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|---|---|---|---|---|---|---|
005edb4aaf741512a4a2c93e5b6758aeab7c8613
|
Phonecharger/Agreements4railroad
|
[
"license:openrail++",
"region:us"
] |
2023-04-30T17:41:33+00:00
|
{"license": "openrail++"}
|
2023-04-30T17:41:33+00:00
|
|
3b060686dc821da895a86ac05198f980894f63fa
|
# Dataset Card for "chunked-wikipedia20220301en-bookcorpusopen"
```
num_examples: 33.5 million
download_size: 15.3 GB
dataset_size: 26.1 GB
```
This dataset combines [wikipedia20220301.en](https://huggingface.co/datasets/wikipedia) and [bookcorpusopen](https://huggingface.co/datasets/bookcorpusopen),
and splits the data into smaller chunks, of size ~820 chars
(such that each item will be at least ~128 tokens for the average tokenizer).
The logic only splits on spaces, so the chunks are likely to be slightly larger than 820 chars.
The dataset has been normalized into lower case, with accents and non-english characters removed.
Items with less than 200 chars or more than 1000 chars have been removed.
The data has not been shuffled (you can either use `dataset.shuffle(...)`,
or download the shuffled version [here](https://huggingface.co/datasets/sradc/chunked-shuffled-wikipedia20220301en-bookcorpusopen),
which will be faster to iterate over).
This dataset is processed for convenience, at the expense of losing some percentage of the tokens due to truncation,
(assuming the training minibatches are truncated to 128 tokens).
|
sradc/chunked-wikipedia20220301en-bookcorpusopen
|
[
"region:us"
] |
2023-04-30T18:18:32+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 26076989556, "num_examples": 33536113}], "download_size": 15221565467, "dataset_size": 26076989556}}
|
2023-05-30T15:52:48+00:00
|
919d81d280bcb5c87ac35b3629938d8468b4b778
|
# Dataset Card for "cool_new_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
AlphaRish/cool_new_dataset
|
[
"region:us"
] |
2023-04-30T18:24:32+00:00
|
{"dataset_info": {"features": [{"name": "name", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "ad", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2576, "num_examples": 5}], "download_size": 6219, "dataset_size": 2576}}
|
2023-04-30T18:24:36+00:00
|
4546f5005a7f79ba7889a9082d84b7a1591f38f9
|
# Dataset Card for "oa_vicuna_dolly_grademath_alpaca_leetcode"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
pvduy/oa_vicuna_dolly_grademath_alpaca_leetcode
|
[
"region:us"
] |
2023-04-30T18:40:26+00:00
|
{"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "label", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 395462299, "num_examples": 176191}], "download_size": 193391047, "dataset_size": 395462299}}
|
2023-05-01T16:15:13+00:00
|
6d7ce63105008e88d91fda29f7d4c55b229982e3
|
WindowsWhistler/the-vehicle-set
|
[
"license:cc-by-3.0",
"region:us"
] |
2023-04-30T19:03:24+00:00
|
{"license": "cc-by-3.0"}
|
2023-04-30T19:03:27+00:00
|
|
b9e18cdaa73ccca111a29ae9d49f3cd8c2b4b151
|
# Dataset Card for "Shadow-Dataset-ControlNet"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Nahrawy/Shadow-Dataset-ControlNet
|
[
"region:us"
] |
2023-04-30T19:21:31+00:00
|
{"dataset_info": {"features": [{"name": "frame", "dtype": "string"}, {"name": "target", "dtype": "image"}, {"name": "shadow", "dtype": "image"}, {"name": "position", "dtype": "string"}, {"name": "heading", "dtype": "string"}, {"name": "direction", "dtype": "string"}, {"name": "elevation", "dtype": "string"}, {"name": "prompt", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2308840037.0, "num_examples": 3000}], "download_size": 2227889206, "dataset_size": 2308840037.0}}
|
2023-04-30T19:42:52+00:00
|
25b3597b12077d5fd2478a81e6620be5ed8f57bc
|
# Dataset Card for "cc_perp_sample_v1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
lowem1/cc_perp_sample_v1
|
[
"region:us"
] |
2023-04-30T19:24:22+00:00
|
{"dataset_info": {"features": [{"name": "title", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "domain", "dtype": "string"}, {"name": "date", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "image_url", "dtype": "string"}, {"name": "length", "dtype": "int64"}, {"name": "hash", "dtype": "string"}, {"name": "mask", "dtype": "string"}, {"name": "filled", "dtype": "string"}, {"name": "edit_distance", "dtype": "int64"}, {"name": "normalized_distance", "dtype": "float64"}, {"name": "adj_distance", "dtype": "float64"}], "splits": [{"name": "train", "num_bytes": 72314835, "num_examples": 21966}], "download_size": 8889118, "dataset_size": 72314835}}
|
2023-05-01T01:37:53+00:00
|
a1e3e006f788eda7e6acde8efe03647b67d43259
|
# Dataset Card for "esg-sentiment"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
TrajanovRisto/esg-sentiment
|
[
"region:us"
] |
2023-04-30T19:28:28+00:00
|
{"dataset_info": {"features": [{"name": "Text", "dtype": "string"}, {"name": "Environmental Negative", "dtype": "int32"}, {"name": "Environmental Neutral", "dtype": "int32"}, {"name": "Environmental Positive", "dtype": "int32"}, {"name": "Governance Negative", "dtype": "int32"}, {"name": "Governance Neutral", "dtype": "int32"}, {"name": "Governance Positive", "dtype": "int32"}, {"name": "Social Negative", "dtype": "int32"}, {"name": "Social Neutral", "dtype": "int32"}, {"name": "Social Positive", "dtype": "int32"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 135470.12812960235, "num_examples": 611}, {"name": "test", "num_bytes": 15076.871870397643, "num_examples": 68}], "download_size": 80141, "dataset_size": 150547.0}}
|
2023-04-30T19:28:31+00:00
|
dd0c3ee1bd64c00c8536c595a79c8974eedf6cc9
|
# Dataset Card for "flower-dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Ambrosiussen/flower-dataset
|
[
"region:us"
] |
2023-04-30T19:28:47+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 11753553.0, "num_examples": 300}], "download_size": 11742671, "dataset_size": 11753553.0}}
|
2023-04-30T20:01:14+00:00
|
4397ea20862c1d6d94a26514d7e4238fb49c1ee4
|
<a href="https://github.com/alxfgh/LLM-Guided-GA/blob/main/SELFIES%20Tokenizer.ipynb">Custom cl100k</a> tokenized version of <a href="https://huggingface.co/datasets/alxfgh/PubChem10M_SELFIES">PubChem10M_SELFIES</a>.
|
alxfgh/PubChem10M_SELFIES_Tokenized
|
[
"size_categories:1M<n<10M",
"source_datasets:PubChem10M",
"chemistry",
"molecules",
"selfies",
"smiles",
"region:us"
] |
2023-04-30T19:51:17+00:00
|
{"size_categories": ["1M<n<10M"], "source_datasets": ["PubChem10M"], "pretty_name": "PubChem10M_Selfies_Tokenized", "tags": ["chemistry", "molecules", "selfies", "smiles"]}
|
2023-04-30T22:59:13+00:00
|
07857e2a97571ab553421ed645ef470bef6fa05c
|
# ChatGPT-Research-Abstracts
This is a dataset created in relation to a bachelor thesis written by Nicolai Thorer Sivesind and Andreas Bentzen Winje. It contains human-produced and machine-generated text samples of scientific research abstracts.
A reformatted version for text-classification is available in the dataset collection [Human-vs-Machine](https://huggingface.co/datasets/NicolaiSivesind/human-vs-machine). In this collection, all samples are split into separate data points for real and generated, and labeled either 0 (human-produced) or 1 (machine-generated).
Specifications:
+ Generated samples are produced using the GPT-3.5 model, _GPT-3.5-turbo-0301_ (Snapshot of the model used in ChatGPT 1st of March, 2023).
+ Target content prompted using title of real abstract
+ Target word count equal to the human-produced abstract
+ Contains 10k data points of each class.
+ Created by Nicolai Thorer Sivesind
More information about production and contents will be added in the end of may 2023.
### Citation
Please use the following citation:
```
@misc {sivesind_2023,
author = { {Nicolai Thorer Sivesind}},
title = { ChatGPT-Generated-Abstracts },
year = 2023,
publisher = { Hugging Face }
}
```
More information about the dataset will be added once the thesis is finished (end of may 2023).
|
NicolaiSivesind/ChatGPT-Research-Abstracts
|
[
"task_categories:text-classification",
"size_categories:10K<n<100k",
"language:en",
"license:cc",
"chatgpt",
"gpt",
"research abstracts",
"region:us"
] |
2023-04-30T20:09:44+00:00
|
{"language": ["en"], "license": "cc", "size_categories": ["10K<n<100k"], "task_categories": ["text-classification"], "pretty_name": "ChatGPT Research Abstracts - Labled text segments produced by humans and ChatGPT", "tags": ["chatgpt", "gpt", "research abstracts"]}
|
2023-05-11T16:00:58+00:00
|
8ea2bd508ce1385bbeac5f5d7dac5376acc8aee5
|
Kasuzu/Laboral_gerencie
|
[
"license:unknown",
"region:us"
] |
2023-04-30T20:11:17+00:00
|
{"license": "unknown"}
|
2023-05-03T20:08:28+00:00
|
|
926a7f5593cef020e269406db6c69c4ead823f84
|
ZeroLynx/aigen
|
[
"license:other",
"region:us"
] |
2023-04-30T20:11:37+00:00
|
{"license": "other"}
|
2023-04-30T20:11:37+00:00
|
|
6a7c2dc061b5e8bab01b1118b0e136dc23b1db4e
|
adeljebali/htt_cli_check
|
[
"license:lgpl-3.0",
"region:us"
] |
2023-04-30T21:22:33+00:00
|
{"license": "lgpl-3.0"}
|
2023-04-30T21:23:34+00:00
|
|
884a773d127d49864ed909a8499b240ceb1040aa
|
# Dataset Card for "webvid-mini-frames"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
gigant/webvid-mini-frames
|
[
"region:us"
] |
2023-04-30T21:37:01+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "prompt", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 843960385.0, "num_examples": 3184}], "download_size": 843331948, "dataset_size": 843960385.0}}
|
2023-04-30T21:49:38+00:00
|
54f5444a3a03219987715c6cf6da1bdaaee95b35
|
489,438 Instructions
|
Dampish/Titan-L_QuickTrain-v4
|
[
"license:cc-by-nc-4.0",
"region:us"
] |
2023-04-30T21:45:25+00:00
|
{"license": "cc-by-nc-4.0"}
|
2023-04-30T21:51:56+00:00
|
7d371c9591154fe51007679e4fc6eacd733e2711
|
# Dataset Card for "Food101_test_google_flan_t5_small_mode_T_SPECIFIC_A_ns_100"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/Food101_test_google_flan_t5_small_mode_T_SPECIFIC_A_ns_100
|
[
"region:us"
] |
2023-04-30T22:07:31+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_clip_tags_ViT_L_14_Attributes_ViT_L_14_descriptors_text_davinci_003_full_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 35548, "num_examples": 100}], "download_size": 0, "dataset_size": 35548}}
|
2023-05-02T02:57:08+00:00
|
f93b0c2d55ff9a2b12fc9d87931e1367e65d48d0
|
Dampish/datatt
|
[
"license:cc-by-nc-4.0",
"region:us"
] |
2023-04-30T22:23:35+00:00
|
{"license": "cc-by-nc-4.0"}
|
2023-04-30T22:23:35+00:00
|
|
cf72c95c803ee2cbd19bc2e2432834e2bebfcdc8
|
Vipulrajput13/SD-TEST
|
[
"license:unknown",
"region:us"
] |
2023-04-30T22:30:29+00:00
|
{"license": "unknown"}
|
2023-04-30T22:30:53+00:00
|
|
68e863bd136d3c568902cb9e55803408dbcf63a9
|
mfidabel/sam-coyo-1k
|
[
"license:mit",
"region:us"
] |
2023-04-30T22:42:33+00:00
|
{"license": "mit", "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "conditioning_image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 914695375.841, "num_examples": 1159}], "download_size": 913350586, "dataset_size": 914695375.841}}
|
2023-05-01T14:32:48+00:00
|
|
1cca663bab6ee67819a5e11e09894a6ba9d9bbfb
|
# Dataset Card for "Shadow-Dataset-ControlNet"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
ParityError/ControlNet-Shadows
|
[
"region:us"
] |
2023-04-30T23:11:30+00:00
|
{"dataset_info": {"features": [{"name": "frame", "dtype": "string"}, {"name": "target", "dtype": "image"}, {"name": "shadow", "dtype": "image"}, {"name": "position", "dtype": "string"}, {"name": "heading", "dtype": "string"}, {"name": "direction", "dtype": "string"}, {"name": "elevation", "dtype": "string"}, {"name": "prompt", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2308840037.0, "num_examples": 3000}], "download_size": 2227889206, "dataset_size": 2308840037.0}}
|
2023-04-30T23:34:42+00:00
|
e9465900c1a6b8121dd9ca00e8153adb6b487b92
|
AmazingGitarAdam/vocaleminbem
|
[
"license:openrail",
"region:us"
] |
2023-05-01T00:13:15+00:00
|
{"license": "openrail"}
|
2023-05-01T00:20:17+00:00
|
|
a80b9d9e6821d158f6f3409c619e6cfbcf819010
|
# Dataset Card for "boolq_fr"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
reaganjlee/boolq_fr
|
[
"region:us"
] |
2023-05-01T00:28:20+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}]}], "dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "passage", "dtype": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "False", "1": "True"}}}}], "splits": [{"name": "train", "num_bytes": 4295148, "num_examples": 9427}, {"name": "validation", "num_bytes": 1485986, "num_examples": 3270}], "download_size": 3536844, "dataset_size": 5781134}}
|
2023-08-18T22:03:09+00:00
|
b43c2f37a04003d628c6661dbb4179b45fc05aff
|
# Dataset Card for "arithmetic_2md_1to1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
sethapun/arithmetic_2md_1to1
|
[
"region:us"
] |
2023-05-01T00:45:54+00:00
|
{"dataset_info": {"features": [{"name": "expression", "dtype": "string"}, {"name": "answer", "dtype": "float64"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "false", "1": "true"}}}}], "splits": [{"name": "train", "num_bytes": 54000, "num_examples": 2000}, {"name": "validation", "num_bytes": 10800, "num_examples": 400}], "download_size": 4984, "dataset_size": 64800}}
|
2023-05-01T00:46:07+00:00
|
88165dae37ea0eb661cc9e09a8b6d38445b09db3
|
# Dataset Card for "arithmetic_2all_1to1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
sethapun/arithmetic_2all_1to1
|
[
"region:us"
] |
2023-05-01T00:47:27+00:00
|
{"dataset_info": {"features": [{"name": "expression", "dtype": "string"}, {"name": "answer", "dtype": "float64"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "false", "1": "true"}}}}], "splits": [{"name": "train", "num_bytes": 54000, "num_examples": 2000}, {"name": "validation", "num_bytes": 10800, "num_examples": 400}], "download_size": 5932, "dataset_size": 64800}}
|
2023-05-01T00:48:07+00:00
|
98d0ef1e389ec9ed73b6922a47c07087dd5a1e78
|
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
Rushali/esca
|
[
"task_categories:feature-extraction",
"size_categories:1K<n<10K",
"license:openrail",
"biology",
"region:us"
] |
2023-05-01T01:13:54+00:00
|
{"license": "openrail", "size_categories": ["1K<n<10K"], "task_categories": ["feature-extraction"], "pretty_name": "ESCA Dataset", "tags": ["biology"]}
|
2023-05-01T01:42:07+00:00
|
85422db9cea545053a13138b00c8ae3ea00641b5
|
miladfa7/Intel-Image-Classification
|
[
"license:other",
"region:us"
] |
2023-05-01T02:34:35+00:00
|
{"license": "other"}
|
2023-05-01T04:00:52+00:00
|
|
3b2cd04ee6f529f560d4847a6e9fd40f0f972dca
|
# Dataset Card for "riffusion-musiccaps-datasets-768"
Converted google/musicCaps to spectograms with audio_to_spectrum with riffusion cli.
