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12d516c8ffd2a3d5828de2b38c8b8f13ca67b57e
|
# Dataset Card for "DepressionDetection"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mangoesai/DepressionDetection
|
[
"region:us"
] |
2023-04-05T16:55:18+00:00
|
{"dataset_info": {"features": [{"name": "clean_text", "dtype": "string"}, {"name": "is_depression", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 2020382.4309921097, "num_examples": 5411}, {"name": "test", "num_bytes": 866251.5690078903, "num_examples": 2320}], "download_size": 1709340, "dataset_size": 2886634.0}}
|
2023-04-05T16:55:23+00:00
|
a79ab54d6a56efd39076413dc0e65b73805a4903
|
alan-txi/dnd-monsters
|
[
"license:mit",
"region:us"
] |
2023-04-05T17:07:01+00:00
|
{"license": "mit"}
|
2023-04-05T18:12:07+00:00
|
|
1443324656307b4147af56a84c8546326f98d6b2
|
# 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]
|
hyrax/Workshop-exampledataset
|
[
"region:us"
] |
2023-04-05T17:17:20+00:00
|
{}
|
2023-04-05T17:17:51+00:00
|
78c776b31cf94f946d2d38b74dd1de9efbf45c2a
|
Test Dataset Card
|
LennonTurner408/job_summaries
|
[
"region:us"
] |
2023-04-05T17:18:37+00:00
|
{}
|
2023-04-05T18:12:58+00:00
|
dc266ec732aaa33f2be20399ad1f63712fcb2d7e
|
# Dataset Card for "skatedecks"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
drumwell/skatedecks
|
[
"region:us"
] |
2023-04-05T17:23:49+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 27957.0, "num_examples": 6}], "download_size": 20088, "dataset_size": 27957.0}}
|
2023-04-05T17:23:50+00:00
|
a82a2631485c7eab40852f5ecd626f474908fce7
|
# Dataset Card for "DepressionDetection"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mangoesai/DepressionDetection-prompted
|
[
"region:us"
] |
2023-04-05T17:31:23+00:00
|
{"dataset_info": {"features": [{"name": "clean_text", "dtype": "string"}, {"name": "is_depression", "dtype": "int64"}, {"name": "instances", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 4664474, "num_examples": 5411}, {"name": "test", "num_bytes": 1897494, "num_examples": 2320}], "download_size": 3523750, "dataset_size": 6561968}}
|
2023-04-05T17:31:28+00:00
|
986b702d46707729690af61204cec2bd0563684c
|
# Dataset Card for "DepressionDetection-prompted"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
rjac/DepressionDetection-prompted
|
[
"region:us"
] |
2023-04-05T17:38:01+00:00
|
{"dataset_info": {"features": [{"name": "clean_text", "dtype": "string"}, {"name": "is_depression", "dtype": "int64"}, {"name": "instances", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 4631512, "num_examples": 5411}, {"name": "test", "num_bytes": 1930456, "num_examples": 2320}], "download_size": 3543125, "dataset_size": 6561968}}
|
2023-04-07T03:20:27+00:00
|
9a405731df2d6ab0a6e8d1c722215f2e07b816f8
|
# Dataset Card for "food_recipe_ner_v1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
kanak8278/food_recipe_ner_v1
|
[
"region:us"
] |
2023-04-05T17:38:51+00:00
|
{"dataset_info": {"features": [{"name": "tokens", "sequence": "string"}, {"name": "ner_tags", "sequence": "int64"}, {"name": "ner_map", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 1746217.1134020619, "num_examples": 6363}, {"name": "val", "num_bytes": 436622.88659793814, "num_examples": 1591}], "download_size": 305891, "dataset_size": 2182840.0}}
|
2023-04-05T17:38:56+00:00
|
a94da484de547cc5c8d12acaf85ddd9107c92e20
|
# Dataset Card for "ml_papers_abstracts"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mikezhu888/ml_papers_abstracts
|
[
"region:us"
] |
2023-04-05T17:45:38+00:00
|
{"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "response", "dtype": "string"}, {"name": "__index_level_0__", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1266188, "num_examples": 20}], "download_size": 676104, "dataset_size": 1266188}}
|
2023-04-05T17:45:40+00:00
|
01412ba699035de68fd55ae36045437c28073a06
|
# Dataset Card for "Kvasir-SEG"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
kowndinya23/Kvasir-SEG
|
[
"region:us"
] |
2023-04-05T17:47:22+00:00
|
{"dataset_info": {"features": [{"name": "name", "dtype": "string"}, {"name": "image", "dtype": "image"}, {"name": "annotation", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 36829616.0, "num_examples": 880}, {"name": "validation", "num_bytes": 8018441.0, "num_examples": 120}], "download_size": 44672597, "dataset_size": 44848057.0}}
|
2023-04-05T17:47:27+00:00
|
6e4de0a6de2cd8281d53b34e13d95ab10e41a793
|
# Dataset Card for "martin_valen_dataset_5"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
akadhim-ai/martin_valen_dataset_5
|
[
"region:us"
] |
2023-04-05T18:01:16+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 165549.0, "num_examples": 20}], "download_size": 82237, "dataset_size": 165549.0}}
|
2023-04-05T18:01:19+00:00
|
4d2de8905778f63eaa68a85daaf2be7512a45050
|
# Dataset Card for "success-llm-instructions"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
enoreyes/success-llm-instructions
|
[
"region:us"
] |
2023-04-05T18:03:05+00:00
|
{"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "completion", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 14435167, "num_examples": 23125}], "download_size": 8773761, "dataset_size": 14435167}}
|
2023-04-05T18:03:07+00:00
|
0f4bca5ef5959c8d0bdd84cc5d3fea5d2b35460d
|
# Dataset Card for "DepressionDetection"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
rjac/DepressionDetection
|
[
"region:us"
] |
2023-04-05T18:03:57+00:00
|
{"dataset_info": {"features": [{"name": "clean_text", "dtype": "string"}, {"name": "is_depression", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 2020382.4309921097, "num_examples": 5411}, {"name": "test", "num_bytes": 866251.5690078903, "num_examples": 2320}], "download_size": 1717661, "dataset_size": 2886634.0}}
|
2023-04-07T03:20:12+00:00
|
e55916bbd2aa8f55da2908f11629fc604019892d
|
Dataset created by crawling the [Drugs.com](https://www.drugs.com/) database - please abide by their [Terms and Conditions](https://www.drugs.com/support/terms.html)
### How the Graph was Created
Most drugs on Drugs.com have a **Related/Similar Drugs** page (e.g. [here](https://www.drugs.com/acetaminophen.html)). In my graph, nodes are drugs in the database, and edges are Related/Similar Drugs linked by the drug's description page.
Note: Not all drugs in the dataset are part of the graph, as not all drugs have a "Related/Similar Drugs" section
|
Ariel4/related-drugs-network
|
[
"size_categories:1K<n<10K",
"license:cc-by-4.0",
"chemistry",
"biology",
"graph",
"network",
"drugs",
"region:us"
] |
2023-04-05T18:08:30+00:00
|
{"license": "cc-by-4.0", "size_categories": ["1K<n<10K"], "pretty_name": "Network of Related Drugs from Drugs.com Database", "tags": ["chemistry", "biology", "graph", "network", "drugs"]}
|
2023-04-05T19:02:19+00:00
|
ccfaf6c39ef0216d00afe4137a5ee7cf1bc571db
|
# Dataset Card for Dataset Name
## Dataset Description
- **Repository: {https://github.com/ar852/chatgpt-scraping}**
### Dataset Summary
scraped-chatgpt-conversations contains ~100k conversations between a user and chatgpt that were shared online through reddit, twitter, or sharegpt. For sharegpt, the conversations were directly scraped from the website. For reddit and twitter, images were downloaded from submissions, segmented, and run through an OCR pipeline to obtain a conversation list. For information on how the each json file is structured, please see `json_guides.md`
### Languages
- twitter 1, twitter 2, and sharegpt json files are multilingual
- reddit and twitter 2 json files are english only
## Dataset Structure
- refer to *json_guide.txt*
## Dataset Creation
This dataset was created by scraping images from twitter, reddit, and sharegpt.com using the pushshift and twitter APIs, respectively. The images are run through a filter to check if they contain a chatgpt conversation, then the image is processed and run through an OCR pipeline to obtain the conversation text. More info can be found in the repository.
### Source Data
- twitter.com
- reddit.com
- sharegpt.com
## Considerations for Using the Data
A significant amount of dicts created from parsing reddit and twitter images may be parsed incorrectly for a number of reasons: cropping done by the image poster, incorrectly classifying the image as containing a chatgpt conversation, incorrect image parsing (segmentation) by the parser, incorrect OCR by pytesseract.
### Licensing Information
[More Information Needed]
### Contributions
[More Information Needed]
|
ar852/scraped-chatgpt-conversations
|
[
"task_categories:question-answering",
"task_categories:text-generation",
"task_categories:conversational",
"size_categories:100K<n<1M",
"region:us"
] |
2023-04-05T18:10:58+00:00
|
{"size_categories": ["100K<n<1M"], "task_categories": ["question-answering", "text-generation", "conversational"]}
|
2023-04-05T20:45:07+00:00
|
3262298f70f530446a968971621c0e9f51079f1c
|
# Dataset Card for "awesome_topic_code_snippets"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
RomanTeucher/awesome_topic_code_snippets
|
[
"region:us"
] |
2023-04-05T18:18:10+00:00
|
{"dataset_info": {"features": [{"name": "instruction", "dtype": "string"}, {"name": "code", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 62884, "num_examples": 131}], "download_size": 36309, "dataset_size": 62884}}
|
2023-04-05T18:18:12+00:00
|
d3fd70368530f936b9f0d3e5a5904c72ad87fc41
|
# 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)
|
lakshmiv/cool_new_dataset
|
[
"region:us"
] |
2023-04-05T18:29:38+00:00
|
{"dataset_info": {"features": [{"name": "name", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "ad", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3152, "num_examples": 5}], "download_size": 7476, "dataset_size": 3152}}
|
2023-04-05T18:29:40+00:00
|
ce62c2078b9c3ef8221825635d16f81da89243e7
|
# Dataset Card for "nobrainers"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
sungile/nobrainers
|
[
"region:us"
] |
2023-04-05T18:32:52+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 246996.0, "num_examples": 12}], "download_size": 248222, "dataset_size": 246996.0}}
|
2023-04-05T18:48:36+00:00
|
b512f8d456700c3f1340e581b9a42a28b1119b43
|
# Synthetic Dataset for Product Descriptions and Ads
The basic process was as follows:
1. Prompt GPT-4 to create a list of 100 sample clothing items and descriptions for those items.
2. Split the output into desired format `{"product" : "<PRODUCT NAME>", "description" : "<DESCRIPTION>"}
3. Prompt GPT-4 to create adverts for each of the 100 samples based on their name and description.
This data was not cleaned or verified manually.
