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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
open-source-metrics/preprocessed_pip | open-source-metrics | 2023-11-23T14:47:47Z | 23 | 0 | null | [
"region:us"
] | 2023-11-23T14:47:47Z | 2023-06-14T19:10:29.000Z | 2023-06-14T19:10:29 | ---
dataset_info:
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dataset_size: 252148
---
# Dataset Card for "preprocessed_pip"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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nisaar/Lawyer_GPT_India | nisaar | 2023-06-16T05:49:30Z | 23 | 7 | null | [
"license:apache-2.0",
"region:us"
] | 2023-06-16T05:49:30Z | 2023-06-14T20:45:23.000Z | 2023-06-14T20:45:23 | ---
license: apache-2.0
---
**Dataset Card for Indian Polity Question-Answer Dataset**
---
**Dataset Summary**
This dataset contains a collection of question-answer pairs on the subject of Indian Polity. The aim is to provide comprehensive answers to a wide range of questions pertaining to the Indian Constitution, judiciary, legislative, and various socio-political issues in India. It serves as a valuable resource for learners, researchers, and AI systems seeking to understand or respond to questions about Indian Polity.
---
**Supported Tasks and Leaderboards**
This dataset is useful for tasks such as question answering, text comprehension, language modelling, and conversational AI development. There's no specific leaderboard associated with this dataset.
---
**Languages**
The dataset is in English.
---
**Dataset Structure**
- **Data Instances**
Each instance in the dataset consists of a pair of a human-posed question and an assistant-provided answer on a specific topic in Indian Polity.
- **Data Fields**
1. Question: A text field containing the question.
2. Answer: A text field containing the corresponding answer.
- **Data Splits**
The dataset isn't divided into standard splits of training, validation, and test sets.
---
**Dataset Creation**
- **Curation Rationale**
The dataset was curated to provide accurate and comprehensive answers to a range of questions about Indian Polity. It covers fundamental rights, constitutional provisions, legislative procedures, and socio-political issues, among others.
- **Source Data**
- **Initial Data Collection and Normalization**
Data collection involved generating questions on Indian Polity topics and providing detailed answers.
- **Who are the source language producers?**
The language was produced by a language model trained by OpenAI.
---
**Annotations**
- **Annotation process**
Not applicable as the dataset doesn't contain annotations.
- **Who are the annotators?**
Not applicable as the dataset doesn't contain annotations.
---
**Personal and Sensitive Information**
The dataset does not contain any personal or sensitive information.
---
**Considerations for Using the Data**
- **Social Impact of Dataset**
The dataset can contribute to the understanding of Indian Polity and Constitution. It can help in educational, research, and AI applications.
- **Discussion of Biases**
There is no obvious bias in the dataset as it provides factual information related to the Indian Constitution and Polity.
- **Other Known Limitations**
The dataset may not cover all possible questions on Indian Polity. Additionally, all answers are in English, which may limit its use for non-English speakers.
---
**Additional Information**
- **Dataset Curators**
The dataset has been curated by an OpenAI language model.
- **Licensing Information**
The dataset follows OpenAI's standard data use policy.
- **Citation Information**
Not applicable as this is an artificial dataset.
- **Contributions**
The dataset was generated by the ChatGPT model trained by OpenAI. | [
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0.106151685118... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
MAPS-research/GEMRec-Roster | MAPS-research | 2023-08-07T04:41:32Z | 23 | 1 | null | [
"task_categories:text-to-image",
"size_categories:n<1K",
"language:en",
"license:openrail",
"art",
"diffusers",
"region:us"
] | 2023-08-07T04:41:32Z | 2023-06-18T09:32:11.000Z | 2023-06-18T09:32:11 | ---
dataset_info:
features:
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license: openrail
task_categories:
- text-to-image
language:
- en
tags:
- art
- diffusers
size_categories:
- n<1K
---
# GEMRec-18k -- Roster
This is the official model checkpoint metadata dataset for the paper [Towards Personalized Prompt-Model Retrieval for Generative Recommendation](https://github.com/MAPS-research/GEMRec).
