4542fc5b
|
This is a synthetic database of questions for testing InstructGPTs on.
It came about as I couldn't think of good examples when asked and got a bit out of hand.
|
Corianas/GPT_Tasks
|
[
"license:apache-2.0",
"region:us"
] |
2023-04-01T09:37:51+00:00
|
{"license": "apache-2.0"}
|
2023-04-01T09:39:08+00:00
|
978346334feecf0837547e806e78d05158121c7a
|
# Dataset Card for "inpaint-finetune"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
nafiturgut/inpaint-finetune
|
[
"region:us"
] |
2023-04-01T09:46:43+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1608841.0, "num_examples": 9}], "download_size": 1610882, "dataset_size": 1608841.0}}
|
2023-04-01T10:06:38+00:00
|
ed0487e2465dcf942072ad2a9cf8526cc5463b26
|
# Dataset Card for "WhisperSmallTestmp3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Nolan1206/WhisperSmallTestmp3
|
[
"region:us"
] |
2023-04-01T10:16:30+00:00
|
{"dataset_info": {"features": [{"name": "audio", "dtype": "audio"}, {"name": "sentence", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 138283843.39, "num_examples": 2586}, {"name": "test", "num_bytes": 7987803.0, "num_examples": 231}], "download_size": 134971232, "dataset_size": 146271646.39}}
|
2023-04-01T10:16:56+00:00
|
cb96367320576642255ae55fdc49f1de6014c434
|
Ellen364/NLPTask
|
[
"language:en",
"region:us"
] |
2023-04-01T10:31:51+00:00
|
{"language": ["en"]}
|
2023-04-01T10:36:46+00:00
|
|
3e6232c0bccc73af69b60c0632463d8f5f161fd0
|
# Dataset Card for "bayc-tiny"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
nielsgl/bayc-tiny
|
[
"region:us"
] |
2023-04-01T10:35:10+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 1569879.0, "num_examples": 40}], "download_size": 1481064, "dataset_size": 1569879.0}}
|
2023-04-01T10:50:42+00:00
|
cfb1a2b0dffe3e963991f3c43169978e1328a7f6
|
Source: https://dumps.wikimedia.org/kkwiki/latest/ [kwiki-latest-pages-articles.xml.bz2]
|
amandyk/kazakh_wiki_articles
|
[
"task_categories:text-generation",
"language:kk",
"license:afl-3.0",
"region:us"
] |
2023-04-01T11:37:47+00:00
|
{"language": ["kk"], "license": "afl-3.0", "task_categories": ["text-generation"]}
|
2023-04-01T14:03:48+00:00
|
7390367a67df274d95e48d0a867a895b40407c5e
|
# Dataset Card for "igbo-translation"
## Dataset Summary
This data set contains translated data from engllish to igbo language for use in training general purpose translation models
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
iamwille/igbo-translation
|
[
"task_categories:translation",
"annotations_creators:found",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"language_creators:found",
"multilinguality:translation",
"size_categories:10K<n<100K",
"source_datasets:extended|igbo_english_machine_translation",
"language:en",
"language:ig",
"license:apache-2.0",
"region:us"
] |
2023-04-01T11:52:31+00:00
|
{"annotations_creators": ["found", "crowdsourced"], "language_creators": ["crowdsourced", "found"], "language": ["en", "ig"], "license": ["apache-2.0"], "multilinguality": ["translation"], "size_categories": ["10K<n<100K"], "source_datasets": ["extended|igbo_english_machine_translation"], "task_categories": ["translation"], "task_ids": [], "pretty_name": "Igbo to English language ", "dataset_info": {"features": [{"name": "English", "dtype": "string"}, {"name": "Igbo", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1983074.9958306309, "num_examples": 8094}, {"name": "test", "num_bytes": 661270.004169369, "num_examples": 2699}], "download_size": 1705526, "dataset_size": 2644345.0}, "tags": []}
|
2023-04-01T13:40:26+00:00
|
a397aca9938ab7013101109b358d8f481febc189
|
# Silver Ukrainian Coreference Dataset
## Dataset Description
### Dataset Summary
A silver coreference resolution dataset for the Ukrainian language. The dataset was generated automatically with the usage of the word alignment method from the following English dataset: https://github.com/d5555/Coreference-dataset.
The word alignment method was implemented by Andrii Kursin ([email protected]).
