id stringlengths 2 115 | author stringlengths 2 42 ⌀ | last_modified timestamp[us, tz=UTC] | downloads int64 0 8.87M | likes int64 0 3.84k | paperswithcode_id stringlengths 2 45 ⌀ | tags list | lastModified timestamp[us, tz=UTC] | createdAt stringlengths 24 24 | key stringclasses 1 value | created timestamp[us] | card stringlengths 1 1.01M | embedding list | library_name stringclasses 21 values | pipeline_tag stringclasses 27 values | mask_token null | card_data null | widget_data null | model_index null | config null | transformers_info null | spaces null | safetensors null | transformersInfo null | modelId stringlengths 5 111 ⌀ | embeddings list |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
cahya/persona_empathetic | cahya | 2022-02-19T22:49:35Z | 153 | 0 | null | [
"license:mit",
"region:us"
] | 2022-02-19T22:49:35Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
license: mit
---
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cakiki/arxiv-metadata | cakiki | 2022-02-03T20:57:23Z | 153 | 0 | null | [
"license:cc0-1.0",
"region:us"
] | 2022-02-03T20:57:23Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
license: cc0-1.0
---
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caythuoc/caoduoclieu | caythuoc | 2023-06-15T10:41:13Z | 153 | 0 | null | [
"region:us"
] | 2023-06-15T10:41:13Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | Entry not found | [
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-0... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
ccccccc/hdjw_94ejrjr | ccccccc | 2021-02-18T07:41:38Z | 153 | 0 | null | [
"region:us"
] | 2021-02-18T07:41:38Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | Entry not found | [
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-0.7852816581726074,
-0.2257382869720459,
-0.9104480743408203,
0.5715669393539429,
-0... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
cdleong/temp_africaNLP_keyword_spotting_for_african_languages | cdleong | 2022-10-25T09:07:32Z | 153 | 0 | null | [
"language:wo",
"language:fuc",
"language:srr",
"language:mnk",
"language:snk",
"region:us"
] | 2022-10-25T09:07:32Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
language:
- wo
- fuc
- srr
- mnk
- snk
---
## Dataset Description
- **Homepage:** https://zenodo.org/record/4661645
TEMPORARY TEST DATASET
Not for actual use! Attempting to test out a dataset script for loading https://zenodo.org/record/4661645
| [
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-... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
cem/film | cem | 2021-12-23T22:02:57Z | 153 | 0 | null | [
"region:us"
] | 2021-12-23T22:02:57Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | Entry not found | [
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0.5715669393539429,
... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
cemigo/taylor_vs_shakes | cemigo | 2021-03-14T23:45:59Z | 153 | 0 | null | [
"region:us"
] | 2021-03-14T23:45:59Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | This dataset has 336 pieces of quotes from William Shakespeare and Taylor Swift (labeled) for supervised classification.
Source: https://www.kaggle.com/kellylougheed/tswift-vs-shakespeare | [
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0.04360641166... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
cemigo/test-data | cemigo | 2021-02-07T23:49:41Z | 153 | 0 | null | [
"region:us"
] | 2021-02-07T23:49:41Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | Entry not found | [
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0.5715669393539429,
... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
csarron/25m-img-caps | csarron | 2022-03-28T18:51:26Z | 153 | 1 | null | [
"region:us"
] | 2022-03-28T18:51:26Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | see https://huggingface.co/datasets/csarron/4m-img-caps for example usage | [
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0.264833718538284... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
csarron/4m-img-caps | csarron | 2022-03-28T18:50:53Z | 153 | 1 | null | [
"region:us"
] | 2022-03-28T18:50:53Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | see [read_pyarrow.py](https://gist.github.com/csarron/df712e53c9e0dcaad4eb6843e7a3d51c#file-read_pyarrow-py) for how to read one pyarrow file.
example PyTorch dataset:
```python
from torch.utils.data import Dataset
class ImageCaptionArrowDataset(Dataset):
def __init__(
self,
dataset_file,
tokenizer,
):
import pyarrow as pa
data = [pa.ipc.open_file(pa.memory_map(f, "rb")).read_all() for f in glob.glob(dataset_file)]
self.data = pa.concat_tables(data)
# do other initialization, like init image preprocessing fn,
def __getitem__(self, index):
# item_id = self.data["id"][index].as_py()
text = self.data["text"][index].as_py() # get text
if isinstance(text, list):
text = random.choice(text)
img_bytes = self.data["image"][index].as_py() # get image bytes
# do some processing with image and text, return the features
# img_feat = self.image_bytes_to_tensor(img_bytes)
# inputs = self.tokenizer(
# text,
# padding="max_length",
# max_length=self.max_text_len,
# truncation=True,
# return_token_type_ids=True,
# return_attention_mask=True,
# add_special_tokens=True,
# return_tensors="pt",
# )
# input_ids = inputs.input_ids.squeeze(0)
# attention_mask = inputs.attention_mask.squeeze(0)
# return {
# # "item_ids": item_id,
# "text_ids": input_ids,
# "input_ids": input_ids,
# "text_masks": attention_mask,
# "pixel_values": img_feat,
# }
def __len__(self):
return len(self.data)
``` | [
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0.02987181767821... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
image-search-2/unsplash_lite_image_dataset | image-search-2 | 2021-11-19T12:44:46Z | 153 | 1 | null | [
"region:us"
] | 2021-11-19T12:44:46Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | # The Unsplash Dataset
The Unsplash Dataset is made up of over 250,000+ contributing global photographers and data sourced from hundreds of millions of searches across a nearly unlimited number of uses and contexts. Due to the breadth of intent and semantics contained within the Unsplash dataset, it enables new opportunities for research and learning.
The Unsplash Dataset is offered in two datasets:
- the Lite dataset: available for commercial and noncommercial usage, containing 25k nature-themed Unsplash photos, 25k keywords, and 1M searches
- the Full dataset: available for noncommercial usage, containing 3M+ high-quality Unsplash photos, 5M keywords, and over 250M searches
As the Unsplash library continues to grow, we’ll release updates to the dataset with new fields and new images, with each subsequent release being [semantically versioned](https://semver.org/).
We welcome any feedback regarding the content of the datasets or their format. With your input, we hope to close the gap between the data we provide and the data that you would like to leverage. You can [open an issue](https://github.com/unsplash/datasets/issues/new/choose) to report a problem or to let us know what you would like to see in the next release of the datasets.
For more on the Unsplash Dataset, see [our announcement](https://unsplash.com/blog/the-unsplash-dataset/) and [site](https://unsplash.com/data).
## Download
### Lite Dataset
The Lite dataset contains all of the same fields as the Full dataset, but is limited to ~25,000 photos. It can be used for both commercial and non-commercial usage, provided you abide by [the terms](https://github.com/unsplash/datasets/blob/master/TERMS.md).
[⬇️ Download the Lite dataset](https://unsplash.com/data/lite/latest) [~650MB compressed, ~1.4GB raw]
### Full Dataset
The Full dataset is available for non-commercial usage and all uses must abide by [the terms](https://github.com/unsplash/datasets/blob/master/TERMS.md). To access, please go to [unsplash.com/data](https://unsplash.com/data) and request access. The dataset weighs ~20 GB compressed (~43GB raw)).
## Documentation
See the [documentation for a complete list of tables and fields](https://github.com/unsplash/datasets/blob/master/DOCS.md).
## Usage
You can follow these examples to load the dataset in these common formats:
- [Load the dataset in a PostgreSQL database](https://github.com/unsplash/datasets/tree/master/how-to/psql)
- [Load the dataset in a Python environment](https://github.com/unsplash/datasets/tree/master/how-to/python)
- [Submit an example doc](https://github.com/unsplash/datasets/blob/master/how-to/README.md#submit-an-example)
## Share your work
We're making this data open and available with the hopes of enabling researchers and developers to discover interesting and useful connections in the data.
We'd love to see what you create, whether that's a research paper, a machine learning model, a blog post, or just an interesting discovery in the data. Send us an email at [data@unsplash.com](mailto:data@unsplash.com).
If you're using the dataset in a research paper, you can attribute the dataset as `Unsplash Lite Dataset 1.2.0` or `Unsplash Full Dataset 1.2.0` and link to the permalink [`unsplash.com/data`](https://unsplash.com/data).
----
The Unsplash Dataset is made available for research purposes. [It cannot be used to redistribute the images contained within](https://github.com/unsplash/datasets/blob/master/TERMS.md). To use the Unsplash library in a product, see [the Unsplash API](https://unsplash.com/developers).
| [
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0.127736344933509... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
jimregan/foinse | jimregan | 2021-10-06T20:42:52Z | 153 | 0 | null | [
"region:us"
] | 2021-10-06T20:42:52Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | Entry not found | [
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jakartaresearch/google-play-review | jakartaresearch | 2022-08-06T16:24:49Z | 153 | 4 | null | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:id",
"license:cc-by-4.0",
"sentiment",
"google-play",
"indonesia... | 2022-08-06T16:24:49Z | 2022-08-06T05:00:32.000Z | 2022-08-06T05:00:32 | ---
annotations_creators:
- found
language:
- id
language_creators:
- found
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: Indonesian Google Play Review
size_categories:
- 1K<n<10K
source_datasets:
- original
tags:
- sentiment
- google-play
- indonesian
task_categories:
- text-classification
task_ids:
- sentiment-classification
---
# Dataset Card for Indonesian Google Play Review
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
Scrapped from e-commerce app on Google Play.
### Supported Tasks and Leaderboards
Sentiment Analysis
### Languages
Indonesian
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@andreaschandra](https://github.com/andreaschandra) for adding this dataset. | [
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0.21590693295... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
cardiffnlp/tweet_topic_single | cardiffnlp | 2022-11-27T11:25:34Z | 153 | 3 | null | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"multilinguality:monolingual",
"size_categories:1k<10K",
"language:en",
"license:other",
"arxiv:2209.09824",
"region:us"
] | 2022-11-27T11:25:34Z | 2022-09-02T00:20:17.000Z | 2022-09-02T00:20:17 | ---
language:
- en
license:
- other
multilinguality:
- monolingual
size_categories:
- 1k<10K
task_categories:
- text-classification
task_ids:
- sentiment-classification
pretty_name: TweetTopicSingle
---
# Dataset Card for "cardiffnlp/tweet_topic_single"
## Dataset Description
- **Paper:** [https://arxiv.org/abs/2209.09824](https://arxiv.org/abs/2209.09824)
- **Dataset:** Tweet Topic Dataset
- **Domain:** Twitter
- **Number of Class:** 6
### Dataset Summary
This is the official repository of TweetTopic (["Twitter Topic Classification
, COLING main conference 2022"](https://arxiv.org/abs/2209.09824)), a topic classification dataset on Twitter with 6 labels.
Each instance of TweetTopic comes with a timestamp which distributes from September 2019 to August 2021.
See [cardiffnlp/tweet_topic_multi](https://huggingface.co/datasets/cardiffnlp/tweet_topic_multi) for multi label version of TweetTopic.
The tweet collection used in TweetTopic is same as what used in [TweetNER7](https://huggingface.co/datasets/tner/tweetner7).
The dataset is integrated in [TweetNLP](https://tweetnlp.org/) too.
### Preprocessing
We pre-process tweets before the annotation to normalize some artifacts, converting URLs into a special token `{{URL}}` and non-verified usernames into `{{USERNAME}}`.
For verified usernames, we replace its display name (or account name) with symbols `{@}`.
For example, a tweet
```
Get the all-analog Classic Vinyl Edition
of "Takin' Off" Album from @herbiehancock
via @bluenoterecords link below:
http://bluenote.lnk.to/AlbumOfTheWeek
```
is transformed into the following text.