## Dataset Intro
`GEMRec-18K` is a prompt-model interaction dataset with 18K images generated by 200 publicly-available generative models paired with a diverse set of 90 textual prompts. We randomly sampled a subset of 197 models from the full set of models (all finetuned from Stable Diffusion) on [Civitai](https://civitai.com/) according to the popularity distribution (i.e., download counts) and added 3 original Stable Diffusion checkpoints (v1.4, v1.5, v2.1) from HuggingFace. All the model checkpoints have been converted to the [Diffusers](https://huggingface.co/docs/diffusers/index) format. The textual prompts were drawn from three sources: 60 prompts were sampled from [Parti Prompts](https://github.com/google-research/parti); 10 prompts were sampled from [Civitai](https://civitai.com/) by popularity; we also handcrafted 10 prompts following the prompting guide from [DreamStudio](https://beta.dreamstudio.ai/prompt-guide), and then extended them to 20 by creating a shortened and simplified version following the tips from [Midjourney](https://docs.midjourney.com/docs/prompts). The textual prompts were classified into 12 categories: abstract, animal, architecture, art, artifact, food, illustration, people, produce & plant, scenery, vehicle, and world knowledge.
## Links
#### Dataset
- [GEMRec-Promptbook](https://huggingface.co/datasets/MAPS-research/GEMRec-PromptBook): The full version of our GemRec-18k dataset (images & metadata).
- [GEMRec-Metadata](https://huggingface.co/datasets/MAPS-research/GEMRec-Metadata): The pruned version of our GemRec-18k dataset (metadata only).
- [GEMRec-Roster](https://huggingface.co/datasets/MAPS-research/GEMRec-Roster): The metadata for the 200 model checkpoints fetched from [Civitai](https://civitai.com/).
#### Space
- [GEMRec-Gallery](https://huggingface.co/spaces/MAPS-research/GEMRec-Gallery): Our web application for browsing and comparing the generated images.
#### Github Code
- [GEMRec](https://github.com/MAPS-research/GEMRec)
## Acknowledgement
This work was supported through the NYU High Performance Computing resources, services, and staff expertise.
## Citation
If you find our work helpful, please consider cite it as follows:
```bibtex
@article{guo2023towards,
title={Towards Personalized Prompt-Model Retrieval for Generative Recommendation},
author={Guo, Yuanhe and Liu, Haoming and Wen, Hongyi},
journal={arXiv preprint arXiv:2308.02205},
year={2023}
}
``` | [
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0.207502573728561... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
ltkw98/fold0 | ltkw98 | 2023-06-22T21:57:31Z | 23 | 0 | null | [
"region:us"
] | 2023-06-22T21:57:31Z | 2023-06-22T21:57:28.000Z | 2023-06-22T21:57:28 | ---
dataset_info:
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download_size: 1334996
dataset_size: 4108210
---
# Dataset Card for "fold0"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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nampdn-ai/tiny-webtext | nampdn-ai | 2023-08-27T00:23:37Z | 23 | 20 | null | [
"task_categories:text-generation",
"size_categories:1M<n<10M",
"source_datasets:tiiuae/falcon-refinedweb",
"language:en",
"license:mit",
"arxiv:2306.01116",
"doi:10.57967/hf/1024",
"region:us"
] | 2023-08-27T00:23:37Z | 2023-06-27T01:13:25.000Z | 2023-06-27T01:13:25 | ---
license: mit
viewer: true
task_categories:
- text-generation
language:
- en
pretty_name: Tiny WebText
size_categories:
- 1M<n<10M
source_datasets:
- tiiuae/falcon-refinedweb
---
# Tiny WebText
The Tiny WebText dataset is designed to help models learn about perception on web text while neutralizing the bias of the source text using critical thinking methods. By providing a rich and diverse set of texts, I aim to improve the ability of models to understand and analyze information in a more objective and unbiased manner.
This dataset can be used to train and evaluate natural language processing and machine learning models, with the goal of improving their perception and critical thinking skills. It is a valuable resource for researchers and developers, especially those working in the fields of machine learning and data engineering.
The dataset is augmented using subset of [Falcon-RefinedWeb](https://arxiv.org/abs/2306.01116), which provides additional augmented text using [Falcon-7B-Instruct](https://huggingface.co/tiiuae/falcon-7b-instruct) model to improve the quality and diversity of the texts.