### Languages
- Ukrainian
## Dataset Structure
### Data Fields
Each sample of the dataset consists of the following fields:
- **doc_key** - document identifier.
- **clusters** - list of clusters, where each cluster consists of the list of mentions. Each mention is represented as a list of two indices: the first index denotes the first word of the mention, the second index denotes the last word of the mention.
- **sentences** - list of sentences where each sentence is represented as a list of words.
- **tokens** - list of words.
- **speakers** - list of speakers which is currently filled with dummy input.
### Data Splits
The dataset is divided into two parts:
- training set;
- validation set.
A test set is absent as far as the dataset is generated automatically.
## Dataset Creation
### Source Data
The dataset was created from the following dataset: https://github.com/d5555/Coreference-dataset.
### Contributions
The code for the translation of samples with further alignment was created by Andrii Kursin ([email protected]). The dataset was generated by Artem Kramov (https://www.linkedin.com/in/artem-kramov-0b3731100/).
|
artemkramov/coreference-dataset-ua
|
[
"task_categories:token-classification",
"size_categories:10K<n<100K",
"language:uk",
"coreference-resolution",
"coreference",
"anaphora",
"region:us"
] |
2023-04-01T12:07:36+00:00
|
{"language": ["uk"], "size_categories": ["10K<n<100K"], "task_categories": ["token-classification"], "pretty_name": "Silver Ukrainian Coreference Dataset ", "tags": ["coreference-resolution", "coreference", "anaphora"]}
|
2023-04-02T10:54:35+00:00
|
d413032a103a5b04fb87fac1b4f504388402938d
|
# azcorpus - The largest open-source NLP corpus for Azerbaijani (1.9M documents, ~ 18M sentences)
__Due to ongoing maintenance activities, only a portion of our corpus is currently available for access.__
In recent years, deep learning models have been widely used in NLP, yielding excellent results. However, most research works in NLP have focused on high-resource languages such as English. There is a significant gap in NLP research for low- resource languages, Azerbaijani being no exception. So, the availability of adequate corpora for most of the languages is still limited, especially for less-resourced languages such as Azerbaijani.
Therefore, this study aimed to contribute to the NLP research community by building the largest NLP corpus for Azerbaijani language.
## Corpus Summary
“azcorpus” built for text generation purposes contains a total of 1.9 million documents, drawn from a variety of sources. The corpus is designed to provide a broad range of linguistic data for natural language processing and organized by genre and topic, with texts covering a range of subjects including politics, economics, science, culture, sport, history, society and etc.
Texts were selected from a variety of sources including newspapers, magazines, academic journals, wikipedia articles and books. The corpus includes both contemporary and historical texts, providing a rich linguistic and cultural context for natural language processing applications.
___
## Corpus structure
### Data fields
- id: Document id
- text - Newline-separated content
- source - Document source
- reliability - Subjective cleaning evaluation rate
- license - Document license
### Data Splits
This corpus has 3 sources(az_books, az_wiki, and az_news) and 1.876.492 cleaned documents.
| Source name | Number of Instances | Size (GB) |
| ------------- | --------------------|:----------------------|
| az_books | 1,540,732 | 19.5 |
| az_wiki | 98,882 | 0.9 |
| az_news | 236,878 | 3.8 |
___
## Methodology
The first step in building "azcorpus" was to collect text data from various sources.
The news websites were selected based on their popularity and the diversity of topics covered.
Additionally, a collection of ebooks in Azerbaijani was obtained from various online sources. We have expanded our collection to encompass not only fictional literature, but also scholarly works, such as physics, chemistry, and etc.
Source-specific cleaning techniques were applied separately to ensure consistency and accuracy in the corpus. Further information regarding the methodology at hand will be expounded upon in our forthcoming academic paper.
To ensure the ethical use of the corpus, we only collected publicly available data, and we did not collect any personal or sensitive information. We also ensured that the corpus was used for research purposes only and not for commercial gain. In accordance with legal considerations, it is not within our current plans to divulge sources at this time.
___
## Corpus Usage
To obtain comprehensive guidance on how to use "azcorpus", please refer to the detailed usage instructions provided in this [notebook](https://github.com/azcorpus/azcorpus_v0/blob/main/azcorpus_v0.ipynb).