|
deelow/restaurant-reviews
|
[
"task_categories:text-generation",
"size_categories:n<1K",
"language:en",
"license:openrail",
"art",
"region:us"
] |
2023-04-05T18:33:26+00:00
|
{"language": ["en"], "license": "openrail", "size_categories": ["n<1K"], "task_categories": ["text-generation"], "pretty_name": "Product Descriptions and Ads", "dataset_info": {"features": [{"name": "name", "dtype": "string"}, {"name": "review", "dtype": "string"}, {"name": "average_review", "dtype": "string"}, {"name": "menu", "dtype": "string"}, {"name": "stars", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 61707, "num_examples": 13}], "dataset_name": "json"}, "tags": ["art"]}
|
2023-04-05T20:22:33+00:00
|
6f0b80b6531e58527070925204b01030b7cc980a
|
# Dataset Card for "nobrainers2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
sungile/nobrainers2
|
[
"region:us"
] |
2023-04-05T18:50:11+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 209821.0, "num_examples": 19}], "download_size": 211013, "dataset_size": 209821.0}}
|
2023-04-05T19:58:26+00:00
|
f7293e87a2920f838090067c3220ee8435f08335
|
madabou/DKT
|
[
"license:mit",
"region:us"
] |
2023-04-05T18:50:50+00:00
|
{"license": "mit", "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 62715.0, "num_examples": 8}], "download_size": 58891, "dataset_size": 62715.0}}
|
2023-04-06T09:06:14+00:00
|
|
f56fa469d084e78184d7d8866e4c5a013138c73c
|
# Dataset Card for "batman_product_ads"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
jasonjones/batman_product_ads
|
[
"region:us"
] |
2023-04-05T18:51:55+00:00
|
{"dataset_info": {"features": [{"name": "product", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "ad", "dtype": "string"}, {"name": "piece_count", "dtype": "int64"}, {"name": "minifigures", "sequence": "string"}, {"name": "price", "dtype": "float64"}, {"name": "release_year", "dtype": "int64"}, {"name": "input_ids", "sequence": "int32"}, {"name": "token_type_ids", "sequence": "int8"}, {"name": "attention_mask", "sequence": "int8"}], "splits": [{"name": "train", "num_bytes": 10432, "num_examples": 5}], "download_size": 15755, "dataset_size": 10432}}
|
2023-04-05T18:51:59+00:00
|
3e940a510c4e7359bfdf5e56465f7c2a847da833
|
# 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)
|
deepaknautiyal/cool_new_dataset
|
[
"region:us"
] |
2023-04-05T18:53:54+00:00
|
{"dataset_info": {"features": [{"name": "name", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "ad", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 18741, "num_examples": 47}], "download_size": 13891, "dataset_size": 18741}}
|
2023-04-05T18:57:04+00:00
|
6eb337357fd62b4d00d9051288243f3d43b371f3
|
# Dataset Card for "demo_martinvalen"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
rthatha/demo_martinvalen
|
[
"region:us"
] |
2023-04-05T18:59:08+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 82775.0, "num_examples": 10}], "download_size": 82557, "dataset_size": 82775.0}}
|
2023-04-05T18:59:12+00:00
|
6e21fd35a04641c58e96322b03fab5eb38b86aa3
|
# 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)
|
narenb7/cool_new_dataset
|
[
"region:us"
] |
2023-04-05T19:13:58+00:00
|
{"dataset_info": {"features": [{"name": "name", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "ad", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 10463, "num_examples": 24}], "download_size": 10493, "dataset_size": 10463}}
|
2023-04-05T19:26:08+00:00
|
5b257e9d8aac5a8cd9b0f6c0eba1b69055d099f3
|
# 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)
|
JUpton/cool_new_dataset
|
[
"region:us"
] |
2023-04-05T19:15:43+00:00
|
{"dataset_info": {"features": [{"name": "name", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "ad", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3230, "num_examples": 5}], "download_size": 7691, "dataset_size": 3230}}
|
2023-04-05T19:15:45+00:00
|
80b175957aaa6806fb66c87e83a1adf3e52e2f48
|
Nisheethjaiswal/demo_dataset
|
[
"license:openrail",
"region:us"
] |
2023-04-05T19:17:04+00:00
|
{"license": "openrail"}
|
2023-04-05T19:17:04+00:00
|
|
bcf5f2eeb5a9f9b2b2cc6e71b9b9a796d27cffc9
|
pydan/mvdataset
|
[
"license:openrail",
"region:us"
] |
2023-04-05T19:18:20+00:00
|
{"license": "openrail", "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 174587.0, "num_examples": 10}], "download_size": 175182, "dataset_size": 174587.0}}
|
2023-05-07T11:24:58+00:00
|
|
6be8d6d77dd60633ac3f475e850c4beaac57f3cc
|
# Dataset Card for "ad_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
schwenkd/ad_dataset
|
[
"region:us"
] |
2023-04-05T19:27:14+00:00
|
{"dataset_info": {"features": [{"name": "name", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "ad", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 21238, "num_examples": 52}], "download_size": 14670, "dataset_size": 21238}}
|
2023-04-05T19:27:16+00:00
|
cf6ff1b135aa6acf72c1bf966f8a2a76363c283a
|
# Dataset Card for "evaluation_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
AlekseyKorshuk/evaluation_dataset
|
[
"region:us"
] |
2023-04-05T19:31:08+00:00
|
{"dataset_info": {"features": [{"name": "input_text", "dtype": "string"}, {"name": "output_text", "dtype": "string"}, {"name": "user_id", "dtype": "string"}, {"name": "tokens", "sequence": "int64"}], "splits": [{"name": "validation", "num_bytes": 116428164, "num_examples": 54881}, {"name": "train", "num_bytes": 465740765, "num_examples": 219524}], "download_size": 320290001, "dataset_size": 582168929}}
|
2023-04-05T19:31:24+00:00
|
b6407a13deb042d50d08a1bbb494c4b4dda1ab0e
|
# Balloons
The [Balloons dataset](https://archive.ics.uci.edu/ml/datasets/Balloons) from the [UCI ML repository](https://archive.ics.uci.edu/ml/datasets).
Predict if the given balloon is inflated.
# Configurations and tasks
| **Configuration** | **Task** | Description |
|--------------------------------------------|---------------------------|--------------------------------------------------------------------------------------------------|
| adult_or_stretch | Binary classification | Balloons are inflated if age == adult or act == stretch. |
| adult_and_stretch | Binary classification | Balloons are inflated if age == adult and act == stretch. |
| yellow_and_small | Binary classification | Balloons are inflated if color == yellow and size == small. |
| yellow_and_small_or_adult_and_stretch | Binary classification | Balloons are inflated if color == yellow and size == small or age == adult and act == stretch. |
# Usage
```python
from datasets import load_dataset
dataset = load_dataset("mstz/balloons", "adult_or_stretch")["train"]
```
# Features
|**Feature** |**Type** | **Description** |
|-------------------|-----------|-------------------|
|`color` |`[string]` | Balloon's color. |
|`size` |`[string]` | Balloon's size. |
|`act` |`[string]` | Balloon's state. |
|`age` |`[string]` | Balloon's age. |
|`is_inflated` | `[int8]`| The inflation status of the baloon.|
|
mstz/balloons
|
[
"task_categories:tabular-classification",
"size_categories:n<1K",
"language:en",
"license:cc",
"balloons",
"tabular_classification",
"binary_classification",
"UCI",
"region:us"
] |
2023-04-05T19:32:06+00:00
|
{"language": ["en"], "license": "cc", "size_categories": ["n<1K"], "task_categories": ["tabular-classification"], "pretty_name": "Balloons", "tags": ["balloons", "tabular_classification", "binary_classification", "UCI"], "configs": ["adult_or_stretch", "adult_and_stretch", "yellow_and_small", "yellow_and_small_or_adult_and_stretch"]}
|
2023-04-15T10:16:18+00:00
|
02181901a748553113fe6587879200d831fe2883
|
pritam1984314/cool_job_dataset
|
[
"task_categories:text-generation",
"size_categories:n<1K",
"language:en",
"license:openrail",
"region:us"
] |
2023-04-05T19:40:04+00:00
|
{"language": ["en"], "license": "openrail", "size_categories": ["n<1K"], "task_categories": ["text-generation"], "pretty_name": "headline"}
|
2023-04-05T20:02:20+00:00
|
|
6d09aa1941d5b1ff7ac83f8417020fe8dc30d016
|
ablack3/synthetic_health1
|
[
"license:openrail",
"region:us"
] |
2023-04-05T19:43:19+00:00
|
{"license": "openrail"}
|
2023-04-05T20:33:12+00:00
|
|
056cb6a305e73b96d627177228ee17f4cb882a5c
|
# Dataset Card for "ask2democracy-cfqa-salud-pension"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
jorge-henao/ask2democracy-cfqa-salud-pension
|
[
"region:us"
] |
2023-04-05T19:44:13+00:00
|
{"dataset_info": {"features": [{"name": "instruction", "dtype": "string"}, {"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}, {"name": "topics", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 7711587, "num_examples": 3805}], "download_size": 880079, "dataset_size": 7711587}}
|
2023-04-05T19:44:18+00:00
|
778b2790f611a3dd705385ccdba46d5b932e0951
|
# Blood
The [Blood Transfusion dataset](https://archive-beta.ics.uci.edu/dataset/176/blood+transfusion+service+center) from the [UCI ML repository](https://archive.ics.uci.edu/ml/datasets).
Census dataset including personal characteristic of a person, and their income threshold.
# Configurations and tasks
| **Configuration** | **Task** | **Description** |
|-------------------|---------------------------|---------------------------------------------------------------|
| blood | Binary classification | Has the person donated blood in the past month? |
# Usage
```python
from datasets import load_dataset
dataset = load_dataset("mstz/blood")["train"]
```
|
mstz/blood
|
[
"task_categories:tabular-classification",
"size_categories:n<1K",
"language:en",
"license:cc",
"blood",
"tabular_classification",
"binary_classification",
"multiclass_classification",
"UCI",
"region:us"
] |
2023-04-05T19:51:24+00:00
|
{"language": ["en"], "license": "cc", "size_categories": ["n<1K"], "task_categories": ["tabular-classification"], "pretty_name": "Blood Transfusion", "tags": ["blood", "tabular_classification", "binary_classification", "multiclass_classification", "UCI"], "configs": ["blood"]}
|
2023-04-15T10:37:04+00:00
|
ca6b48c8ce435b4750efc895d3c40bfab8bd7d9a
|
# GPT Detectors Are Biased Against Non-Native English Writers
[](https://lbesson.mit-license.org/)
[](https://www.python.org/downloads/release/python-390/)
[](https://jupyter.org/try)
This repository contains the data and supplementary materials for our paper:
**GPT Detectors Are Biased Against Non-Native English Writers**\
Weixin Liang*, Mert Yuksekgonul*, Yining Mao*, Eric Wu*, James Zou\
arXiv: [2304.02819](https://arxiv.org/abs/2304.02819)
```bibtex
@article{liang2023gpt,
title={GPT detectors are biased against non-native English writers},
author={Weixin Liang and Mert Yuksekgonul and Yining Mao and Eric Wu and James Zou},
year={2023},
eprint={2304.02819},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
## Abstract
*The rapid adoption of generative language models has brought about substantial advancements in digital communication, while simultaneously raising concerns regarding the potential misuse of AI-generated content. Although numerous detection methods have been proposed to differentiate between AI and human-generated content, the fairness and robustness of these detectors remain underexplored. In this study, we evaluate the performance of several widely-used GPT detectors using writing samples from native and non-native English writers. Our findings reveal that these detectors consistently misclassify non-native English writing samples as AI-generated, whereas native writing samples are accurately identified. Furthermore, we demonstrate that simple prompting strategies can not only mitigate this bias but also effectively bypass GPT detectors, suggesting that GPT detectors may unintentionally penalize writers with constrained linguistic expressions. Our results call for a broader conversation about the ethical implications of deploying ChatGPT content detectors and caution against their use in evaluative or educational settings, particularly when they may inadvertently penalize or exclude non-native English speakers from the global discourse.*
<p align='center'>
<img width="636" src="https://user-images.githubusercontent.com/32794044/230640445-8d1221d4-8651-4cf4-b6d7-b6d440d6e0f5.png">
<br>
<b>Figure 1: Bias in GPT detectors against non-native English writing samples.</b>
</p>
(a) Performance comparison of seven widely-used GPT detectors. More than half of the non-native-authored TOEFL (Test of English as a Foreign Language) essays are incorrectly classified as "AI-generated," while detectors exhibit near-perfect accuracy for college essays.
Using ChatGPT-4 to improve the word choices in TOEFL essays (Prompt: "Enhance the word choices to sound more like that of a native speaker.") significantly reduces misclassification as AI-generated text.
(b) TOEFL essays unanimously misclassified as AI-generated show significantly lower perplexity compared to others, suggesting that GPT detectors might penalize authors with limited linguistic expressions.
<p align='center'>
<img width="100%" src="https://user-images.githubusercontent.com/32794044/230640270-e6c3d0ca-aabd-4d13-8527-15fed1491050.png">
<br>
<b>Figure 2: Simple prompts effectively bypass GPT detectors.</b>
</p>
(a) For ChatGPT-3.5 generated college admission essays, the performance of seven widely-used GPT detectors declines markedly when a second-round self-edit prompt ("Elevate the provided text by employing literary language") is applied, with detection rates dropping from up to 100% to up to 13%.
(b) ChatGPT-3.5 generated essays initially exhibit notably low perplexity; however, applying the self-edit prompt leads to a significant increase in perplexity.
(c) Similarly, in detecting ChatGPT-3.5 generated scientific abstracts, a second-round self-edit prompt ("Elevate the provided text by employing advanced technical language") leads to a reduction in detection rates from up to 68% to up to 28%.
(d) ChatGPT-3.5 generated abstracts have slightly higher perplexity than the generated essays but remain low. Again, the self-edit prompt significantly increases the perplexity.
## Repo Structure Overview
```
.
├── README.md
├── data/
├── human_data/
├── TOEFL_real_91/
├── name.json
├── data.json
├── TOEFL_gpt4polished_91/
├── ...
├── CollegeEssay_real_70/
├── CS224N_real_145/
├── gpt_data/
├── CollegeEssay_gpt3_31/
├── CollegeEssay_gpt3PromptEng_31/
├── CS224N_gpt3_145/
├── CS224N_gpt3PromptEng_145/
```
The `data` folder contains the human-written and AI-generated datasets used in our study. Each subfolder contains a `name.json` file, which provides the metadata, and a `data.json` file, which contains the text samples.