Random 7.68 sec for each music in musicCaps.
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Hyeon2/riffusion_musiccaps_datasets_768
|
[
"task_categories:text-to-image",
"size_categories:1K<n<10K",
"language:en",
"license:cc-by-4.0",
"riffusion",
"region:us"
] |
2023-05-01T02:52:46+00:00
|
{"language": ["en"], "license": "cc-by-4.0", "size_categories": ["1K<n<10K"], "task_categories": ["text-to-image"], "pretty_name": "r", "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 453812212.472, "num_examples": 5464}], "download_size": 451327913, "dataset_size": 453812212.472}, "tags": ["riffusion"]}
|
2023-05-03T23:12:40+00:00
|
6f0562e9c1bde3e76916825409459a6edeaaec23
|
# Dataset Card for blognone-20230430
## Dataset Summary
[Blognone](https://www.blognone.com/) posts from January 1, 2020 to April 30, 2023.
## Features
- title: (str)
- author: (str)
- date: (str)
- tags: (list)
- content: (str)
## Licensing Information
Blognone posts are published are licensed under the [Creative Commons Attribution 3.0 Thailand](https://creativecommons.org/licenses/by/3.0/th/deed.en) (CC BY 3.0 TH).
|
Noxturnix/blognone-20230430
|
[
"task_categories:text-generation",
"task_categories:text-classification",
"size_categories:10K<n<100K",
"language:th",
"license:cc-by-3.0",
"region:us"
] |
2023-05-01T03:24:03+00:00
|
{"language": ["th"], "license": "cc-by-3.0", "size_categories": ["10K<n<100K"], "task_categories": ["text-generation", "text-classification"], "dataset_info": {"features": [{"name": "title", "dtype": "string"}, {"name": "author", "dtype": "string"}, {"name": "date", "dtype": "string"}, {"name": "tags", "sequence": "string"}, {"name": "content", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 51748027, "num_examples": 18623}], "download_size": 21759892, "dataset_size": 51748027}}
|
2023-05-05T20:47:56+00:00
|
c8d5151038ed1ea299cd1d05dccf044770f17ba1
|
justquick/pdf12step
|
[
"license:apache-2.0",
"region:us"
] |
2023-05-01T03:49:00+00:00
|
{"license": "apache-2.0"}
|
2023-05-01T03:49:00+00:00
|
|
ba2c1089d912a84487971061274e89a25b3f9951
|
Hikam/PreprocessedReviewDataset
|
[
"license:apache-2.0",
"region:us"
] |
2023-05-01T04:03:31+00:00
|
{"license": "apache-2.0"}
|
2023-05-01T04:03:31+00:00
|
|
bf53e9038f1d5572bc7d5f49d52033e1b1bb00da
|
# Dataset Card for "newAnim_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
ambrosemcduffy/newAnim_dataset
|
[
"region:us"
] |
2023-05-01T04:23:10+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 24444584.0, "num_examples": 53}], "download_size": 24332541, "dataset_size": 24444584.0}}
|
2023-07-09T01:11:46+00:00
|
884ca501c1cf78481916102a05977d799615668f
|
# Dataset Card for "Food101_test_google_flan_t5_small_mode_T_SPECIFIC_A_ns_25250"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/Food101_test_google_flan_t5_small_mode_T_SPECIFIC_A_ns_25250
|
[
"region:us"
] |
2023-05-01T04:27:55+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_clip_tags_ViT_L_14_Attributes_ViT_L_14_descriptors_text_davinci_003_full_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 10964309, "num_examples": 25250}], "download_size": 0, "dataset_size": 10964309}}
|
2023-05-01T04:46:23+00:00
|
31102bd93bd6605876ea960500fba7f6ef0972b6
|
The emo163 dataset comprises approximately 395,000 entries of music emotion labels. Each entry includes three primary columns: Song ID, Playlist ID, and the emotional label of the song. Sourced from the official website of Netease Cloud Music, this dataset offers comprehensive information regarding the emotional annotations of songs. The Song ID serves as a unique identifier for each track, while the Playlist ID denotes the playlist to which the song belongs. The emotional labels assign categorical emotional tags to each song, facilitating in-depth exploration within the field of music emotion analysis for researchers and data scientists. With its substantial scale, the dataset is suitable for constructing emotion analysis models, conducting data mining, and gaining a profound understanding of the relationship between music and emotion.
## Maintenance
```bash
GIT_LFS_SKIP_SMUDGE=1 git clone [email protected]:datasets/monet-joe/emo163
```
## Usage
```python
from datasets import load_dataset
dataset = load_dataset("monet-joe/emo163")
for item in dataset["train"]:
print(item)
for item in dataset["validation"]:
print(item)
for item in dataset["test"]:
print(item)
```
## Mirror
<https://www.modelscope.cn/datasets/monetjoe/emo163>
## Reference
[1] <https://music.163.com/#/discover/playlist>
|
monet-joe/emo163
|
[
"task_categories:audio-classification",
"task_categories:image-classification",
"size_categories:1M<n<10M",
"language:en",
"license:mit",
"music",
"art",
"region:us"
] |
2023-05-01T04:45:31+00:00
|
{"language": ["en"], "license": "mit", "size_categories": ["1M<n<10M"], "task_categories": ["audio-classification", "image-classification"], "pretty_name": "emo163 dataset", "tags": ["music", "art"]}
|
2024-01-12T10:52:20+00:00
|
c3e9fa5588ef4e1a4ef0c73254a3301861d9bb3d
|
# Dataset Card for "paws-wiki"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
sharad/paws-wiki
|
[
"region:us"
] |
2023-05-01T04:56:29+00:00
|
{"dataset_info": {"features": [{"name": "sentence1", "dtype": "string"}, {"name": "sentence2", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 80177815, "num_examples": 344655}, {"name": "test", "num_bytes": 2822062, "num_examples": 12075}], "download_size": 59255186, "dataset_size": 82999877}}
|
2023-05-03T16:36:29+00:00
|
ea83930e0cc747d40fa70f775bc0d193ac9a4364
|
yongchoooon/fire_images
|
[
"task_categories:text-to-image",
"annotations_creators:machine-generated",
"language_creators:other",
"multilinguality:monolingual",
"size_categories:n<1K",
"language:en",
"license:cc-by-nc-sa-4.0",
"region:us"
] |
2023-05-01T05:06:05+00:00
|
{"annotations_creators": ["machine-generated"], "language_creators": ["other"], "language": ["en"], "license": "cc-by-nc-sa-4.0", "multilinguality": ["monolingual"], "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "task_ids": [], "pretty_name": "fire_images", "tags": []}
|
2023-05-02T04:55:42+00:00
|
|
6f72f785b73f2f5852af10cfce5d6bb88399f0f8
|
Hikam22/ReviewDataset
|
[
"license:apache-2.0",
"region:us"
] |
2023-05-01T05:14:15+00:00
|
{"license": "apache-2.0"}
|
2023-05-01T05:19:43+00:00
|
|
0404d6876fa6ec8a116b911df831a1daee4d4762
|
tayamaken/DashaStyle
|
[
"license:unknown",
"region:us"
] |
2023-05-01T05:19:55+00:00
|
{"license": "unknown"}
|
2023-06-12T18:37:39+00:00
|
|
a03dc45955019159cad3156c98860855fdb11150
|
# Dataset Card for "medmcqa-rule-neg"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
joey234/medmcqa-rule-neg
|
[
"region:us"
] |
2023-05-01T05:41:15+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "opa", "dtype": "string"}, {"name": "opb", "dtype": "string"}, {"name": "opc", "dtype": "string"}, {"name": "opd", "dtype": "string"}, {"name": "cop", "dtype": {"class_label": {"names": {"0": "a", "1": "b", "2": "c", "3": "d"}}}}, {"name": "choice_type", "dtype": "string"}, {"name": "exp", "dtype": "string"}, {"name": "subject_name", "dtype": "string"}, {"name": "topic_name", "dtype": "string"}, {"name": "question", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 1417364, "num_examples": 6150}, {"name": "validation", "num_bytes": 2233369, "num_examples": 4183}], "download_size": 2422050, "dataset_size": 3650733}}
|
2023-05-03T00:41:55+00:00
|
bf03871585d431fe16484ce45d5b55120358410c
|
# Dataset Card for "arithmetic_2all_1to250"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
sethapun/arithmetic_2all_1to250
|
[
"region:us"
] |
2023-05-01T05:50:44+00:00
|
{"dataset_info": {"features": [{"name": "expression", "dtype": "string"}, {"name": "answer", "dtype": "float64"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "false", "1": "true"}}}}], "splits": [{"name": "train", "num_bytes": 60348, "num_examples": 2000}, {"name": "validation", "num_bytes": 12054, "num_examples": 400}], "download_size": 30282, "dataset_size": 72402}}
|
2023-05-01T05:50:47+00:00
|
f93389e0b7ad5043bd73f3b01606a9f74c5c6e28
|
# Dataset Card for "arithmetic_2all_1to750"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
sethapun/arithmetic_2all_1to750
|
[
"region:us"
] |
2023-05-01T05:50:48+00:00
|
{"dataset_info": {"features": [{"name": "expression", "dtype": "string"}, {"name": "answer", "dtype": "float64"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "false", "1": "true"}}}}], "splits": [{"name": "train", "num_bytes": 61408, "num_examples": 2000}, {"name": "validation", "num_bytes": 12266, "num_examples": 400}], "download_size": 33411, "dataset_size": 73674}}
|
2023-05-01T05:50:51+00:00
|
80d4dd4b58b63ab11471218d19630ad7d00f7fa1
|
# Dataset Card for "arithmetic_2as_1to250"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
sethapun/arithmetic_2as_1to250
|
[
"region:us"
] |
2023-05-01T05:52:00+00:00
|
{"dataset_info": {"features": [{"name": "expression", "dtype": "string"}, {"name": "answer", "dtype": "int64"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "false", "1": "true"}}}}], "splits": [{"name": "train", "num_bytes": 60140, "num_examples": 2000}, {"name": "validation", "num_bytes": 12060, "num_examples": 400}], "download_size": 23655, "dataset_size": 72200}}
|
2023-05-01T05:52:05+00:00
|
8abd24999be49431b92ce467c38b644b4a186a5f
|
# Dataset Card for "arithmetic_2as_1to750"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
sethapun/arithmetic_2as_1to750
|
[
"region:us"
] |
2023-05-01T05:52:05+00:00
|
{"dataset_info": {"features": [{"name": "expression", "dtype": "string"}, {"name": "answer", "dtype": "int64"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "false", "1": "true"}}}}], "splits": [{"name": "train", "num_bytes": 61432, "num_examples": 2000}, {"name": "validation", "num_bytes": 12264, "num_examples": 400}], "download_size": 27596, "dataset_size": 73696}}
|
2023-05-01T05:52:08+00:00
|
193eaf6908a6382000d9ecf685843c5853393d5a
|
# Dataset Card for "arithmetic_2md_1to250"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
sethapun/arithmetic_2md_1to250
|
[
"region:us"
] |
2023-05-01T05:52:54+00:00
|
{"dataset_info": {"features": [{"name": "expression", "dtype": "string"}, {"name": "answer", "dtype": "float64"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "false", "1": "true"}}}}], "splits": [{"name": "train", "num_bytes": 60236, "num_examples": 2000}, {"name": "validation", "num_bytes": 11988, "num_examples": 400}], "download_size": 32920, "dataset_size": 72224}}
|
2023-05-01T05:52:58+00:00
|
ebdb39e743667a49c0fcd974a0c8ffe8e7445ae6
|
# Dataset Card for "arithmetic_2md_1to750"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
sethapun/arithmetic_2md_1to750
|
[
"region:us"
] |
2023-05-01T05:52:58+00:00
|
{"dataset_info": {"features": [{"name": "expression", "dtype": "string"}, {"name": "answer", "dtype": "float64"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "false", "1": "true"}}}}], "splits": [{"name": "train", "num_bytes": 61402, "num_examples": 2000}, {"name": "validation", "num_bytes": 12284, "num_examples": 400}], "download_size": 35644, "dataset_size": 73686}}