```python
corpus = AzCorpus(access_token = "your_token")
# To obtain a corpus in the raw JSON format
corpus.generate_samples()
```
The download of the entire corpus is a process that entails a time span of approximately 25 minutes to 2 hours, contingent upon the velocity of your internet connection. Presently, our team is engrossed in the refinement of the download script with the objective of enhancing efficiency.
___
## Considerations for Using the Corpus
#### Social Impact
Our work has the potential to contribute to the community by providing a valuable resource for development of new text generation tools in Azerbaijani.
"azcorpus" demonstrates the importance of building large NLP corpora for under-resourced languages, and highlights the social impact of such resources. By making this corpus available to the wider community, we hope to stimulate further research and development in the field of Azerbaijani text generation, and contribute to the broader goal of promoting linguistic diversity and cultural heritage. Future studies could explore the potential community impact of our work.
#### Biases and Limitations
Addressing potential bias in machine learning corpuses is a common concern in research.
In this study, we acknowledge that our dataset may be subject to bias and to mitigate this issue, we employed several techniques.
However, we recognize that our approach may still have limitations.
So, It is important to exercise caution with models trained on a "azcorpus" that has not been adequately filtered,
as this may have an impact on the resulting models. In particular, it is crucial to be mindful of any biases
that may be present in the "azcorpus_v0".
Future work could further investigate these issues and explore additional
methods to address bias in the corpus.
___
## Additional Information
#### Corpus authors
The corpus was put together by [Huseyn Kishiyev](https://www.linkedin.com/in/huseynkishiyev/), [Jafar Isbarov](https://www.linkedin.com/in/jafar-isbarov/), [Kanan Suleymanli](https://www.linkedin.com/in/kanan-suleyman/), [Khazar Heydarli](https://www.linkedin.com/in/xezer-heyderli/), [Leyla Eminova](https://www.linkedin.com/in/leyla-eminova/) and [Nijat Zeynalov](https://www.linkedin.com/in/nijat-zeynalov-064163142/).
The authors' names have been arranged in alphabetical order. All authors have equal rights and contributed equally to this work.
The authors declare no conflict of interest. There are no founding sponsors and no other role in the design of the work other than the authors; in the collection, analysis, or interpretation of data; in the writing of the manuscript, and in the decision to publish the corpus.
___
|
azcorpus/azcorpus_v0
|
[
"license:openrail",
"region:us"
] |
2023-04-01T12:37:10+00:00
|
{"license": "openrail", "extra_gated_prompt": "You agree to not use the dataset to conduct experiments that cause harm to human subjects.", "extra_gated_fields": {"Name and Surname": "text", "Email": "text", "Company": "text", "Purpose of Use": "text", "I agree to use this dataset for non-commercial use ONLY": "checkbox"}}
|
2023-09-20T09:24:11+00:00
|
3e5e71b49f4a6558b03d32a7db17806d8ff39f80
|
# Dataset Card for digits dataset
Optical recognition of handwritten digits dataset
## Dataset Description
- **Homepage:** https://scikit-learn.org/stable/datasets/toy_dataset.html#digits-dataset
## Note - How to load this dataset directly with the datasets library
```
from datasets import load_dataset
dataset = load_dataset("sklearn-docs/digits",header=None)
```
### Dataset Summary
This is a copy of the test set of the UCI ML hand-written digits datasets https://archive.ics.uci.edu/ml/datasets/Optical+Recognition+of+Handwritten+Digits
The data set contains images of hand-written digits: 10 classes where each class refers to a digit.
Preprocessing programs made available by NIST were used to extract normalized bitmaps of handwritten digits from a preprinted form. From a total of 43 people, 30 contributed to the training set and different 13 to the test set. 32x32 bitmaps are divided into nonoverlapping blocks of 4x4 and the number of on pixels are counted in each block. This generates an input matrix of 8x8 where each element is an integer in the range 0..16. This reduces dimensionality and gives invariance to small distortions.
For info on NIST preprocessing routines, see M. D. Garris, J. L. Blue, G. T. Candela, D. L. Dimmick, J. Geist, P. J. Grother, S. A. Janet, and C. L. Wilson, NIST Form-Based Handprint Recognition System, NISTIR 5469, 1994.
### Data Instances
Number of Instances:
1797
Number of Attributes:
64
Attribute Information:
8x8 image of integer pixels in the range 0..16.