## Reference
```bibtex
@article{liang2023gpt,
title={GPT detectors are biased against non-native English writers},
author={Weixin Liang and Mert Yuksekgonul and Yining Mao and Eric Wu and James Zou},
year={2023},
eprint={2304.02819},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
WxWx/ChatGPT-Detector-Bias
|
[
"task_categories:text-classification",
"size_categories:n<1K",
"language:en",
"license:mit",
"ChatGPT",
"GPT Detector",
"ChatGPT Detector",
"arxiv:2304.02819",
"region:us"
] |
2023-04-05T19:57:48+00:00
|
{"language": ["en"], "license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-classification"], "tags": ["ChatGPT", "GPT Detector", "ChatGPT Detector"]}
|
2023-04-09T23:48:06+00:00
|
cbaf886f7704ac93c3fb9a5479b60625ed641dae
|
# Dataset Card for "bk_gen_ai"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
bryantaekim/bk_gen_ai
|
[
"region:us"
] |
2023-04-05T20:08:47+00:00
|
{"dataset_info": {"features": [{"name": "name", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "ad", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3022, "num_examples": 5}], "download_size": 6806, "dataset_size": 3022}}
|
2023-04-05T20:15:45+00:00
|
2f111066a70ab9533fb00cf9e030383c618d2195
|
data-corentinv/dataset
|
[
"license:mit",
"region:us"
] |
2023-04-05T20:10:31+00:00
|
{"license": "mit"}
|
2023-04-05T20:10:31+00:00
|
|
0df5fb2dd4696d283930c1cbb7422810274d053c
|
# 🚢 Stanford Human Preferences Dataset (SHP) (Italian Translation)
The Stanford Human Preferences Dataset (SHP) is a collection of responses to questions and instructions in 18 different subject areas, ranging from cooking to legal advice. This version of the dataset is a **partial** Italian translation of the original English dataset.
Please note that the quality of the translations has not been verified. However, the dataset may still be useful for training models.
Each example in the dataset consists of a Reddit post that includes a question or instruction and a pair of top-level comments. The comments are ranked according to their perceived helpfulness by Reddit users. SHP uses the fact that if comment A has a higher score than comment B despite being written after B, then A is considered more preferred.
The preference labels in the dataset reflect the helpfulness of a response, rather than identifying harmful responses. This approach differs from previous work that focused on identifying harmful responses.
|
efederici/shp-partial-it
|
[
"task_categories:text-generation",
"task_categories:question-answering",
"size_categories:10K<n<100K",
"language:it",
"RLHF",
"preferences",
"RL",
"human feedback",
"reddit",
"region:us"
] |
2023-04-05T20:16:13+00:00
|
{"language": ["it"], "size_categories": ["10K<n<100K"], "task_categories": ["text-generation", "question-answering"], "tags": ["RLHF", "preferences", "RL", "human feedback", "reddit"]}
|
2023-04-05T20:27:00+00:00
|
42a6b223b7ac7613d1ddfebb626776c15c5b0f55
|
# Chess Rock VS Pawn
The [Chess Rock VS Pawn dataset](https://archive-beta.ics.uci.edu/dataset/22/chess+king+rook+vs+king+pawn) from the [UCI ML repository](https://archive.ics.uci.edu/ml/datasets).
# Configurations and tasks
| **Configuration** | **Task** | **Description** |
|-------------------|---------------------------|--------------------------|
| chess | Binary classification | Can the white piece win? |
# Usage
```python
from datasets import load_dataset
dataset = load_dataset("mstz/chess_rock_vs_pawn")["train"]
```
|
mstz/chess_rock_vs_pawn
|
[
"task_categories:tabular-classification",
"size_categories:1K<n<10K",
"language:en",
"license:cc",
"chess",
"tabular_classification",
"binary_classification",
"multiclass_classification",
"UCI",
"region:us"
] |
2023-04-05T20:23:27+00:00
|
{"language": ["en"], "license": "cc", "size_categories": ["1K<n<10K"], "task_categories": ["tabular-classification"], "pretty_name": "Chess Rock VS Pawn", "tags": ["chess", "tabular_classification", "binary_classification", "multiclass_classification", "UCI"], "configs": ["chess"]}
|
2023-04-16T16:01:23+00:00
|
454f7346046ed87776678dd3b2019225a8f5d9c3
|
# Dataset Card for "news_summarization_vi_prompt"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
nlplabtdtu/summarization_sft_65K_prompted
|
[
"language:vi",
"region:us"
] |
2023-04-05T20:24:14+00:00
|
{"language": "vi", "dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 3305091, "num_examples": 1000}, {"name": "train", "num_bytes": 217481218, "num_examples": 65361}], "download_size": 101604861, "dataset_size": 220786309}}
|
2023-07-15T08:20:25+00:00
|
a705c549706eec3165e7e9ff5cbfd57fc39d99d5
|
madabou/dkt_dataset
|
[
"license:mit",
"region:us"
] |
2023-04-05T20:24:31+00:00
|
{"license": "mit", "dataset_info": {"features": [{"name": "image", "dtype": "binary"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1302979, "num_examples": 4}], "download_size": 1254355, "dataset_size": 1302979}}
|
2023-04-05T20:24:56+00:00
|
|
cd1dd72d408229a2234bed42fdac4596f1084c51
|
herrius/alpace-es-backup
|
[
"license:apache-2.0",
"region:us"
] |
2023-04-05T20:31:53+00:00
|
{"license": "apache-2.0"}
|
2023-04-05T20:31:53+00:00
|
|
01879d375dfa2a0f6d9d1f437d7a7f0788b9f095
|
efederici/lfqa-preprocessed-it
|
[
"task_categories:question-answering",
"task_categories:text-generation",
"language:it",
"lfqa",
"mt",
"region:us"
] |
2023-04-05T20:38:00+00:00
|
{"language": ["it"], "task_categories": ["question-answering", "text-generation"], "tags": ["lfqa", "mt"]}
|
2023-04-13T20:32:54+00:00
|
|
def61abd9dd8d05a3264a4585ce2d1181185be65
|
# Dataset Card for "roleplay-io"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
AlekseyKorshuk/roleplay-io
|
[
"region:us"
] |
2023-04-05T20:44:55+00:00
|
{"dataset_info": {"features": [{"name": "input_text", "dtype": "string"}, {"name": "output_text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2495441, "num_examples": 3146}], "download_size": 1543319, "dataset_size": 2495441}}
|
2023-04-05T20:44:58+00:00
|
2b7ec01cc0cfb399df35edef74e6ef02771315c2
|
This dataset is taken from https://github.com/arnav-gudibande/koala-test-set
|
HuggingFaceH4/Koala-test-set
|
[
"license:apache-2.0",
"region:us"
] |
2023-04-05T20:53:39+00:00
|
{"license": "apache-2.0"}
|
2023-04-05T20:54:31+00:00
|
330e48b1e92552e97e8610bd0fba1271b2b6f3c5
|
Armenian wikipedia at date 04.2023
80M tokens
296.539 articles
|
armvectores/hy_wikipedia_2023
|
[
"task_categories:text-generation",
"size_categories:10M<n<100M",
"language:hy",
"wikipedia",
"region:us"
] |
2023-04-05T20:56:34+00:00
|
{"language": ["hy"], "size_categories": ["10M<n<100M"], "task_categories": ["text-generation"], "dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "title", "dtype": "string"}, {"name": "article", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1059747688, "num_examples": 296538}], "download_size": 0, "dataset_size": 1059747688}, "tags": ["wikipedia"]}
|
2023-04-09T09:24:13+00:00
|
048d11b98f5bd56938c048302c3f90ac6144acbd
|
halbast/Saul_Goodman
|
[
"license:cc",
"region:us"
] |
2023-04-05T21:00:00+00:00
|
{"license": "cc"}
|
2023-04-05T21:00:00+00:00
|
|
4f488f6f85dc1c835e5fdb71435776605f0f90f5
|
# Dataset Card for "iwslt-2023-en-vi-train-val-split-0.1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
shreevigneshs/iwslt-2023-en-vi-train-val-split-0.1
|
[
"region:us"
] |
2023-04-05T21:38:15+00:00
|
{"dataset_info": {"features": [{"name": "en", "dtype": "string"}, {"name": "vi", "dtype": "string"}, {"name": "vi_annotated", "dtype": "string"}, {"name": "styles", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 326525.0, "num_examples": 720}, {"name": "val", "num_bytes": 36694.0, "num_examples": 80}, {"name": "if_test", "num_bytes": 275045.0, "num_examples": 598}, {"name": "f_test", "num_bytes": 294897.0, "num_examples": 598}, {"name": "f_flores", "num_bytes": 337966, "num_examples": 1012}, {"name": "if_flores", "num_bytes": 337966, "num_examples": 1012}], "download_size": 409674, "dataset_size": 1609093.0}}
|
2023-04-17T22:51:29+00:00
|
ea7a34c68295667b3fcaba3db1fbe19d10dcb184
|
# Dataset Card for "iwslt-2023-en-ko-train-val-split-0.1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
shreevigneshs/iwslt-2023-en-ko-train-val-split-0.1
|
[
"region:us"
] |
2023-04-05T21:39:06+00:00
|
{"dataset_info": {"features": [{"name": "en", "dtype": "string"}, {"name": "ko", "dtype": "string"}, {"name": "ko_annotated", "dtype": "string"}, {"name": "styles", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 283232.0, "num_examples": 720}, {"name": "val", "num_bytes": 32220.0, "num_examples": 80}, {"name": "if_test", "num_bytes": 238485.0, "num_examples": 597}, {"name": "f_test", "num_bytes": 249702.0, "num_examples": 597}, {"name": "f_flores", "num_bytes": 312159, "num_examples": 1012}, {"name": "if_flores", "num_bytes": 312159, "num_examples": 1012}], "download_size": 702238, "dataset_size": 1427957.0}}
|
2023-04-17T22:52:20+00:00
|
0bcce1dd49f2063ce632ed04f02c4167b6d994ad
|
0.7M tokens
|
armvectores/hy_parallel_rnc_2023
|
[
"task_categories:text-generation",
"size_categories:100K<n<1M",
"language:hy",
"region:us"
] |
2023-04-05T21:51:05+00:00
|
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\u056b\u0576\u0579-\u0578\u0580 \u0562\u0561\u0576 \u056f\u0561\u0580\u0578\u0572 \u0567 \u0561\u0572\u0561\u057e\u0561\u0572\u057e\u0565\u056c\u0589 \u054d\u057f\u0561\u056c\u056b\u0576\u0576 \u056b\u0576\u0584\u0576 \u0567 \u0561\u057d\u0578\u0582\u0574 \u056a\u0578\u0572\u0578\u057e\u0580\u0564\u056b\u0576 \u0561\u0575\u0576, \u056b\u0576\u0579 \u0570\u0561\u0580\u056f \u0567 \u0570\u0561\u0574\u0561\u0580\u0578\u0582\u0574\u0589 \u0544\u0561\u0580\u056f \u0531\u056c\u0565\u0584\u057d\u0561\u0576\u0564\u0580\u0578\u057e\u056b\u0579\u0568 \u056a\u0561\u0574\u056f\u0565\u057f\u0576\u0565\u0580\u0568, \u0585\u0562\u0575\u0565\u056f\u057f\u0576\u0565\u0580\u0576 \u0567\u0580 \u0561\u057d\u0578\u0582\u0574`\u0570\u0565\u0576\u0581 \u0564\u0561 \u0567\u056c \u054d\u057f\u0561\u056c\u056b\u0576\u056b \u056f\u0561\u0574\u0584\u0576 \u0567\u0580\u0589 \u0533\u056c\u056d\u0561\u057e\u0578\u0580\u0576 \u0561\u0575\u0576 \u0567\u0580, \u0578\u0580 \u0574\u0561\u0580\u057f\u0565\u0576\u0575\u0561\u0576 \u0565\u0580\u056f\u0580\u0578\u0580\u0564 \u0561\u0580\u057f\u0561\u0564\u0580\u0561\u0574\u0561\u057d\u056b \u0577\u056b\u0576\u0561\u0580\u0561\u0580\u0578\u0582\u0569\u0575\u0578\u0582\u0576\u0576 \u0561\u057e\u0561\u0580\u057f\u0565\u056c\u0578\u0582 \u056a\u0561\u0574\u056f\u0565\u057f\u0568 \u0570\u0565\u057f\u0561\u0571\u0563\u057e\u0561\u056e \u0567 \u0574\u0565\u056f \u057f\u0561\u0580\u0578\u057e\u0589 \u0534\u0561 \u056f\u0561\u0576\u056d\u0561\u0563\u0578\u0582\u0577\u0561\u056f\u0578\u0582\u0574 \u0567\u0580 \u0576\u0578\u0580, \u057c\u0565\u0561\u056c\u056b\u057d\u057f\u0561\u056f\u0561\u0576 \u0574\u0578\u057f\u0565\u0581\u0578\u0582\u0574`\u0565\u0580\u056f\u0580\u0578\u0580\u0564 \u0570\u0576\u0563\u0561\u0574\u0575\u0561\u056f\u056b \u057a\u056c\u0561\u0576\u0568 \u056f\u0561\u0566\u0574\u0565\u056c\u056b\u057d. \u0574\u0565\u057f\u0561\u0572\u0576", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1525824, "num_examples": 27}], "download_size": 1413391, "dataset_size": 1525824}}
|
2023-04-06T21:30:31+00:00
|
34a012dbad35041ac8ab74a4a7ebc94da8b895b5
|
# joha
|
aymanelmar/joha
|
[
"region:us"
] |
2023-04-05T21:56:20+00:00
|
{}
|
2023-04-12T23:31:31+00:00
|
9c4ac2078c4764622054913f436464dd2f88236a
|
echodpp/fer2013_preprocess
|
[
"license:openrail",
"region:us"
] |
2023-04-05T23:46:31+00:00
|
{"license": "openrail"}
|
2023-04-05T23:49:43+00:00
|
|
91520ba3b44ac063a8b70a6459f3e73b18060cee
|
FalconRR/Voseo_Tweets
|
[
"task_categories:text-classification",
"size_categories:10K<n<100K",
"license:mit",
"region:us"
] |
2023-04-05T23:53:34+00:00
|
{"license": "mit", "size_categories": ["10K<n<100K"], "task_categories": ["text-classification"]}
|
2023-04-05T23:56:01+00:00
|
|
5c1fdc26986af05d0d89c772c56dada6894f9c5e
|
J2005dotcom/Bone-isms
|
[
"task_categories:conversational",
"task_categories:question-answering",
"language:en",
"license:apache-2.0",
"region:us"
] |
2023-04-06T00:04:40+00:00
|
{"language": ["en"], "license": "apache-2.0", "task_categories": ["conversational", "question-answering"]}
|
2023-04-15T23:30:03+00:00
|
|
aefdeb1ff1a739b92ac5338682cc2610a0328505
|
<p align="center"><img src="images/banner.png"/></p>
# Overview
Information of **more than 85,000 games** published on Steam. Maintained by **[Fronkon Games](https://github.com/FronkonGames)**.