I welcome any feedback or contributions. Thank you for your interest in my work! | [
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InfImagine/FakeImageDataset | InfImagine | 2023-11-20T05:37:00Z | 23 | 7 | null | [
"license:apache-2.0",
"arxiv:2304.13023",
"region:us"
] | 2023-11-20T05:37:00Z | 2023-07-07T04:08:51.000Z | 2023-07-07T04:08:51 | ---
license: apache-2.0
---
# Fake Image Dataset
Fake Image Dataset is now open-sourced at [huggingface (InfImagine Organization)](https://huggingface.co/datasets/InfImagine/FakeImageDataset/tree/main/ImageData/train) and [openxlab](https://openxlab.org.cn/datasets/whlzy/FakeImageDataset/tree/main). ↗ It consists of two folders, *ImageData* and *MetaData*. *ImageData* contains the compressed packages of the Fake Image Dataset, while *MetaData* contains the labeling information of the corresponding data indicating whether they are real or fake.
Sentry-Image is now open-sourced at [Sentry-Image (github repository)](https://github.com/Inf-imagine/Sentry) which provides the SOTA fake image detection models in [Sentry-Image Leaderboard](http://sentry.infimagine.com/) pretraining in [Fake Image Dataset](https://huggingface.co/datasets/InfImagine/FakeImageDataset/tree/main/ImageData/train) to detect whether the image provided is an AI-generated or real image.
## Why we need [Fake Image Dataset](https://huggingface.co/datasets/InfImagine/FakeImageDataset/tree/main/ImageData/train) and [Sentry-Image](http://sentry.infimagine.com/)?
* 🧐 Recent [study](https://arxiv.org/abs/2304.13023) have shown that humans struggle significantly to distinguish real photos from AI-generated ones, with a misclassification rate of **38.7%**.
* 🤗 To help people confirm whether the images they see are real images or AI-generated images, we launched the Sentry-Image project.
* 💻 Sentry-Image is an open source project which provides the SOTA fake image detection models in [Sentry-Image Leaderboard](http://sentry.infimagine.com/) to detect whether the image provided is an AI-generated or real image.
# Dataset card for Fake Image Dataset
## Dataset Description
* **Homepage:** [Sentry-Image](http://sentry.infimagine.com/)
* **Paper:** [https://arxiv.org/pdf/2304.13023.pdf](https://arxiv.org/pdf/2304.13023.pdf)
* **Point of Contact:** [contact@infimagine.com](mailto:contact@infimagine.com)
## How to Download
You can use following codes to download the dataset：
```shell
git lfs install
git clone https://huggingface.co/datasets/InfImagine/FakeImageDataset
```
You can use following codes to extract the files in each subfolder (take the *IF-CC95K* subfolder in ImageData/val/IF-CC95K as an example)：
```shell
cat IF-CC95K.tar.gz.* > IF-CC95K.tar.gz
tar -xvf IF-CC95K.tar.gz
```
## Dataset Summary
FakeImageDataset was created to serve as an large-scale dataset for the pretraining of detecting fake images.
It was built on StableDiffusion v1.5, IF and StyleGAN3.
## Supported Tasks and Leaderboards
FakeImageDataset is intended to be primarly used as a pretraining dataset for detecting fake images.
## Sub Dataset
### Training Dataset (Fake2M)
| Dataset | SD-V1.5Real-dpms-25 | IF-V1.0-dpms++-25 | StyleGAN3 |
| :----------- | :-----------: | :-----------: | :-----------: |
| Generator | Diffusion | Diffusion | GAN |
| Numbers | 1M | 1M | 87K |
| Resolution | 512 | 256 | (>=512) |
| Caption | CC3M-Train | CC3M-Train | - |
| ImageData Path | ImageData/train/SDv15R-CC1M | ImageData/train/IFv1-CC1M | ImageData/train/stylegan3-80K |
| MetaData Path | MetaData/train/SDv15R-CC1M.csv | MetaData/train/IF-CC1M.csv | MetaData/train/stylegan3-80K.csv |
### Validation Dataset (MPBench)
| Dataset | SDv15 | SDv21 | IF | Cogview2 | StyleGAN3 | Midjourneyv5 |
| :---------- | :-----------: | :-----------: | :-----------: | :-----------: | :-----------: | :-----------: |
| Generator | Diffusion | Diffusion | Diffusion | AR | GAN | - |
| Numbers | 30K | 15K | 95K | 22K | 60K | 5K |
| Resolution | 512 | 512 | 256 | 480 | (>=512) | (>=512) |
| Caption | CC15K-val | CC15K-val | CC15K-val | CC15K-val | - | - |
| ImageData Path | ImageData/val/SDv15-CC30K | ImageData/val/SDv21-CC15K | ImageData/val/IF-CC95K | ImageData/val/cogview2-22K | ImageData/val/stylegan3-60K | ImageData/val/Midjourneyv5-5K|
| MetaData Path | MetaData/val/SDv15-CC30K.csv| MetaData/val/SDv21-CC15K.csv | MetaData/val/IF-CC95K.csv | MetaData/val/cogview2-22K.csv | MetaData/val/stylegan3-60K.csv | MetaData/val/Midjourneyv5-5K.csv |
# News
* [2023/07] We open source the [Sentry-Image repository](https://github.com/Inf-imagine/Sentry) and [Sentry-Image Demo & Leaderboard](http://sentry.infimagine.com/).