```
Get the all-analog Classic Vinyl Edition
of "Takin' Off" Album from {@herbiehancock@}
via {@bluenoterecords@} link below: {{URL}}
```
A simple function to format tweet follows below.
```python
import re
from urlextract import URLExtract
extractor = URLExtract()
def format_tweet(tweet):
# mask web urls
urls = extractor.find_urls(tweet)
for url in urls:
tweet = tweet.replace(url, "{{URL}}")
# format twitter account
tweet = re.sub(r"\b(\s*)(@[\S]+)\b", r'\1{\2@}', tweet)
return tweet
target = """Get the all-analog Classic Vinyl Edition of "Takin' Off" Album from @herbiehancock via @bluenoterecords link below: http://bluenote.lnk.to/AlbumOfTheWeek"""
target_format = format_tweet(target)
print(target_format)
'Get the all-analog Classic Vinyl Edition of "Takin\' Off" Album from {@herbiehancock@} via {@bluenoterecords@} link below: {{URL}}'
```
### Data Splits
| split | number of texts | description |
|:------------------------|-----:|------:|
| test_2020 | 376 | test dataset from September 2019 to August 2020 |
| test_2021 | 1693 | test dataset from September 2020 to August 2021 |
| train_2020 | 2858 | training dataset from September 2019 to August 2020 |
| train_2021 | 1516 | training dataset from September 2020 to August 2021 |
| train_all | 4374 | combined training dataset of `train_2020` and `train_2021` |
| validation_2020 | 352 | validation dataset from September 2019 to August 2020 |
| validation_2021 | 189 | validation dataset from September 2020 to August 2021 |
| train_random | 2830 | randomly sampled training dataset with the same size as `train_2020` from `train_all` |
| validation_random | 354 | randomly sampled training dataset with the same size as `validation_2020` from `validation_all` |
| test_coling2022_random | 3399 | random split used in the COLING 2022 paper |
| train_coling2022_random | 3598 | random split used in the COLING 2022 paper |
| test_coling2022 | 3399 | temporal split used in the COLING 2022 paper |
| train_coling2022 | 3598 | temporal split used in the COLING 2022 paper |
For the temporal-shift setting, model should be trained on `train_2020` with `validation_2020` and evaluate on `test_2021`.
In general, model would be trained on `train_all`, the most representative training set with `validation_2021` and evaluate on `test_2021`.
**IMPORTANT NOTE:** To get a result that is comparable with the results of the COLING 2022 Tweet Topic paper, please use `train_coling2022` and `test_coling2022` for temporal-shift, and `train_coling2022_random` and `test_coling2022_random` fir random split (the coling2022 split does not have validation set).
### Models
| model | training data | F1 | F1 (macro) | Accuracy |
|:------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------|---------:|-------------:|-----------:|
| [cardiffnlp/roberta-large-tweet-topic-single-all](https://huggingface.co/cardiffnlp/roberta-large-tweet-topic-single-all) | all (2020 + 2021) | 0.896043 | 0.800061 | 0.896043 |
| [cardiffnlp/roberta-base-tweet-topic-single-all](https://huggingface.co/cardiffnlp/roberta-base-tweet-topic-single-all) | all (2020 + 2021) | 0.887773 | 0.79793 | 0.887773 |
| [cardiffnlp/twitter-roberta-base-2019-90m-tweet-topic-single-all](https://huggingface.co/cardiffnlp/twitter-roberta-base-2019-90m-tweet-topic-single-all) | all (2020 + 2021) | 0.892499 | 0.774494 | 0.892499 |
| [cardiffnlp/twitter-roberta-base-dec2020-tweet-topic-single-all](https://huggingface.co/cardiffnlp/twitter-roberta-base-dec2020-tweet-topic-single-all) | all (2020 + 2021) | 0.890136 | 0.776025 | 0.890136 |
| [cardiffnlp/twitter-roberta-base-dec2021-tweet-topic-single-all](https://huggingface.co/cardiffnlp/twitter-roberta-base-dec2021-tweet-topic-single-all) | all (2020 + 2021) | 0.894861 | 0.800952 | 0.894861 |
| [cardiffnlp/roberta-large-tweet-topic-single-2020](https://huggingface.co/cardiffnlp/roberta-large-tweet-topic-single-2020) | 2020 only | 0.878913 | 0.70565 | 0.878913 |
| [cardiffnlp/roberta-base-tweet-topic-single-2020](https://huggingface.co/cardiffnlp/roberta-base-tweet-topic-single-2020) | 2020 only | 0.868281 | 0.729667 | 0.868281 |
| [cardiffnlp/twitter-roberta-base-2019-90m-tweet-topic-single-2020](https://huggingface.co/cardiffnlp/twitter-roberta-base-2019-90m-tweet-topic-single-2020) | 2020 only | 0.882457 | 0.740187 | 0.882457 |
| [cardiffnlp/twitter-roberta-base-dec2020-tweet-topic-single-2020](https://huggingface.co/cardiffnlp/twitter-roberta-base-dec2020-tweet-topic-single-2020) | 2020 only | 0.87596 | 0.746275 | 0.87596 |
| [cardiffnlp/twitter-roberta-base-dec2021-tweet-topic-single-2020](https://huggingface.co/cardiffnlp/twitter-roberta-base-dec2021-tweet-topic-single-2020) | 2020 only | 0.877732 | 0.746119 | 0.877732 |
Model fine-tuning script can be found [here](https://huggingface.co/datasets/cardiffnlp/tweet_topic_single/blob/main/lm_finetuning.py).
## Dataset Structure
### Data Instances
An example of `train` looks as follows.
```python
{
"text": "Game day for {{USERNAME}} U18\u2019s against {{USERNAME}} U18\u2019s. Even though it\u2019s a \u2018home\u2019 game for the people that have settled in Mid Wales it\u2019s still a 4 hour round trip for us up to Colwyn Bay. Still enjoy it though!",
"date": "2019-09-08",
"label": 4,
"id": "1170606779568463874",
"label_name": "sports_&_gaming"
}
```
### Label ID
The label2id dictionary can be found at [here](https://huggingface.co/datasets/tner/tweet_topic_single/raw/main/dataset/label.single.json).
```python
{
"arts_&_culture": 0,
"business_&_entrepreneurs": 1,
"pop_culture": 2,
"daily_life": 3,
"sports_&_gaming": 4,
"science_&_technology": 5
}
```
### Citation Information
```
@inproceedings{dimosthenis-etal-2022-twitter,
title = "{T}witter {T}opic {C}lassification",
author = "Antypas, Dimosthenis and
Ushio, Asahi and
Camacho-Collados, Jose and
Neves, Leonardo and
Silva, Vitor and
Barbieri, Francesco",
booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
month = oct,
year = "2022",
address = "Gyeongju, Republic of Korea",
publisher = "International Committee on Computational Linguistics"
}
``` | [
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LinhDuong/chatdoctor-5k | LinhDuong | 2023-03-28T07:32:21Z | 153 | 0 | null | [
"license:apache-2.0",
"arxiv:2303.14070",
"region:us"
] | 2023-03-28T07:32:21Z | 2023-03-28T07:23:57.000Z | 2023-03-28T07:23:57 | ---
license: apache-2.0
---
This ChatDoctor-5K dataset is collected from this paper https://arxiv.org/pdf/2303.14070.pdf
Alternatively, you can download the original dataset from this link https://drive.google.com/file/d/1nDTKZ3wZbZWTkFMBkxlamrzbNz0frugg/view?usp=sharing | [
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bigheiniuJ/EvalMetaICLAll | bigheiniuJ | 2023-07-24T06:39:16Z | 153 | 0 | null | [
"region:us"
] | 2023-07-24T06:39:16Z | 2023-07-23T20:34:43.000Z | 2023-07-23T20:34:43 | ---
dataset_info:
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---
# Dataset Card for "EvalMetaICLAll"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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RealTimeData/bbc_latest | RealTimeData | 2023-11-27T00:35:05Z | 153 | 0 | null | [
"region:us"
] | 2023-11-27T00:35:05Z | 2023-08-19T23:03:43.000Z | 2023-08-19T23:03:43 | ---
{}
---
# Latest BBC News
You could always access the latest BBC News articles via this dataset.
We update the dataset weekly, on every Sunday. So the dataset always provides the latest BBC News article from the last week.
The current dataset on main branch contains the latest BBC News articles submitted from 2023-11-20 to 2023-11-27.
The data collection is conducted on 2023-11-27.
Use the dataset via:
```
ds = datasets.load_dataset('RealTimeData/bbc_latest')
```
# Previsou versions
You could access previous versions by requesting different branches.
For example, you could find the 2023-08-20 version via:
```
ds = datasets.load_dataset('RealTimeData/bbc_latest', revision = '2023-08-20')
```
Check all available versions by clicking the "Files and versions" button on the top bar.
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jamescalam/agent-conversations-retrieval-tool | jamescalam | 2023-08-27T12:57:37Z | 153 | 7 | null | [
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EleutherAI/sycophancy | EleutherAI | 2023-09-05T15:14:40Z | 153 | 1 | null | [
"region:us"
] | 2023-09-05T15:14:40Z | 2023-08-29T07:58:29.000Z | 2023-08-29T07:58:29 | Entry not found | [
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benjis/bigvul | benjis | 2023-08-31T03:02:50Z | 153 | 0 | null | [
"region:us"
] | 2023-08-31T03:02:50Z | 2023-08-31T02:55:32.000Z | 2023-08-31T02:55:32 | ---
configs:
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data_files:
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path: data/train-*
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path: data/validation-*
- split: test
path: data/test-*
dataset_info:
features:
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- name: vul
dtype: int8
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- name: test
num_bytes: 88687280.64632414
num_examples: 33050
download_size: 252969708
dataset_size: 582322563.1230518
---
# Dataset Card for "bigvul"
Unofficial, not affiliated with the authors.
- **Paper:** https://doi.org/10.1145/3379597.3387501
- **Repository:** https://github.com/ZeoVan/MSR_20_Code_vulnerability_CSV_Dataset | [
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legacy107/spamming-email-classification | legacy107 | 2023-10-02T09:39:55Z | 153 | 0 | null | [
"region:us"
] | 2023-10-02T09:39:55Z | 2023-09-25T14:22:14.000Z | 2023-09-25T14:22:14 | ---
configs:
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path: data/train-*
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path: data/test-*
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---
# Dataset Card for "spamming-email-classification"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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LDJnr/Pure-Dove | LDJnr | 2023-11-21T17:55:19Z | 153 | 21 | null | [
"task_categories:conversational",
"task_categories:question-answering",
"task_categories:text-generation",
"size_categories:1K<n<10K",
"language:en",
"license:apache-2.0",
"Physics",
"Biology",
"Math",
"Chemistry",
"Culture",
"Logic",
"Roleplay",
"region:us"
] | 2023-11-21T17:55:19Z | 2023-09-26T02:06:24.000Z | 2023-09-26T02:06:24 | ---
license: apache-2.0
task_categories:
- conversational
- question-answering
- text-generation
language:
- en
tags:
- Physics
- Biology
- Math
- Chemistry
- Culture
- Logic
- Roleplay
pretty_name: Pure-Dove
size_categories:
- 1K<n<10K
---
## This is the Official Pure-Dove dataset. Over 3K multi-turn examples, and many more coming soon!
This dataset aims to be the largest highest quality cluster of real human back and forth conversations with GPT-4.
Steps have even been done to ensure that only the best GPT-4 conversations in comparisons are kept, there are many instances where two GPT-4 responses are rated as equal to eachother or as both bad. We exclude all such responses from Pure Dove and make sure to only include ChatBot Arena responses that are voted as being better even against another instance of GPT-4.