This dataset has been created with **[this code (MIT)](https://github.com/FronkonGames/Steam-Games-Scraper)** and use the API provided by _Steam_, the largest gaming platform on PC. Data is also collected from _Steam Spy_.
Only published games, _no DLCs, episodes, music, videos, etc_.
Here is a simple example of how to parse json information:
```
# Simple parse of the 'games.json' file.
import os
import json
dataset = {}
if os.path.exists('games.json'):
with open('games.json', 'r', encoding='utf-8') as fin:
text = fin.read()
if len(text) > 0:
dataset = json.loads(text)
for app in dataset:
appID = app # AppID, unique identifier for each app (string).
game = dataset[app]
name = game['name'] # Game name (string).
releaseDate = game['release_date'] # Release date (string).
estimatedOwners = game['estimated_owners'] # Estimated owners (string, e.g.: "0 - 20000").
peakCCU = game['peak_ccu'] # Number of concurrent users, yesterday (int).
required_age = game['required_age'] # Age required to play, 0 if it is for all audiences (int).
price = game['price'] # Price in USD, 0.0 if its free (float).
dlcCount = game['dlc_count'] # Number of DLCs, 0 if you have none (int).
longDesc = game['detailed_description'] # Detailed description of the game (string).
shortDesc = game['short_description'] # Brief description of the game,
# does not contain HTML tags (string).
languages = game['supported_languages'] # Comma-separated enumeration of supporting languages.
fullAudioLanguages = game['full_audio_languages'] # Comma-separated enumeration of languages with audio support.
reviews = game['reviews'] #
headerImage = game['header_image'] # Header image URL in the store (string).
website = game['website'] # Game website (string).
supportWeb = game['support_url'] # Game support URL (string).
supportEmail = game['support_email'] # Game support email (string).
supportWindows = game['windows'] # Does it support Windows? (bool).
supportMac = game['mac'] # Does it support Mac? (bool).
supportLinux = game['linux'] # Does it support Linux? (bool).
metacriticScore = game['metacritic_score'] # Metacritic score, 0 if it has none (int).
metacriticURL = game['metacritic_url'] # Metacritic review URL (string).
userScore = game['user_score'] # Users score, 0 if it has none (int).
positive = game['positive'] # Positive votes (int).
negative = game['negative'] # Negative votes (int).
scoreRank = game['score_rank'] # Score rank of the game based on user reviews (string).
achievements = game['achievements'] # Number of achievements, 0 if it has none (int).
recommens = game['recommendations'] # User recommendations, 0 if it has none (int).
notes = game['notes'] # Extra information about the game content (string).
averagePlaytime = game['average_playtime_forever'] # Average playtime since March 2009, in minutes (int).
averageplaytime2W = game['average_playtime_2weeks'] # Average playtime in the last two weeks, in minutes (int).
medianPlaytime = game['median_playtime_forever'] # Median playtime since March 2009, in minutes (int).
medianPlaytime2W = game['median_playtime_2weeks'] # Median playtime in the last two weeks, in minutes (int).
packages = game['packages'] # Available packages.
for pack in packages:
title = pack['title'] # Package title (string).
packDesc = pack['description'] # Package description (string).
subs = pack['subs'] # Subpackages.
for sub in subs:
text = sub['text'] # Subpackage title (string).
subDesc = sub['description'] # Subpackage description (string).
subPrice = sub['price'] # Subpackage price in USD (float).
developers = game['developers'] # Game developers.
for developer in developers:
developerName = developer # Developer name (string).
publishers = game['publishers'] # Game publishers.
for publisher in publishers:
publisherName = publisher # Publisher name (string).
categories = game['categories'] # Game categories.
for category in categories:
categoryName = category # Category name (string).
genres = game['genres'] # Game genres.
for gender in genres:
genderName = gender # Gender name (string).
screenshots = game['scrennshots'] # Game screenshots.
for screenshot in screenshots:
scrennshotsURL = screenshot # Game screenshot URL (string).
movies = game['movies'] # Game movies.
for movie in movies:
movieURL = movie # Game movie URL (string).
tags = game['tags'] # Tags.
for tag in tags:
tagKey = tag # Tag key (string, int).
```
|
FronkonGames/steam-games-dataset
|
[
"task_categories:text-generation",
"task_categories:text2text-generation",
"size_categories:10K<n<100K",
"language:en",
"license:cc-by-4.0",
"games",
"steam",
"video games",
"gamedev",
"doi:10.57967/hf/0511",
"region:us"
] |
2023-04-06T00:17:47+00:00
|
{"language": ["en"], "license": "cc-by-4.0", "size_categories": ["10K<n<100K"], "task_categories": ["text-generation", "text2text-generation"], "pretty_name": "Steam Games Dataset", "tags": ["games", "steam", "video games", "gamedev"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-00000-of-00001-e2ed184370a06932.parquet"}]}]}
|
2024-01-11T23:21:37+00:00
|
b869f2501070b1d726589c2a589e488fd58c417b
|
ntnxx2/license_plate_roboflow
|
[
"license:cc-by-4.0",
"region:us"
] |
2023-04-06T00:18:39+00:00
|
{"license": "cc-by-4.0"}
|
2023-04-06T00:40:47+00:00
|
|
fe1f5d1e0c2eba2d30043362cabd9ae45d5fc6c6
|
# Dataset Card for "uesp-wiki-content"
This dataset contains the content of the pages from the [Unofficial Elder Scrolls Pages](https://en.uesp.net/wiki/Main_Page).
**License**: The contents of this dataset are licensed under the [Creative Commons by-sa 2.5 License](http://creativecommons.org/licenses/by-sa/2.5/).
**Source**:
* The content of this dataset was taken from the [dumps](http://dumps.uesp.net/) subdomain of [uesp.net](https://uesp.net)
* The contents of this dataset come from the file named "`uespwiki-2022-02-09-current.xml.bz2`", as that was the most recent version available at the time of dataset creation.
* The archive file was processed by [mediawiki-dump](https://github.com/macbre/mediawiki-dump)
* Using [my own fork](https://github.com/Netruk44/mediawiki-dump/tree/namespace-fix) to fix a bug with cleaning the text.
**Caveats**:
* The `content_cleaned` column has some known issues.
* Words may occasionally be missing if they were a special link type in the original content.
|
Netruk44/uesp-wiki-content
|
[
"size_categories:100K<n<1M",
"language:en",
"license:other",
"region:us"
] |
2023-04-06T00:33:46+00:00
|
{"language": ["en"], "license": "other", "size_categories": ["100K<n<1M"], "dataset_info": {"features": [{"name": "namespace", "dtype": "int64"}, {"name": "page_id", "dtype": "int64"}, {"name": "url", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "content", "dtype": "string"}, {"name": "revision_id", "dtype": "int64"}, {"name": "timestamp", "dtype": "string"}, {"name": "contributor", "dtype": "string"}, {"name": "content_cleaned", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 757966297, "num_examples": 324930}], "download_size": 363485644, "dataset_size": 757966297}}
|
2023-04-10T19:20:54+00:00
|
855ec788f355750c578e231de1cd13f69f57e233
|
# Dataset Card for "MIMIC-medical-report"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
IndianaUniversityDatasetsModels/MIMIC-medical-report
|
[
"region:us"
] |
2023-04-06T01:46:47+00:00
|
{"dataset_info": {"features": [{"name": "FileName", "dtype": "string"}, {"name": "INDICATION", "dtype": "string"}, {"name": "IMPRESSION", "dtype": "string"}, {"name": "FINDINGS", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 45203432.183416, "num_examples": 83971}, {"name": "test", "num_bytes": 461341.9082919998, "num_examples": 857}, {"name": "validation", "num_bytes": 461341.9082919998, "num_examples": 857}], "download_size": 20175619, "dataset_size": 46126116.00000001}}
|
2023-04-06T01:47:09+00:00
|
4b7d5263868b7f277391ebe5cbea4e4ca136fb14
|
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:** [email protected]
### 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]
|
lambdasec/gh-top-1000-projects-vulns
|
[
"task_categories:text-generation",
"size_categories:n<1K",
"language:code",
"license:apache-2.0",
"code",
"region:us"
] |
2023-04-06T02:15:56+00:00
|
{"language": ["code"], "license": "apache-2.0", "size_categories": ["n<1K"], "task_categories": ["text-generation"], "pretty_name": "Github Top 1000 Projects Vulnerabilities", "tags": ["code"]}
|
2023-04-11T02:31:45+00:00
|
768a138f1bb52279cb442fad218af7ff519111d1
|
siddicky/offsec-help-docs
|
[
"license:cc-by-nc-4.0",
"region:us"
] |
2023-04-06T02:26:40+00:00
|
{"license": "cc-by-nc-4.0"}
|
2023-04-06T02:27:52+00:00
|
|
468fdbffdf3481cd3076643f0cb0fe4d129ce594
|
A collection of images from a very silly cat, these are all from @fatfatmillycat in twitter. Intended to be used with stable-diffusion-v1-4
|
spongus/milly-images
|
[
"task_categories:text-to-image",
"task_categories:image-classification",
"task_categories:image-segmentation",
"size_categories:n<1K",
"language:en",
"license:unlicense",
"image",
"cat",
"silly",
"calico",
"region:us"
] |
2023-04-06T02:45:01+00:00
|
{"language": ["en"], "license": "unlicense", "size_categories": ["n<1K"], "task_categories": ["text-to-image", "image-classification", "image-segmentation"], "pretty_name": "Milly Images", "tags": ["image", "cat", "silly", "calico"]}
|
2023-04-15T16:41:37+00:00
|
38b41cbad92897b51164affca12064adc7348aba
|
emmettroberts12/GrapeAIGrapeMind
|
[
"license:c-uda",
"region:us"
] |
2023-04-06T02:55:53+00:00
|
{"license": "c-uda"}
|
2023-04-06T02:56:53+00:00
|
|
169c1bbc0d3e071eea1bb4639858c1ad207df947
|
Joe02/Belm_style_refs
|
[
"license:other",
"region:us"
] |
2023-04-06T03:10:58+00:00
|
{"license": "other"}
|
2023-04-06T03:11:16+00:00
|
|
11a11fb6f9ef24b9055908971af60781847f89fb
|
Rhitrao/YL1
|
[
"license:other",
"region:us"
] |
2023-04-06T03:48:19+00:00
|
{"license": "other"}
|
2023-04-06T03:48:19+00:00
|
|
4eee0162f9e83d22c69d1c2d55ac865ffc25b55c
|
sin3142/memes-500
|
[
"task_categories:image-classification",
"size_categories:n<1K",
"region:us"
] |
2023-04-06T03:56:19+00:00
|
{"size_categories": ["n<1K"], "task_categories": ["image-classification"]}
|
2023-04-06T03:57:22+00:00
|
|
28be995efe3e604be18a89317a2b63b59bf6f959
|
FabioInspires/hoodies
|
[
"license:openrail",
"region:us"
] |
2023-04-06T04:06:03+00:00
|
{"license": "openrail", "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 82775.0, "num_examples": 10}], "download_size": 82557, "dataset_size": 82775.0}}
|
2023-04-06T04:07:11+00:00
|
|
8b09571ebde489058d122fc1e8da100cdac20d18
|
sin3142/memes-1500
|
[
"task_categories:image-classification",
"size_categories:1K<n<10K",
"region:us"
] |
2023-04-06T04:10:52+00:00
|
{"size_categories": ["1K<n<10K"], "task_categories": ["image-classification"]}
|
2023-04-06T04:11:46+00:00
|
|
a155eb69a77f6cf4a7f231bc4f9c57c30cf8a97f
|
azezza/fef
|
[
"license:other",
"region:us"
] |
2023-04-06T04:20:39+00:00
|
{"license": "other"}
|
2023-04-06T04:20:39+00:00
|
|
95d74355ef481905b8b118405c1b04088b42d47b
|
# Dataset Card for "audio-diffusion"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
pseudohappy/audio-diffusion-256
|
[
"region:us"
] |
2023-04-06T05:12:45+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "audio_file", "dtype": "string"}, {"name": "slice", "dtype": "int16"}], "splits": [{"name": "train", "num_bytes": 51493597.25, "num_examples": 1198}], "download_size": 51415901, "dataset_size": 51493597.25}}
|
2023-04-06T05:13:49+00:00
|
00325591518b137014dc75e50ad585a3b74cc8b7
|
bhadresh-savani/translate_code_geeksforgeeks_for_t5
|
[
"size_categories:1K<n<10K",
"language:en",
"license:mit",
"code",
"c++",
"python",
"java",
"region:us"
] |
2023-04-06T05:15:01+00:00
|
{"language": ["en"], "license": "mit", "size_categories": ["1K<n<10K"], "pretty_name": "CodeT5-Translate", "tags": ["code", "c++", "python", "java"]}
|
2023-04-06T05:20:16+00:00
|
|
c2b70220a467a1e6eb35c1b1ccbd72ea7730fc1f
|
# Dataset Card for "food_recipe_ner_v2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
kanak8278/food_recipe_ner_v2
|
[
"region:us"
] |
2023-04-06T05:17:08+00:00
|
{"dataset_info": {"features": [{"name": "tokens", "sequence": "string"}, {"name": "ner_tags", "sequence": "int64"}, {"name": "ner_map", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 1947368.6267281107, "num_examples": 7117}, {"name": "val", "num_bytes": 487047.3732718894, "num_examples": 1780}], "download_size": 339459, "dataset_size": 2434416.0}}
|
2023-04-07T17:29:21+00:00
|
49393e5301b7155407e0910b015cc7a635910242
|
# MQuAD
The Medical Question and Answering dataset(MQuAD) has been refined, including the following datasets. You can download it through the Hugging Face dataset. Use the DATASETS method as follows.