* [2023/07] We open source the [Sentry-Image dataset](https://huggingface.co/datasets/InfImagine/FakeImageDataset).
Stay tuned for this project! Feel free to contact [contact@infimagine.com](contact@infimagine.com)! 😆
# License
This project is open-sourced under the [Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0). These weights and datasets are fully open for academic research and can be used for commercial purposes with official written permission. If you find our open-source models and datasets useful for your business, we welcome your donation to support the development of the next-generation Sentry-Image model. Please contact [contact@infimagine.com](contact@infimagine.com) for commercial licensing and donation inquiries.
# Citation
The code and model in this repository is mostly developed for or derived from the paper below. Please cite it if you find the repository helpful.
```
@misc{sentry-image-leaderboard,
title = {Sentry-Image Leaderboard},
author = {Zeyu Lu, Di Huang, Chunli Zhang, Chengyue Wu, Xihui Liu, Lei Bai, Wanli Ouyang},
year = {2023},
publisher = {InfImagine, Shanghai AI Laboratory},
howpublished = "\url{https://github.com/Inf-imagine/Sentry}"
},
@misc{lu2023seeing,
title = {Seeing is not always believing: Benchmarking Human and Model Perception of AI-Generated Images},
author = {Zeyu Lu, Di Huang, Lei Bai, Jingjing Qu, Chengyue Wu, Xihui Liu, Wanli Ouyang},
year = {2023},
eprint = {2304.13023},
archivePrefix = {arXiv},
primaryClass = {cs.AI}
}
``` | [
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juanberasategui/Crypto_Tweets | juanberasategui | 2023-07-24T08:27:32Z | 23 | 1 | null | [
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CohleM/english-to-nepali | CohleM | 2023-07-27T10:02:48Z | 23 | 0 | null | [
"license:apache-2.0",
"region:us"
] | 2023-07-27T10:02:48Z | 2023-07-27T09:32:58.000Z | 2023-07-27T09:32:58 | ---
license: apache-2.0
configs:
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data_files:
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path: data/train-*
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features:
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---
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ChanceFocus/flare-edtsum | ChanceFocus | 2023-07-28T05:23:42Z | 23 | 0 | null | [
"region:us"
] | 2023-07-28T05:23:42Z | 2023-07-28T05:23:36.000Z | 2023-07-28T05:23:36 | ---
dataset_info:
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download_size: 10794666
dataset_size: 20411227
---
# Dataset Card for "flare-edtsum"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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umaru97/flickr30k_train_val_test | umaru97 | 2023-08-04T06:07:36Z | 23 | 0 | null | [
"region:us"
] | 2023-08-04T06:07:36Z | 2023-08-04T06:03:32.000Z | 2023-08-04T06:03:32 | ---
configs:
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num_examples: 1014
- name: test
num_bytes: 142117238.54065907
num_examples: 1000
download_size: 4305964964
dataset_size: 4100200368.4279995
---
# Dataset Card for "flickr30k_train_val_test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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foduucom/table-detection-yolo | foduucom | 2023-08-05T14:42:23Z | 23 | 5 | null | [
"task_categories:object-detection",
"size_categories:1K<n<10K",
"language:en",
"foduuai",
"table",
"Documents",
"bordered table",
"borderless table",
"unstructured document",
"region:us"
] | 2023-08-05T14:42:23Z | 2023-08-05T11:43:51.000Z | 2023-08-05T11:43:51 | ---
task_categories:
- object-detection
tags:
- foduuai
- table
- Documents
- bordered table
- borderless table
- unstructured document
language:
- en
pretty_name: TableBorderNet
size_categories:
- 1K<n<10K
---
<div align="center">
<img width="640" alt="foduucom/table-detection-yolo" src="https://huggingface.co/datasets/foduucom/table-detection-yolo/resolve/main/thumbnail.jpg">
</div>
### Dataset Labels
```
['bordered', 'borderless']
```
### Number of Images
```json
{'test': 34, 'train': 238, 'valid': 70}
```
### How to Use
- Install [datasets](https://pypi.org/project/datasets/):
```bash
pip install datasets
```
- Load the dataset:
```python
from datasets import load_dataset
ds = load_dataset("foduucom/table-detection-yolo", name="full")
example = ds['train'][0]
```
### Dataset Summary
Certainly! Here's a dataset summary for your dataset of images containing tables that are categorized as border and borderless, provided in YOLO format:
## Dataset Summary
The **Table Detection Dataset** is a curated collection of images, each depicting tables that are classified as either 'bordered' or 'borderless'. The dataset is provided in YOLO format, featuring annotations for accurate object detection and classification. It serves as a valuable resource for researchers, developers, and practitioners working on table detection tasks, with a specific focus on distinguishing between tables with distinct visual characteristics.