|
2023-05-01T05:53:01+00:00
|
5da273dd1f65c0268a08530fa1eab2ae89ba98a6
|
rnpatien/ddpm-butterflies-128
|
[
"license:openrail",
"region:us"
] |
2023-05-01T05:57:55+00:00
|
{"license": "openrail"}
|
2023-05-01T05:57:55+00:00
|
|
3d8c5e08335d2af56e1067e2c98d5203ee512e47
|
# Dataset Card for "chai-davinci-chatml"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
AlekseyKorshuk/chai-davinci-chatml
|
[
"region:us"
] |
2023-05-01T06:01:31+00:00
|
{"dataset_info": {"features": [{"name": "conversation", "list": [{"name": "content", "dtype": "string"}, {"name": "do_train", "dtype": "bool"}, {"name": "role", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 335276416, "num_examples": 75549}], "download_size": 0, "dataset_size": 335276416}}
|
2023-06-12T19:44:55+00:00
|
b8142da2ca361ec977fbc28f5b893690745010f6
|
# Dataset Card for "oge_prob"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
batalovme/oge_prob
|
[
"region:us"
] |
2023-05-01T06:08:10+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "solution", "dtype": "string"}, {"name": "answer", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 48579.0, "num_examples": 61}, {"name": "validation", "num_bytes": 48579.0, "num_examples": 61}], "download_size": 38527, "dataset_size": 97158.0}}
|
2023-05-01T06:08:17+00:00
|
7c1a76c2c1864abf434453e312f034d0cf211fff
|
linkanjarad/Wikitext-TL39
|
[
"task_categories:text-generation",
"size_categories:1M<n<10M",
"language:tl",
"region:us"
] |
2023-05-01T06:28:15+00:00
|
{"language": ["tl"], "size_categories": ["1M<n<10M"], "task_categories": ["text-generation"], "pretty_name": "Wikitext TL39"}
|
2023-05-01T07:41:45+00:00
|
|
ff3582df7399aa2d2d5491c6ae6786e8f0b1b7ee
|
# Dataset Card for "music-berkeley-emotions"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
akhmedsakip/music-berkeley-emotions
|
[
"region:us"
] |
2023-05-01T07:02:17+00:00
|
{"dataset_info": {"features": [{"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}, {"name": "label", "dtype": {"class_label": {"names": {"0": "L", "1": "A", "2": "K", "3": "D", "4": "C", "5": "H", "6": "F", "7": "E", "8": "J", "9": "G", "10": "M", "11": "B", "12": "I"}}}}], "splits": [{"name": "train", "num_bytes": 225090972.57043225, "num_examples": 1392}, {"name": "test", "num_bytes": 39838241.765567765, "num_examples": 246}], "download_size": 263271905, "dataset_size": 264929214.33600003}}
|
2023-05-01T08:13:21+00:00
|
e89476888a1cc36eba85af623321853c2760ee5a
|
WilliamWen/ner_mat
|
[
"task_categories:token-classification",
"language:en",
"license:apache-2.0",
"region:us"
] |
2023-05-01T07:11:29+00:00
|
{"language": ["en"], "license": "apache-2.0", "task_categories": ["token-classification"]}
|
2023-05-02T14:47:10+00:00
|
|
309bbc6e849df8dd885dac2ec1aea53134317a68
|
# 📝 BUOD Article Scraper
Authors: [James Esguerra](https://huggingface.co/jamesesguerra), [Julia Avila](), [Hazielle Bugayong](https://huggingface.co/0xhaz)
- Article Scraper for the KAMI-3000 dataset used in the BUOD [distilBART](https://huggingface.co/ateneoscsl/BUOD_distilBART_TM) and [bert2bert](https://huggingface.co/ateneoscsl/BUOD_bert2bert_TM) Transformer Models. This was also used for the text summarization tasks in the Filipino Language.
### Setup
1. Clone the repository.
```sh
# https
git clone https://github.com/avila-bugayong-esguerra/article-scraper.git
# or
# ssh
git clone [email protected]:avila-bugayong-esguerra/article-scraper.git
```
2. Change directory into project folder.
```sh
cd article_scraper
```
3. Create a virtual environment.
```sh
python -m venv venv
```
4. Activate the virtual environment.
```sh
# windows
\venv\Scripts\activate
# unix
source venv/bin/activate
```
5. Install the dependencies.
```sh
pip install -r article_scraper/requirements.txt
```
6. Change directory into the Scrapy project.
```sh
cd article_scraper
```
|
ateneoscsl/BUOD_articlescraper
|
[
"task_categories:summarization",
"language:tl",
"language:en",
"region:us"
] |
2023-05-01T07:28:14+00:00
|
{"language": ["tl", "en"], "task_categories": ["summarization"]}
|
2023-05-01T07:48:09+00:00
|
16d80daa00cca330347839286d4454fc094bbf60
|
# Dataset Card for "mongolian-ner"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Blgn94/mongolian-ner
|
[
"region:us"
] |
2023-05-01T07:29:42+00:00
|
{"dataset_info": {"features": [{"name": "tokens", "sequence": "string"}, {"name": "ner_tags", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 4006355, "num_examples": 10162}], "download_size": 1026335, "dataset_size": 4006355}}
|
2023-05-01T07:29:43+00:00
|
b4e5f32d4b58aebdcb6822924c09a38198ae5af8
|
# Dataset Card for "wine_reviews_all_text"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
james-burton/wine_reviews_all_text
|
[
"region:us"
] |
2023-05-01T07:38:55+00:00
|
{"dataset_info": {"features": [{"name": "country", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "points", "dtype": "string"}, {"name": "price", "dtype": "string"}, {"name": "province", "dtype": "string"}, {"name": "variety", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 20853718, "num_examples": 71504}, {"name": "validation", "num_bytes": 3678893, "num_examples": 12619}, {"name": "test", "num_bytes": 6134091, "num_examples": 21031}], "download_size": 0, "dataset_size": 30666702}}
|
2023-05-11T10:21:11+00:00
|
13482b90d55c482ca825d21f70de16300c2af552
|
# Dataset Card for "fake_job_postings2_all_text"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
james-burton/fake_job_postings2_all_text
|
[
"region:us"
] |
2023-05-01T07:39:06+00:00
|
{"dataset_info": {"features": [{"name": "title", "dtype": "string"}, {"name": "salary_range", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "required_experience", "dtype": "string"}, {"name": "required_education", "dtype": "string"}, {"name": "fraudulent", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 14698550, "num_examples": 10816}, {"name": "validation", "num_bytes": 2500568, "num_examples": 1909}, {"name": "test", "num_bytes": 4379198, "num_examples": 3182}], "download_size": 0, "dataset_size": 21578316}}
|
2023-05-02T14:59:18+00:00
|
6fc59bac42bc44d2a06c015fdf3bc417c31da95d
|
# Dataset Card for "product_sentiment_machine_hack_all_text"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
james-burton/product_sentiment_machine_hack_all_text
|
[
"region:us"
] |
2023-05-01T07:39:19+00:00
|
{"dataset_info": {"features": [{"name": "Product_Description", "dtype": "string"}, {"name": "Product_Type", "dtype": "string"}, {"name": "Sentiment", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 526902, "num_examples": 4327}, {"name": "validation", "num_bytes": 92808, "num_examples": 764}, {"name": "test", "num_bytes": 155969, "num_examples": 1273}], "download_size": 0, "dataset_size": 775679}}
|
2023-05-02T14:59:37+00:00
|
f2e01d5dcbe86010c1edfdfdede2a8a28bd6a8e0
|
# Dataset Card for "kick_starter_funding_all_text"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
james-burton/kick_starter_funding_all_text
|
[
"region:us"
] |
2023-05-01T07:39:33+00:00
|
{"dataset_info": {"features": [{"name": "name", "dtype": "string"}, {"name": "desc", "dtype": "string"}, {"name": "goal", "dtype": "string"}, {"name": "keywords", "dtype": "string"}, {"name": "disable_communication", "dtype": "string"}, {"name": "country", "dtype": "string"}, {"name": "currency", "dtype": "string"}, {"name": "deadline", "dtype": "string"}, {"name": "created_at", "dtype": "string"}, {"name": "final_status", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 21884995, "num_examples": 73526}, {"name": "validation", "num_bytes": 3869495, "num_examples": 12976}, {"name": "test", "num_bytes": 6434631, "num_examples": 21626}], "download_size": 0, "dataset_size": 32189121}}
|
2023-05-02T14:59:46+00:00
|
1c635f49a6f4386cd0dfc895e024af2a2e07db9e
|
# Dataset Card for "jigsaw_unintended_bias100K_all_text"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
james-burton/jigsaw_unintended_bias100K_all_text
|
[
"region:us"
] |
2023-05-01T07:39:52+00:00
|
{"dataset_info": {"features": [{"name": "comment_text", "dtype": "string"}, {"name": "asian", "dtype": "string"}, {"name": "atheist", "dtype": "string"}, {"name": "bisexual", "dtype": "string"}, {"name": "black", "dtype": "string"}, {"name": "buddhist", "dtype": "string"}, {"name": "christian", "dtype": "string"}, {"name": "female", "dtype": "string"}, {"name": "heterosexual", "dtype": "string"}, {"name": "hindu", "dtype": "string"}, {"name": "homosexual_gay_or_lesbian", "dtype": "string"}, {"name": "intellectual_or_learning_disability", "dtype": "string"}, {"name": "jewish", "dtype": "string"}, {"name": "latino", "dtype": "string"}, {"name": "male", "dtype": "string"}, {"name": "muslim", "dtype": "string"}, {"name": "other_disability", "dtype": "string"}, {"name": "other_gender", "dtype": "string"}, {"name": "other_race_or_ethnicity", "dtype": "string"}, {"name": "other_religion", "dtype": "string"}, {"name": "other_sexual_orientation", "dtype": "string"}, {"name": "physical_disability", "dtype": "string"}, {"name": "psychiatric_or_mental_illness", "dtype": "string"}, {"name": "transgender", "dtype": "string"}, {"name": "white", "dtype": "string"}, {"name": "funny", "dtype": "string"}, {"name": "wow", "dtype": "string"}, {"name": "sad", "dtype": "string"}, {"name": "likes", "dtype": "string"}, {"name": "disagree", "dtype": "string"}, {"name": "target", "dtype": "int64"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 43474162, "num_examples": 85000}, {"name": "validation", "num_bytes": 7667244, "num_examples": 15000}, {"name": "test", "num_bytes": 12792522, "num_examples": 25000}], "download_size": 0, "dataset_size": 63933928}}
|
2023-05-02T14:59:59+00:00
|
3641d02bdc29ac7e108f439158a21802e2166d27
|
# Dataset Card for "GPTTokenizer_THUCNews_10000_to_lm_datasets"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
oyxy2019/GPTTokenizer_THUCNews_10000_to_lm_datasets
|
[
"region:us"
] |
2023-05-01T07:44:10+00:00
|
{"dataset_info": {"features": [{"name": "input_ids", "sequence": "int32"}, {"name": "attention_mask", "sequence": "int8"}, {"name": "labels", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 134080000, "num_examples": 80000}, {"name": "validation", "num_bytes": 13408000, "num_examples": 8000}, {"name": "test", "num_bytes": 1340800, "num_examples": 800}], "download_size": 24032981, "dataset_size": 148828800}}
|
2023-05-01T13:59:39+00:00
|
938f8b9465f8fb1fe700c9f5c2c41581d78e3faf
|
# Dataset Card for "wine_reviews_ordinal"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
james-burton/wine_reviews_ordinal
|
[
"region:us"
] |
2023-05-01T07:53:41+00:00
|