## Quick Guide
```python
from datasets import load_dataset
dataset = load_dataset("danielpark/MQuAD-v1")
```
Medical Q/A datasets gathered from the following websites.
- eHealth Forum
- iCliniq
- Question Doctors
- WebMD
Data was gathered at the 5th of May 2017.
The MQuAD provides embedded question and answer arrays in string format, so it is recommended to convert the string-formatted arrays into float format as follows. This measure has been applied to save resources and time used for embedding.
```python
from datasets import load_dataset
from utilfunction import col_convert
import pandas as pd
qa = load_dataset("danielpark/MQuAD-v1", "csv")
df_qa = pd.DataFrame(qa['train'])
df_qa = col_convert(df_qa, ['Q_FFNN_embeds', 'A_FFNN_embeds'])
```
|
danielpark/MQuAD-v1
|
[
"task_categories:question-answering",
"task_categories:text-generation",
"language:en",
"language:ko",
"license:apache-2.0",
"biology",
"region:us"
] |
2023-04-06T05:31:15+00:00
|
{"language": ["en", "ko"], "license": "apache-2.0", "task_categories": ["question-answering", "text-generation"], "pretty_name": "Medical domain QA dataset for training a medical chatbot.", "tags": ["biology"]}
|
2023-04-07T11:21:48+00:00
|
9f15a454ad48b1f5e9e57939d3777cbf215533ed
|
# Dataset Card for "retrived_data"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
carnival13/retrived_data
|
[
"region:us"
] |
2023-04-06T05:33:24+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "context", "struct": [{"name": "sentences", "sequence": {"sequence": "string"}}, {"name": "title", "sequence": "string"}]}], "splits": [{"name": "validation", "num_bytes": 7001551, "num_examples": 6332}, {"name": "test", "num_bytes": 8082223, "num_examples": 7405}], "download_size": 9771504, "dataset_size": 15083774}}
|
2023-04-06T05:33:30+00:00
|
e99102c7e3d4cb00649028335da0e7791a4a23dd
|
We release instruction corpora with 8M examples for training a small ChatGPT, naming HugChat.
How to use this data for training:
- download the corpora, and unzip;
- clone the HugNLP library: https://github.com/wjn1996/HugNLP
```
git clone https://github.com/wjn1996/HugNLP.git
```
- Put the corpora into user-defined path, and perform:
```
bash ./application/instruction_prompting/instruction_tuning/run_causal_instruction.sh
```
More details can be found in the HugNLP documents: [HugNLP: Generative Instruction-tuning](https://github.com/wjn1996/HugNLP/blob/main/documents/instruction_prompting/generative_instruction_tuning.md)
|
wjn1996/hugnlp-instruction-corpora
|
[
"license:apache-2.0",
"region:us"
] |
2023-04-06T05:57:56+00:00
|
{"license": "apache-2.0"}
|
2023-05-06T15:02:28+00:00
|
c66e6c049405a238cee94c4f59577bcebd71aa75
|
ArthurCui/shenq
|
[
"license:mit",
"region:us"
] |
2023-04-06T06:28:50+00:00
|
{"license": "mit"}
|
2023-04-06T06:29:47+00:00
|
|
a6433fc20eacc6726f0c4734e982077801869afc
|
# Congress
The [Congress dataset](https://archive.ics.uci.edu/ml/datasets/Congress) from the [UCI ML repository](https://archive.ics.uci.edu/ml/datasets).
Congressmen of two different parties vote on a series of bills. Guess the party of each voter on the basis of their votes.
# Configurations and tasks
| **Configuration** | **Task** | **Description** |
|-------------------|---------------------------|---------------------------------------------------------------|
| voting | Binary classification | What's the party of the voter? |
# Usage
```python
from datasets import load_dataset
dataset = load_dataset("mstz/congress", "voting")["train"]
```
|
mstz/congress
|
[
"task_categories:tabular-classification",
"size_categories:n<1K",
"language:en",
"license:cc",
"congress",
"tabular_classification",
"binary_classification",
"UCI",
"region:us"
] |
2023-04-06T06:41:17+00:00
|
{"language": ["en"], "license": "cc", "size_categories": ["n<1K"], "task_categories": ["tabular-classification"], "pretty_name": "Congress", "tags": ["congress", "tabular_classification", "binary_classification", "UCI"], "configs": ["voting"]}
|
2023-04-16T16:01:56+00:00
|
beae5b5bea4cc994571724edcda5824c7a2a8202
|
# Dataset Card for "alpaca-es-hackaton"
La base de datos original ha sido proporcionada por SomosNLP y se encuentra [aquí](https://huggingface.co/datasets/somosnlp/somos-clean-alpaca-es)
Este conjunto de datos es una traducción del dataset Clean Alpaca al Español y sirve como referencia para el esfuerzo colaborativo de limpieza y mejora del dataset durante el [Hackathon Somos NLP 2023](https://somosnlp.org/hackathon). *Nota: No es necesario participar en el hackathon para contribuir a esta tarea.*
Los scripts, modelos y, en general, el código asociado a estas tareas se puede encontrar en el Github de [Burra](https://github.com/maxserras/burra)
En este reto, hemos anotado manualmente unos cuantos ejemplos y hemos analizado de manera semi-automática el dataset para identificar ciertas inconsistencias.
1- Instrucciones mal traducidas: los ejemplos que tengan la misma instrucción tanto en el corpus de [inglés](https://github.com/maxserras/burra/blob/master/corpus/alpaca_data_cleaned.json) como el de castellano han sido etiquetados automáticamente como "BAD INSTRUCTION"
2- Identificación de ejemplos mal traducidos usando LangID, se identifican a nivel de metadatos:
- tr-flag-1-instruction: True, para el caso de que la instrucción esté mal traducida
- tr-flag-2-input: True, para el caso de que la entrada esté mal traducida
- tr-flag-3-outcome: True, no hace falta que lo expliquemos, ¿no?
4- Evaluar dos modelos de setfit para detección de ejemplos no procesables como URLs, Fotografías, Imágenes, y todos aquellos elemenos donde el modelo no tiene capacidad de asimilación.
- st-multi-unprocessable-score: float - para la evaluación realizada con el [modelo](https://huggingface.co/hackathon-somos-nlp-2023/setfit-alpaca-es-unprocessable-sample-detection-multi) entrenado sobre la base multilingue.
- st-unprocessable-score: float - para la evaluación realizada con el [modelo](https://huggingface.co/hackathon-somos-nlp-2023/setfit-alpaca-es-unprocessable-sample-detection) sin base multilingue.
5- Alinear el corpus de EN y ES a nivel de traducción usando [LASER](https://github.com/facebookresearch/LASER). No todos los elementos han podido ser alineados, pero por lo general, si no se han podido alinear, la traducción o el ejemplo suelen presentar errores, con lo cual recomendamos descartar esos ejemplos.
- en_index: int, el metadato asociado al índice de la lista del [corpus inicial en inglés](https://github.com/maxserras/burra/blob/master/corpus/alpaca_data_cleaned.json).
6- Analizar los ejemplos con modelos pre-entrenados de [Bias Detection](https://huggingface.co/d4data/bias-detection-model) y [Hate Speech Detection](https://huggingface.co/Hate-speech-CNERG/bert-base-uncased-hatexplain) y volcar los resultados en los metadatos de:
- hate_score.label, hate_score.score
- bias_score.label, bias_score.label
|
hackathon-somos-nlp-2023/alpaca-es-auto-filter
|
[
"region:us"
] |
2023-04-06T06:45:28+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "null"}, {"name": "inputs", "struct": [{"name": "1-instruction", "dtype": "string"}, {"name": "2-input", "dtype": "string"}, {"name": "3-output", "dtype": "string"}]}, {"name": "prediction", "dtype": "null"}, {"name": "prediction_agent", "dtype": "null"}, {"name": "annotation", "dtype": "string"}, {"name": "annotation_agent", "dtype": "string"}, {"name": "vectors", "struct": [{"name": "input", "sequence": "float64"}, {"name": "instruction", "sequence": "float64"}, {"name": "output", "sequence": "float64"}]}, {"name": "multi_label", "dtype": "bool"}, {"name": "explanation", "dtype": "null"}, {"name": "id", "dtype": "string"}, {"name": "metadata", "struct": [{"name": "bias_score.label", "dtype": "string"}, {"name": "bias_score.score", "dtype": "float64"}, {"name": "en_index", "dtype": "int64"}, {"name": "hate_score.label", "dtype": "string"}, {"name": "hate_score.score", "dtype": "float64"}, {"name": "sf-multi-unprocessable-score", "dtype": "float64"}, {"name": "sf-unprocessable-score", "dtype": "float64"}, {"name": "tr-flag-1-instruction", "dtype": "bool"}, {"name": "tr-flag-2-input", "dtype": "bool"}, {"name": "tr-flag-3-output", "dtype": "bool"}]}, {"name": "status", "dtype": "string"}, {"name": "event_timestamp", "dtype": "timestamp[us]"}, {"name": "metrics", "struct": [{"name": "text_length", "dtype": "int64"}]}], "splits": [{"name": "train", "num_bytes": 986677188, "num_examples": 51942}], "download_size": 653488377, "dataset_size": 986677188}}
|
2023-04-09T19:20:23+00:00
|
99b95125875f1ef0e47f89801cb435d14911fffa
|
Race-C : additional data for race (high school/middle school) but for college level
https://github.com/mrcdata/race-c
```bib
@InProceedings{pmlr-v101-liang19a,
title={A New Multi-choice Reading Comprehension Dataset for Curriculum Learning},
author={Liang, Yichan and Li, Jianheng and Yin, Jian},
booktitle={Proceedings of The Eleventh Asian Conference on Machine Learning},
pages={742--757},
year={2019}
}
```
|
tasksource/race-c
|
[
"task_categories:question-answering",
"task_categories:multiple-choice",
"language:en",
"region:us"
] |
2023-04-06T06:49:42+00:00
|
{"language": ["en"], "task_categories": ["question-answering", "multiple-choice"]}
|
2023-05-31T07:39:38+00:00
|
74abd7a87e5fa8994426650433701ce3ab234a40
|
*Copyright (c) 2023 MinWoo Park*
<br>
# KMI2122-dataset: A Collection of Korea Macroeconomic Indicators from 2021-2022 (Monthly, 24 Sequences)
GitHub: https://github.com/dsdanielpark/kmi2122-dataset
[](https://colab.research.google.com/drive/1ETKhELKSwmo4YP5Cpsbx-xGmWmmurcI4?usp=sharing) <br>
Please, check this before use.