**Key Features:**
- **Image Variety:** The dataset encompasses a diverse range of images, capturing tables from various real-world scenarios and environments.
- **Annotation Precision:** Each image is meticulously annotated with bounding box coordinates and class labels, indicating whether the table is 'bordered' or 'borderless'.
- **YOLO Format:** Annotations follow the YOLO format, making it suitable for training and evaluating object detection models.
- **Research and Development:** The dataset is designed to facilitate advancements in table detection algorithms and technologies, enabling the development of models capable of accurately identifying and classifying different types of tables.
Whether you are working on document analysis, data extraction, or image-based content recognition, the Table Detection Dataset provides an essential foundation for enhancing the capabilities of object detection models in identifying tables with varying visual attributes. By offering a comprehensive collection of border and borderless tables, this dataset empowers the AI community to tackle challenges in table detection across a wide range of applications.
For more details and access to the dataset, please refer to info@foduu.com . | [
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---
# Dataset Card for "govreport-test-tokenized"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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owkin/medical_knowledge_from_extracts | owkin | 2023-09-22T09:37:42Z | 23 | 4 | null | [
"task_categories:summarization",
"language:en",
"license:apache-2.0",
"region:us"
] | 2023-09-22T09:37:42Z | 2023-08-10T09:18:11.000Z | 2023-08-10T09:18:11 | ---
license: apache-2.0
task_categories:
- summarization
language:
- en
---
This dataset is used to train LLMs for medical knowledge extraction tasks | [
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klima7/polish-prose | klima7 | 2023-08-10T22:05:45Z | 23 | 1 | null | [
"region:us"
] | 2023-08-10T22:05:45Z | 2023-08-10T22:01:08.000Z | 2023-08-10T22:01:08 | ---
configs:
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---
# Dataset Card for "polish-prose"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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notrichardren/refuse-to-answer-prompts | notrichardren | 2023-08-24T21:18:55Z | 23 | 0 | null | [
"region:us"
] | 2023-08-24T21:18:55Z | 2023-08-21T16:25:09.000Z | 2023-08-21T16:25:09 | ---
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configs:
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path: data/train-*
---
# Dataset Card for "refuse-to-answer-prompts"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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mtc/german_seahorse_dataset_with_articles | mtc | 2023-08-24T14:01:36Z | 23 | 0 | null | [
"region:us"
] | 2023-08-24T14:01:36Z | 2023-08-24T14:01:31.000Z | 2023-08-24T14:01:31 | ---
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---
# Dataset Card for "german_seahorse_dataset_with_articles"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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bennsalter/caulking_images | bennsalter | 2023-09-06T14:04:31Z | 23 | 0 | null | [
"language:en",
"region:us"
] | 2023-09-06T14:04:31Z | 2023-09-06T13:40:19.000Z | 2023-09-06T13:40:19 | ---
language:
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SinghShweta/loan | SinghShweta | 2023-09-10T10:57:03Z | 23 | 0 | null | [
"license:llama2",
"region:us"
] | 2023-09-10T10:57:03Z | 2023-09-10T08:42:05.000Z | 2023-09-10T08:42:05 | ---
license: llama2
---
| [
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tingchih/multi-class | tingchih | 2023-09-12T04:21:02Z | 23 | 0 | null | [
"region:us"
] | 2023-09-12T04:21:02Z | 2023-09-12T00:25:48.000Z | 2023-09-12T00:25:48 | ---
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download_size: 77717077
dataset_size: 141032407
---
# Dataset Card for "multi-class"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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khalidalt/xlsum_clm | khalidalt | 2023-09-12T04:36:27Z | 23 | 0 | null | [
"region:us"
] | 2023-09-12T04:36:27Z | 2023-09-12T04:35:17.000Z | 2023-09-12T04:35:17 | ---
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---