{"dataset_info": {"features": [{"name": "country", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "points", "dtype": "int64"}, {"name": "price", "dtype": "float64"}, {"name": "province", "dtype": "string"}, {"name": "variety", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 21009429, "num_examples": 71504}, {"name": "validation", "num_bytes": 3706451, "num_examples": 12619}, {"name": "test", "num_bytes": 6180000, "num_examples": 21031}], "download_size": 0, "dataset_size": 30895880}}
|
2023-05-11T10:21:14+00:00
|
a40fe1e3f77ec45b3cae24d1e6e158a56dec8060
|
# Dataset Card for "jigsaw_unintended_bias100K_ordinal"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
james-burton/jigsaw_unintended_bias100K_ordinal
|
[
"region:us"
] |
2023-05-01T07:54:23+00:00
|
{"dataset_info": {"features": [{"name": "comment_text", "dtype": "string"}, {"name": "asian", "dtype": "float64"}, {"name": "atheist", "dtype": "float64"}, {"name": "bisexual", "dtype": "float64"}, {"name": "black", "dtype": "float64"}, {"name": "buddhist", "dtype": "float64"}, {"name": "christian", "dtype": "float64"}, {"name": "female", "dtype": "float64"}, {"name": "heterosexual", "dtype": "float64"}, {"name": "hindu", "dtype": "float64"}, {"name": "homosexual_gay_or_lesbian", "dtype": "float64"}, {"name": "intellectual_or_learning_disability", "dtype": "float64"}, {"name": "jewish", "dtype": "float64"}, {"name": "latino", "dtype": "float64"}, {"name": "male", "dtype": "float64"}, {"name": "muslim", "dtype": "float64"}, {"name": "other_disability", "dtype": "float64"}, {"name": "other_gender", "dtype": "float64"}, {"name": "other_race_or_ethnicity", "dtype": "float64"}, {"name": "other_religion", "dtype": "float64"}, {"name": "other_sexual_orientation", "dtype": "float64"}, {"name": "physical_disability", "dtype": "float64"}, {"name": "psychiatric_or_mental_illness", "dtype": "float64"}, {"name": "transgender", "dtype": "float64"}, {"name": "white", "dtype": "float64"}, {"name": "funny", "dtype": "int64"}, {"name": "wow", "dtype": "int64"}, {"name": "sad", "dtype": "int64"}, {"name": "likes", "dtype": "int64"}, {"name": "disagree", "dtype": "int64"}, {"name": "target", "dtype": "int64"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 46984927, "num_examples": 85000}, {"name": "validation", "num_bytes": 8285559, "num_examples": 15000}, {"name": "test", "num_bytes": 13825536, "num_examples": 25000}], "download_size": 0, "dataset_size": 69096022}}
|
2023-05-02T15:00:05+00:00
|
416c3894366080659293f50c59f471fdeedc6721
|
# Dataset Card for "imdb_genre_prediction_ordinal"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
james-burton/imdb_genre_prediction_ordinal
|
[
"region:us"
] |
2023-05-01T07:56:05+00:00
|
{"dataset_info": {"features": [{"name": "Rank", "dtype": "int64"}, {"name": "Title", "dtype": "string"}, {"name": "Description", "dtype": "string"}, {"name": "Director", "dtype": "string"}, {"name": "Actors", "dtype": "string"}, {"name": "Year", "dtype": "int64"}, {"name": "Runtime (Minutes)", "dtype": "int64"}, {"name": "Rating", "dtype": "float64"}, {"name": "Votes", "dtype": "int64"}, {"name": "Revenue (Millions)", "dtype": "float64"}, {"name": "Metascore", "dtype": "float64"}, {"name": "Genre_is_Drama", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 224587, "num_examples": 680}, {"name": "validation", "num_bytes": 39612, "num_examples": 120}, {"name": "test", "num_bytes": 65442, "num_examples": 200}], "download_size": 0, "dataset_size": 329641}}
|
2023-05-02T15:00:14+00:00
|
670f62c8e43402eff0e6b0dd0e6e14c1a732b116
|
# Dataset Card for "ms-marco-es"
QA asymmetric Spanish dataset filtered from [multilingual version of MS Marco](https://huggingface.co/datasets/unicamp-dl/mmarco)
```python
import datasets
ms_marco_es = datasets.load_dataset('unicamp-dl/mmarco', name='spanish', split='train')
ms_marco_es.push_to_hub("dariolopez/ms-marco-es", token=os.environ['hg_token'])
```
|
dariolopez/ms-marco-es
|
[
"task_categories:question-answering",
"size_categories:10M<n<100M",
"language:es",
"license:apache-2.0",
"region:us"
] |
2023-05-01T08:35:57+00:00
|
{"language": ["es"], "license": "apache-2.0", "size_categories": ["10M<n<100M"], "task_categories": ["question-answering"], "dataset_info": {"features": [{"name": "query", "dtype": "string"}, {"name": "positive", "dtype": "string"}, {"name": "negative", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 34534407690, "num_examples": 39780811}], "download_size": 13523306019, "dataset_size": 34534407690}}
|
2023-05-01T15:11:29+00:00
|
747b1b6981292cc909ba992a2506dd4e60a9569d
|
# Dataset Card for "kdd2023-it"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
WahtsMyName/kdd2023-it
|
[
"region:us"
] |
2023-05-01T08:38:53+00:00
|
{"dataset_info": {"features": [{"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 193321252, "num_examples": 126925}], "download_size": 87432578, "dataset_size": 193321252}}
|
2023-05-01T08:41:36+00:00
|
88af80dc8bae3bda39767aa7884ebee2cdd1e39c
|
# Dataset Card for "oasst1_en_code"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
philschmid/oasst1_en_code
|
[
"region:us"
] |
2023-05-01T08:45:22+00:00
|
{"dataset_info": {"features": [{"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 3183667, "num_examples": 1387}], "download_size": 1315559, "dataset_size": 3183667}}
|
2023-05-01T08:47:25+00:00
|
b851b6972dfb68587b263c8718fdab270d82b503
|
# Dataset Card for "kdd2023-ES"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
WahtsMyName/kdd2023-ES
|
[
"region:us"
] |
2023-05-01T08:47:16+00:00
|
{"dataset_info": {"features": [{"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 142107497, "num_examples": 89047}], "download_size": 63013011, "dataset_size": 142107497}}
|
2023-05-01T08:48:04+00:00
|
4b31fd766bbf9ec6f165bf5eddd3aae0046a6c6a
|
# Dataset Card for "kdd2023-FR"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
WahtsMyName/kdd2023-FR
|
[
"region:us"
] |
2023-05-01T08:48:10+00:00
|
{"dataset_info": {"features": [{"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 181342267, "num_examples": 117561}], "download_size": 82064276, "dataset_size": 181342267}}
|
2023-05-01T08:48:46+00:00
|
92edb12dec0ba7aa204c3e649420e9e9a7243faa
|
# Dataset Card for "time"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
polinaeterna/time
|
[
"region:us"
] |
2023-05-01T09:18:54+00:00
|
{"dataset_info": {"features": [{"name": "time", "dtype": "timestamp[us]"}], "splits": [{"name": "train", "num_bytes": 48, "num_examples": 6}], "download_size": 897, "dataset_size": 48}, "builder_config": {"data_files": [{"split": "train", "pattern": "data/train-*"}]}}
|
2023-05-01T09:18:57+00:00
|
7d87d7bb78e6e8227342081501ad42972d14c588
|
This dataset is machine-translated version of [databricks-dolly-15k.jsonl](https://github.com/databrickslabs/dolly/tree/master/data) into Turkish.
Used `googletrans==3.1.0a0` to translation.
|
atasoglu/databricks-dolly-15k-tr
|
[
"task_categories:question-answering",
"size_categories:10K<n<100K",
"language:tr",
"license:cc-by-sa-3.0",
"region:us"
] |
2023-05-01T09:22:31+00:00
|
{"language": ["tr"], "license": "cc-by-sa-3.0", "size_categories": ["10K<n<100K"], "task_categories": ["question-answering"], "pretty_name": "databricks-dolly-15k-tr"}
|
2023-05-01T09:30:39+00:00
|
a6ccb4954e31c53bb4f36ebafd7e756e7a3abf02
|
This is the version 1.1.0 of the original PET dataset.
in this version we fixed ``the Performs Relations'' and few minor errors.
Please refer to the original [PET Dataset repository](https://huggingface.co/datasets/patriziobellan/PET) for more info.
|
patriziobellan/PETv11
|
[
"region:us"
] |
2023-05-01T09:30:40+00:00
|
{"dataset_info": {"features": [{"name": "document name", "dtype": "string"}, {"name": "tokens", "sequence": "string"}, {"name": "tokens-IDs", "sequence": "int8"}, {"name": "ner_tags", "sequence": "string"}, {"name": "sentence-IDs", "sequence": "int8"}, {"name": "relations", "sequence": [{"name": "source-head-sentence-ID", "dtype": "int8"}, {"name": "source-head-word-ID", "dtype": "int8"}, {"name": "relation-type", "dtype": "string"}, {"name": "target-head-sentence-ID", "dtype": "int8"}, {"name": "target-head-word-ID", "dtype": "int8"}]}], "splits": [{"name": "test", "num_bytes": 203379, "num_examples": 45}], "download_size": 38326, "dataset_size": 203379}}
|
2023-05-01T09:38:03+00:00
|
42317f5076412d41f89d14e732c208026a1f50d0
|
# Dataset Card for "merged_preprocessed_parliament_commonvoice"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
jkot/merged_preprocessed_parliament_commonvoice
|
[
"region:us"
] |
2023-05-01T09:37:03+00:00
|
{"dataset_info": {"features": [{"name": "input_features", "sequence": {"sequence": "float32"}}, {"name": "labels", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 210499135424, "num_examples": 219101}, {"name": "test", "num_bytes": 11099630080, "num_examples": 11555}], "download_size": 65027813279, "dataset_size": 221598765504}}
|
2023-05-01T12:35:28+00:00
|
ed48b9fa949f5a7452ad138ad24926974d767f2d
|
# Fork of [b-mc2/sql-create-context](https://huggingface.co/datasets/b-mc2/sql-create-context)
#### Overview
This dataset builds from [WikiSQL](https://huggingface.co/datasets/wikisql) and [Spider](https://huggingface.co/datasets/spider).
There are 78,577 examples of natural language queries, SQL CREATE TABLE statements, and SQL Query answering the question using the CREATE statement as context. This dataset was built with text-to-sql LLMs in mind, intending to prevent hallucination of column and table names often seen when trained on text-to-sql datasets. The CREATE TABLE statement can often be copy and pasted from different DBMS and provides table names, column names and their data types. By providing just the CREATE TABLE statement as context, we can hopefully provide better grounding for models without having to provide actual rows of data, limiting token usage and exposure to private, sensitive, or proprietary data.
#### Cleansing and Augmentation
Cleansing and data augmentation has been done on the combined WikiSQL and Spider data. I used [SQLGlot](https://github.com/tobymao/sqlglot) on queries from Spider and WikiSQL and parsed them into different tables and columns, I then inferred column data types based on usage of `>` `<` operators as well as the use of `MIN()` `MAX()` `AVG()` `SUM()` on columns. While this isn't perfect, it increases the likelihood of inferring the correct datatype for a column, the columns otherwise default to VARCHAR type. These tables and columns are then used to generate CREATE TABLE statements using the inferred types. SQLGlot is used again to ensure both the SQL queries and CREATE TABLE statements parse without errors.
Some queries that do not have column names, e.g. SELECT * FROM table, have a default Id column added to the CREATE TABLE statement. Some other queries which use the generic `table` as the FROM table have instead been changed to a variation of `table_name_1` or some other number which is also reflected in the CREATE TABLE statement.
#### TODO
- Further augment the data by converting queries and CREATE TABLE statements into different SQL dialects, this can be done with SQLGlot. Reference to the dialect might also be added to the question.