## Install
```
pip install kmi2122
```
or simply download the raw dataset:
```
curl -LO https://raw.githubusercontent.com/DSDanielPark/kmi2122-dataset/master/kmi2122_dataset/dataset.json
```
Alternatively, you can use the dataset uploaded to Hugging Face as follows.
```python
from datasets import load_dataset
dataset = load_dataset("danielpark/KMI2122-v1", "csv")
df = pd.DataFrame(dataset['train'])
```
<br>
## Usage
```python
import kmi2122_dataset as kd
raw_dataset = kd.Dataset() # you can see raw dataset (dict)
kmi = kd.KMI2122()
df = kmi.get_df() # you can get a dataframe (pandas.DataFrame)
column_dict = dict(kmi.column_info()) # information of columns (dict)
```
- tutorial: https://github.com/DSDanielPark/kmi2122-dataset/blob/main/docs/tutorial.ipynb
- file list: https://github.com/DSDanielPark/kmi2122-dataset/blob/main/docs/file_list.csv
You can check the list of KOSIS statistical files used to create the following dataframe at the above url.
<br>
## Stats
- 24 monthly sequences from Jan, 2022 to Dec, 2023
- 2,593 Korean statistical columns from 57 tables in one dataframe
<br>
## Description
This dataset merges monthly economic indicators provided by the Korean Statistical Information Service (KOSIS) into a single data frame. The data covers a total of 24 sequences from January 2021 to December 2022 and includes information in 24 columns.
To solve the problem of encoding Korean characters and having many columns in one data frame, the column names were changed to table{int}_col{int}. This change is reflected in the data, making it easy to use, as demonstrated in the following example.
This dataset provides valuable information for time series data analysis and modeling, enabling users to extract information on which indicators require more focus and predict values using several economic indicators. It is expected that this data and lagging techniques will enable the identification of the successors of economic indicators.
<br>
## Contents
- This dataset has a total of 2593 columns with 24 sequences (monthly from 2021.01 to 2022.12) as the number of columns.
- Those 2593 columns were extracted from 57 tables.
- Missing values do not exist.
- The published statistical data were original, and no separate preprocessing was performed.
| | file_name | description_in_eng |
|---:|:------------|:-----------------------------------------------------------------------|
| 0 | table0 | ELW Trading Volume |
| 1 | table1 | ELW Investor Trading Volume |
| 2 | table2 | ETF Trading Volume |
| 3 | table3 | ETF Investor Trading Volume |
| 4 | table4 | ETN Trading Volume |
| 5 | table5 | ETN Investor Trading Volume |
| 6 | table6 | KRX 100 |
| 7 | table7 | KRX 300 |
| 8 | table8 | KRX Dividend Yield |
| 9 | table9 | KRX Listed Stock Trading Volume |
| 10 | table10 | KRX Listed Stock Summary |
| 11 | table11 | KRX Listed Stock Investor Trading Volume |
| 12 | table12 | KRX Price to Book Ratio (PBR) |
| 13 | table13 | KRX Price to Earnings Ratio (PER) |
| 14 | table14 | KRX Major Price Index |
| 15 | table15 | KTOP 30 |
| 16 | table16 | Composite Stock Price Index 2015100 10th |
| 17 | table17 | Composite Stock Price Index 2015100 Composition Index Time Series 10th |
| 18 | table18 | Expected Inflation Rate National Monthly |
| 19 | table19 | Production Diffusion Index |
| 20 | table20 | Consumer Price Index 2020100 |
| 21 | table21 | Consumer Price Index Including Self-Contained Housing 2020100 |
| 22 | table22 | Monthly Consumer Price Index Fluctuation Rate |
| 23 | table23 | Korea Exchange Securities Trading Volume and Trading Volume per Trade |
| 24 | table24 | Korea Exchange Securities Trading Volume |
| 25 | table25 | Korea Exchange Securities Industry Market Capitalization |
| 26 | table26 | Korea Exchange Securities Listed Company Dividend Status |
| 27 | table27 | Korea Exchange Securities Listed Company Revenue Status |
| 28 | table28 | Korea Exchange Securities Listed Stock Summary |
| 29 | table29 | Korea Exchange Securities Investor Trading Volume |
| 30 | table30 | Korea Exchange Securities Program Trading Trading Volume |
| 31 | table31 | Consumer Price Index Including Self-Contained Housing 2020100 |
| 32 | table32 | Consumer Price Index by Expenditure Purpose Including Items 2020100 |
| 33 | table33 | KOSDAQ 150 Sector Index |
| 34 | table34 | KOSDAQ Trading Volume |
| 35 | table35 | KOSDAQ Dividend Yield |
| 36 | table36 | KOSDAQ Industry Revenue Status |
| 37 | table37 | KOSDAQ Industry Financial Status |
| 38 | table38 | KOSDAQ Industry Stock Price Index |
| 39 | table39 | KOSDAQ Industry Investment Indicators |
| 40 | table40 | KOSDAQ Listed Company Dividend Status |
| 41 | table41 | KOSDAQ Listed Company Revenue Status |
| 42 | table42 | KOSDAQ Price to Book Ratio (PBR) |
| 43 | table43 | KOSDAQ Price to Earnings Ratio (PER) |
| 44 | table44 | KOSDAQ Major Price Index |
| 45 | table45 | KOSDAQ Index |
| 46 | table46 | KOSDAQ Investor Trading Volume |
| 47 | table47 | KOSPI 200 Sector Index |
| 48 | table48 | KOSPI 200 Index |
| 49 | table49 | KOSPI Dividend Yield |
| 50 | table50 | KOSPI Industry Stock Price Index |
| 51 | table51 | KOSPI Industry Investment Indicators |
| 52 | table52 | KOSPI Price to Book Ratio (PBR) |
| 53 | table53 | KOSPI Price to Earnings Ratio (PER) |
| 54 | table54 | KOSPI Major Price Index |
| 55 | table55 | KOSPI Index |
| 56 | table56 | Consumer Price Index by Item and Item Characteristic 2020100 |
| 57 | table57 | Consumer Price Index by Item Characteristic 2020100 |
<br>
The column names of the dataset have been maintained in Korean. In addition, you can check the 57 statistics file names in Korean through the key value of table_list in kmi_dataset_column_info. We apologize for the inconvenience, but we do not provide English-translated values as changing Korean to English can make it difficult for users to compare with the original data on KOSIS.
Also, if there are columns with missing values in some tables, the columns with missing values have been removed. Therefore, please note that the entire dataset does not have any missing values.
<br>
## Format
```
{
'kmi_dataset_column_info': {'table0_col1': 'Number of trading days',
'table0_col10': 'Trading volume',
'table0_col11': 'Trading value (unit: million KRW)',
'table0_col12': 'Trading value (unit: million KRW)',
'table0_col13': 'Trading value (unit: million KRW)',
...
'table9_date': 'Trading volume of listed stocks'
},
'kmi_dataset_main': '{"date":{"0":2021.01,"1":2021.02,"2":2021.03,"3":2021.04,"4" ....}' ....}
}
```
<br>
## How to Cite
If you extend or use this work, please cite using this.
```
Data provided by The Korean Statistical Information Service(KOSIS), "KMI-2122" dataset, accessed from the 'kmi2122-dataset' repository on GitHub (https://github.com/DSDanielPark/kmi2122-dataset) on {Data Citation Date}
```
Please use this badge.
```
```
<br>
# Further Reading
## What is the Korean Statistical Information Service (KOSIS)?
The Korean Statistical Information Service [(KOSIS)](https://kosis.kr/serviceInfo/kosisIntroduce.do) is a one-stop statistical service provided by the National Statistical Office. It helps users find the statistics they need by collecting key domestic, international, and North Korean statistics in one place. KOSIS contains all government-approved statistics on the economy, society, and environment from over 400 organizations. It also provides the latest statistics on international finance and economy from the IMF, Worldbank, and OECD.
Through an easy and convenient search function, various contents, and statistical explanatory data services that even the general public can understand, KOSIS allows users to quickly and accurately find the statistical data they need.
<br>
## DataSets
You can find datasets on the latest macroeconomic indicators for Korea from official data providers such as the Bank of Korea (https://www.bok.or.kr/) or the Korea Statistics Portal (https://kosis.kr/)
1. Blockchain datasets
- Blockchain.com (https://www.blockchain.com/): provides blockchain transaction data for cryptocurrencies such as Bitcoin and Ethereum.
- Coinmetrics.io (https://coinmetrics.io/): collects and analyzes various blockchain data.
- Kaggle (https://www.kaggle.com/datasets?tags=14120-blockchain): a platform that provides various blockchain datasets.
2. Macroeconomic indicators for each country
- International Monetary Fund (IMF) (https://www.imf.org/en/Data): provides macroeconomic indicator data for countries around the world.
- World Bank (https://data.worldbank.org/): provides data on macroeconomic indicators, population, poverty, and more for each country.
- OECD (https://stats.oecd.org/): provides data on macroeconomic indicators, social issues, environmental issues, and more for OECD member countries.
3. Stock datasets
- Yahoo Finance (https://finance.yahoo.com/): provides data for US stocks, futures, options, and more.
- Google Finance (https://www.google.com/finance): provides data for US stocks, indices, and more.
- Investing.com (https://www.investing.com/): provides data for stocks, indices, funds, and more worldwide.
|
danielpark/kmi2122-v1
|
[
"language:ko",
"language:en",
"license:apache-2.0",
"finance",
"region:us"
] |
2023-04-06T06:52:28+00:00
|
{"language": ["ko", "en"], "license": "apache-2.0", "pretty_name": "Korea Macroeconomic Indicators from 2021-2022", "tags": ["finance"]}
|
2023-10-03T18:58:51+00:00
|
62c1e2ac361aba58044e15b28347bac4aad84ebb
|
# Dataset Card for "rm-static-m2m100-zh"
Traditional Chinese translation of the [Dahoas/rm-static](https://huggingface.co/datasets/Dahoas/rm-static) dataset.
The dataset is first translated into Simplified Chinese using [facebook/m2m100-12B-last-ckpt](https://huggingface.co/facebook/m2m100-12B-last-ckpt) and greedy decoding.
The translation is then filtered and further translated into Traditional Chinese using [OpenCC](https://github.com/BYVoid/OpenCC)
The dataset may contain samples with translation errors, we plan to release a filtered version of this dataset in the future.
|
liswei/rm-static-zhTW
|
[
"task_categories:text2text-generation",
"task_categories:text-generation",
"task_categories:text-classification",
"size_categories:10K<n<100K",
"language:zh",
"instruction-finetuning",
"rlhf",
"region:us"
] |
2023-04-06T06:53:00+00:00
|
{"language": ["zh"], "size_categories": ["10K<n<100K"], "task_categories": ["text2text-generation", "text-generation", "text-classification"], "pretty_name": "rm-static-zhTW", "dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "response", "dtype": "string"}, {"name": "chosen", "dtype": "string"}, {"name": "rejected", "dtype": "string"}, {"name": "prompt_zh", "dtype": "string"}, {"name": "response_zh", "dtype": "string"}, {"name": "chosen_zh", "dtype": "string"}, {"name": "rejected_zh", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 198602975, "num_examples": 76256}, {"name": "test", "num_bytes": 13365684, "num_examples": 5103}], "download_size": 129737844, "dataset_size": 211968659}, "tags": ["instruction-finetuning", "rlhf"]}
|
2023-05-19T08:22:44+00:00
|
88aa159b2f12070b337712bc488815bfeac642f9
|
# Dataset Card for "DKT-img-to-img"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
madabou/DKT-img-to-img
|
[
"region:us"
] |
2023-04-06T06:58:38+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1302980.0, "num_examples": 4}], "download_size": 1256181, "dataset_size": 1302980.0}}
|
2023-04-06T08:39:32+00:00
|
bc5f86b38d36204842cf97bf4cc01b6e4e376439
|
Korakoe/Vicuna-Uncleaned-Alpaca-Format
|
[
"task_categories:text-generation",
"language:en",
"instruct",
"alpaca",
"vicuna",
"region:us"
] |
2023-04-06T07:06:59+00:00
|
{"language": ["en"], "task_categories": ["text-generation"], "pretty_name": "Vicuna Uncleaned - Alpaca Format", "tags": ["instruct", "alpaca", "vicuna"]}
|
2023-04-06T07:14:10+00:00
|
|
60b407278a63b5c1b513a4729cad6b3bc2033e2f
|
Muvaraimodel/Textsummarization
|
[
"license:other",
"region:us"
] |
2023-04-06T07:22:22+00:00
|
{"license": "other"}
|
2023-04-06T07:22:22+00:00
|
|
1077516ec469db2b4cebe06b8bf225a0653f7b95
|
# Malayalam Morphology Analyser dataset
This is a dataset of 801585 Malayalam words and their morphology analysis using [Mlmorph Malayalam morphology analyser](morph.smc.org.in/).