Missing Attribute Values:
None
Creator:
5. Alpaydin (alpaydin ‘@’ boun.edu.tr)
Date:
July; 1998
### Citation Information
References
C. Kaynak (1995) Methods of Combining Multiple Classifiers and Their Applications to Handwritten Digit Recognition, MSc Thesis, Institute of Graduate Studies in Science and Engineering, Bogazici University.
Alpaydin, C. Kaynak (1998) Cascading Classifiers, Kybernetika.
Ken Tang and Ponnuthurai N. Suganthan and Xi Yao and A. Kai Qin. Linear dimensionalityreduction using relevance weighted LDA. School of Electrical and Electronic Engineering Nanyang Technological University. 2005.
Claudio Gentile. A New Approximate Maximal Margin Classification Algorithm. NIPS. 2000.
|
sklearn-docs/digits
|
[
"size_categories:1K<n<10K",
"license:cc0-1.0",
"region:us"
] |
2023-04-01T13:09:07+00:00
|
{"license": "cc0-1.0", "size_categories": ["1K<n<10K"]}
|
2023-04-06T18:05:28+00:00
|
250db7417fe37e09562c7ec256fc51cc6ec38ea6
|
eng-ali-ramadan/sha3rawy-speech
|
[
"license:unknown",
"region:us"
] |
2023-04-01T13:56:56+00:00
|
{"license": "unknown", "dataset_info": {"features": [{"name": "audio", "dtype": "audio"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 4014804.0, "num_examples": 10}], "download_size": 4014912, "dataset_size": 4014804.0}}
|
2023-04-01T22:18:13+00:00
|
|
79242abbe3474c6454f40786439a4565390327bf
|
martingrzzler/sensory-modality-ratings
|
[
"task_categories:text-classification",
"language:en",
"psycholingistics",
"norare",
"region:us"
] |
2023-04-01T14:25:19+00:00
|
{"language": ["en"], "task_categories": ["text-classification"], "tags": ["psycholingistics", "norare"]}
|
2023-04-01T14:36:51+00:00
|
|
c915955195b4ea02e6eb3f1cfa3163ee6994aafb
|
# Dataset Card for "somos-clean-alpaca-es-validations"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
maga12/somos-clean-alpaca-es-validations
|
[
"region:us"
] |
2023-04-01T14:26:53+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "null"}, {"name": "inputs", "struct": [{"name": "1-instruction", "dtype": "string"}, {"name": "2-input", "dtype": "string"}, {"name": "3-output", "dtype": "string"}]}, {"name": "prediction", "list": [{"name": "label", "dtype": "string"}, {"name": "score", "dtype": "float64"}]}, {"name": "prediction_agent", "dtype": "string"}, {"name": "annotation", "dtype": "string"}, {"name": "annotation_agent", "dtype": "string"}, {"name": "vectors", "struct": [{"name": "input", "sequence": "float64"}, {"name": "instruction", "sequence": "float64"}, {"name": "output", "sequence": "float64"}]}, {"name": "multi_label", "dtype": "bool"}, {"name": "explanation", "dtype": "null"}, {"name": "id", "dtype": "string"}, {"name": "metadata", "struct": [{"name": "tr-flag-1-instruction", "dtype": "bool"}, {"name": "tr-flag-2-input", "dtype": "bool"}, {"name": "tr-flag-3-output", "dtype": "bool"}]}, {"name": "status", "dtype": "string"}, {"name": "event_timestamp", "dtype": "timestamp[us]"}, {"name": "metrics", "struct": [{"name": "text_length", "dtype": "int64"}]}], "splits": [{"name": "train", "num_bytes": 2569677, "num_examples": 136}], "download_size": 0, "dataset_size": 2569677}}
|
2023-07-08T22:14:11+00:00
|
637d00c58f59e132a8b9b27910d944160ac9dd43
|
jkot/czech_parliament_plenary_hearings
|
[
"task_categories:automatic-speech-recognition",
"size_categories:10B<n<100B",