- Support other informative contexts beyond CREATE TABLE
Random sample:
```json
{
"question": "Please show the themes of competitions with host cities having populations larger than 1000.",
"context": "CREATE TABLE city (City_ID VARCHAR, Population INTEGER); CREATE TABLE farm_competition (Theme VARCHAR, Host_city_ID VARCHAR)",
"answer": "SELECT T2.Theme FROM city AS T1 JOIN farm_competition AS T2 ON T1.City_ID = T2.Host_city_ID WHERE T1.Population > 1000"
},
{
"question": "Please show the different statuses of cities and the average population of cities with each status.",
"context": "CREATE TABLE city (Status VARCHAR, Population INTEGER)",
"answer": "SELECT Status, AVG(Population) FROM city GROUP BY Status"
},
```
|
philschmid/sql-create-context-copy
|
[
"task_categories:text-generation",
"task_categories:question-answering",
"task_categories:table-question-answering",
"size_categories:10K<n<100K",
"language:en",
"license:cc-by-4.0",
"SQL",
"code",
"NLP",
"text-to-sql",
"context-sql",
"spider",
"wikisql",
"sqlglot",
"region:us"
] |
2023-05-01T09:37:03+00:00
|
{"language": ["en"], "license": "cc-by-4.0", "size_categories": ["10K<n<100K"], "task_categories": ["text-generation", "question-answering", "table-question-answering"], "pretty_name": "sql-create-context", "tags": ["SQL", "code", "NLP", "text-to-sql", "context-sql", "spider", "wikisql", "sqlglot"], "duplicated_from": "b-mc2/sql-create-context"}
|
2023-05-01T09:37:47+00:00
|
255b17481b8c39ce68e55e2857825f8ded381d93
|
# Dataset Card for "ravdess-singing-emotions"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
akhmedsakip/ravdess-singing-emotions
|
[
"region:us"
] |
2023-05-01T09:48:18+00:00
|
{"dataset_info": {"features": [{"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}, {"name": "label", "dtype": {"class_label": {"names": {"0": "fearful", "1": "neutral", "2": "calm", "3": "happy", "4": "sad", "5": "angry"}}}}], "splits": [{"name": "train", "num_bytes": 120236438.52470355, "num_examples": 809}, {"name": "test", "num_bytes": 30381090.47529644, "num_examples": 203}], "download_size": 115056541, "dataset_size": 150617529.0}}
|
2023-05-01T09:53:15+00:00
|
973e100112a4b4ccf4c48fa0c52d607c90db75a6
|
mfidabel/sam-coyo-2k
|
[
"license:mit",
"region:us"
] |
2023-05-01T09:49:30+00:00
|
{"license": "mit", "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "conditioning_image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1717753206.88, "num_examples": 2240}], "download_size": 1815819421, "dataset_size": 1717753206.88}}
|
2023-05-01T14:22:55+00:00
|
|
bc934e8a30c91fb60f1b9b600a37ed3abf6e2a41
|
# Dataset Card for "hh_shp_oa_gpt4_rm_dataset_vicuna_formatoa"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
pvduy/hh_shp_oa_gpt4_rm_dataset_vicuna_formatoa
|
[
"region:us"
] |
2023-05-01T10:43:42+00:00
|
{"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "chosen", "dtype": "string"}, {"name": "rejected", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 455500395, "num_examples": 281457}, {"name": "test", "num_bytes": 34927691, "num_examples": 21643}], "download_size": 227074554, "dataset_size": 490428086}}
|
2023-05-01T10:44:14+00:00
|
0561de173eee10a7dc15831e2bde047732f83ee9
|
Abunga/misaka
|
[
"region:us"
] |
2023-05-01T11:14:13+00:00
|
{}
|
2023-05-01T11:15:26+00:00
|
|
9022de7da8177dffc435a998296b3d9ed7e4bfea
|
# Dataset Card for "chip2_en_code"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
philschmid/chip2_en_code
|
[
"region:us"
] |
2023-05-01T11:28:08+00:00
|
{"dataset_info": {"features": [{"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 1750565, "num_examples": 3300}], "download_size": 517363, "dataset_size": 1750565}}
|
2023-05-01T11:30:50+00:00
|
fc4eddc7c834ffe58ef8f937cdf0d5fe93dd8ffb
|
# Dataset Card for "chip2_oasst1_en_code"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
philschmid/chip2_oasst1_en_code
|
[
"region:us"
] |
2023-05-01T11:31:10+00:00
|
{"dataset_info": {"features": [{"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 4934232, "num_examples": 4687}], "download_size": 1866641, "dataset_size": 4934232}}
|
2023-05-01T11:31:13+00:00
|
7e33e674727b2ad939e887f0d2a84da18fdddd09
|
Generated from https://github.com/ironSource/parquetjs
|
elonmuskceo/parquet-fruits
|
[
"license:apache-2.0",
"region:us"
] |
2023-05-01T11:32:59+00:00
|
{"license": "apache-2.0"}
|
2023-05-01T11:49:44+00:00
|
62b3730b6c0c1e4c648103723f8f7fb81ad4707f
|
# Dataset Card for JGLUE
[](https://aclanthology.org/2022.lrec-1.317)
書籍『大規模言語モデル入門』で使用する、JGLUEのデータセットです。
[オリジナルのリポジトリ](https://github.com/yahoojapan/JGLUE)で公開されているデータセットを利用しています。
### Licence
コードのライセンスは Creative Commons Attribution-ShareAlike 4.0 International License です。
データそのもののライセンスは[配布元](https://github.com/yahoojapan/JGLUE)のライセンスに従ってください。
### Citation
```bibtex
@inproceedings{kurihara-etal-2022-jglue,
title = "{JGLUE}: {J}apanese General Language Understanding Evaluation",
author = "Kurihara, Kentaro and
Kawahara, Daisuke and
Shibata, Tomohide",
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-1.317",
pages = "2957--2966",
abstract = "To develop high-performance natural language understanding (NLU) models, it is necessary to have a benchmark to evaluate and analyze NLU ability from various perspectives. While the English NLU benchmark, GLUE, has been the forerunner, benchmarks are now being released for languages other than English, such as CLUE for Chinese and FLUE for French; but there is no such benchmark for Japanese. We build a Japanese NLU benchmark, JGLUE, from scratch without translation to measure the general NLU ability in Japanese. We hope that JGLUE will facilitate NLU research in Japanese.",
}
```
```bibtex
@InProceedings{Kurihara_nlp2022,
author = "栗原健太郎 and 河原大輔 and 柴田知秀",
title = "JGLUE: 日本語言語理解ベンチマーク",
booktitle = "言語処理学会第 28 回年次大会",
year = "2022",
url = "https://www.anlp.jp/proceedings/annual_meeting/2022/pdf_dir/E8-4.pdf"
note= "in Japanese"
}
```
### Contributions
データセット作成者である [Kentaro Kurihara](https://twitter.com/kkurihara_cs), [Daisuke Kawahara](https://twitter.com/daisukekawahar1), [Tomohide Shibata](https://twitter.com/stomohide) に感謝を申し上げます。
また本リポジトリのコードは [Shunsuke Kitada](https://twitter.com/shunk031)の[こちらのリポジトリ](https://huggingface.co/datasets/shunk031/JGLUE)を基に作成されたものです。
|
llm-book/JGLUE
|
[
"task_categories:multiple-choice",
"task_categories:question-answering",
"task_categories:sentence-similarity",
"task_categories:text-classification",
"task_ids:multiple-choice-qa",
"task_ids:open-domain-qa",
"task_ids:multi-class-classification",
"task_ids:sentiment-classification",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"source_datasets:original",
"language:ja",
"license:cc-by-4.0",
"MARC",
"STS",
"NLI",
"SQuAD",
"CommonsenseQA",
"region:us"
] |
2023-05-01T12:00:36+00:00
|
{"annotations_creators": ["crowdsourced"], "language_creators": ["crowdsourced", "found"], "language": ["ja"], "license": ["cc-by-4.0"], "multilinguality": ["monolingual"], "size_categories": [], "source_datasets": ["original"], "task_categories": ["multiple-choice", "question-answering", "sentence-similarity", "text-classification"], "task_ids": ["multiple-choice-qa", "open-domain-qa", "multi-class-classification", "sentiment-classification"], "pretty_name": "JGLUE", "tags": ["MARC", "STS", "NLI", "SQuAD", "CommonsenseQA"]}
|
2023-10-05T23:58:24+00:00
|
b14c741b627ddcf3f604865d9b9c2695243014ac
|
# AutoTrain Dataset for project: test-sa-gam
## Dataset Description
This dataset has been automatically processed by AutoTrain for project test-sa-gam.
### 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": "It is easy to navigate and update programs",
"target": "[([6, 7], [2]), ([4], [2])]"
},
{
"text": "The big screen allows you to enjoy watching movies , pictures and etc",
"target": "[([2], [1])]"
}
]
```
### 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 | 1016 |
| valid | 112 |
|
Adongua/autotrain-data-test-sa-gam
|
[
"task_categories:summarization",
"language:en",
"region:us"
] |
2023-05-01T12:24:23+00:00
|
{"language": ["en"], "task_categories": ["summarization"]}
|
2023-05-01T12:49:05+00:00
|
75b07837aa59ceee083fc5f0be959da4318c39f9
|
abobster/pushkin
|
[
"license:cc",
"region:us"
] |
2023-05-01T13:17:45+00:00
|
{"license": "cc"}
|
2023-05-01T14:55:26+00:00
|
|
831e1fc0486745793ee7332f9e1bdd0796bf611c
|
# Dataset Card for "audio_test_dataset"
This dataset consists of the first 5 samples of [mozilla-foundation/common_voice_13_0](https://huggingface.co/datasets/mozilla-foundation/common_voice_13_0) and is only used for unit testing.
|
alexandrainst/audio_test_dataset
|
[
"size_categories:n<1K",
"language:da",
"license:cc0-1.0",
"region:us"
] |
2023-05-01T13:24:51+00:00
|
{"language": ["da"], "license": "cc0-1.0", "size_categories": ["n<1K"], "dataset_info": {"features": [{"name": "client_id", "dtype": "string"}, {"name": "path", "dtype": "string"}, {"name": "audio", "dtype": {"audio": {"sampling_rate": 48000}}}, {"name": "sentence", "dtype": "string"}, {"name": "up_votes", "dtype": "int64"}, {"name": "down_votes", "dtype": "int64"}, {"name": "age", "dtype": "string"}, {"name": "gender", "dtype": "string"}, {"name": "accent", "dtype": "string"}, {"name": "locale", "dtype": "string"}, {"name": "segment", "dtype": "string"}, {"name": "variant", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 108571, "num_examples": 5}, {"name": "validation", "num_bytes": 116850, "num_examples": 5}, {"name": "test", "num_bytes": 78943, "num_examples": 5}, {"name": "other", "num_bytes": 101436, "num_examples": 5}, {"name": "invalidated", "num_bytes": 156925, "num_examples": 5}], "download_size": 590682, "dataset_size": 562725}}
|
2023-05-01T13:28:58+00:00
|
6f30d827a719eb908be74b2f8481436cbb769478
|
# Dataset Card for "SecurityEval"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
moyix/SecurityEval
|
[
"region:us"
] |
2023-05-01T13:39:28+00:00
|
{"dataset_info": {"features": [{"name": "ID", "dtype": "string"}, {"name": "Prompt", "dtype": "string"}, {"name": "Insecure_code", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 72854, "num_examples": 130}], "download_size": 46036, "dataset_size": 72854}}
|
2023-05-01T18:08:57+00:00
|
337d23f826127ec785f8fe88e583b8a586ed0cf4
|
This is artificial Faroese OCR training data created by collection real OCR errors and inserting them into 38 million tokens of non-OCRed text.
The parallel data is set up as a TSV file with the first column (fo_err) being the text with OCR errors, while the second column (fo_corr) is without OCR errors.
This dataset was created by using scripts from https://github.com/atlijas/ocr-post-processing.
Two ByT5 models have been fine-tuned with the data: https://huggingface.co/svanhvit/byt5-ocr-post-processing-faroese-ai-yfirlestur and https://huggingface.co/svanhvit/byt5-ocr-post-processing-faroese
This is a work on progress and more version of the dataset and the models are on the way.