- Comprised of over 3000 highly filtered multi-turn conversations between GPT-4 and real humans.
- Average context length per conversation is over 800 tokens.
## Purpose?
- This dataset is not particularly intended to be trained on by itself, however, the size and quality of this dataset can work wonderfully as a supplemmentary addition to virtually any multi-turn compatible dataset. I encourage this use, all I ask is proper credits given for such!
## Quality filtering and cleaning.
- The conversations were sourced from openly datasets such as ShareGPT and ChatBotArena by Lmsys, however, a large portion of these chats were riddled with hallucinations and abnormal distributions of different languages.
- Extensive cleaning was done to filter out instances of overt AI moralizing or related behaviour, such as "As an AI language model" and "September 2021", not just in english, but other languages too!
## Credits
During the curation process, there can be some relatively arduos steps when it comes to actually executing on the best experimentation or concepts for how to filter examples out.
Luckily there is folks over at NousResearch that helped expedite this process with little to no sacrifices in quality, big credit to J-Supha within NousResearch specifically for making these types of significant contributions.
## Future Plans & How you can help!
This is a relatively early build amongst the grand plans for the future of what I plan to work on!
In the near future we plan on leveraging the help of domain specific expert volunteers to eliminate any mathematically/verifiably incorrect answers from training curations of different types of datasets.
If you have at-least a bachelors in mathematics, physics, biology or chemistry and would like to volunteer even just 30 minutes of your expertise time, please contact LDJ on discord!
Citation:
```
@article{daniele2023amplify-instruct,
title={Amplify-Instruct: Synthetically Generated Diverse Multi-turn Conversations for Effecient LLM Training.},
author={Daniele, Luigi and Suphavadeeprasit},
journal={arXiv preprint arXiv:(comming soon)},
year={2023}
}
``` | [
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0.071703262627124... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
maxolotl/must-c-en-es-wait3-02 | maxolotl | 2023-10-22T07:48:24Z | 153 | 0 | null | [
"region:us"
] | 2023-10-22T07:48:24Z | 2023-10-22T07:48:05.000Z | 2023-10-22T07:48:05 | ---
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---
# Dataset Card for "must-c-en-es-wait3-02"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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Karavet/pioNER-Armenian-Named-Entity | Karavet | 2022-10-21T16:07:06Z | 152 | 1 | null | [
"task_ids:named-entity-recognition",
"multilinguality:monolingual",
"language:hy",
"license:apache-2.0",
"region:us"
] | 2022-10-21T16:07:06Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
language: [hy]
task_categories: [named-entity-recognition]
multilinguality: [monolingual]
task_ids: [named-entity-recognition]
license: [apache-2.0]
---
## Table of Contents
- [Table of Contents](#table-of-contents)
- [pioNER - named entity annotated datasets](#pioNER---named-entity-annotated-datasets)
- [Silver-standard dataset](#silver-standard-dataset)
- [Gold-standard dataset](#gold-standard-dataset)
# pioNER - named entity annotated datasets
pioNER corpus provides gold-standard and automatically generated named-entity datasets for the Armenian language.
Alongside the datasets, we release 50-, 100-, 200-, and 300-dimensional GloVe word embeddings trained on a collection of Armenian texts from Wikipedia, news, blogs, and encyclopedia.
## Silver-standard dataset
The generated corpus is automatically extracted and annotated using Armenian Wikipedia. We used a modification of [Nothman et al](https://www.researchgate.net/publication/256660013_Learning_multilingual_named_entity_recognition_from_Wikipedia) and [Sysoev and Andrianov](http://www.dialog-21.ru/media/3433/sysoevaaandrianovia.pdf) approaches to create this corpus. This approach uses links between Wikipedia articles to extract fragments of named-entity annotated texts.
The corpus is split into train and development sets.
*Table 1. Statistics for pioNER train, development and test sets*
| dataset | #tokens | #sents | annotation | texts' source |
|-------------|:--------:|:-----:|:--------:|:-----:|
| train | 130719 | 5964 | automatic | Wikipedia |
| dev | 32528 | 1491 | automatic | Wikipedia |
| test | 53606 | 2529 | manual | iLur.am |
## Gold-standard dataset
This dataset is a collection of over 250 news articles from iLur.am with manual named-entity annotation. It includes sentences from political, sports, local and world news, and is comparable in size with the test sets of other languages (Table 2).
We aim it to serve as a benchmark for future named entity recognition systems designed for the Armenian language.
The dataset contains annotations for 3 popular named entity classes:
people (PER), organizations (ORG), and locations (LOC), and is released in CoNLL03 format with IOB tagging scheme.
During annotation, we generally relied on categories and [guidelines assembled by BBN](https://catalog.ldc.upenn.edu/docs/LDC2005T33/BBN-Types-Subtypes.html) Technologies for TREC 2002 question answering track
Tokens and sentences were segmented according to the UD standards for the Armenian language from [ArmTreebank project](http://armtreebank.yerevann.com/tokenization/process/).
*Table 2. Comparison of pioNER gold-standard test set with test sets for English, Russian, Spanish and German*
| test dataset | #tokens | #LOC | #ORG | #PER |
|-------------|:--------:|:-----:|:--------:|:-----:|
| Armenian pioNER | 53606 | 1312 | 1338 | 1274 |
| Russian factRuEval-2016 | 59382 | 1239 | 1595 | 1353 |
| German CoNLL03 | 51943 | 1035 | 773 | 1195 |
| Spanish CoNLL02 | 51533 | 1084 | 1400 | 735 |
| English CoNLL03 | 46453 | 1668 | 1661 | 1671 | | [
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Niciu/test-cre-dataset-issues | Niciu | 2022-03-01T14:06:43Z | 152 | 0 | null | [
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WyrdCurt/AO4W | WyrdCurt | 2021-07-26T12:03:27Z | 152 | 0 | null | [
"region:us"
] | 2021-07-26T12:03:27Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | # Archive Of Our Own Original Works (AO4W)
**Warning! Many/most of these files may be NSFW!**
Approximately 2GB of text files from Archive of Our Own; specifically, files labeled "original work" or some variation. For training fiction models. I recommend that you clean the text as needed for your purposes. | [
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abidlabs/crowdsourced-speech3 | abidlabs | 2022-01-21T16:12:06Z | 152 | 0 | null | [
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abwicke/koplo | abwicke | 2021-03-18T15:43:39Z | 152 | 0 | null | [
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albertvillanova/tmp-tests | albertvillanova | 2021-12-02T14:12:12Z | 152 | 0 | null | [
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aliabd/crowdsourced-speech4 | aliabd | 2022-01-21T17:36:51Z | 152 | 0 | null | [
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alireza655/alireza655 | alireza655 | 2021-02-08T23:24:50Z | 152 | 0 | null | [
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clem/autonlp-data-french_word_detection | clem | 2021-09-14T09:45:38Z | 152 | 1 | null | [
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] | 2021-09-14T09:45:38Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | This is a very good dataset! | [
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cnrcastroli/aaaa | cnrcastroli | 2021-03-04T21:51:21Z | 152 | 0 | null | [
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coala/kkk | coala | 2021-09-14T07:56:22Z | 152 | 0 | null | [
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congpt/dstc23_asr | congpt | 2021-04-06T18:04:04Z | 152 | 0 | null | [
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crich/cider | crich | 2021-11-15T18:26:56Z | 152 | 0 | null | [
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csarron/image-captions | csarron | 2021-11-29T04:31:34Z | 152 | 0 | null | [
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dasago78/dasago78dataset | dasago78 | 2021-04-02T17:57:24Z | 152 | 0 | null | [
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davanstrien/hipe2020 | davanstrien | 2022-02-15T11:40:24Z | 152 | 0 | null | [
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davanstrien/iiif_labeled | davanstrien | 2022-02-28T11:06:07Z | 152 | 0 | null | [
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dongpil/test | dongpil | 2021-07-29T10:34:34Z | 152 | 0 | null | [
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eason929/test | eason929 | 2021-03-15T04:02:59Z | 152 | 0 | null | [
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ehcalabres/ravdess_speech | ehcalabres | 2022-10-24T15:51:41Z | 152 | 3 | null | [
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] | 2022-10-24T15:51:41Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
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language_creators:
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language:
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license:
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multilinguality:
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size_categories:
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source_datasets:
- original
task_categories:
- audio-classification
task_ids:
- speech-emotion-recognition
---
# Dataset Card for ravdess_speech
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** [Needs More Information]
- **Repository:** https://zenodo.org/record/1188976#.YUS4MrozZdS
- **Paper:** https://doi.org/10.1371/journal.pone.0196391
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** ravdess@gmail.com
### Dataset Summary
The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) contains 24 professional actors (12 female, 12 male), vocalizing two lexically-matched statements in a neutral North American accent. Speech includes calm, happy, sad, angry, fearful, surprise, and disgust expressions. Each expression is produced at two levels of emotional intensity (normal, strong), with an additional neutral expression. The conditions of the audio files are: 16bit, 48kHz .wav.
### Supported Tasks and Leaderboards
- audio-classification: The dataset can be used to train a model for Audio Classification tasks, which consists in predict the latent emotion presented on the audios.
### Languages
The audios available in the dataset are in English spoken by actors in a neutral North American accent.
## Dataset Structure
### Data Instances
[Needs More Information]
### Data Fields
[Needs More Information]
### Data Splits
[Needs More Information]
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
The RAVDESS is released under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License, CC BY-NC-SA 4.0
Commercial licenses for the RAVDESS can also be purchased. For more information, please visit our license fee page, or contact us at ravdess@gmail.com.
### Citation Information
Livingstone SR, Russo FA (2018) The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS): A dynamic, multimodal set of facial and vocal expressions in North American English. PLoS ONE 13(5): e0196391. https://doi.org/10.1371/journal.pone.0196391. | [
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-0.045269902795553... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Norod78/simpsons-blip-captions | Norod78 | 2022-11-09T16:27:19Z | 152 | 3 | null | [
"task_categories:text-to-image",
"annotations_creators:machine-generated",
"language_creators:other",
"multilinguality:monolingual",
"size_categories:n<1K",
"language:en",
"license:cc-by-nc-sa-4.0",
"region:us"
] | 2022-11-09T16:27:19Z | 2022-11-06T11:11:36.000Z | 2022-11-06T11:11:36 | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 51605730.0
num_examples: 755
download_size: 50553165
dataset_size: 51605730.0
pretty_name: 'Simpsons BLIP captions'
size_categories:
- n<1K
tags: []
task_categories:
- text-to-image
license: cc-by-nc-sa-4.0
annotations_creators:
- machine-generated
language:
- en
language_creators:
- other
multilinguality:
- monolingual
---
# Dataset Card for "simpsons-blip-captions"
| [
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0.114859238266944... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Jzuluaga/uwb_atcc | Jzuluaga | 2022-12-05T11:15:20Z | 152 | 0 | null | [
"task_categories:automatic-speech-recognition",
"multilinguality:monolingual",
"language:en",
"license:cc-by-nc-sa-4.0",
"audio",
"automatic-speech-recognition",
"en-atc",
"en",
"noisy-speech-recognition",
"speech-recognition",
"arxiv:2203.16822",
"region:us"
] | 2022-12-05T11:15:20Z | 2022-11-28T07:12:02.000Z | 2022-11-28T07:12:02 | ---
dataset_info:
features:
- name: id
dtype: string
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: text
dtype: string
- name: segment_start_time
dtype: float32
- name: segment_end_time
dtype: float32
- name: duration
dtype: float32
splits:
- name: test
num_bytes: 140620332.25
num_examples: 2822
- name: train
num_bytes: 608597323.625
num_examples: 11291
download_size: 711464914
dataset_size: 749217655.875
tags:
- audio
- automatic-speech-recognition
- en-atc
- en
- noisy-speech-recognition
- speech-recognition
task_categories:
- automatic-speech-recognition
language:
- en
multilinguality:
- monolingual
license:
- cc-by-nc-sa-4.0
---
# Dataset Card for UWB-ATCC corpus
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages and Other Details](#languages-and-other-details)
- [Dataset Structure](#dataset-structure)
- [Data Fields](#data-fields)
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** [UWB-ATCC corpus homepage](https://lindat.mff.cuni.cz/repository/xmlui/handle/11858/00-097C-0000-0001-CCA1-0)
- **Repository:** [GitHub repository (used in research)](https://github.com/idiap/w2v2-air-traffic)
- **Paper:** [Air traffic control communication (ATCC) speech corpora and their use for ASR and TTS development](https://link.springer.com/article/10.1007/s10579-019-09449-5)
- **Paper of this research:** [How Does Pre-trained Wav2Vec 2.0 Perform on Domain Shifted ASR? An Extensive Benchmark on Air Traffic Control Communications](https://arxiv.org/abs/2203.16822)
### Dataset Summary
The UWB-ATCC Corpus is provided provided by University of West Bohemia, Department of Cybernetics. The corpus contains recordings of communication between air traffic controllers and pilots. The speech is manually transcribed and labeled with the information about the speaker (pilot/controller, not the full identity of the person). The corpus is currently small (20 hours) but we plan to search for additional data next year. The audio data format is: 8kHz, 16bit PCM, mono.