"language:cs",
"license:cc-by-4.0",
"region:us"
] |
2023-04-01T14:50:50+00:00
|
{"language": ["cs"], "license": "cc-by-4.0", "size_categories": ["10B<n<100B"], "task_categories": ["automatic-speech-recognition"], "pretty_name": "Czech parliament plenary hearings"}
|
2023-04-07T19:16:55+00:00
|
|
9171d25565d68c9a3b5312213885121d0662c419
|
martingrzzler/mrc_imageability_ratings
|
[
"task_categories:text-classification",
"size_categories:1K<n<10K",
"language:en",
"psycholinguistics",
"mrc",
"region:us"
] |
2023-04-01T15:00:14+00:00
|
{"language": ["en"], "size_categories": ["1K<n<10K"], "task_categories": ["text-classification"], "tags": ["psycholinguistics", "mrc"]}
|
2023-04-01T15:01:02+00:00
|
|
87cea21e18d4a5650e01d8f011b3fb7739f63d22
|
ajoshi-6/insincere-subset
|
[
"license:mit",
"region:us"
] |
2023-04-01T15:06:50+00:00
|
{"license": "mit"}
|
2023-04-01T15:13:37+00:00
|
|
d2a5f1e783e5969739833d7213c9293712b0b41a
|
i have no idea how to add data
|
123tozi123/dog-voise
|
[
"task_categories:text-to-speech",
"size_categories:n<1K",
"license:cc-by-sa-4.0",
"region:us"
] |
2023-04-01T15:16:48+00:00
|
{"license": "cc-by-sa-4.0", "size_categories": ["n<1K"], "task_categories": ["text-to-speech"]}
|
2023-04-01T15:20:21+00:00
|
c3a67c850990820d7632be43eb6342daef27d6fb
|
123tozi123/toki-pona-tts
|
[
"task_categories:text-to-speech",
"size_categories:n<1K",
"license:cc-by-sa-4.0",
"region:us"
] |
2023-04-01T15:22:34+00:00
|
{"license": "cc-by-sa-4.0", "size_categories": ["n<1K"], "task_categories": ["text-to-speech"]}
|
2023-04-01T21:01:41+00:00
|
|
297cd110b221cedd0b63e5cc278ecf3fcef5a093
|
# Dataset Card for "indexed-open-image-v4-test-set"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Isamu136/indexed-open-image-v4-test-set
|
[
"region:us"
] |
2023-04-01T15:38:55+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "caption", "sequence": "string"}, {"name": "image_id", "dtype": "string"}, {"name": "ibot_b_16_embedding", "sequence": "float32"}, {"name": "moco_vitb_imagenet_embeddings_without_last_layer", "sequence": "float32"}, {"name": "clip_vision_l14", "sequence": "float32"}, {"name": "clip_l14", "sequence": "float32"}], "splits": [{"name": "train", "num_bytes": 40537274594.0, "num_examples": 125436}], "download_size": 40917861964, "dataset_size": 40537274594.0}}
|
2023-04-04T07:35:11+00:00
|
85dad333bf9e225ad1c3e66ed6d05c12f358fcd5
|
# Dataset Card for CARDIO.DE
## Dataset Description
- **Homepage:** https://heidata.uni-heidelberg.de/dataset.xhtml?persistentId=doi:10.11588/data/AFYQDY
- **Pubmed:** False
- **Public:** False
- **Tasks:** NER
We present CARDIO:DE, the first freely available and distributable large German clinical corpus from the cardiovascular domain. CARDIO:DE encompasses 500 clinical routine German doctor’s letters from Heidelberg University Hospital, which were manually annotated. Our prospective study design complies well with current data protection regulations and allows us to keep the original structure of clinical documents consistent. In order to ease access to our corpus, we manually de-identified all letters. To enable various information extraction tasks the temporal information in the documents was preserved. We added two high-quality manual annotation layers to CARDIO:DE, (1) medication information and (2) CDA-compliant section classes.