|
AnnikaSimonsen/FO-OCRtrain
|
[
"region:us"
] |
2023-05-01T13:40:34+00:00
|
{}
|
2023-05-01T13:47:05+00:00
|
f3e69e6d13817167d1f29e7bbba105adefe831a7
|
nathancday/imagenet_sketch_mini
|
[
"license:apache-2.0",
"region:us"
] |
2023-05-01T13:43:25+00:00
|
{"license": "apache-2.0", "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "tench, Tinca tinca", "1": "goldfish, Carassius auratus", "2": "great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias", "3": "tiger shark, Galeocerdo cuvieri", "4": "hammerhead, hammerhead shark", "5": "electric ray, crampfish, numbfish, torpedo", "6": "stingray", "7": "cock", "8": "hen", "9": "ostrich, Struthio camelus", "10": "brambling, Fringilla montifringilla", "11": "goldfinch, Carduelis carduelis", "12": "house finch, linnet, Carpodacus mexicanus", "13": "junco, snowbird", "14": "indigo bunting, indigo finch, indigo bird, Passerina cyanea", "15": "robin, American robin, Turdus migratorius", "16": "bulbul", "17": "jay", "18": "magpie", "19": "chickadee", "20": "water ouzel, dipper", "21": "kite", "22": "bald eagle, American eagle, Haliaeetus leucocephalus", "23": "vulture", "24": "great grey owl, great gray owl, Strix nebulosa", "25": "European fire salamander, Salamandra salamandra", "26": "common newt, Triturus vulgaris", "27": "eft", "28": "spotted salamander, Ambystoma maculatum", "29": "axolotl, mud puppy, Ambystoma mexicanum", "30": "bullfrog, Rana catesbeiana", "31": "tree frog, tree-frog", "32": "tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui", "33": "loggerhead, loggerhead turtle, Caretta caretta", "34": "leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea", "35": "mud turtle", "36": "terrapin", "37": "box turtle, box tortoise", "38": "banded gecko", "39": "common iguana, iguana, Iguana iguana", "40": "American chameleon, anole, Anolis carolinensis", "41": "whiptail, whiptail lizard", "42": "agama", "43": "frilled lizard, Chlamydosaurus kingi", "44": "alligator lizard", "45": "Gila monster, Heloderma suspectum", "46": "green lizard, Lacerta viridis", "47": "African chameleon, Chamaeleo chamaeleon", "48": "Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis", "49": "African crocodile, Nile crocodile, Crocodylus niloticus", "50": "American alligator, Alligator mississipiensis", "51": "triceratops", "52": "thunder snake, worm snake, Carphophis amoenus", "53": "ringneck snake, ring-necked snake, ring snake", "54": "hognose snake, puff adder, sand viper", "55": "green snake, grass snake", "56": "king snake, kingsnake", "57": "garter snake, grass snake", "58": "water snake", "59": "vine snake", "60": "night snake, Hypsiglena torquata", "61": "boa constrictor, Constrictor constrictor", "62": "rock python, rock snake, Python sebae", "63": "Indian cobra, Naja naja", "64": "green mamba", "65": "sea snake", "66": "horned viper, cerastes, sand viper, horned asp, Cerastes cornutus", "67": "diamondback, diamondback rattlesnake, Crotalus adamanteus", "68": "sidewinder, horned rattlesnake, Crotalus cerastes", "69": "trilobite", "70": "harvestman, daddy longlegs, Phalangium opilio", "71": "scorpion", "72": "black and gold garden spider, Argiope aurantia", "73": "barn spider, Araneus cavaticus", "74": "garden spider, Aranea diademata", "75": "black widow, Latrodectus mactans", "76": "tarantula", "77": "wolf spider, hunting spider", "78": "tick", "79": "centipede", "80": "black grouse", "81": "ptarmigan", "82": "ruffed grouse, partridge, Bonasa umbellus", "83": "prairie chicken, prairie grouse, prairie fowl", "84": "peacock", "85": "quail", "86": "partridge", "87": "African grey, African gray, Psittacus erithacus", "88": "macaw", "89": "sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita", "90": "lorikeet", "91": "coucal", "92": "bee eater", "93": "hornbill", "94": "hummingbird", "95": "jacamar", "96": "toucan", "97": "drake", "98": "red-breasted merganser, Mergus serrator", "99": "goose", "100": "black swan, Cygnus atratus", "101": "tusker", "102": "echidna, spiny anteater, anteater", "103": "platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus", "104": "wallaby, brush kangaroo", "105": "koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus", "106": "wombat", "107": "jellyfish", "108": "sea anemone, anemone", "109": "brain coral", "110": "flatworm, platyhelminth", "111": "nematode, nematode worm, roundworm", "112": "conch", "113": "snail", "114": "slug", "115": "sea slug, nudibranch", "116": "chiton, coat-of-mail shell, sea cradle, polyplacophore", "117": "chambered nautilus, pearly nautilus, nautilus", "118": "Dungeness crab, Cancer magister", "119": "rock crab, Cancer irroratus", "120": "fiddler crab", "121": "king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica", "122": "American lobster, Northern lobster, Maine lobster, Homarus americanus", "123": "spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish", "124": "crayfish, crawfish, crawdad, crawdaddy", "125": "hermit crab", "126": "isopod", "127": "white stork, Ciconia ciconia", "128": "black stork, Ciconia nigra", "129": "spoonbill", "130": "flamingo", "131": "little blue heron, Egretta caerulea", "132": "American egret, great white heron, Egretta albus", "133": "bittern", "134": "crane", "135": "limpkin, Aramus pictus", "136": "European gallinule, Porphyrio porphyrio", "137": "American coot, marsh hen, mud hen, water hen, Fulica americana", "138": "bustard", "139": "ruddy turnstone, Arenaria interpres", "140": "red-backed sandpiper, dunlin, Erolia alpina", "141": "redshank, Tringa totanus", "142": "dowitcher", "143": "oystercatcher, oyster catcher", "144": "pelican", "145": "king penguin, Aptenodytes patagonica", "146": "albatross, mollymawk", "147": "grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus", "148": "killer whale, killer, orca, grampus, sea wolf, Orcinus orca", "149": "dugong, Dugong dugon", "150": "sea lion", "151": "Chihuahua", "152": "Japanese spaniel", "153": "Maltese dog, Maltese terrier, Maltese", "154": "Pekinese, Pekingese, Peke", "155": "Shih-Tzu", "156": "Blenheim spaniel", "157": "papillon", "158": "toy terrier", "159": "Rhodesian ridgeback", "160": "Afghan hound, Afghan", "161": "basset, basset hound", "162": "beagle", "163": "bloodhound, sleuthhound", "164": "bluetick", "165": "black-and-tan coonhound", "166": "Walker hound, Walker foxhound", "167": "English foxhound", "168": "redbone", "169": "borzoi, Russian wolfhound", "170": "Irish wolfhound", "171": "Italian greyhound", "172": "whippet", "173": "Ibizan hound, Ibizan Podenco", "174": "Norwegian elkhound, elkhound", "175": "otterhound, otter hound", "176": "Saluki, gazelle hound", "177": "Scottish deerhound, deerhound", "178": "Weimaraner", "179": "Staffordshire bullterrier, Staffordshire bull terrier", "180": "American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier", "181": "Bedlington terrier", "182": "Border terrier", "183": "Kerry blue terrier", "184": "Irish terrier", "185": "Norfolk terrier", "186": "Norwich terrier", "187": "Yorkshire terrier", "188": "wire-haired fox terrier", "189": "Lakeland terrier", "190": "Sealyham terrier, Sealyham", "191": "Airedale, Airedale terrier", "192": "cairn, cairn terrier", "193": "Australian terrier", "194": "Dandie Dinmont, Dandie Dinmont terrier", "195": "Boston bull, Boston terrier", "196": "miniature schnauzer", "197": "giant schnauzer", "198": "standard schnauzer", "199": "Scotch terrier, Scottish terrier, Scottie", "200": "Tibetan terrier, chrysanthemum dog", "201": "silky terrier, Sydney silky", "202": "soft-coated wheaten terrier", "203": "West Highland white terrier", "204": "Lhasa, Lhasa apso", "205": "flat-coated retriever", "206": "curly-coated retriever", "207": "golden retriever", "208": "Labrador retriever", "209": "Chesapeake Bay retriever", "210": "German short-haired pointer", "211": "vizsla, Hungarian pointer", "212": "English setter", "213": "Irish setter, red setter", "214": "Gordon setter", "215": "Brittany spaniel", "216": "clumber, clumber spaniel", "217": "English springer, English springer spaniel", "218": "Welsh springer spaniel", "219": "cocker spaniel, English cocker spaniel, cocker", "220": "Sussex spaniel", "221": "Irish water spaniel", "222": "kuvasz", "223": "schipperke", "224": "groenendael", "225": "malinois", "226": "briard", "227": "kelpie", "228": "komondor", "229": "Old English sheepdog, bobtail", "230": "Shetland sheepdog, Shetland sheep dog, Shetland", "231": "collie", "232": "Border collie", "233": "Bouvier des Flandres, Bouviers des Flandres", "234": "Rottweiler", "235": "German shepherd, German shepherd dog, German police dog, alsatian", "236": "Doberman, Doberman pinscher", "237": "miniature pinscher", "238": "Greater Swiss Mountain dog", "239": "Bernese mountain dog", "240": "Appenzeller", "241": "EntleBucher", "242": "boxer", "243": "bull mastiff", "244": "Tibetan mastiff", "245": "French bulldog", "246": "Great Dane", "247": "Saint Bernard, St Bernard", "248": "Eskimo dog, husky", "249": "malamute, malemute, Alaskan malamute", "250": "Siberian husky", "251": "dalmatian, coach dog, carriage dog", "252": "affenpinscher, monkey pinscher, monkey dog", "253": "basenji", "254": "pug, pug-dog", "255": "Leonberg", "256": "Newfoundland, Newfoundland dog", "257": "Great Pyrenees", "258": "Samoyed, Samoyede", "259": "Pomeranian", "260": "chow, chow chow", "261": "keeshond", "262": "Brabancon griffon", "263": "Pembroke, Pembroke Welsh corgi", "264": "Cardigan, Cardigan Welsh corgi", "265": "toy poodle", "266": "miniature poodle", "267": "standard poodle", "268": "Mexican hairless", "269": "timber wolf, grey wolf, gray wolf, Canis lupus", "270": "white wolf, Arctic wolf, Canis lupus tundrarum", "271": "red wolf, maned wolf, Canis rufus, Canis niger", "272": "coyote, prairie wolf, brush wolf, Canis latrans", "273": "dingo, warrigal, warragal, Canis dingo", "274": "dhole, Cuon alpinus", "275": "African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus", "276": "hyena, hyaena", "277": "red fox, Vulpes vulpes", "278": "kit fox, Vulpes macrotis", "279": "Arctic fox, white fox, Alopex lagopus", "280": "grey fox, gray fox, Urocyon cinereoargenteus", "281": "tabby, tabby cat", "282": "tiger cat", "283": "Persian cat", "284": "Siamese cat, Siamese", "285": "Egyptian cat", "286": "cougar, puma, catamount, mountain lion, painter, panther, Felis concolor", "287": "lynx, catamount", "288": "leopard, Panthera pardus", "289": "snow leopard, ounce, Panthera uncia", "290": "jaguar, panther, Panthera onca, Felis onca", "291": "lion, king of beasts, Panthera leo", "292": "tiger, Panthera tigris", "293": "cheetah, chetah, Acinonyx jubatus", "294": "brown bear, bruin, Ursus arctos", "295": "American black bear, black bear, Ursus americanus, Euarctos americanus", "296": "ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus", "297": "sloth bear, Melursus ursinus, Ursus ursinus", "298": "mongoose", "299": "meerkat, mierkat", "300": "tiger beetle", "301": "ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle", "302": "ground beetle, carabid beetle", "303": "long-horned beetle, longicorn, longicorn beetle", "304": "leaf beetle, chrysomelid", "305": "dung beetle", "306": "rhinoceros beetle", "307": "weevil", "308": "fly", "309": "bee", "310": "ant, emmet, pismire", "311": "grasshopper, hopper", "312": "cricket", "313": "walking stick, walkingstick, stick insect", "314": "cockroach, roach", "315": "mantis, mantid", "316": "cicada, cicala", "317": "leafhopper", "318": "lacewing, lacewing fly", "319": "dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk", "320": "damselfly", "321": "admiral", "322": "ringlet, ringlet butterfly", "323": "monarch, monarch butterfly, milkweed butterfly, Danaus plexippus", "324": "cabbage butterfly", "325": "sulphur butterfly, sulfur butterfly", "326": "lycaenid, lycaenid butterfly", "327": "starfish, sea star", "328": "sea urchin", "329": "sea cucumber, holothurian", "330": "wood rabbit, cottontail, cottontail rabbit", "331": "hare", "332": "Angora, Angora rabbit", "333": "hamster", "334": "porcupine, hedgehog", "335": "fox squirrel, eastern fox squirrel, Sciurus niger", "336": "marmot", "337": "beaver", "338": "guinea pig, Cavia cobaya", "339": "sorrel", "340": "zebra", "341": "hog, pig, grunter, squealer, Sus scrofa", "342": "wild boar, boar, Sus scrofa", "343": "warthog", "344": "hippopotamus, hippo, river horse, Hippopotamus amphibius", "345": "ox", "346": "water buffalo, water ox, Asiatic buffalo, Bubalus bubalis", "347": "bison", "348": "ram, tup", "349": "bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis", "350": "ibex, Capra ibex", "351": "hartebeest", "352": "impala, Aepyceros melampus", "353": "gazelle", "354": "Arabian camel, dromedary, Camelus dromedarius", "355": "llama", "356": "weasel", "357": "mink", "358": "polecat, fitch, foulmart, foumart, Mustela putorius", "359": "black-footed ferret, ferret, Mustela