Important, from the `<id (string)>` field, you can obtain the speaker roles. For instance:
- `_PI`: segment with only pilot speech
- `_AT`: segment with only ATCO speech
- `PIAT`: segment with both, ATCO and pilot speech
### Supported Tasks and Leaderboards
- `automatic-speech-recognition`. Already adapted/fine-tuned models are available here --> [XLS-R-300m](https://huggingface.co/Jzuluaga/wav2vec2-large-960h-lv60-self-en-atc-atcosim).
### Languages and other details
The text and the recordings are in English. The authors took advantage of the fact that one of their industrial partners develops complex IT solutions for several ATC authorities and airports and, as such, has access to the ATC communication recordings collected in the Czech airspace. This partner was able to secure the following data:
- Ground control—communication before takeoff and after landing—19.2 h of data.
- Tower control—communication during takeoff, landing and landing standby—22.5 h.
- Approach control—communication during landing approach—25.5 h.
- Area control—communication during overflights and cruises—71.3 h.
(Not all data is released. Check their website [here](https://lindat.mff.cuni.cz/repository/xmlui/handle/11858/00-097C-0000-0001-CCA1-0))
## Dataset Structure
### Data Fields
- `id (string)`: a string of recording identifier for each example, corresponding to its.
- `audio (audio)`: audio data for the given ID
- `text (string)`: transcript of the file already normalized. Follow these repositories for more details [w2v2-air-traffic](https://github.com/idiap/w2v2-air-traffic) and [bert-text-diarization-atc](https://github.com/idiap/bert-text-diarization-atc)
- `segment_start_time (float32)`: segment start time (normally 0)
- `segment_end_time (float32): segment end time
- `duration (float32)`: duration of the recording, compute as segment_end_time - segment_start_time
## Additional Information
### Licensing Information
The licensing status of the dataset hinges on the legal status of the [UWB-ATCC corpus](https://lindat.mff.cuni.cz/repository/xmlui/handle/11858/00-097C-0000-0001-CCA1-0) creators.
They used [Creative Commons - Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/) licensing.
### Citation Information
Contributors who prepared, processed, normalized and uploaded the dataset in HuggingFace:
```
@article{zuluaga2022how,
title={How Does Pre-trained Wav2Vec2. 0 Perform on Domain Shifted ASR? An Extensive Benchmark on Air Traffic Control Communications},
author={Zuluaga-Gomez, Juan and Prasad, Amrutha and Nigmatulina, Iuliia and Sarfjoo, Saeed and others},
journal={IEEE Spoken Language Technology Workshop (SLT), Doha, Qatar},
year={2022}
}
@article{zuluaga2022bertraffic,
title={BERTraffic: BERT-based Joint Speaker Role and Speaker Change Detection for Air Traffic Control Communications},
author={Zuluaga-Gomez, Juan and Sarfjoo, Seyyed Saeed and Prasad, Amrutha and others},
journal={IEEE Spoken Language Technology Workshop (SLT), Doha, Qatar},
year={2022}
}
@article{zuluaga2022atco2,
title={ATCO2 corpus: A Large-Scale Dataset for Research on Automatic Speech Recognition and Natural Language Understanding of Air Traffic Control Communications},
author={Zuluaga-Gomez, Juan and Vesel{\`y}, Karel and Sz{\"o}ke, Igor and Motlicek, Petr and others},
journal={arXiv preprint arXiv:2211.04054},
year={2022}
}
```
Authors of the dataset:
```
@article{vsmidl2019air,
title={Air traffic control communication (ATCC) speech corpora and their use for ASR and TTS development},
author={{\v{S}}m{\'\i}dl, Lubo{\v{s}} and {\v{S}}vec, Jan and Tihelka, Daniel and Matou{\v{s}}ek, Jind{\v{r}}ich and Romportl, Jan and Ircing, Pavel},
journal={Language Resources and Evaluation},
volume={53},
number={3},
pages={449--464},
year={2019},
publisher={Springer}
}
```
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-0.1574173867702... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
12ml/e-CARE | 12ml | 2023-01-06T18:50:03Z | 152 | 1 | null | [
"task_categories:multiple-choice",
"region:us"
] | 2023-01-06T18:50:03Z | 2022-12-21T11:38:01.000Z | 2022-12-21T11:38:01 | ---
task_categories:
- multiple-choice
---
# Dataset of (Du et al., 2022)
## Abstract
>Understanding causality has vital importance for various Natural Language Processing (NLP) applications. Beyond the labeled instances, conceptual explanations of the causality can provide deep understanding of the causal fact to facilitate the causal reasoning process. However, such explanation information still remains absent in existing causal reasoning resources. In this paper, we fill this gap by presenting a human-annotated explainable CAusal REasoning dataset (e-CARE), which contains over 20K causal reasoning questions, together with natural language formed explanations of the causal questions. Experimental results show that generating valid explanations for causal facts still remains especially challenging for the state-of-the-art models, and the explanation information can be helpful for promoting the accuracy and stability of causal reasoning models.
## Notes
Please note that the original dataset has been modified so that the variable names match with those in the COPA dataset (Roemmele et al., 2011). In addition, only the training and the development sets are [publicly available](https://github.com/waste-wood/e-care).
## References
Du, L., Ding, X., Xiong, K., Liu, T., & Qin, B. (2022). e-CARE: a New Dataset for Exploring Explainable Causal Reasoning. arXiv preprint arXiv:2205.05849.
Roemmele, M., Bejan, C., and Gordon, A. (2011) Choice of Plausible Alternatives: An Evaluation of Commonsense Causal Reasoning. AAAI Spring Symposium on Logical Formalizations of Commonsense Reasoning, Stanford University, March 21-23, 2011. | [
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-0.4125695526599884,
0.39446032047271... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
heegyu/news-category-balanced-top10 | heegyu | 2023-02-13T02:56:31Z | 152 | 1 | null | [
"license:cc-by-4.0",
"region:us"
] | 2023-02-13T02:56:31Z | 2023-02-13T02:45:28.000Z | 2023-02-13T02:45:28 | ---
license: cc-by-4.0
---
### Top10 sampled news category dataset
randomly sampled news data
original dataset: https://www.kaggle.com/datasets/rmisra/news-category-dataset
### Value Counts per Category
```
ENTERTAINMENT 10000
POLITICS 10000
WELLNESS 10000
TRAVEL 9900
STYLE & BEAUTY 9814
PARENTING 8791
HEALTHY LIVING 6694
QUEER VOICES 6347
FOOD & DRINK 6340
BUSINESS 5992
``` | [
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0.01241811644285917... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
FastJobs/Visual_Emotional_Analysis | FastJobs | 2023-03-13T06:31:17Z | 152 | 8 | null | [
"task_categories:image-classification",
"size_categories:10K<n<100K",
"language:en",
"region:us"
] | 2023-03-13T06:31:17Z | 2023-03-03T06:23:19.000Z | 2023-03-03T06:23:19 | ---
task_categories:
- image-classification
language:
- en
size_categories:
- 10K<n<100K
--- | [
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-0.0478255338966846... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
llm-book/aio-retriever | llm-book | 2023-10-25T15:31:08Z | 152 | 0 | null | [
"size_categories:10K<n<100K",
"language:ja",
"region:us"
] | 2023-10-25T15:31:08Z | 2023-07-04T04:53:47.000Z | 2023-07-04T04:53:47 | ---
language:
- ja
size_categories:
- 10K<n<100K
dataset_info:
features:
- name: qid
dtype: string
- name: competition
dtype: string
- name: timestamp
dtype: string
- name: section
dtype: string
- name: number
dtype: string
- name: original_question
dtype: string
- name: original_answer
dtype: string
- name: original_additional_info
dtype: string
- name: question
dtype: string
- name: answers
list: string
- name: passages
list:
- name: passage_id
dtype: int32
- name: title
dtype: string
- name: text
dtype: string
- name: positive_passage_indices
list: int32
- name: negative_passage_indices
list: int32
splits:
- name: train
num_bytes: 1742881639
num_examples: 22335
- name: validation
num_bytes: 78671502
num_examples: 1000
download_size: 665253451
dataset_size: 1821553141
---
# Dataset Card for llm-book/aio-retriever
書籍『大規模言語モデル入門』で使用する、「AI王」コンペティションのQAデータセット（文書検索モデル訓練用）です。
GitHub リポジトリ [cl-tohoku/quiz-datasets](https://github.com/cl-tohoku/quiz-datasets) で公開されているデータセットを利用しています。
## Licence
本データセットに含まれる一部のクイズ問題の著作権は [abc/EQIDEN 実行委員会](https://abc-dive.com/portal/)に帰属するものであり、これらのクイズ問題は本書における使用許諾を得ているものです。
本データセットに含まれる一部のクイズ問題は[株式会社キュービック](http://www.qbik.co.jp/)および[株式会社カプリティオ](https://capriccio.tokyo/)に依頼し作成したものであり、これらのクイズ問題は[クリエイティブ・コモンズ表示・継承ライセンス 4.0 (CC BY-SA 4.0)](https://creativecommons.org/licenses/by-sa/4.0/deed.ja) ライセンスの下に提供されています。
本データセットにパッセージとして付与されている Wikipedia のコンテンツは、[クリエイティブ・コモンズ表示・継承ライセンス 3.0 (CC BY-SA 3.0)](https://creativecommons.org/licenses/by-sa/3.0/deed.ja) および [GNU 自由文書ライセンス (GFDL)](https://www.gnu.org/licenses/fdl.html) の下に配布されているものです。
クイズ問題のライセンスについて、詳細は [cl-tohoku/quiz-datasets](https://github.com/cl-tohoku/quiz-datasets) を参照してください。
| [
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0.136961743235588... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
TrainingDataPro/email-spam-classification | TrainingDataPro | 2023-09-14T16:37:38Z | 152 | 1 | null | [
"task_categories:text-classification",
"language:en",
"license:cc-by-nc-nd-4.0",
"finance",
"code",
"region:us"
] | 2023-09-14T16:37:38Z | 2023-07-25T12:09:29.000Z | 2023-07-25T12:09:29 | ---
license: cc-by-nc-nd-4.0
task_categories:
- text-classification
language:
- en
tags:
- finance
- code
---
# Email Spam Classification
The dataset consists of a collection of emails categorized into two major classes: **spam** and **not spam**. It is designed to facilitate the development and evaluation of spam detection or email filtering systems.