## Citation Information
```
@data{
data/AFYQDY_2022,
author = {Christoph Dieterich},
publisher = {heiDATA},
title = {{CARDIO:DE}},
year = {2022},
version = {V5},
doi = {10.11588/data/AFYQDY},
url = {https://doi.org/10.11588/data/AFYQDY}
}
```
|
bigbio/cardiode
|
[
"multilinguality:monolingual",
"language:ger",
"license:other",
"region:us"
] |
2023-04-01T15:40:12+00:00
|
{"language": ["ger"], "license": "other", "multilinguality": "monolingual", "pretty_name": "CARDIO:DE", "bigbio_language": ["German"], "homepage": "https://heidata.uni-heidelberg.de/dataset.xhtml?persistentId=doi:10.11588/data/AFYQDY", "bigbio_pubmed": false, "bigbio_public": false, "bigbio_tasks": ["NAMED_ENTITY_RECOGNITION"]}
|
2023-04-05T00:14:13+00:00
|
95c41b1ec74d08bac12feb769ced791ff39ed069
|
# Dataset Card for BRONCO150
## Dataset Description
- **Homepage:** https://www2.informatik.hu-berlin.de/~leser/bronco/index.html
- **Pubmed:** False
- **Public:** False
- **Tasks:** NER, NED
BRONCO150 is a corpus containing selected sentences of 150 German discharge summaries of cancer patients (hepatocelluar carcinoma or melanoma) treated at Charite Universitaetsmedizin Berlin or Universitaetsklinikum Tuebingen. All discharge summaries were manually anonymized. The original documents were scrambled at the sentence level to make reconstruction of individual reports impossible.
## Citation Information
```
@article{10.1093/jamiaopen/ooab025,
author = {Kittner, Madeleine and Lamping, Mario and Rieke, Damian T and Götze, Julian and Bajwa, Bariya and Jelas, Ivan and Rüter, Gina and Hautow, Hanjo and Sänger, Mario and Habibi, Maryam and Zettwitz, Marit and Bortoli, Till de and Ostermann, Leonie and Ševa, Jurica and Starlinger, Johannes and Kohlbacher, Oliver and Malek, Nisar P and Keilholz, Ulrich and Leser, Ulf},
title = "{Annotation and initial evaluation of a large annotated German oncological corpus}",
journal = {JAMIA Open},
volume = {4},
number = {2},
year = {2021},
month = {04},
issn = {2574-2531},
doi = {10.1093/jamiaopen/ooab025},
url = {https://doi.org/10.1093/jamiaopen/ooab025},
note = {ooab025},
eprint = {https://academic.oup.com/jamiaopen/article-pdf/4/2/ooab025/38830128/ooab025.pdf},
}
```
|
bigbio/bronco
|
[
"multilinguality:monolingual",
"language:de",
"region:us"
] |
2023-04-01T15:46:42+00:00
|
{"language": ["de"], "multilinguality": "monolingual", "pretty_name": "BRONCO150", "bigbio_language": ["German"], "homepage": "https://www2.informatik.hu-berlin.de/~leser/bronco/index.html", "bigbio_pubmed": false, "bigbio_public": false, "bigbio_tasks": ["NAMED_ENTITY_RECOGNITION", "NAMED_ENTITY_DISAMBIGUATION"]}
|
2023-04-01T15:47:31+00:00
|
a379767b3700f7b72ec08ca42892aa9721bd792c
|
# Dataset Card for GGPONC2
## Dataset Description
- **Homepage:** https://www.leitlinienprogramm-onkologie.de/projekte/ggponc-english/
- **Pubmed:** False
- **Public:** False
- **Tasks:** NER
The GGPONC project aims to provide a freely distributable corpus of German medical text for NLP researchers.
Clinical guidelines are particularly suitable to create such corpora, as they contain no protected health information
(PHI), which distinguishes them from other kinds of medical text.
The second version of the corpus (GGPONC 2.0) consists of 30 German oncology guidelines with 1.87 million tokens.
It has been completely manually annotated on the entity level by 7 medical students using the INCEpTION platform over a
time frame of 6 months in more than 1200 hours of work. This makes GGPONC 2.0 the largest annotated, freely
distributable corpus of German medical text at the moment.
Annotated entities are Findings (Diagnosis / Pathology, Other Finding), Substances (Clinical Drug, Nutrients / Body
Substances, External Substances) and Procedures (Therapeutic, Diagnostic), as well as Specifications for these entities.
In total, annotators have created more than 200000 entity annotations. In addition, fragment relationships have been
annotated to explicitly indicate elliptical coordinated noun phrases, a common phenomenon in German text.