## License
Creative Commons Attribution Share Alike 4.0
## Contact
Contact santhosh.thottingal @ gmail.com
|
santhosh/malayalam-morphology-analyser
|
[
"size_categories:1M<n<10M",
"language:ml",
"license:cc-by-sa-4.0",
"malayalam",
"morphology",
"doi:10.57967/hf/0508",
"region:us"
] |
2023-04-06T07:29:07+00:00
|
{"language": ["ml"], "license": "cc-by-sa-4.0", "size_categories": ["1M<n<10M"], "tags": ["malayalam", "morphology"]}
|
2023-04-06T08:28:15+00:00
|
6dbbc00bc8e17dc7166ada6374e8601fa69f8962
|
guilgautier/dkt-images
|
[
"license:mit",
"region:us"
] |
2023-04-06T07:56:39+00:00
|
{"license": "mit", "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 62581.0, "num_examples": 8}], "download_size": 59096, "dataset_size": 62581.0}}
|
2023-04-06T14:35:18+00:00
|
|
4dd9e2d71fa6c31601d2989159d68c2d8bf9b63f
|
# Dataset Card for "alpaca-es-hackaton-backup"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mserras/alpaca-es-hackaton-backup
|
[
"region:us"
] |
2023-04-06T08:03:49+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "null"}, {"name": "inputs", "struct": [{"name": "1-instruction", "dtype": "string"}, {"name": "2-input", "dtype": "string"}, {"name": "3-output", "dtype": "string"}]}, {"name": "prediction", "dtype": "null"}, {"name": "prediction_agent", "dtype": "null"}, {"name": "annotation", "sequence": "string"}, {"name": "annotation_agent", "dtype": "null"}, {"name": "vectors", "struct": [{"name": "input", "sequence": "float64"}, {"name": "instruction", "sequence": "float64"}, {"name": "output", "sequence": "float64"}]}, {"name": "multi_label", "dtype": "bool"}, {"name": "explanation", "dtype": "null"}, {"name": "id", "dtype": "string"}, {"name": "metadata", "struct": [{"name": "tr-flag-1-instruction", "dtype": "bool"}, {"name": "tr-flag-2-input", "dtype": "bool"}, {"name": "tr-flag-3-output", "dtype": "bool"}]}, {"name": "status", "dtype": "string"}, {"name": "event_timestamp", "dtype": "timestamp[us]"}, {"name": "metrics", "dtype": "null"}], "splits": [{"name": "train", "num_bytes": 982796255, "num_examples": 51942}], "download_size": 650895383, "dataset_size": 982796255}}
|
2023-04-06T08:18:45+00:00
|
aac7ace63d89bac03a860ab78c5e9449879fbbb5
|
# English Malayalam names
This dataset has 27814162 person names both in English and Malayalam.
The source for this dataset is various election roles published by Government.
Potential usages:
1. English <-> Malayalam name transliteration tasks
2. Named entity recognition
3. Person name recognition
## License
Creative commons Attribution Share Alike 4.0
## Contact
Santhosh Thottingal santhosh.thottingal @ gmail.com
|
santhosh/english-malayalam-names
|
[
"task_categories:text2text-generation",
"size_categories:10M<n<100M",
"language:en",
"language:ml",
"license:cc-by-sa-4.0",
"malayalam",
"region:us"
] |
2023-04-06T08:16:11+00:00
|
{"language": ["en", "ml"], "license": "cc-by-sa-4.0", "size_categories": ["10M<n<100M"], "task_categories": ["text2text-generation"], "tags": ["malayalam"]}
|
2023-04-06T08:26:40+00:00
|
73962de89059076e6a0c5541a79fb7272cf58a47
|
# Dataset Card for "m2e_6_4_padded_merged_tags_no_augs"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Sunbird/m2e_6_4_padded_merged_tags_no_augs
|
[
"region:us"
] |
2023-04-06T08:23:13+00:00
|
{"dataset_info": {"features": [{"name": "input_ids", "sequence": "int32"}, {"name": "attention_mask", "sequence": "int8"}, {"name": "labels", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 4401362036, "num_examples": 2626111}, {"name": "valid", "num_bytes": 4190000, "num_examples": 2500}], "download_size": 388982529, "dataset_size": 4405552036}}
|
2023-04-06T08:24:35+00:00
|
af766a23ca54a16689fe88bf41b7d501cd4bc1e6
|
# Fertility
The [Fertility dataset](https://archive.ics.uci.edu/ml/datasets/Fertility) from the [UCI ML repository](https://archive.ics.uci.edu/ml/datasets).
Classify fertility abnormalities of patients.
# Configurations and tasks
| **Configuration** | **Task** | **Description** |
|-------------------|---------------------------|------------------------------------------|
| encoding | | Encoding dictionary |
| fertility | Binary classification | Does the patient have fertility issues? |
# Usage
```python
from datasets import load_dataset
dataset = load_dataset("mstz/fertility", "fertility")["train"]
```
# Features
|**Feature** |**Type** |
|----------------------------------------|------------------|
| season_of_sampling | `[string]` |
| age_at_time_of_sampling | `[int8]` |
| has_had_childhood_diseases | `[bool]` |
| has_had_serious_trauma | `[bool]` |
| has_had_surgical_interventions | `[bool]` |
| has_had_high_fevers_in_the_past_year | `[string]` |
| frequency_of_alcohol_consumption | `[float16]` |
| smoking_frequency | `[string]` |
| number_of_sitting_hours_per_day | `[float16]` |
|
mstz/fertility
|
[
"task_categories:tabular-classification",
"size_categories:n<1K",
"language:en",
"license:cc",
"fertility",
"tabular_classification",
"binary_classification",
"multiclass_classification",
"UCI",
"region:us"
] |
2023-04-06T08:27:16+00:00
|
{"language": ["en"], "license": "cc", "size_categories": ["n<1K"], "task_categories": ["tabular-classification"], "pretty_name": "Fertility", "tags": ["fertility", "tabular_classification", "binary_classification", "multiclass_classification", "UCI"], "configs": ["encoding", "fertility"]}
|
2023-04-16T16:28:42+00:00
|
0347095e70b030151f099f361740d51f26a0fdff
|
# Dataset Card for Indonesian Alpaca-Cleaned
- **Repository:** https://github.com/gururise/AlpacaDataCleaned
## Dataset Description
This is the Indonesian translated version of the cleaned original Alpaca Dataset released by Stanford. The following issues have been identified in the original release and fixed in this dataset:
1. **Hallucinations:** Many instructions in the original dataset had instructions referencing data on the internet, which just caused GPT3 to hallucinate an answer.
```
"instruction":"Summarize the given article in 200 Words.",
"input": "https://www.bbc.com/news/world-51461830",
"output": "The recent protests in Belarus are part of a growing wave of discontent against the rule of Alexander Lukashenko, who has led the country since 1994. Demonstrations against his government have been ongoing since August, and come in the wake of an election widely seen as rigged. As police and security forces have cracked down on protesters, the movement has only grown, with hundreds of thousands of people taking to the streets to call for new leadership. Human rights activists have accused the government of using extreme violence in order prosecute protesters, leading to an international outcry and sanctions from the European Union. As the situation in Belarus continues to unfold, the international community is monitoring the situation closely - hoping for a peaceful resolution, while also standing firm against the oppressive tactics employed by the Belarusian state."
"instruction": "Classify the following shape according to simple geometry.",
"input": "<Picture Attached>",
"output": "Triangle"
"instruction": "Given a recent news article, write a headline for it.",
"input": "https://www.news.example.com/article-1",
"output": "\"NASA Launches a Mars 2020 Mission to Search for Signs of Ancient Life\""
```
2. **Merged Instructions:** There were many instructions that were merged together in the original dataset for some reason.
```
"instruction": "Should I invest in stocks?",
"input": "",
"output": "It depends on your individual situation. Investing in stocks carries a certain amount of risk but can also offer potential rewards. Make sure to research the different types of stocks, understand the risks and rewards, and develop a plan that works for you.\n##\n18. Instruction: Identify the noun in the following sentence:\n18. Input:\nThe large apple was delicious.\n18. Output:\nApple"
```
3. **Empty outputs:** Some entries in the original dataset had empty outputs.
```
"instruction": "Calculate the area of the following shape in square centimeters.",
"input": "rectangle of size 4 cm x 5 cm",
"output": ""
```
4. **Empty code examples:** Some descriptions in the original dataset were missing code examples, making it difficult to understand the intended behavior of the code.
5. **Instructions to generate images:** Some descriptions in the original dataset included instructions to generate images, something obviously not possible.
```
"instruction": "Create a graphic or logo that visually represents the word \"courage\".",
"input": "",
"output": "<No Output>"
```
6. **N/A outputs:** Some code snippets in the original dataset had N/A outputs.
7. **Inconsistent input field:** The original dataset had inconsistent usage of the input field when it was supposed to be empty.
```
"input":"<no input>"
"input":"No input"
"input":"noinput"
"input":"<noinput>"
```
8. **Wrong answers:** Some instructions/questions in the original dataset had incorrect answers. About 80% of the math problems are estimated to have incorrect answers.
```
"instruction": "Calculate the median of the following data set.",
"input": "1, 2, 4, 5, 8, 9",
"output": "5"
"instruction": "Convert 25m to km.",
"input": "",
"output": "25km"
```
9. **Non-Sensical/Unclear instructions:** Many instructions are unclear, we try to clarify (or re-write) if instructions are non-sensical. Instructions that are slightly unclear, but where one could deduce the meaning are not altered.
```
"instruction": "Freeze the following sample of yogurt for 10 minutes.",
"input": "Yogurt sample",
"output": "<noinput>"
"instruction": "Increase the font size to 12 points.",
"input": "",
"output": "The font size has been increased to 12 points."
```
10. **Extraneous escape and control characters:** The original dataset had several entries with extraneous escape and control characters.
### Original Alpaca Dataset Summary
Alpaca is a dataset of 52,000 instructions and demonstrations generated by OpenAI's `text-davinci-003` engine. This instruction data can be used to conduct instruction-tuning for language models and make the language model follow instruction better.
The authors built on the data generation pipeline from [Self-Instruct framework](https://github.com/yizhongw/self-instruct) and made the following modifications:
- The `text-davinci-003` engine to generate the instruction data instead of `davinci`.
- A [new prompt](https://github.com/tatsu-lab/stanford_alpaca/blob/main/prompt.txt) was written that explicitly gave the requirement of instruction generation to `text-davinci-003`.
- Much more aggressive batch decoding was used, i.e., generating 20 instructions at once, which significantly reduced the cost of data generation.
- The data generation pipeline was simplified by discarding the difference between classification and non-classification instructions.
- Only a single instance was generated for each instruction, instead of 2 to 3 instances as in Self-Instruct.
This produced an instruction-following dataset with 52K examples obtained at a much lower cost (less than $500).
In a preliminary study, the authors also found that the 52K generated data to be much more diverse than the data released by [Self-Instruct](https://github.com/yizhongw/self-instruct/blob/main/data/seed_tasks.jsonl).
### Supported Tasks and Leaderboards
The Alpaca dataset designed for instruction training pretrained language models.
### Languages
The data in Alpaca are in English (BCP-47 en).
## Dataset Structure
### Data Instances
An example of "train" looks as follows:
```json
{
"instruction": "Create a classification task by clustering the given list of items.",
"input": "Apples, oranges, bananas, strawberries, pineapples",
"output": "Class 1: Apples, Oranges\nClass 2: Bananas, Strawberries\nClass 3: Pineapples",
"text": "Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n\n### Instruction:\nCreate a classification task by clustering the given list of items.\n\n### Input:\nApples, oranges, bananas, strawberries, pineapples\n\n### Response:\nClass 1: Apples, Oranges\nClass 2: Bananas, Strawberries\nClass 3: Pineapples",
}
```
### Data Fields
The data fields are as follows:
* `instruction`: describes the task the model should perform. Each of the 52K instructions is unique.
* `input`: optional context or input for the task. For example, when the instruction is "Summarize the following article", the input is the article. Around 40% of the examples have an input.
* `output`: the answer to the instruction as generated by `text-davinci-003`.
* `text`: the `instruction`, `input` and `output` formatted with the [prompt template](https://github.com/tatsu-lab/stanford_alpaca#data-release) used by the authors for fine-tuning their models.