**The spam emails** in the dataset are typically unsolicited and unwanted messages that aim to promote products or services, spread malware, or deceive recipients for various malicious purposes. These emails often contain misleading subject lines, excessive use of advertisements, unauthorized links, or attempts to collect personal information.
The **non-spam emails** in the dataset are genuine and legitimate messages sent by individuals or organizations. They may include personal or professional communication, newsletters, transaction receipts, or any other non-malicious content.
The dataset encompasses emails of varying *lengths, languages, and writing styles*, reflecting the inherent heterogeneity of email communication. This diversity aids in training algorithms that can generalize well to different types of emails, making them robust against different spammer tactics and variations in non-spam email content.
### The dataset's possible applications:
- spam detection
- fraud detection
- email filtering systems
- customer support automation
- natural language processing
# Get the dataset
### This is just an example of the data
Leave a request on [**https://trainingdata.pro/data-market**](https://trainingdata.pro/data-market?utm_source=huggingface&utm_medium=cpc&utm_campaign=email-spam-classification) to discuss your requirements, learn about the price and buy the dataset.
# File with the extension .csv
includes the following information:
- **title**: title of the email,
- **text**: text of the email,
- **type**: type of the email
# Email spam might be collected in accordance with your requirements.
## [**TrainingData**](https://trainingdata.pro/data-market?utm_source=huggingface&utm_medium=cpc&utm_campaign=email-spam-classification) provides high-quality data annotation tailored to your needs
More datasets in TrainingData's Kaggle account: **https://www.kaggle.com/trainingdatapro/datasets**
TrainingData's GitHub: **https://github.com/Trainingdata-datamarket/TrainingData_All_datasets**
| [
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0.0051321787759... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
jitx/Methods2Test_java_unit_test_code | jitx | 2023-08-30T19:31:25Z | 152 | 3 | null | [
"task_categories:text-generation",
"language:en",
"license:mit",
"unit test",
"java",
"code",
"arxiv:2203.12776",
"region:us"
] | 2023-08-30T19:31:25Z | 2023-08-30T18:59:03.000Z | 2023-08-30T18:59:03 | ---
license: mit
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: target
dtype: string
- name: src_fm
dtype: string
- name: src_fm_fc
dtype: string
- name: src_fm_fc_co
dtype: string
- name: src_fm_fc_ms
dtype: string
- name: src_fm_fc_ms_ff
dtype: string
splits:
- name: train
num_bytes: 3399525755
num_examples: 624022
- name: test
num_bytes: 907751466
num_examples: 156922
download_size: 558984469
dataset_size: 4307277221
task_categories:
- text-generation
language:
- en
tags:
- unit test
- java
- code
---
## Dataset Description
Microsoft created this large dataset of Java Junit test cases with its corresponding focal methods.
It contains 780k pairs of JUnit test cases and focal methods which were extracted from a total of 91K
Java open source project hosted on GitHub.
The mapping between test case and focal methods are based heuristics rules and Java developer's best practice.
More information could be found here:
- [methods2test Github repo](https://github.com/microsoft/methods2test)
- [Methods2Test: A dataset of focal methods mapped to test cases](https://arxiv.org/pdf/2203.12776.pdf)
## Dataset Schema
```
target: <TEST_CASE>
src_fm: <FOCAL_METHOD>
src_fm_fc: <FOCAL_CLASS_NAME> <FOCAL_METHOD>
src_fm_fc_co: <FOCAL_CLASS_NAME> <FOCAL_METHOD> <CONTRSUCTORS>
src_fm_fc_ms: <FOCAL_CLASS_NAME> <FOCAL_METHOD> <CONTRSUCTORS> <METHOD_SIGNATURES>
src_fm_fc_ms_ff: <FOCAL_CLASS_NAME> <FOCAL_METHOD> <CONTRSUCTORS> <METHOD_SIGNATURES> <FIELDS>
```
## Focal Context
- fm: this representation incorporates exclusively the source
code of the focal method. Intuitively, this contains the most
important information for generating accurate test cases for
the given method.
- fm+fc: this representations adds the focal class name, which
can provide meaningful semantic information to the model.
- fm+fc+c: this representation adds the signatures of the constructor methods of the focal class. The idea behind this
augmentation is that the test case may require instantiating
an object of the focal class in order to properly test the focal
method.
- fm+fc+c+m: this representation adds the signatures of the
other public methods in the focal class. The rationale which
motivated this inclusion is that the test case may need to
invoke other auxiliary methods within the class (e.g., getters,
setters) to set up or tear down the testing environment.
- fm+fc+c+m+f : this representation adds the public fields of
the focal class. The motivation is that test cases may need to
inspect the status of the public fields to properly test a focal
method.
The different levels of focal contexts are the following:
```
FM: focal method
FM_FC: focal method + focal class name
FM_FC_CO: focal method + focal class name + constructor signatures
FM_FC_MS: focal method + focal class name + constructor signatures + public method signatures
FM_FC_MS_FF: focal method + focal class name + constructor signatures + public method signatures + public fields
```
## Lmitations
The original authors validate the heuristics by inspecting a
statistically significant sample (confidence level of 95% within 10%
margin of error) of 97 samples from the training set. Two authors
independently evaluated the sample, then met to discuss the disagreements. We found that 90.72% of the samples have a correct
link between the test case and the corresponding focal method
## Contribution
All the thanks to the original authors. | [
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-0.02452009171... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
taishi-i/awesome-japanese-nlp-classification-dataset | taishi-i | 2023-09-09T11:09:04Z | 152 | 3 | null | [
"task_categories:text-classification",
"size_categories:1K<n<10K",
"language:en",
"language:ja",
"license:other",
"code",
"region:us"
] | 2023-09-09T11:09:04Z | 2023-09-09T06:37:36.000Z | 2023-09-09T06:37:36 | ---
license: other
task_categories:
- text-classification
language:
- en
- ja
tags:
- code
size_categories:
- 1K<n<10K
---
# Dataset overview
This dataset identifies whether a GitHub repository description pertains to Japanese natural language processing (NLP).
The labels are categorized as **"Relevant (1)" and "Not Relevant (0)"**.
Problem Setting:
- Training Data: Repository descriptions from before 2022
- Test Data: Repository descriptions from 2023
- Objective: To detect repositories related to Japanese NLP
Data Collection:
- Positive Examples: Repositories listed in "[awesome-japanese-nlp-resources](https://github.com/taishi-i/awesome-japanese-nlp-resources)" as of September 9, 2023
- Negative Examples: Collected from the GitHub API and visually confirmed
- Note: The annotation process is subjective
Dataset Features:
- Subjective labeling
- Mixed English and Japanese descriptions
- Imbalanced label distribution
**These dataset features mirror real-world challenges and are ideal for evaluating models.**
Based on GitHub's terms of service, please use this dataset for research purposes only.
# How to use this dataset
How to load in Python.
```python
from datasets import load_dataset
dataset = load_dataset("taishi-i/awesome-japanese-nlp-classification-dataset")
```
Details of the dataset.
```python
DatasetDict({
train: Dataset({
features: ['label', 'text', 'url', 'created_at'],
num_rows: 5496
})
validation: Dataset({
features: ['label', 'text', 'url', 'created_at'],
num_rows: 400
})
test: Dataset({
features: ['label', 'text', 'url', 'created_at'],
num_rows: 856
})
})
```
# Baseline
Baseline trained with [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased).
Please use the baseline model from [here](https://huggingface.co/taishi-i/awesome-japanese-nlp-classification-model).
The F1-score for label 1 is important for this task.
| Label | Precision | Recall | F1-Score | Support |
|--------------|-----------|--------|----------|---------|
| 0 | 0.98 | 0.99 | 0.98 | 796 |
| 1 | 0.79 | 0.70 | **0.74** | 60 |
| Accuracy | | | 0.97 | 856 |
| Macro Avg | 0.89 | 0.84 | 0.86 | 856 |
| Weighted Avg | 0.96 | 0.97 | 0.97 | 856 |
# Dataset stats
Label distribution:
| Dataset | Label 0 (%) | Label 1 (%) |
|------------|-------------|-------------|
| Train | 92.59 | 7.41 |
| Validation | 95.75 | 4.25 |
| Test | 92.99 | 7.01 |
Relevant sample:
```python
{
"label": 1,
"text": "JGLUE: Japanese General Language Understanding Evaluation for huggingface datasets",
"url": "https://github.com/shunk031/huggingface-datasets_JGLUE",
"created_at": "2023-02-25T04:33:03Z"
}
```
Not Relevant sample:
```python
{
"label": 0,
"text": "Official repository of FaceLit: Neural 3D Relightable Faces (CVPR 2023)",
"url": "https://github.com/apple/ml-facelit",
"created_at": "2023-04-03T22:47:29Z"
}
```
Number of texts, average number of characters per text, minimum number of characters, maximum number of characters:
| Dataset | Text Count | Average Length | Min Length | Max Length |
|------------|------------|----------------|------------|------------|
| Train | 5496 | 58.05 | 2.0 | 609.0 |
| Validation | 400 | 54.33 | 8.0 | 226.0 |
| Test | 856 | 58.85 | 3.0 | 341.0 |
Proportion of text languages:
| Dataset | English (%) | Japanese (%) |
|------------|-------------|--------------|
| Train | 89.34 | 10.66 |
| Validation | 82.00 | 18.00 |
| Test | 83.18 | 16.82 |
Time range:
| Dataset | Start Date | End Date |
|---------|---------------------------|---------------------------|
| Train | 2008-02-11 22:55:26+00:00 | 2022-09-30 19:45:09+00:00 |
| Validation | 2022-10-01 06:02:56+00:00 | 2022-12-31 12:12:41+00:00 |
| Test | 2023-01-01 06:15:03+00:00 | 2023-08-21 15:30:53+00:00 |
# License
We collect and publish this dataset under [GitHub Acceptable Use Policies - 7. Information Usage Restrictions](https://docs.github.com/en/site-policy/acceptable-use-policies/github-acceptable-use-policies#7-information-usage-restrictions) and [GitHub Terms of Service - H. API Terms](https://docs.github.com/en/site-policy/github-terms/github-terms-of-service#h-api-terms) for research purposes. This dataset should be used solely for research verification purposes. Adhering to GitHub's regulations is mandatory.
| [
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dwadden/covidfact_entailment | dwadden | 2023-10-31T00:33:56Z | 152 | 0 | null | [
"task_categories:text-classification",
"task_ids:fact-checking",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc-by-nc-2.0",
"region:us"
] | 2023-10-31T00:33:56Z | 2023-10-30T22:26:59.000Z | 2023-10-30T22:26:59 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license:
- cc-by-nc-2.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- fact-checking
pretty_name: CovidFact
dataset_info:
features:
- name: claim_id
dtype: int32
- name: claim
dtype: string
- name: abstract_id
dtype: int32
- name: title
dtype: string
- name: abstract
sequence: string
- name: verdict
dtype: string
- name: evidence
sequence: int32
splits:
- name: train
num_bytes: 1547185
num_examples: 940
- name: test
num_bytes: 523542
num_examples: 317
download_size: 3610222
dataset_size: 2070727
---
# Dataset Card for "covidfact_entailment"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Dataset Structure](#dataset-structure)
- [Data Fields](#data-fields)
## Dataset Description
- **Repository:** <https://github.com/asaakyan/covidfact>
- **Point of Contact:** [David Wadden](mailto:davidw@allenai.org)
### Dataset Summary
COVID-FACT is a dataset of claims about COVID-19. For this version of the dataset, we follow the preprocessing from the MultiVerS modeling paper https://github.com/dwadden/multivers, verifying claims against abstracts of scientific research articles. Entailment labels and rationales are included.