## Citation Information
```
@inproceedings{borchert-etal-2022-ggponc,
title = "{GGPONC} 2.0 - The {G}erman Clinical Guideline Corpus for Oncology: Curation Workflow, Annotation Policy, Baseline {NER} Taggers",
author = "Borchert, Florian and
Lohr, Christina and
Modersohn, Luise and
Witt, Jonas and
Langer, Thomas and
Follmann, Markus and
Gietzelt, Matthias and
Arnrich, Bert and
Hahn, Udo and
Schapranow, Matthieu-P.",
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-1.389",
pages = "3650--3660",
}
```
|
bigbio/ggponc2
|
[
"multilinguality:monolingual",
"language:de",
"region:us"
] |
2023-04-01T15:49:04+00:00
|
{"language": ["de"], "multilinguality": "monolingual", "pretty_name": "GGPONC2", "bigbio_language": ["German"], "homepage": "https://www.leitlinienprogramm-onkologie.de/projekte/ggponc-english/", "bigbio_pubmed": false, "bigbio_public": "flase", "bigbio_tasks": ["NAMED_ENTITY_RECOGNITION"]}
|
2023-04-05T00:15:05+00:00
|
97fe579a4a26d862964ca54512432359af8f9d54
|
# Dataset Card for "quran_tafseer"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
M-AI-C/quran_tafseer
|
[
"region:us"
] |
2023-04-01T15:58:10+00:00
|
{"dataset_info": {"features": [{"name": "en-ahmedali", "dtype": "string"}, {"name": "en-ahmedraza", "dtype": "string"}, {"name": "en-arberry", "dtype": "string"}, {"name": "en-asad", "dtype": "string"}, {"name": "en-daryabadi", "dtype": "string"}, {"name": "en-hilali", "dtype": "string"}, {"name": "en-itani", "dtype": "string"}, {"name": "en-maududi", "dtype": "string"}, {"name": "en-mubarakpuri", "dtype": "string"}, {"name": "en-pickthall", "dtype": "string"}, {"name": "en-qarai", "dtype": "string"}, {"name": "en-qaribullah", "dtype": "string"}, {"name": "en-sahih", "dtype": "string"}, {"name": "en-sarwar", "dtype": "string"}, {"name": "en-shakir", "dtype": "string"}, {"name": "en-transliterati", "dtype": "string"}, {"name": "en-wahiduddi", "dtype": "string"}, {"name": "en-yusufali", "dtype": "string"}, {"name": "ayah", "dtype": "int64"}, {"name": "sorah", "dtype": "int64"}, {"name": "sentence", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 17616789, "num_examples": 6235}], "download_size": 9631631, "dataset_size": 17616789}}
|
2023-04-01T16:03:14+00:00
|
5530f6640b3f41edbf6fb01220c71483ace00ad2
|
# Dataset Card for "quran_en_translations"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
M-AI-C/quran_en_translations
|
[
"region:us"
] |
2023-04-01T16:12:39+00:00
|
{"dataset_info": {"features": [{"name": "en-ahmedali", "dtype": "string"}, {"name": "en-ahmedraza", "dtype": "string"}, {"name": "en-arberry", "dtype": "string"}, {"name": "en-asad", "dtype": "string"}, {"name": "en-daryabadi", "dtype": "string"}, {"name": "en-hilali", "dtype": "string"}, {"name": "en-itani", "dtype": "string"}, {"name": "en-maududi", "dtype": "string"}, {"name": "en-mubarakpuri", "dtype": "string"}, {"name": "en-pickthall", "dtype": "string"}, {"name": "en-qarai", "dtype": "string"}, {"name": "en-qaribullah", "dtype": "string"}, {"name": "en-sahih", "dtype": "string"}, {"name": "en-sarwar", "dtype": "string"}, {"name": "en-shakir", "dtype": "string"}, {"name": "en-transliterati", "dtype": "string"}, {"name": "en-wahiduddi", "dtype": "string"}, {"name": "en-yusufali", "dtype": "string"}, {"name": "ayah", "dtype": "int64"}, {"name": "sorah", "dtype": "int64"}, {"name": "sentence", "dtype": "string"}, {"name": "en-tafsir-mokhtasar-html", "dtype": "string"}, {"name": "en-tafsir-mokhtasar-text", "dtype": "string"}, {"name": "en-tafsir-maarif-html", "dtype": "string"}, {"name": "en-tafsir-maarif-text", "dtype": "string"}, {"name": "en-tafsir-ibn-kathir-html", "dtype": "string"}, {"name": "en-tafsir-ibn-kathir-text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 66065359, "num_examples": 6235}], "download_size": 35313084, "dataset_size": 66065359}}
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2023-04-02T13:34:43+00:00
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