# Dataset Card for "xlsum_clm"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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GokhanAI/AGENT_V2 | GokhanAI | 2023-11-16T15:04:30Z | 23 | 0 | null | [
"region:us"
] | 2023-11-16T15:04:30Z | 2023-09-12T10:58:43.000Z | 2023-09-12T10:58:43 | ---
configs:
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---
# Dataset Card for "AGENT_V2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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mnazari/nena_speech_1_0_test | mnazari | 2023-10-27T08:58:56Z | 23 | 0 | null | [
"task_categories:automatic-speech-recognition",
"task_categories:text-to-speech",
"task_categories:translation",
"annotations_creators:crowdsourced",
"annotations_creators:Geoffrey Khan",
"language_creators:crowdsourced",
"multilinguality:multilingual",
"size_categories:10K<n<100K",
"size_categories... | 2023-10-27T08:58:56Z | 2023-09-20T04:23:27.000Z | 2023-09-20T04:23:27 | ---
pretty_name: NENA Speech Dataset 1.0 (test)
annotations_creators:
- crowdsourced
- Geoffrey Khan
language_creators:
- crowdsourced
language:
- aii
- cld
- huy
- lsd
- trg
- aij
- bhn
- hrt
- kqd
- syn
license:
- cc0-1.0
multilinguality:
- multilingual
task_categories:
- automatic-speech-recognition
- text-to-speech
- translation
size_categories:
- 10K<n<100K
- 1K<n<10K
- n<1K
---
# Dataset Card for NENA Speech Dataset 1.0 (test)
## Table of Contents
- [Dataset Summary](#dataset-summary)
- [Dataset Description](#dataset-description)
- [Languages](#languages)
- [How to Use](#how-to-use)
- [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) -->
- [Building the Dataset](#building-the-dataset)
- [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)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## ⚠️ This is a temperary repository that will be replaced by end of 2023
## Dataset Summary
NENA Speech is a multimodal dataset to help teach machines how real people speak the Northeastern Neo-Aramaic (NENA) dialects.
The NENA dialects form a very diverse group of Aramaic dialects spoken by Christian and Jewish communities indigenous to northwestern Iran, northern Iraq, and southeastern Türkiye.
NENA Speech consists of multimodal examples of speech of the NENA dialects. While all documented NENA dialects are included, not all have data yet, and some will never due to recent loss of their final speakers.
## Dataset Description
- **Homepage**: https://crowdsource.nenadb.dev/
- **Point of Contact:** [Matthew Nazari](mailto:matthewnazari@college.harvard.edu)
## Languages
The NENA dialects form a very diverse group of Aramaic dialects spoken by Christian and Jewish communities indigenous to northwestern Iran, northern Iraq, and southeastern Türkiye.
Speakers of the Christian dialects call their language Assyrian and Chaldean in English. In their language these speakers use multiple different terms (e.g. suráy, sureth, ḥadiṯan, senaya). Speakers of the Jewish dialects call their language lišana deni, lišanət noshan, lišana nosha, lišana didan, all meaning "our language". Some names reflect the consciousness of it being a specifically Jewish language (e.g. lišan hozaye, hulaula).
NENA Speech has a subset for all of the over 150 NENA dialects. Not all dialects have examples available yet. Some dialects will never have examples available due to the loss of their final speakers in recent years.
## How to Use
The `datasets` library allows you to load and pre-process your dataset in pure Python, at scale. The dataset can be downloaded and prepared in one call to your local drive by using the `load_dataset` function.
For example, simply specify the corresponding language config name (e.g., "urmi (christian)" for the dialect of the Assyrian Christians of Urmi):
```python
from datasets import load_dataset
nena_speech = load_dataset("mnazari/nena_speech_1_0_test", "urmi (christian)", split="train")
```
To find out more about loading and preparing audio datasets, head over to [hf.co/blog/audio-datasets](https://huggingface.co/blog/audio-datasets).
## Dataset Structure
### Data Instances
The NENA Speech dataset is a multimodal dataset that consists of three different kinds of examples:
1. **Unlabeled speech examples:** these contain audio of speech (`audio`) but no accompanying transcription (`transcription`) or translation (`translation`). This is useful for representation learning.