## Dataset Structure
### Data fields
- `claim_id`: An `int32` claim identifier.
- `claim`: A `string`.
- `abstract_id`: An `int32` abstract identifier.
- `title`: A `string`.
- `abstract`: A list of `strings`, one for each sentence in the abstract.
- `verdict`: The fact-checking verdict, a `string`.
- `evidence`: A list of sentences from the abstract which provide evidence for the verdict.
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magnifi/hl-codellama-chat-response-v2 | magnifi | 2023-11-02T16:47:51Z | 152 | 0 | null | [
"region:us"
] | 2023-11-02T16:47:51Z | 2023-11-02T16:47:40.000Z | 2023-11-02T16:47:40 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: Query
dtype: string
- name: Result
dtype: string
- name: chat_response
dtype: string
splits:
- name: train
num_bytes: 1321860.461185117
num_examples: 1523
- name: test
num_bytes: 567627.5388148829
num_examples: 654
download_size: 109799
dataset_size: 1889488.0
---
# Dataset Card for "hl-codellama-chat-response-v2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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Champion/vpc2020_clear_anon_speech | Champion | 2021-10-12T14:19:45Z | 151 | 0 | null | [
"region:us"
] | 2021-10-12T14:19:45Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | Repo to share original and anonymized speech of vpc2020
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cristinakuo/latino40 | cristinakuo | 2021-12-27T19:24:12Z | 151 | 0 | null | [
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ctgowrie/chessgames | ctgowrie | 2021-12-05T00:43:39Z | 151 | 0 | null | [
"region:us"
] | 2021-12-05T00:43:39Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | Entry not found | [
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cyko/books | cyko | 2021-11-27T12:09:21Z | 151 | 0 | null | [
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davanstrien/ads-test | davanstrien | 2022-01-18T12:27:37Z | 151 | 0 | null | [
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debajyotidatta/biosses | debajyotidatta | 2022-02-01T01:46:29Z | 151 | 0 | null | [
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"region:us"
] | 2022-02-01T01:46:29Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
license: gpl-3.0
---
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dev/untitled_imgs | dev | 2021-12-11T14:14:27Z | 151 | 0 | null | [
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diiogo/annotations | diiogo | 2023-10-27T12:16:36Z | 151 | 0 | null | [
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dispenst/jhghdghfd | dispenst | 2021-03-28T15:24:20Z | 151 | 0 | null | [
"region:us"
] | 2021-03-28T15:24:20Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | <a href="https://jobs.acm.org/jobs/watch-godzilla-vs-kong-2021-full-1818658-cd">.</a>
<a href="https://jobs.acm.org/jobs/123movies-watch-godzilla-vs-kong-online-2021-full-f-r-e-e-1818655-cd">.</a>
<a href="https://jobs.acm.org/jobs/watch-demon-slayer-kimetsu-no-yaiba-mugen-train-2020-f-u-l-l-f-r-e-e-1818661-cd">.</a>
<a href="https://jobs.acm.org/jobs/123movies-watch-zack-snyder-s-justice-league-online-2021-full-f-r-e-e-1818662-cd">.</a>
<a href="https://jobs.acm.org/jobs/hd-watch-godzilla-vs-kong-2021-version-full-hbomax-1818659-cd">.</a>
<a href="https://jobs.acm.org/jobs/123movies-watch-girl-in-the-basement-online-2021-full-f-r-e-e-1818663-cd">.</a>
<a href="https://jobs.acm.org/jobs/watch-godzilla-vs-kong-2021-f-u-l-l-h-d-1818660-cd">.</a>
<a href="https://jobs.acm.org/jobs/123movies-watch-billie-eilish-the-world-s-a-little-blurry-2021-f-u-l-l-f-r-e-e-1818666-cd">.</a>
<a href="https://jobs.acm.org/jobs/123movies-watch-monster-hunter-2020-f-u-l-l-f-r-e-e-1818667-cd">.</a>
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<a href="https://onlinegdb.com/BJaH8WR4O">.</a> | [
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0.01638515293598... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
dispix/test-dataset | dispix | 2021-02-08T12:22:38Z | 151 | 0 | null | [
"region:us"
] | 2021-02-08T12:22:38Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | Entry not found | [
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florianbussmann/FUNSD-vu2020revising | florianbussmann | 2022-10-25T09:20:31Z | 151 | 0 | null | [
"multilinguality:monolingual",
"language:en",
"arxiv:2010.05322",
"region:us"
] | 2022-10-25T09:20:31Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
language:
- en
multilinguality:
- monolingual
language_bcp47:
- en-US
---
# Dataset Card for FUNSD-vu2020revising
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Paper:** [https://arxiv.org/abs/2010.05322](https://arxiv.org/abs/2010.05322)
### Dataset Summary
This is the revised version of the [FUNSD dataset](https://huggingface.co/datasets/nielsr/funsd) as proposed by [Vu, H. M., & Nguyen, D. T. N. (2020)](https://arxiv.org/abs/2010.05322).
### Supported Tasks and Leaderboards
The Form Understanding challenge comprises three tasks, namely word grouping, semantic-entity labeling, and entity linking.
## Dataset Structure
### Data Instances
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Data Fields
The data fields are the same among all splits.
- `id`: a `string` feature - GUID.
- `words`: a `list` of `string` features.
- `bboxes`: a `list` of `list` with four (`int`) features.
- `ner_tags`: a `list` of classification labels (`int`). Full tagset with indices:
```python
{'O': 0, 'B-HEADER': 1, 'I-HEADER': 2, 'B-QUESTION': 3, 'I-QUESTION': 4, 'B-ANSWER': 5, 'I-ANSWER': 6}
```
- `image_path`: a `string` feature.
### Data Splits
| name |train|test|
|------------|----:|---:|
|FUNSD-vu2020| 149| 50|
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@article{vu2020revising,
title={Revising FUNSD dataset for key-value detection in document images},
author={Vu, Hieu M and Nguyen, Diep Thi-Ngoc},
journal={arXiv preprint arXiv:2010.05322},
year={2020}
}
``` | [
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-0.0352938696742... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
laion/laion2B-en-joined | laion | 2022-03-31T07:44:37Z | 151 | 7 | null | [
"license:cc-by-4.0",
"region:us"
] | 2022-03-31T07:44:37Z | 2022-03-29T22:02:50.000Z | 2022-03-29T22:02:50 | ---
license: cc-by-4.0
---
| [
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-0.0478255338966846... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
sanchit-gandhi/concatenated_librispeech | sanchit-gandhi | 2023-01-26T11:45:39Z | 151 | 0 | null | [
"region:us"
] | 2023-01-26T11:45:39Z | 2023-01-26T10:26:12.000Z | 2023-01-26T10:26:12 | ---
dataset_info:
features:
- name: audio
dtype: audio
splits:
- name: train
num_bytes: 707889.0
num_examples: 1
download_size: 0
dataset_size: 707889.0
---
# Dataset Card for "concatenated_librispeech"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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jonathan-roberts1/Brazilian_Coffee_Scenes | jonathan-roberts1 | 2023-03-31T15:27:06Z | 151 | 0 | null | [
"task_categories:image-classification",
"license:other",
"region:us"
] | 2023-03-31T15:27:06Z | 2023-02-14T18:27:36.000Z | 2023-02-14T18:27:36 | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': coffee
'1': no coffee
splits:
- name: train
num_bytes: 4256968.464
num_examples: 2876
download_size: 2830232
dataset_size: 4256968.464
license: other
task_categories:
- image-classification
---
# Dataset Card for "Brazilian_Coffee_Scenes"
## Dataset Description
- **Paper** [Do deep features generalize from everyday objects to remote sensing and aerial scenes domains?](https://www.cv-foundation.org/openaccess/content_cvpr_workshops_2015/W13/papers/Penatti_Do_Deep_Features_2015_CVPR_paper.pdf)
### Licensing Information
[CC BY-NC]
## Citation Information
[Do deep features generalize from everyday objects to remote sensing and aerial scenes domains?](https://www.cv-foundation.org/openaccess/content_cvpr_workshops_2015/W13/papers/Penatti_Do_Deep_Features_2015_CVPR_paper.pdf)
```
@inproceedings{penatti2015deep,
title = {Do deep features generalize from everyday objects to remote sensing and aerial scenes domains?},
author = {Penatti, Ot{\'a}vio AB and Nogueira, Keiller and Dos Santos, Jefersson A},
year = 2015,
booktitle = {Proceedings of the IEEE conference on computer vision and pattern recognition workshops},
pages = {44--51}
}
``` | [
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-0.02754010260105... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
rcds/swiss_law_area_prediction | rcds | 2023-07-20T07:38:52Z | 151 | 3 | null | [
"task_categories:text-classification",
"annotations_creators:machine-generated",
"language_creators:expert-generated",
"multilinguality:multilingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:de",
"language:fr",
"language:it",
"license:cc-by-sa-4.0",
"arxiv:2306.09237",... | 2023-07-20T07:38:52Z | 2023-03-25T10:51:36.000Z | 2023-03-25T10:51:36 | ---
license: cc-by-sa-4.0
annotations_creators:
- machine-generated
language:
- de
- fr
- it
language_creators:
- expert-generated
multilinguality:
- multilingual
pretty_name: Law Area Prediction
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-classification
---
# Dataset Card for Law Area Prediction
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
The dataset contains cases to be classified into the four main areas of law: Public, Civil, Criminal and Social
These can be classified further into sub-areas:
```
"public": ['Tax', 'Urban Planning and Environmental', 'Expropriation', 'Public Administration', 'Other Fiscal'],
"civil": ['Rental and Lease', 'Employment Contract', 'Bankruptcy', 'Family', 'Competition and Antitrust', 'Intellectual Property'],
'criminal': ['Substantive Criminal', 'Criminal Procedure']
```
### Supported Tasks and Leaderboards
Law Area Prediction can be used as text classification task
### Languages
Switzerland has four official languages with three languages German, French and Italian being represenated. The decisions are written by the judges and clerks in the language of the proceedings.
| Language | Subset | Number of Documents|
|------------|------------|--------------------|
| German | **de** | 127K |
| French | **fr** | 156K |
| Italian | **it** | 46K |
## Dataset Structure
- decision_id: unique identifier for the decision
- facts: facts section of the decision
- considerations: considerations section of the decision
- law_area: label of the decision (main area of law)
- law_sub_area: sub area of law of the decision
- language: language of the decision
- year: year of the decision
- court: court of the decision
- chamber: chamber of the decision
- canton: canton of the decision
- region: region of the decision
### Data Fields
[More Information Needed]
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
The dataset was split date-stratisfied
- Train: 2002-2015
- Validation: 2016-2017
- Test: 2018-2022
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
The original data are published from the Swiss Federal Supreme Court (https://www.bger.ch) in unprocessed formats (HTML). The documents were downloaded from the Entscheidsuche portal (https://entscheidsuche.ch) in HTML.
#### Who are the source language producers?
The decisions are written by the judges and clerks in the language of the proceedings.
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
The dataset contains publicly available court decisions from the Swiss Federal Supreme Court. Personal or sensitive information has been anonymized by the court before publication according to the following guidelines: https://www.bger.ch/home/juridiction/anonymisierungsregeln.html.