nigripes", "360": "otter", "361": "skunk, polecat, wood pussy", "362": "badger", "363": "armadillo", "364": "three-toed sloth, ai, Bradypus tridactylus", "365": "orangutan, orang, orangutang, Pongo pygmaeus", "366": "gorilla, Gorilla gorilla", "367": "chimpanzee, chimp, Pan troglodytes", "368": "gibbon, Hylobates lar", "369": "siamang, Hylobates syndactylus, Symphalangus syndactylus", "370": "guenon, guenon monkey", "371": "patas, hussar monkey, Erythrocebus patas", "372": "baboon", "373": "macaque", "374": "langur", "375": "colobus, colobus monkey", "376": "proboscis monkey, Nasalis larvatus", "377": "marmoset", "378": "capuchin, ringtail, Cebus capucinus", "379": "howler monkey, howler", "380": "titi, titi monkey", "381": "spider monkey, Ateles geoffroyi", "382": "squirrel monkey, Saimiri sciureus", "383": "Madagascar cat, ring-tailed lemur, Lemur catta", "384": "indri, indris, Indri indri, Indri brevicaudatus", "385": "Indian elephant, Elephas maximus", "386": "African elephant, Loxodonta africana", "387": "lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens", "388": "giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca", "389": "barracouta, snoek", "390": "eel", "391": "coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch", "392": "rock beauty, Holocanthus tricolor", "393": "anemone fish", "394": "sturgeon", "395": "gar, garfish, garpike, billfish, Lepisosteus osseus", "396": "lionfish", "397": "puffer, pufferfish, blowfish, globefish", "398": "abacus", "399": "abaya", "400": "academic gown, academic robe, judge's robe", "401": "accordion, piano accordion, squeeze box", "402": "acoustic guitar", "403": "aircraft carrier, carrier, flattop, attack aircraft carrier", "404": "airliner", "405": "airship, dirigible", "406": "altar", "407": "ambulance", "408": "amphibian, amphibious vehicle", "409": "analog clock", "410": "apiary, bee house", "411": "apron", "412": "ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin", "413": "assault rifle, assault gun", "414": "backpack, back pack, knapsack, packsack, rucksack, haversack", "415": "bakery, bakeshop, bakehouse", "416": "balance beam, beam", "417": "balloon", "418": "ballpoint, ballpoint pen, ballpen, Biro", "419": "Band Aid", "420": "banjo", "421": "bannister, banister, balustrade, balusters, handrail", "422": "barbell", "423": "barber chair", "424": "barbershop", "425": "barn", "426": "barometer", "427": "barrel, cask", "428": "barrow, garden cart, lawn cart, wheelbarrow", "429": "baseball", "430": "basketball", "431": "bassinet", "432": "bassoon", "433": "bathing cap, swimming cap", "434": "bath towel", "435": "bathtub, bathing tub, bath, tub", "436": "beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon", "437": "beacon, lighthouse, beacon light, pharos", "438": "beaker", "439": "bearskin, busby, shako", "440": "beer bottle", "441": "beer glass", "442": "bell cote, bell cot", "443": "bib", "444": "bicycle-built-for-two, tandem bicycle, tandem", "445": "bikini, two-piece", "446": "binder, ring-binder", "447": "binoculars, field glasses, opera glasses", "448": "birdhouse", "449": "boathouse", "450": "bobsled, bobsleigh, bob", "451": "bolo tie, bolo, bola tie, bola", "452": "bonnet, poke bonnet", "453": "bookcase", "454": "bookshop, bookstore, bookstall", "455": "bottlecap", "456": "bow", "457": "bow tie, bow-tie, bowtie", "458": "brass, memorial tablet, plaque", "459": "brassiere, bra, bandeau", "460": "breakwater, groin, groyne, mole, bulwark, seawall, jetty", "461": "breastplate, aegis, egis", "462": "broom", "463": "bucket, pail", "464": "buckle", "465": "bulletproof vest", "466": "bullet train, bullet", "467": "butcher shop, meat market", "468": "cab, hack, taxi, taxicab", "469": "caldron, cauldron", "470": "candle, taper, wax light", "471": "cannon", "472": "canoe", "473": "can opener, tin opener", "474": "cardigan", "475": "car mirror", "476": "carousel, carrousel, merry-go-round, roundabout, whirligig", "477": "carpenter's kit, tool kit", "478": "carton", "479": "car wheel", "480": "cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM", "481": "cassette", "482": "cassette player", "483": "castle", "484": "catamaran", "485": "CD player", "486": "cello, violoncello", "487": "cellular telephone, cellular phone, cellphone, cell, mobile phone", "488": "chain", "489": "chainlink fence", "490": "chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour", "491": "chain saw, chainsaw", "492": "chest", "493": "chiffonier, commode", "494": "chime, bell, gong", "495": "china cabinet, china closet", "496": "Christmas stocking", "497": "church, church building", "498": "cinema, movie theater, movie theatre, movie house, picture palace", "499": "cleaver, meat cleaver, chopper", "500": "cliff dwelling", "501": "cloak", "502": "clog, geta, patten, sabot", "503": "cocktail shaker", "504": "coffee mug", "505": "coffeepot", "506": "coil, spiral, volute, whorl, helix", "507": "combination lock", "508": "computer keyboard, keypad", "509": "confectionery, confectionary, candy store", "510": "container ship, containership, container vessel", "511": "convertible", "512": "corkscrew, bottle screw", "513": "cornet, horn, trumpet, trump", "514": "cowboy boot", "515": "cowboy hat, ten-gallon hat", "516": "cradle", "517": "crane2", "518": "crash helmet", "519": "crate", "520": "crib, cot", "521": "Crock Pot", "522": "croquet ball", "523": "crutch", "524": "cuirass", "525": "dam, dike, dyke", "526": "desk", "527": "desktop computer", "528": "dial telephone, dial phone", "529": "diaper, nappy, napkin", "530": "digital clock", "531": "digital watch", "532": "dining table, board", "533": "dishrag, dishcloth", "534": "dishwasher, dish washer, dishwashing machine", "535": "disk brake, disc brake", "536": "dock, dockage, docking facility", "537": "dogsled, dog sled, dog sleigh", "538": "dome", "539": "doormat, welcome mat", "540": "drilling platform, offshore rig", "541": "drum, membranophone, tympan", "542": "drumstick", "543": "dumbbell", "544": "Dutch oven", "545": "electric fan, blower", "546": "electric guitar", "547": "electric locomotive", "548": "entertainment center", "549": "envelope", "550": "espresso maker", "551": "face powder", "552": "feather boa, boa", "553": "file, file cabinet, filing cabinet", "554": "fireboat", "555": "fire engine, fire truck", "556": "fire screen, fireguard", "557": "flagpole, flagstaff", "558": "flute, transverse flute", "559": "folding chair", "560": "football helmet", "561": "forklift", "562": "fountain", "563": "fountain pen", "564": "four-poster", "565": "freight car", "566": "French horn, horn", "567": "frying pan, frypan, skillet", "568": "fur coat", "569": "garbage truck, dustcart", "570": "gasmask, respirator, gas helmet", "571": "gas pump, gasoline pump, petrol pump, island dispenser", "572": "goblet", "573": "go-kart", "574": "golf ball", "575": "golfcart, golf cart", "576": "gondola", "577": "gong, tam-tam", "578": "gown", "579": "grand piano, grand", "580": "greenhouse, nursery, glasshouse", "581": "grille, radiator grille", "582": "grocery store, grocery, food market, market", "583": "guillotine", "584": "hair slide", "585": "hair spray", "586": "half track", "587": "hammer", "588": "hamper", "589": "hand blower, blow dryer, blow drier, hair dryer, hair drier", "590": "hand-held computer, hand-held microcomputer", "591": "handkerchief, hankie, hanky, hankey", "592": "hard disc, hard disk, fixed disk", "593": "harmonica, mouth organ, harp, mouth harp", "594": "harp", "595": "harvester, reaper", "596": "hatchet", "597": "holster", "598": "home theater, home theatre", "599": "honeycomb", "600": "hook, claw", "601": "hoopskirt, crinoline", "602": "horizontal bar, high bar", "603": "horse cart, horse-cart", "604": "hourglass", "605": "iPod", "606": "iron, smoothing iron", "607": "jack-o'-lantern", "608": "jean, blue jean, denim", "609": "jeep, landrover", "610": "jersey, T-shirt, tee shirt", "611": "jigsaw puzzle", "612": "jinrikisha, ricksha, rickshaw", "613": "joystick", "614": "kimono", "615": "knee pad", "616": "knot", "617": "lab coat, laboratory coat", "618": "ladle", "619": "lampshade, lamp shade", "620": "laptop, laptop computer", "621": "lawn mower, mower", "622": "lens cap, lens cover", "623": "letter opener, paper knife, paperknife", "624": "library", "625": "lifeboat", "626": "lighter, light, igniter, ignitor", "627": "limousine, limo", "628": "liner, ocean liner", "629": "lipstick, lip rouge", "630": "Loafer", "631": "lotion", "632": "loudspeaker, speaker, speaker unit, loudspeaker system, speaker system", "633": "loupe, jeweler's loupe", "634": "lumbermill, sawmill", "635": "magnetic compass", "636": "mailbag, postbag", "637": "mailbox, letter box", "638": "maillot", "639": "maillot, tank suit", "640": "manhole cover", "641": "maraca", "642": "marimba, xylophone", "643": "mask", "644": "matchstick", "645": "maypole", "646": "maze, labyrinth", "647": "measuring cup", "648": "medicine chest, medicine cabinet", "649": "megalith, megalithic structure", "650": "microphone, mike", "651": "microwave, microwave oven", "652": "military uniform", "653": "milk can", "654": "minibus", "655": "miniskirt, mini", "656": "minivan", "657": "missile", "658": "mitten", "659": "mixing bowl", "660": "mobile home, manufactured home", "661": "Model T", "662": "modem", "663": "monastery", "664": "monitor", "665": "moped", "666": "mortar", "667": "mortarboard", "668": "mosque", "669": "mosquito net", "670": "motor scooter, scooter", "671": "mountain bike, all-terrain bike, off-roader", "672": "mountain tent", "673": "mouse, computer mouse", "674": "mousetrap", "675": "moving van", "676": "muzzle", "677": "nail", "678": "neck brace", "679": "necklace", "680": "nipple", "681": "notebook, notebook computer", "682": "obelisk", "683": "oboe, hautboy, hautbois", "684": "ocarina, sweet potato", "685": "odometer, hodometer, mileometer, milometer", "686": "oil filter", "687": "organ, pipe organ", "688": "oscilloscope, scope, cathode-ray oscilloscope, CRO", "689": "overskirt", "690": "oxcart", "691": "oxygen mask", "692": "packet", "693": "paddle, boat paddle", "694": "paddlewheel, paddle wheel", "695": "padlock", "696": "paintbrush", "697": "pajama, pyjama, pj's, jammies", "698": "palace", "699": "panpipe, pandean pipe, syrinx", "700": "paper towel", "701": "parachute, chute", "702": "parallel bars, bars", "703": "park bench", "704": "parking meter", "705": "passenger car, coach, carriage", "706": "patio, terrace", "707": "pay-phone, pay-station", "708": "pedestal, plinth, footstall", "709": "pencil box, pencil case", "710": "pencil sharpener", "711": "perfume, essence", "712": "Petri dish", "713": "photocopier", "714": "pick, plectrum, plectron", "715": "pickelhaube", "716": "picket fence, paling", "717": "pickup, pickup truck", "718": "pier", "719": "piggy bank, penny bank", "720": "pill bottle", "721": "pillow", "722": "ping-pong ball", "723": "pinwheel", "724": "pirate, pirate ship", "725": "pitcher, ewer", "726": "plane, carpenter's plane, woodworking plane", "727": "planetarium", "728": "plastic bag", "729": "plate rack", "730": "plow, plough", "731": "plunger, plumber's helper", "732": "Polaroid camera, Polaroid Land camera", "733": "pole", "734": "police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria", "735": "poncho", "736": "pool table, billiard table, snooker table", "737": "pop bottle, soda bottle", "738": "pot, flowerpot", "739": "potter's wheel", "740": "power drill", "741": "prayer rug, prayer mat", "742": "printer", "743": "prison, prison house", "744": "projectile, missile", "745": "projector", "746": "puck, hockey puck", "747": "punching bag, punch bag, punching ball, punchball", "748": "purse", "749": "quill, quill pen", "750": "quilt, comforter, comfort, puff", "751": "racer, race car, racing car", "752": "racket, racquet", "753": "radiator", "754": "radio, wireless", "755": "radio telescope, radio reflector", "756": "rain barrel", "757": "recreational vehicle, RV, R.V.", "758": "reel", "759": "reflex camera", "760": "refrigerator, icebox", "761": "remote control, remote", "762": "restaurant, eating house, eating place, eatery", "763": "revolver, six-gun, six-shooter", "764": "rifle", "765": "rocking chair, rocker", "766": "rotisserie", "767": "rubber eraser, rubber, pencil eraser", "768": "rugby ball", "769": "rule, ruler", "770": "running shoe", "771": "safe", "772": "safety pin", "773": "saltshaker, salt shaker", "774": "sandal", "775": "sarong", "776": "sax, saxophone", "777": "scabbard", "778": "scale, weighing machine", "779": "school bus", "780": "schooner", "781": "scoreboard", "782": "screen, CRT screen", "783": "screw", "784": "screwdriver", "785": "seat belt, seatbelt", "786": "sewing machine", "787": "shield, buckler", "788": "shoe shop, shoe-shop, shoe store", "789": "shoji", "790": "shopping basket", "791": "shopping cart", "792": "shovel", "793": "shower