2. **Transcribed speech examples:** these contain both audio and transcription of speech. These are useful for machine learning tasks like automatic speech recognition and speech synthesis.
3. **Transcribed and translated speech examples:** these kinds of examples contain audio, transcription, and translation of speech. These are useful for tasks like multimodal translation.
Make sure to filter for the kinds of examples you need for your task before before using it.
```json
{
"transcription": "gu-mdìta.ˈ",
"translation": "in the town.",
"audio": {
"path": "et/train/nena_speech_0uk14ofpom196aj.mp3",
"array": array([-0.00048828, -0.00018311, -0.00137329, ..., 0.00079346, 0.00091553, 0.00085449], dtype=float32),
"sampling_rate": 48000
},
"locale": "IRN",
"proficiency": "proficient as mom",
"age": "70's",
"crowdsourced": true,
"unlabeled": true,
"interrupted": true,
"client_id": "gwurt1g1ln" ,
"path": "et/train/nena_speech_0uk14ofpom196aj.mp3",
}
```
### Data Fields
- `transcription (string)`: The transcription of what was spoken (e.g. `"beta"`)
- `translation (string)`: The translation of what was spoken in English (e.g. `"house"`)
- `audio (dict)`: 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]`.
- `locale (string)`: The locale of the speaker
- `proficiency (string)`: The proficiency of the speaker
- `age (string)`: The age of the speaker (e.g. `"20's"`, `"50's"`, `"100+"`)
- `crowdsourced (bool)`: Indicates whether the example was crowdsourced as opposed to collected from existing language documentation resources
- `interrupted (bool)`: Indicates whether the example was interrupted with the speaker making sound effects or switching into another language
- `client_id (string)`: An id for which client (voice) made the recording
- `path (string)`: The path to the audio file
### Data Splits
The examples have been subdivided into three portions:
1. **dev:** the validation split (10%)
3. **test:** the test split (10%)
2. **train:** the train split (80%)
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
#### Language Documentation Resources
[Needs More Information]
#### Webscraping Facebook
[Needs More Information]
#### Crowdsourcing
[Needs More Information]
### Annotations
[Needs More Information] -->
### Building the Dataset
The NENA Speech dataset itself is built using `build.py`.
First, install the necessary requirements.
```
pip install -r requirements.txt
```
Next, build the dataset.
```
python build.py --build
```
Finally, push to the HuggingFace dataset repository.
## 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.
## Data Preprocessing
The dataset consists of three different kinds of examples (see [Data Instances](#data-instances)).
Make sure to filter for the kinds of examples you need for your task before before using it. For example, for automatic speech recognition you will want to filter for examples with transcriptions.
In most tasks, you will want to filter out examples that are interrupted (e.g. by the speaker making sound effects, by the speaker switching into a another language).
```python
from datasets import load_dataset
ds = load_dataset("mnazari/nena_speech_1_0_test", "urmi (christian)", split="train")
def filter_for_asr(example):
return example['transcription'] and not example['interrupted']
ds = ds.filter(filter_for_asr, desc="filter dataset")
```
Transcriptions include markers of linguistic and acoustic features which may be removed in certain tasks (e.g. word stress, nuclear stress, intonation group markers, vowel length).
```python
from datasets import load_dataset
ds = load_dataset("mnazari/nena_speech_1_0_test", "urmi (christian)", split="train")
def prepare_dataset(batch):
chars_to_remove = ['ˈ', '̀', '́', '̄', '̆', '.', ',', '?', '!']
for char in chars_to_remove:
batch["transcription"] = batch["transcription"].replace(char, "")
return batch
ds = ds.map(prepare_dataset, desc="preprocess dataset")
```
<!-- ## 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
### Licensing Information
Public Domain, [CC-0](https://creativecommons.org/share-your-work/public-domain/cc0/).
### Citation Information
This work has not yet been published.
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SEACrowd/indolem_nerui | SEACrowd | 2023-09-26T12:31:26Z | 23 | 0 | null | [
"language:ind",
"license:cc-by-4.0",
"named-entity-recognition",
"arxiv:2011.00677",
"region:us"
] | 2023-09-26T12:31:26Z | 2023-09-26T11:15:40.000Z | 2023-09-26T11:15:40 | ---
license: cc-by-4.0
tags:
- named-entity-recognition
language:
- ind
---
# indolem_nerui
NER UI is a Named Entity Recognition dataset that contains 2,125 sentences obtained via an annotation assignment in an NLP course at the University of Indonesia in 2016.