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
We release the data under CC-BY-4.0 which complies with the court licensing (https://www.bger.ch/files/live/sites/bger/files/pdf/de/urteilsveroeffentlichung_d.pdf)
© Swiss Federal Supreme Court, 2002-2022
The copyright for the editorial content of this website and the consolidated texts, which is owned by the Swiss Federal Supreme Court, is licensed under the Creative Commons Attribution 4.0 International licence. This means that you can re-use the content provided you acknowledge the source and indicate any changes you have made.
Source: https://www.bger.ch/files/live/sites/bger/files/pdf/de/urteilsveroeffentlichung_d.pdf
### Citation Information
Please cite our [ArXiv-Preprint](https://arxiv.org/abs/2306.09237)
```
@misc{rasiah2023scale,
title={SCALE: Scaling up the Complexity for Advanced Language Model Evaluation},
author={Vishvaksenan Rasiah and Ronja Stern and Veton Matoshi and Matthias Stürmer and Ilias Chalkidis and Daniel E. Ho and Joel Niklaus},
year={2023},
eprint={2306.09237},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### Contributions
| [
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0.6023238897323608,
-0.5670843124389648,
-0.9053836464881897,
-0.702889084815979,
-0.035848293453454... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
suolyer/pile_wikipedia | suolyer | 2023-03-27T03:58:20Z | 151 | 0 | null | [
"license:apache-2.0",
"region:us"
] | 2023-03-27T03:58:20Z | 2023-03-26T16:40:41.000Z | 2023-03-26T16:40:41 | ---
license: apache-2.0
---
| [
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-0.7102247476577759,
-0.0478255338966846... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
tomaarsen/conll2003 | tomaarsen | 2023-05-08T13:34:35Z | 151 | 0 | conll-2003 | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"task_ids:part-of-speech",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|other-reuters-corpus",
"language:en",
"lice... | 2023-05-08T13:34:35Z | 2023-05-08T13:33:26.000Z | 2023-05-08T13:33:26 | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
license:
- other
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|other-reuters-corpus
task_categories:
- token-classification
task_ids:
- named-entity-recognition
- part-of-speech
paperswithcode_id: conll-2003
pretty_name: CoNLL-2003
dataset_info:
features:
- name: id
dtype: string
- name: tokens
sequence: string
- name: pos_tags
sequence:
class_label:
names:
'0': '"'
'1': ''''''
'2': '#'
'3': $
'4': (
'5': )
'6': ','
'7': .
'8': ':'
'9': '``'
'10': CC
'11': CD
'12': DT
'13': EX
'14': FW
'15': IN
'16': JJ
'17': JJR
'18': JJS
'19': LS
'20': MD
'21': NN
'22': NNP
'23': NNPS
'24': NNS
'25': NN|SYM
'26': PDT
'27': POS
'28': PRP
'29': PRP$
'30': RB
'31': RBR
'32': RBS
'33': RP
'34': SYM
'35': TO
'36': UH
'37': VB
'38': VBD
'39': VBG
'40': VBN
'41': VBP
'42': VBZ
'43': WDT
'44': WP
'45': WP$
'46': WRB
- name: chunk_tags
sequence:
class_label:
names:
'0': O
'1': B-ADJP
'2': I-ADJP
'3': B-ADVP
'4': I-ADVP
'5': B-CONJP
'6': I-CONJP
'7': B-INTJ
'8': I-INTJ
'9': B-LST
'10': I-LST
'11': B-NP
'12': I-NP
'13': B-PP
'14': I-PP
'15': B-PRT
'16': I-PRT
'17': B-SBAR
'18': I-SBAR
'19': B-UCP
'20': I-UCP
'21': B-VP
'22': I-VP
- name: ner_tags
sequence:
class_label:
names:
'0': O
'1': B-PER
'2': I-PER
'3': B-ORG
'4': I-ORG
'5': B-LOC
'6': I-LOC
'7': B-MISC
'8': I-MISC
config_name: conll2003
splits:
- name: train
num_bytes: 6931345
num_examples: 14041
- name: validation
num_bytes: 1739223
num_examples: 3250
- name: test
num_bytes: 1582054
num_examples: 3453
download_size: 982975
dataset_size: 10252622
train-eval-index:
- config: conll2003
task: token-classification
task_id: entity_extraction
splits:
train_split: train
eval_split: test
col_mapping:
tokens: tokens
ner_tags: tags
metrics:
- type: seqeval
name: seqeval
---
# Dataset Card for "conll2003"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://www.aclweb.org/anthology/W03-0419/](https://www.aclweb.org/anthology/W03-0419/)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of downloaded dataset files:** 4.85 MB
- **Size of the generated dataset:** 10.26 MB
- **Total amount of disk used:** 15.11 MB
### Dataset Summary
The shared task of CoNLL-2003 concerns language-independent named entity recognition. We will concentrate on
four types of named entities: persons, locations, organizations and names of miscellaneous entities that do
not belong to the previous three groups.
The CoNLL-2003 shared task data files contain four columns separated by a single space. Each word has been put on
a separate line and there is an empty line after each sentence. The first item on each line is a word, the second
a part-of-speech (POS) tag, the third a syntactic chunk tag and the fourth the named entity tag. The chunk tags
and the named entity tags have the format I-TYPE which means that the word is inside a phrase of type TYPE. Only
if two phrases of the same type immediately follow each other, the first word of the second phrase will have tag
B-TYPE to show that it starts a new phrase. A word with tag O is not part of a phrase. Note the dataset uses IOB2
tagging scheme, whereas the original dataset uses IOB1.
For more details see https://www.clips.uantwerpen.be/conll2003/ner/ and https://www.aclweb.org/anthology/W03-0419
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Dataset Structure
### Data Instances
#### conll2003
- **Size of downloaded dataset files:** 4.85 MB
- **Size of the generated dataset:** 10.26 MB
- **Total amount of disk used:** 15.11 MB
An example of 'train' looks as follows.
```
{
"id": "0",
"document_id": 1,
"sentence_id": 3,
"tokens": ["The", "European", "Commission", "said", "on", "Thursday", "it", "disagreed", "with", "German", "advice", "to", "consumers", "to", "shun", "British", "lamb", "until", "scientists", "determine", "whether", "mad", "cow", "disease", "can", "be", "transmitted", "to", "sheep", "."]
"pos_tags": [12, 22, 22, 38, 15, 22, 28, 38, 15, 16, 21, 35, 24, 35, 37, 16, 21, 15, 24, 41, 15, 16, 21, 21, 20, 37, 40, 35, 21, 7],
"ner_tags": [0, 3, 4, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
"chunk_tags": [11, 12, 12, 21, 13, 11, 11, 21, 13, 11, 12, 13, 11, 21, 22, 11, 12, 17, 11, 21, 17, 11, 12, 12, 21, 22, 22, 13, 11, 0],
}
```
The original data files have `-DOCSTART-` lines used to separate documents, but these lines are removed here.
Indeed `-DOCSTART-` is a special line that acts as a boundary between two different documents, and it is filtered out in this implementation.
### Data Fields
The data fields are the same among all splits.
#### conll2003
- `id`: a `string` feature.
- `document_id`: an `int32` feature tracking which document the sample is from.
- `sentence_id`: an `int32` feature tracking which sentence in this document the sample is from.
- `tokens`: a `list` of `string` features.
- `pos_tags`: a `list` of classification labels (`int`). Full tagset with indices:
```python
{'"': 0, "''": 1, '#': 2, '$': 3, '(': 4, ')': 5, ',': 6, '.': 7, ':': 8, '``': 9, 'CC': 10, 'CD': 11, 'DT': 12,
'EX': 13, 'FW': 14, 'IN': 15, 'JJ': 16, 'JJR': 17, 'JJS': 18, 'LS': 19, 'MD': 20, 'NN': 21, 'NNP': 22, 'NNPS': 23,
'NNS': 24, 'NN|SYM': 25, 'PDT': 26, 'POS': 27, 'PRP': 28, 'PRP$': 29, 'RB': 30, 'RBR': 31, 'RBS': 32, 'RP': 33,
'SYM': 34, 'TO': 35, 'UH': 36, 'VB': 37, 'VBD': 38, 'VBG': 39, 'VBN': 40, 'VBP': 41, 'VBZ': 42, 'WDT': 43,
'WP': 44, 'WP$': 45, 'WRB': 46}
```
- `chunk_tags`: a `list` of classification labels (`int`). Full tagset with indices:
```python
{'O': 0, 'B-ADJP': 1, 'I-ADJP': 2, 'B-ADVP': 3, 'I-ADVP': 4, 'B-CONJP': 5, 'I-CONJP': 6, 'B-INTJ': 7, 'I-INTJ': 8,
'B-LST': 9, 'I-LST': 10, 'B-NP': 11, 'I-NP': 12, 'B-PP': 13, 'I-PP': 14, 'B-PRT': 15, 'I-PRT': 16, 'B-SBAR': 17,
'I-SBAR': 18, 'B-UCP': 19, 'I-UCP': 20, 'B-VP': 21, 'I-VP': 22}
```
- `ner_tags`: a `list` of classification labels (`int`). Full tagset with indices:
```python
{'O': 0, 'B-PER': 1, 'I-PER': 2, 'B-ORG': 3, 'I-ORG': 4, 'B-LOC': 5, 'I-LOC': 6, 'B-MISC': 7, 'I-MISC': 8}
```
### Data Splits
| name |train|validation|test|
|---------|----:|---------:|---:|
|conll2003|14041| 3250|3453|
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
From the [CoNLL2003 shared task](https://www.clips.uantwerpen.be/conll2003/ner/) page:
> The English data is a collection of news wire articles from the Reuters Corpus. The annotation has been done by people of the University of Antwerp. Because of copyright reasons we only make available the annotations. In order to build the complete data sets you will need access to the Reuters Corpus. It can be obtained for research purposes without any charge from NIST.
The copyrights are defined below, from the [Reuters Corpus page](https://trec.nist.gov/data/reuters/reuters.html):
> The stories in the Reuters Corpus are under the copyright of Reuters Ltd and/or Thomson Reuters, and their use is governed by the following agreements:
>
> [Organizational agreement](https://trec.nist.gov/data/reuters/org_appl_reuters_v4.html)
>
> This agreement must be signed by the person responsible for the data at your organization, and sent to NIST.
>
> [Individual agreement](https://trec.nist.gov/data/reuters/ind_appl_reuters_v4.html)
>
> This agreement must be signed by all researchers using the Reuters Corpus at your organization, and kept on file at your organization.
### Citation Information
```
@inproceedings{tjong-kim-sang-de-meulder-2003-introduction,
title = "Introduction to the {C}o{NLL}-2003 Shared Task: Language-Independent Named Entity Recognition",
author = "Tjong Kim Sang, Erik F. and
De Meulder, Fien",
booktitle = "Proceedings of the Seventh Conference on Natural Language Learning at {HLT}-{NAACL} 2003",
year = "2003",
url = "https://www.aclweb.org/anthology/W03-0419",
pages = "142--147",
}
```
### Contributions
Thanks to [@jplu](https://github.com/jplu), [@vblagoje](https://github.com/vblagoje), [@lhoestq](https://github.com/lhoestq) for adding this dataset.
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"arxiv:2305.19840",
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] | 2023-06-07T08:18:37Z | 2023-06-06T22:10:02.000Z | 2023-06-06T22:10:02 | ---
language:
- pl
---
Part of **BEIR-PL: Zero Shot Information Retrieval Benchmark for the Polish Language**.