cap", "794": "shower curtain", "795": "ski", "796": "ski mask", "797": "sleeping bag", "798": "slide rule, slipstick", "799": "sliding door", "800": "slot, one-armed bandit", "801": "snorkel", "802": "snowmobile", "803": "snowplow, snowplough", "804": "soap dispenser", "805": "soccer ball", "806": "sock", "807": "solar dish, solar collector, solar furnace", "808": "sombrero", "809": "soup bowl", "810": "space bar", "811": "space heater", "812": "space shuttle", "813": "spatula", "814": "speedboat", "815": "spider web, spider's web", "816": "spindle", "817": "sports car, sport car", "818": "spotlight, spot", "819": "stage", "820": "steam locomotive", "821": "steel arch bridge", "822": "steel drum", "823": "stethoscope", "824": "stole", "825": "stone wall", "826": "stopwatch, stop watch", "827": "stove", "828": "strainer", "829": "streetcar, tram, tramcar, trolley, trolley car", "830": "stretcher", "831": "studio couch, day bed", "832": "stupa, tope", "833": "submarine, pigboat, sub, U-boat", "834": "suit, suit of clothes", "835": "sundial", "836": "sunglass", "837": "sunglasses, dark glasses, shades", "838": "sunscreen, sunblock, sun blocker", "839": "suspension bridge", "840": "swab, swob, mop", "841": "sweatshirt", "842": "swimming trunks, bathing trunks", "843": "swing", "844": "switch, electric switch, electrical switch", "845": "syringe", "846": "table lamp", "847": "tank, army tank, armored combat vehicle, armoured combat vehicle", "848": "tape player", "849": "teapot", "850": "teddy, teddy bear", "851": "television, television system", "852": "tennis ball", "853": "thatch, thatched roof", "854": "theater curtain, theatre curtain", "855": "thimble", "856": "thresher, thrasher, threshing machine", "857": "throne", "858": "tile roof", "859": "toaster", "860": "tobacco shop, tobacconist shop, tobacconist", "861": "toilet seat", "862": "torch", "863": "totem pole", "864": "tow truck, tow car, wrecker", "865": "toyshop", "866": "tractor", "867": "trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi", "868": "tray", "869": "trench coat", "870": "tricycle, trike, velocipede", "871": "trimaran", "872": "tripod", "873": "triumphal arch", "874": "trolleybus, trolley coach, trackless trolley", "875": "trombone", "876": "tub, vat", "877": "turnstile", "878": "typewriter keyboard", "879": "umbrella", "880": "unicycle, monocycle", "881": "upright, upright piano", "882": "vacuum, vacuum cleaner", "883": "vase", "884": "vault", "885": "velvet", "886": "vending machine", "887": "vestment", "888": "viaduct", "889": "violin, fiddle", "890": "volleyball", "891": "waffle iron", "892": "wall clock", "893": "wallet, billfold, notecase, pocketbook", "894": "wardrobe, closet, press", "895": "warplane, military plane", "896": "washbasin, handbasin, washbowl, lavabo, wash-hand basin", "897": "washer, automatic washer, washing machine", "898": "water bottle", "899": "water jug", "900": "water tower", "901": "whiskey jug", "902": "whistle", "903": "wig", "904": "window screen", "905": "window shade", "906": "Windsor tie", "907": "wine bottle", "908": "wing", "909": "wok", "910": "wooden spoon", "911": "wool, woolen, woollen", "912": "worm fence, snake fence, snake-rail fence, Virginia fence", "913": "wreck", "914": "yawl", "915": "yurt", "916": "web site, website, internet site, site", "917": "comic book", "918": "crossword puzzle, crossword", "919": "street sign", "920": "traffic light, traffic signal, stoplight", "921": "book jacket, dust cover, dust jacket, dust wrapper", "922": "menu", "923": "plate", "924": "guacamole", "925": "consomme", "926": "hot pot, hotpot", "927": "trifle", "928": "ice cream, icecream", "929": "ice lolly, lolly, lollipop, popsicle", "930": "French loaf", "931": "bagel, beigel", "932": "pretzel", "933": "cheeseburger", "934": "hotdog, hot dog, red hot", "935": "mashed potato", "936": "head cabbage", "937": "broccoli", "938": "cauliflower", "939": "zucchini, courgette", "940": "spaghetti squash", "941": "acorn squash", "942": "butternut squash", "943": "cucumber, cuke", "944": "artichoke, globe artichoke", "945": "bell pepper", "946": "cardoon", "947": "mushroom", "948": "Granny Smith", "949": "strawberry", "950": "orange", "951": "lemon", "952": "fig", "953": "pineapple, ananas", "954": "banana", "955": "jackfruit, jak, jack", "956": "custard apple", "957": "pomegranate", "958": "hay", "959": "carbonara", "960": "chocolate sauce, chocolate syrup", "961": "dough", "962": "meat loaf, meatloaf", "963": "pizza, pizza pie", "964": "potpie", "965": "burrito", "966": "red wine", "967": "espresso", "968": "cup", "969": "eggnog", "970": "alp", "971": "bubble", "972": "cliff, drop, drop-off", "973": "coral reef", "974": "geyser", "975": "lakeside, lakeshore", "976": "promontory, headland, head, foreland", "977": "sandbar, sand bar", "978": "seashore, coast, seacoast, sea-coast", "979": "valley, vale", "980": "volcano", "981": "ballplayer, baseball player", "982": "groom, bridegroom", "983": "scuba diver", "984": "rapeseed", "985": "daisy", "986": "yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum", "987": "corn", "988": "acorn", "989": "hip, rose hip, rosehip", "990": "buckeye, horse chestnut, conker", "991": "coral fungus", "992": "agaric", "993": "gyromitra", "994": "stinkhorn, carrion fungus", "995": "earthstar", "996": "hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa", "997": "bolete", "998": "ear, spike, capitulum", "999": "toilet tissue, toilet paper, bathroom tissue"}}}}], "splits": [{"name": "train", "num_bytes": 43316148.77032365, "num_examples": 255}], "download_size": 42640144, "dataset_size": 43316148.77032365}}
|
2023-05-01T13:53:26+00:00
|
|
e3807aca1d1163f3f895645818fe52f52673142c
|
tw0fold/behboud
|
[
"region:us"
] |
2023-05-01T13:43:53+00:00
|
{}
|
2023-05-01T13:48:41+00:00
|
|
24c5bc60f5b38c53afe36a3efb1fae3206964625
|
dadsqwe/clash
|
[
"license:other",
"region:us"
] |
2023-05-01T14:01:44+00:00
|
{"license": "other"}
|
2023-05-02T09:21:01+00:00
|
|
a849599d01e3eda048756a4833b8843bd35322a9
|
shawmoon/ekattor_alpaca2
|
[
"license:apache-2.0",
"region:us"
] |
2023-05-01T14:25:45+00:00
|
{"license": "apache-2.0"}
|
2023-05-01T14:26:56+00:00
|
|
3ce126f687a7d75ec7b56840135e00d49082b0c6
|
ikezoe/alpha-test
|
[
"license:afl-3.0",
"region:us"
] |
2023-05-01T14:37:58+00:00
|
{"license": "afl-3.0"}
|
2023-05-01T14:41:42+00:00
|
|
e6a763a8409c6a36a043343c23577a09785d2418
|
mncai/kin_med_2M
|
[
"task_categories:conversational",
"language:ko",
"license:gpl-3.0",
"medical",
"region:us"
] |
2023-05-01T14:43:09+00:00
|
{"language": ["ko"], "license": "gpl-3.0", "task_categories": ["conversational"], "tags": ["medical"]}
|
2023-05-01T15:00:39+00:00
|
|
cc1bb9bfd38574777a9f077ab8042ad633098b16
|
# Dataset Card for "ms-marco-es-500k"
QA asymmetric Spanish dataset filtered from [multilingual version of MS Marco](https://huggingface.co/datasets/unicamp-dl/mmarco) and sampled on 500k rows.
```python
import datasets
ms_marco_es = datasets.load_dataset('unicamp-dl/mmarco', name='spanish', split='train')
ms_marco_es.select(range(500_000)).push_to_hub("dariolopez/ms-marco-es-500k", token=os.environ['hg_token'])
```
|
dariolopez/ms-marco-es-500k
|
[
"task_categories:question-answering",
"size_categories:100K<n<1M",
"language:es",
"license:apache-2.0",
"region:us"
] |
2023-05-01T15:09:13+00:00
|
{"language": ["es"], "license": "apache-2.0", "size_categories": ["100K<n<1M"], "task_categories": ["question-answering"], "dataset_info": {"features": [{"name": "query", "dtype": "string"}, {"name": "positive", "dtype": "string"}, {"name": "negative", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 433633520, "num_examples": 500000}], "download_size": 170119229, "dataset_size": 433633520}}
|
2023-05-01T15:12:05+00:00
|
2045bfcf91aaaf1ec3eaefa17a32f715a942f6fa
|
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|
marblepolishing/Marble-Polishing-Service-in-Delhi
|
[
"region:us"
] |
2023-05-01T15:43:44+00:00
|
{}
|
2023-05-01T15:44:54+00:00
|
09ab2a873da57ebb656beaec4eebdcc404b644db
|
# Dataset Card for "openbookqa_retrieved_by_colbert"
This is the `main/test` set of [OBQA](https://huggingface.co/datasets/openbookqa/viewer/main/test), with each question retrieved from [ColBERT v2](https://github.com/stanford-futuredata/ColBERT/tree/main) trained on MS MARCO Passage Ranking (`https://downloads.cs.stanford.edu/nlp/data/colbert/colbertv2/colbertv2.0.tar.gz`).
We index the question part of the train set using doc_maxlen=30, nbits=2. We search each question of test set with k=10 and put the results in the `retrieved` column.
|
cnut1648/openbookqa_retrieved_by_colbert
|
[
"region:us"
] |
2023-05-01T15:51:52+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "question_stem", "dtype": "string"}, {"name": "choices", "sequence": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": "string"}]}, {"name": "answerKey", "dtype": "string"}, {"name": "retrieved", "list": [{"name": "answerKey", "dtype": "string"}, {"name": "choices", "struct": [{"name": "label", "sequence": "string"}, {"name": "text", "sequence": "string"}]}, {"name": "passage", "dtype": "string"}, {"name": "rank", "dtype": "int64"}, {"name": "score", "dtype": "float64"}]}], "splits": [{"name": "test", "num_bytes": 1096660, "num_examples": 500}], "download_size": 220149, "dataset_size": 1096660}}
|
2023-05-06T02:36:14+00:00
|
4cf2a8dcd1bc7d7fb80b264c2b9fab2ecc95b679
|
# Dataset Card for "commonsense_qa_retrieved_by_colbert"
This is the validation set of [CSQA](https://huggingface.co/datasets/commonsense_qa/viewer/default/validation), with each question retrieved from [ColBERT v2](https://github.com/stanford-futuredata/ColBERT/tree/main) trained on MS MARCO Passage Ranking (`https://downloads.cs.stanford.edu/nlp/data/colbert/colbertv2/colbertv2.0.tar.gz`).
We index the question part of the train set using doc_maxlen=30, nbits=2. We search each question of validation set with k=10 and put the results in the `retrieved` column.
|
cnut1648/commonsense_qa_retrieved_by_colbert
|
[
"region:us"
] |
2023-05-01T15:53:20+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "question_concept", "dtype": "string"}, {"name": "choices", "sequence": [{"name": "label", "dtype": "string"}, {"name": "text", "dtype": "string"}]}, {"name": "answerKey", "dtype": "string"}, {"name": "retrieved", "list": [{"name": "answerKey", "dtype": "string"}, {"name": "choices", "struct": [{"name": "label", "sequence": "string"}, {"name": "text", "sequence": "string"}]}, {"name": "passage", "dtype": "string"}, {"name": "rank", "dtype": "int64"}, {"name": "score", "dtype": "float64"}]}], "splits": [{"name": "validation", "num_bytes": 2646054, "num_examples": 1221}], "download_size": 755467, "dataset_size": 2646054}}
|
2023-05-09T06:09:12+00:00
|
2a96c92a34c5544016f8b6022ba3655bf6ed5407
|
# Dataset Card for "Food101_test_google_flan_t5_small_mode_T_SPECIFIC_A_ns_1000"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/Food101_test_google_flan_t5_small_mode_T_SPECIFIC_A_ns_1000
|
[
"region:us"
] |
2023-05-01T16:07:23+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_clip_tags_ViT_L_14_Attributes_ViT_L_14_descriptors_text_davinci_003_full_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 393056, "num_examples": 1000}], "download_size": 46286, "dataset_size": 393056}}
|
2023-05-01T16:07:25+00:00
|
f0d0e64b3db617e22ffe3913e342a2e83be67b7c
|
umoubuton/m4singer
|
[
"license:mit",
"region:us"
] |
2023-05-01T16:11:51+00:00
|
{"license": "mit"}
|
2023-05-01T16:19:02+00:00
|
|
265ba94756dfeed6ee7c82fefb8dfcb2b7341f89
|
# Dataset Card for "Food101_test_google_flan_t5_small_mode_T_SPECIFIC_A_ns_10000"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/Food101_test_google_flan_t5_small_mode_T_SPECIFIC_A_ns_10000
|
[
"region:us"
] |
2023-05-01T16:19:53+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_clip_tags_ViT_L_14_Attributes_ViT_L_14_descriptors_text_davinci_003_full_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 4198605, "num_examples": 10000}], "download_size": 506659, "dataset_size": 4198605}}
|
2023-05-01T16:19:55+00:00
|
2cf0d79c3922b10ef47cf80a72b77c8e695a798f
|
derpyplops/test
|
[
"license:mit",
"region:us"
] |
2023-05-01T16:24:51+00:00
|
{"license": "mit"}
|
2023-05-01T16:24:51+00:00
|
|
add5497a5597307c91df6c605c63342e7bb062eb
|
# Dataset Card for "processed_bert_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Hikam22/processed_bert_dataset
|
[
"region:us"
] |
2023-05-01T16:41:15+00:00
|
{"dataset_info": {"features": [{"name": "input_ids", "sequence": "int32"}, {"name": "token_type_ids", "sequence": "int8"}, {"name": "attention_mask", "sequence": "int8"}, {"name": "special_tokens_mask", "sequence": "int8"}], "splits": [{"name": "train", "num_bytes": 8629178400.0, "num_examples": 2396994}], "download_size": 2322333341, "dataset_size": 8629178400.0}}
|
2023-05-01T17:24:34+00:00
|
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