### Data Splits
| | train |
|---------------|------:|
| alpaca | 52002 |
## 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
Excerpt the [blog post](https://crfm.stanford.edu/2023/03/13/alpaca.html) accompanying the release of this dataset:
> We believe that releasing the above assets will enable the academic community to perform controlled scientific studies on instruction-following language models, resulting in better science and ultimately new techniques to address the existing deficiencies with these models. At the same time, any release carries some risk. First, we recognize that releasing our training recipe reveals the feasibility of certain capabilities. On one hand, this enables more people (including bad actors) to create models that could cause harm (either intentionally or not). On the other hand, this awareness might incentivize swift defensive action, especially from the academic community, now empowered by the means to perform deeper safety research on such models. Overall, we believe that the benefits for the research community outweigh the risks of this particular release. Given that we are releasing the training recipe, we believe that releasing the data, model weights, and training code incur minimal further risk, given the simplicity of the recipe. At the same time, releasing these assets has enormous benefits for reproducible science, so that the academic community can use standard datasets, models, and code to perform controlled comparisons and to explore extensions. Deploying an interactive demo for Alpaca also poses potential risks, such as more widely disseminating harmful content and lowering the barrier for spam, fraud, or disinformation. We have put into place two risk mitigation strategies. First, we have implemented a content filter using OpenAI’s content moderation API, which filters out harmful content as defined by OpenAI’s usage policies. Second, we watermark all the model outputs using the method described in Kirchenbauer et al. 2023, so that others can detect (with some probability) whether an output comes from Alpaca 7B. Finally, we have strict terms and conditions for using the demo; it is restricted to non-commercial uses and to uses that follow LLaMA’s license agreement. We understand that these mitigation measures can be circumvented once we release the model weights or if users train their own instruction-following models. However, by installing these mitigations, we hope to advance the best practices and ultimately develop community norms for the responsible deployment of foundation models.
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
The `alpaca` data is generated by a language model (`text-davinci-003`) and inevitably contains some errors or biases. We encourage users to use this data with caution and propose new methods to filter or improve the imperfections.
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
The dataset is available under the [Creative Commons NonCommercial (CC BY-NC 4.0)](https://creativecommons.org/licenses/by-nc/4.0/legalcode).
### Citation Information
```
@misc{alpaca,
author = {Rohan Taori and Ishaan Gulrajani and Tianyi Zhang and Yann Dubois and Xuechen Li and Carlos Guestrin and Percy Liang and Tatsunori B. Hashimoto },
title = {Stanford Alpaca: An Instruction-following LLaMA model},
year = {2023},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/tatsu-lab/stanford_alpaca}},
}
```
### Contributions
[More Information Needed]
|
cahya/alpaca-id-cleaned
|
[
"task_categories:text-generation",
"language:id",
"license:cc-by-4.0",
"instruction-finetuning",
"region:us"
] |
2023-04-06T08:45:24+00:00
|
{"language": ["id"], "license": "cc-by-4.0", "task_categories": ["text-generation"], "pretty_name": "Alpaca-Cleaned", "tags": ["instruction-finetuning"]}
|
2023-09-23T18:38:23+00:00
|
9cb6a0020844117e5aecdae1356d2328193bdb1e
|
# Haberman
The [Haberman dataset](https://archive.ics.uci.edu/ml/datasets/Haberman) from the [UCI ML repository](https://archive.ics.uci.edu/ml/datasets).
Has the patient survived surgery?
# Configurations and tasks
| **Configuration** | **Task** | **Description** |
|-------------------|---------------------------|------------------------------------|
| sruvival | Binary classification | Has the patient survived surgery? |
# Usage
```python
from datasets import load_dataset
dataset = load_dataset("mstz/haberman", "survival")["train"]
```
|
mstz/haberman
|
[
"task_categories:tabular-classification",
"size_categories:n<1K",
"language:en",
"license:cc",
"haberman",
"tabular_classification",
"binary_classification",
"multiclass_classification",
"region:us"
] |
2023-04-06T08:49:53+00:00
|
{"language": ["en"], "license": "cc", "size_categories": ["n<1K"], "task_categories": ["tabular-classification"], "pretty_name": "Haberman", "tags": ["haberman", "tabular_classification", "binary_classification", "multiclass_classification"], "configs": ["survival"]}
|
2023-04-07T12:50:58+00:00
|
de321cce0506b76cb888a56f9d6f1a1cae717a58
|
emmas96/Lenselink
|
[
"license:gpl-3.0",
"region:us"
] |
2023-04-06T08:49:59+00:00
|
{"license": "gpl-3.0"}
|
2023-04-06T08:54:02+00:00
|
|
daae747d4dd89de24097bda9bfc207741891fd8d
|
koishicyann/ai
|
[
"license:openrail",
"region:us"
] |
2023-04-06T09:01:31+00:00
|
{"license": "openrail"}
|
2023-05-03T06:08:31+00:00
|
|
751b22400b6487410c67617bc0c095a16fff9237
|
# Heart
The [Heart dataset](https://archive.ics.uci.edu/ml/datasets/Heart) from the [UCI ML repository](https://archive.ics.uci.edu/ml/datasets).
Does the patient have heart disease?
# Configurations and tasks
| **Configuration** | **Task** |
|-------------------|---------------------------|
| hungary | Binary classification |
# Usage
```python
from datasets import load_dataset
dataset = load_dataset("mstz/heart", "hungary")["train"]
```
|
mstz/heart
|
[
"task_categories:tabular-classification",
"size_categories:n<1K",
"language:en",
"license:cc",
"heart",
"tabular_classification",
"binary_classification",
"UCI",
"region:us"
] |
2023-04-06T09:18:50+00:00
|
{"language": ["en"], "license": "cc", "size_categories": ["n<1K"], "task_categories": ["tabular-classification"], "pretty_name": "Heart", "tags": ["heart", "tabular_classification", "binary_classification", "UCI"], "configs": ["cleveland", "va", "switzerland", "hungary"]}
|
2023-04-16T16:31:05+00:00
|
f6c59c5dbc1645bfda3a400fa49b161c4cd645ab
|
# Wizzypedia
These datasets are created from the Forgotten Runes Wizard's Cult Wizzypedia.
You can find the [Wizzypedia here](http://wizzypedia.forgottenrunes.com/).
Guide to the datasets:
* `tokenized-wizzypedia-400.jsonl` - 400 token chunks encoded with tiktoken `cl100k_base` encoding
|
magicmachine/wizzypedia
|
[
"size_categories:1K<n<10K",
"language:en",
"license:cc-by-nc-3.0",
"art",
"region:us"
] |
2023-04-06T09:56:08+00:00
|
{"language": ["en"], "license": "cc-by-nc-3.0", "size_categories": ["1K<n<10K"], "pretty_name": "Wizzypedia", "tags": ["art"]}
|
2023-04-09T21:16:42+00:00
|
a2208e04ed9ff63869d65520ef744ca47314ba77
|
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:**
- **Repository:** https://github.com/mskandalis/gqnli-french
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This repository contains a manually translated French version of the [GQNLI](https://github.com/ruixiangcui/GQNLI) challenge dataset, originally written in English. GQNLI is an evaluation corpus that is aimed for testing language model's generalized quantifier reasoning ability.
### Supported Tasks and Leaderboards
This dataset can be used for the task of Natural Language Inference (NLI), also known as Recognizing Textual Entailment (RTE), which is a sentence-pair classification task.
## Dataset Structure
### Data Fields
- `uid`: Index number.
- `premise`: The translated premise in the target language.
- `hypothesis`: The translated premise in the target language.
- `label`: The classification label, with possible values 0 (`entailment`), 1 (`neutral`), 2 (`contradiction`).
- `label_text`: The classification label, with possible values `entailment` (0), `neutral` (1), `contradiction` (2).
- `premise_original`: The original premise from the English source dataset.
- `hypothesis_original`: The original hypothesis from the English source dataset.
### Data Splits
| name |entailment|neutral|contradiction|
|-------------|---------:|------:|------------:|
| test | 97 | 100 | 103 |
## Additional Information
### Citation Information
**BibTeX:**
````BibTeX
@inproceedings{cui-etal-2022-generalized-quantifiers,
title = "Generalized Quantifiers as a Source of Error in Multilingual {NLU} Benchmarks",
author = "Cui, Ruixiang and
Hershcovich, Daniel and
S{\o}gaard, Anders",
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.naacl-main.359",
doi = "10.18653/v1/2022.naacl-main.359",
pages = "4875--4893",
abstract = "Logical approaches to representing language have developed and evaluated computational models of quantifier words since the 19th century, but today{'}s NLU models still struggle to capture their semantics. We rely on Generalized Quantifier Theory for language-independent representations of the semantics of quantifier words, to quantify their contribution to the errors of NLU models. We find that quantifiers are pervasive in NLU benchmarks, and their occurrence at test time is associated with performance drops. Multilingual models also exhibit unsatisfying quantifier reasoning abilities, but not necessarily worse for non-English languages. To facilitate directly-targeted probing, we present an adversarial generalized quantifier NLI task (GQNLI) and show that pre-trained language models have a clear lack of robustness in generalized quantifier reasoning.",
}
````
**ACL:**
Ruixiang Cui, Daniel Hershcovich, and Anders Søgaard. 2022. [Generalized Quantifiers as a Source of Error in Multilingual NLU Benchmarks](https://aclanthology.org/2022.naacl-main.359). In *Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*, pages 4875–4893, Seattle, United States. Association for Computational Linguistics.
### Acknowledgements
This work was supported by the Defence Innovation Agency (AID) of the Directorate General of Armament (DGA) of the French Ministry of Armed Forces, and by the ICO, _Institut Cybersécurité Occitanie_, funded by Région Occitanie, France.
|
maximoss/gqnli-fr
|
[
"task_categories:text-classification",
"task_ids:natural-language-inference",
"task_ids:multi-input-text-classification",
"size_categories:n<1K",
"language:fr",
"license:cc-by-4.0",
"region:us"
] |
2023-04-06T10:48:26+00:00
|
{"language": ["fr"], "license": "cc-by-4.0", "size_categories": ["n<1K"], "task_categories": ["text-classification"], "task_ids": ["natural-language-inference", "multi-input-text-classification"]}
|
2024-02-04T12:30:02+00:00
|
c6532306366f1762d13bf2de8d9d7b12d42743a7
|
KanziRAY222/fuego-20230406-115747-446647
|
[
"fuego",
"region:us"
] |
2023-04-06T10:57:48+00:00
|
{"tags": ["fuego"], "fuego": {"id": "20230406-115747-446647", "status": "preparing", "script": "make_a_video.py", "space_id": "KanziRAY222/fuego-20230406-115747-446647", "space_hardware": "cpu-basic", "github_repo_id": "lucidrains/make-a-video-pytorch", "github_repo_branch": "main", "github_repo_sha": "4430a87da4474b2cec58fe3e0e1c581a328472e5"}}
|
2023-04-06T10:57:50+00:00
|
|
9009ef5378d489e3bb36f2f82e9ffc0307591b28
|
Amirkid/redditjokes
|
[
"license:creativeml-openrail-m",
"region:us"
] |
2023-04-06T11:02:53+00:00
|
{"license": "creativeml-openrail-m", "dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 103220535, "num_examples": 578634}], "download_size": 67652707, "dataset_size": 103220535}}
|
2023-04-06T11:04:59+00:00
|
|
1ed18ccc425ec9360ed27085627b309514a2a42f
|
# Description
Dataset used to train our mT5 based model for machine translation, extracted from various text sources of National Library of Azerbaijan: [mT5-translation-enaz](https://huggingface.co/learningmachineaz/mt5-enaz-10m) \
It has only clean texts. Wiki articles wasn't used as they contain a lot of irrelevant data.
| Key point | Info |
|-------------------------|---------|
| Rows | ~10mil. EN-AZ sentence pairs |
| Size | 975M (zipped) / 2.8G (unzipped) |
| Format | TSV (tab separated pairs) |
| English | Google Translate |
| Azerbaijani | Original cleaned text |
## Author
Collected and prepared by [Renat Kalimulin](https://www.linkedin.com/in/rinat-kalimulin-16853358/)
|
learningmachineaz/translate_enaz_10m
|
[
"task_categories:translation",
"task_categories:text-generation",
"task_categories:text2text-generation",
"size_categories:1M<n<10M",
"language:en",
"language:az",
"license:openrail",
"azerbaijani books",
"azerbaijani news",
"azerbaijani poems",
"azerbaijani articles",
"azerbaijani dataset",
"region:us"
] |
2023-04-06T11:12:45+00:00
|
{"language": ["en", "az"], "license": "openrail", "size_categories": ["1M<n<10M"], "task_categories": ["translation", "text-generation", "text2text-generation"], "pretty_name": "English-Azerbaijani Dataset", "tags": ["azerbaijani books", "azerbaijani news", "azerbaijani poems", "azerbaijani articles", "azerbaijani dataset"]}
|
2023-04-07T14:57:38+00:00
|
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