Link to arxiv: https://arxiv.org/pdf/2305.19840.pdf
Contact: konrad.wojtasik@pwr.edu.pl | [
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"license:apache-2.0",
"region:us"
] | 2023-11-03T20:37:34Z | 2023-08-14T02:12:25.000Z | 2023-08-14T02:12:25 | ---
license: apache-2.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: title
dtype: string
- name: image
dtype: string
- name: story
dtype: string
splits:
- name: train
num_bytes: 4356048
num_examples: 72
download_size: 3538794
dataset_size: 4356048
---
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formermagic/github_python_1m | formermagic | 2022-10-21T16:45:17Z | 150 | 1 | null | [
"task_ids:language-modeling",
"task_ids:slot-filling",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:py",
"license:mit",
"region:us"
] | 2022-10-21T16:45:17Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- found
language:
- py
license:
- mit
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
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task_categories:
- sequence-modeling
- conditional-text-generation
task_ids:
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- slot-filling
- code-generation
---
# Dataset Card for Github Python 1M | [
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license: cc-by-nc-4.0
---
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CodedotAI/code_clippy | CodedotAI | 2022-11-17T19:54:28Z | 149 | 10 | null | [
"task_categories:text-generation",
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annotations_creators:
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language:
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license:
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multilinguality:
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size_categories:
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source_datasets:
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task_categories:
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task_ids:
- language-modeling
pretty_name: Code Clippy
---
# Dataset Card for Code Clippy Data
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://the-eye.eu/public/AI/training_data/code_clippy_data/
- **Repository:** https://github.com/ncoop57/gpt-code-clippy
- **Paper:** [Not yet :)]
- **Leaderboard:** [Not yet :)]
- **Point of Contact:** [Nathan Cooper](mailto@nacooper01@email.wm.edu)
### Dataset Summary
This dataset was generated by selecting GitHub repositories from a large collection of repositories. These repositories were collected from https://seart-ghs.si.usi.ch/ and Github portion of [The Pile](https://github.com/EleutherAI/github-downloader) (performed on July 7th, 2021). The goal of this dataset is to provide a training set for pretraining large language models on code data for helping software engineering researchers better understand their impacts on software related tasks such as autocompletion of code. The dataset is split into train, validation, and test splits. There is a version containing duplicates (209GBs compressed) and ones where exact duplicates (132GBs compressed) are removed. Contains mostly JavaScript and Python code, but other programming languages are included as well to various degrees.
### Supported Tasks and Leaderboards
- `language-modeling`: The dataset can be used to train a model for language modeling for modeling programming languages, which consists of pretraining/finetuning a model to predict missing tokens, either causally or masked, given some context. Success on this task is typically measured by achieving a *low* perplexity score.
### Languages
Multiple programming languages are included in the dataset.
## Dataset Structure
### Data Instances
```
{
"id": datasets.Value("int64"),
"text": datasets.Value("string"),
"repo_name": datasets.Value("string"),
"stars": datasets.Value("string"),
"repo_language": datasets.Value("string"),
"file_name": datasets.Value("string"),
"mime_type": datasets.Value("string")
}
```
### Data Fields
- `id`: A unique identifier for the data instance.
- `text`: The text of the code.
- `repo_name`: The name of the repository.
- `stars`: The number of stars the repository has.
- `repo_language`: The programming language of the repository.
- `file_name`: The name of the file.
- `mime_type`: The MIME type of the file.
### Data Splits
| Size in GBs | Tain | Valid | Test |
| ----- | ------ | ----- | ---- |
| Duplicate | 194 | 9 | 6.3 |
| Deduplicate | 126 | 3.3 | 3.1 |
## Dataset Creation
### Curation Rationale
To have a code dataset that is large enough to properly train a large language model on.
### Source Data
#### Initial Data Collection and Normalization
- [The Pile](https://github.com/EleutherAI/github-downloader)
- [Seart-GHS](https://seart-ghs.si.usi.ch/)
Repositories were collected from both sources and the helper script from https://github.com/EleutherAI/github-downloader was used to download the repositories. Files were scrapped from the downloaded repositories, but ignored files that had certain extensions associated with binary or other non-textual/autogenerated content, and the output was converted into the [LM_Dataformat](https://pypi.org/project/lm-dataformat/) format.
#### Who are the source language producers?
Software developers.
### Annotations
#### Annotation process
No annotation was performed.
#### Who are the annotators?
N/A
### Personal and Sensitive Information
Since this data was collected from public repositories, there exists potential for personal and sensitive information to be included in the data through developers accidentally or on purpose uploading their secret keys, passwords, API keys, emails, etc.
## Considerations for Using the Data
### Social Impact of Dataset
The paper ["Evaluating Large Language Models Trained on Code"](https://arxiv.org/abs/2107.03374) from OpenAI has a good discussion on what the impact of a large language model trained on code could be. Therefore, some parts of their discuss are highlighted here as it pertains to this dataset and models that may be trained from it. **As well as some differences in views from the paper, particularly around legal implications**.
1. **Over-reliance:** A language model trained on large datasets such as this one for the task of autogenerating code may generate plausible solutions that may appear correct, but are not necessarily the correct solution. Not properly evaluating the generated code may cause have negative consequences such as the introduction of bugs, or the introduction of security vulnerabilities. Therefore, it is important that users are aware of the limitations and potential negative consequences of using a language model trained on this dataset.
2. **Economic and labor market impacts:** Large language models trained on large code datasets such as this one that are capable of generating high-quality code have the potential to automate part of the software development process. This may negatively impact software developers. However, as discussed in the paper, as shown in the Summary Report of software developers from [O*NET OnLine](https://www.onetonline.org/link/summary/15-1252.00), developers don't just write software.
3. **Security implications:** No filtering or checking of vulnerabilities or buggy code was performed. This means that the dataset may contain code that may be malicious or contain vulnerabilities. Therefore, any model trained on this dataset may generate vulnerable, buggy, or malicious code. In safety critical software, this could lead to software that may work improperly and could result in serious consequences depending on the software. Additionally, a model trained on this dataset may be used to generate malicious code on purpose in order to perform ransomware or other such attacks.
4. **Legal implications:** No filtering was performed on licensed code. This means that the dataset may contain restrictive licensed code. As discussed in the paper, public Github repositories may fall under "fair use." However, there has been little to no previous cases of such usages of licensed publicly available code. Therefore, any model trained on this dataset may be required to obey license terms that align with the software it was trained on such as GPL-3.0, which is why we purposefully put this dataset under the GPL-3.0 license. It is unclear the legal ramifications of using a language model trained on this dataset.
### Discussion of Biases
The programming languages most represented in this dataset are those of Javascript and Python. Therefore, other, still popular languages such as C and C++, are less represented and therefore model performance for these languages will be less comparatively. Additionally, this dataset only contains public repositories and so may not be representative of code written by private developers. No filtering was performed for potential racist, offensive, or otherwise inappropriate content. Therefore there may be such content in the dataset that will be reflected in models trained on it.
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
Nathan Coooper, Artashes Arutiunian, Santiago Hincapié-Potes, Ben Trevett, Arun Raja, Erfan Hossami, Mrinal Mathur, and contributors!
### Licensing Information
This repository is under the GPL-3.0 license.
### Citation Information
```
@misc{cooper-2021-code-clippy-data,
author = {Nathan Coooper, Artashes Arutiunian, Santiago Hincapié-Potes, Ben Trevett, Arun Raja, Erfan Hossami, Mrinal Mathur, and contributors},
title = {{Code Clippy Data: A large dataset of code data from Github for research into code language models}},
month = jul,
year = 2021,
version = {1.0},
publisher = {GitHub},
url = {https://github.com/ncoop57/gpt-code-clippy}
}
```
### Contributions
Thanks to [@ncoop57](https://github.com/ncoop57), [@arampacha](https://github.com/arampacha), [@shpotes](https://github.com/shpotes), [@bentrevett](https://github.com/bentrevett), [@arunraja-hub](https://github.com/arunraja-hub), [@taisazero](https://github.com/taisazero), [@Mrinal18](https://github.com/Mrinal18), and contributors for adding this dataset.
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timeout-minutes: 9999
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Publication date 2021-06-07 \
Usage Attribution-ShareAlike 4.0 International Creative Commons License by sa \
Topics Stack Exchange Data Dump \
Contributor Stack Exchange Community
Please see the license information at:
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] | 2022-01-25T05:30:29Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | Dataset Summary
The Common Voice dataset consists of a unique MP3 and corresponding text file. Many of the 9,283 recorded hours in the dataset also include demographic metadata like age, sex, and accent that can help train the accuracy of speech recognition engines.
The dataset currently consists of 7,335 validated hours in 60 languages, but were always adding more voices and languages. Take a look at our Languages page to request a language or start contributing.
Supported Tasks and Leaderboards
[Needs More Information]
Languages
English
Dataset Structure
Data Instances
A typical data point comprises the path to the audio file, called path and its sentence. Additional fields include accent, age, client_id, up_votes down_votes, gender, locale and segment.
{'accent': 'netherlands', 'age': 'fourties', 'client_id': 'bbbcb732e0f422150c30ff3654bbab572e2a617da107bca22ff8b89ab2e4f124d03b6a92c48322862f60bd0179ae07baf0f9b4f9c4e11d581e0cec70f703ba54', 'down_votes': 0, 'gender': 'male', 'locale': 'nl', 'path': 'nl/clips/common_voice_nl_23522441.mp3', 'segment': "''", 'sentence': 'Ik vind dat een dubieuze procedure.', 'up_votes': 2, 'audio': {'path':nl/clips/common_voice_nl_23522441.mp3', 'array': array([-0.00048828, -0.00018311, -0.00137329, ..., 0.00079346, 0.00091553, 0.00085449], dtype=float32), 'sampling_rate': 48000} `
Data Fields
client_id: An id for which client (voice) made the recording
path: The path to the audio file
audio: A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: dataset[0]["audio"] the audio file is automatically decoded and resampled to dataset.features["audio"].sampling_rate. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the "audio" column, i.e. dataset[0]["audio"] should always be preferred over dataset["audio"][0].
sentence: The sentence the user was prompted to speak
up_votes: How many upvotes the audio file has received from reviewers
down_votes: How many downvotes the audio file has received from reviewers
age: The age of the speaker.
gender: The gender of the speaker
accent: Accent of the speaker
locale: The locale of the speaker
segment: Usually empty field
Data Splits
The speech material has been subdivided into portions for dev, train, test, validated, invalidated, reported and other.
The validated data is data that has been validated with reviewers and recieved upvotes that the data is of high quality.
The invalidated data is data has been invalidated by reviewers and recieved downvotes that the data is of low quality.
The reported data is data that has been reported, for different reasons.
The other data is data that has not yet been reviewed.
The dev, test, train are all data that has been reviewed, deemed of high quality and split into dev, test and train.
Dataset Creation
Curation Rationale
[Needs More Information]
Source Data
Initial Data Collection and Normalization
[Needs More Information]
Who are the source language producers?
[Needs More Information]
Annotations
Annotation process
[Needs More Information]
Who are the annotators?
[Needs More Information]
Personal and Sensitive Information
The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in the Common Voice dataset.
Considerations for Using the Data
Social Impact of Dataset
The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in the Common Voice dataset.
Discussion of Biases
[More Information Needed]
Other Known Limitations
[More Information Needed]
Additional Information
Dataset Curators
[More Information Needed]
Licensing Information
Public Domain, CC-0
Citation Information
@inproceedings{commonvoice:2020,
author = {Ardila, R. and Branson, M. and Davis, K. and Henretty, M. and Kohler, M. and Meyer, J. and Morais, R. and Saunders, L. and Tyers, F. M. and Weber, G.},
title = {Common Voice: A Massively-Multilingual Speech Corpus},
booktitle = {Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020)},
pages = {4211--4215},
year = 2020
}
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