The corpus has three named entity classes: location, organisation, and person with training/dev/test distribution: 1,530/170/42 and based on 5-fold cross validation.
## Dataset Usage
Run `pip install nusacrowd` before loading the dataset through HuggingFace's `load_dataset`.
## Citation
```
@INPROCEEDINGS{8275098,
author={Gultom, Yohanes and Wibowo, Wahyu Catur},
booktitle={2017 International Workshop on Big Data and Information Security (IWBIS)},
title={Automatic open domain information extraction from Indonesian text},
year={2017},
volume={},
number={},
pages={23-30},
doi={10.1109/IWBIS.2017.8275098}}
@article{DBLP:journals/corr/abs-2011-00677,
author = {Fajri Koto and
Afshin Rahimi and
Jey Han Lau and
Timothy Baldwin},
title = {IndoLEM and IndoBERT: {A} Benchmark Dataset and Pre-trained Language
Model for Indonesian {NLP}},
journal = {CoRR},
volume = {abs/2011.00677},
year = {2020},
url = {https://arxiv.org/abs/2011.00677},
eprinttype = {arXiv},
eprint = {2011.00677},
timestamp = {Fri, 06 Nov 2020 15:32:47 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-2011-00677.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
## License
Creative Commons Attribution 4.0
## Homepage
[https://indolem.github.io/](https://indolem.github.io/)
### NusaCatalogue
For easy indexing and metadata: [https://indonlp.github.io/nusa-catalogue](https://indonlp.github.io/nusa-catalogue) | [
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] | 2023-09-29T18:51:07Z | 2023-09-29T18:50:56.000Z | 2023-09-29T18:50:56 | ---
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---
# Dataset Card for "full_opinions_1994_2020"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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benayas/snips | benayas | 2023-11-28T05:19:33Z | 23 | 0 | null | [
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"region:us"
] | 2023-11-28T05:19:33Z | 2023-10-09T01:10:41.000Z | 2023-10-09T01:10:41 | ---
license: apache-2.0
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"region:us"
] | 2023-10-18T09:37:08Z | 2023-10-18T09:37:03.000Z | 2023-10-18T09:37:03 | ---
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# Dataset Card for "usmle_self_assesment"
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PeterLawrence/processed_demo | PeterLawrence | 2023-11-21T16:48:09Z | 23 | 0 | null | [
"region:us"
] | 2023-11-21T16:48:09Z | 2023-10-24T13:46:34.000Z | 2023-10-24T13:46:34 | ---
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---
# Dataset Card for "processed_demo"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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kardosdrur/opensubtitles-da-sv | kardosdrur | 2023-10-26T07:12:17Z | 23 | 0 | null | [
"license:mit",
"region:us"
] | 2023-10-26T07:12:17Z | 2023-10-25T13:45:35.000Z | 2023-10-25T13:45:35 | ---
license: mit
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---
# OpenSubtitles Danish-Swedish
Aligned sentences with heuristic-based filters from OpenSubtitles in Danish and in Swedish.
The source code for producing the dataset is included in the repository.
The dataset was created to aid training sentence transformers in the Danish Foundation Models project.
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# Dataset Card for "newsqa"
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] | 2023-11-01T01:40:17Z | 2023-10-26T17:45:37.000Z | 2023-10-26T17:45:37 | ---
license: mit
---
This dataset is a work in progress. It will be used to train execution of a subset of Emacs Lisp within the LLM according to the techniques described in this paper: https://arxiv.org/abs/2305.05383 | [
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# Dataset Card for "pile-pythia-code-vs-nl-scores"
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# Dataset Card for "soict_private_test_fix"
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---
# Dataset Card for "dolly-llama2-1k"
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gauravsirola/sas_to_python_base_dataset | gauravsirola | 2023-10-30T16:28:08Z | 23 | 0 | null | [
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] | 2023-10-30T16:28:08Z | 2023-10-30T16:27:28.000Z | 2023-10-30T16:27:28 | ---
license: apache-2.0
---
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Franman/billetes-argentinos | Franman | 2023-11-01T22:05:10Z | 23 | 0 | null | [
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avinashrajavarapu/Common_Voice | avinashrajavarapu | 2023-11-01T09:42:15Z | 23 | 0 | null | [
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"region:us"
] | 2023-11-01T09:42:15Z | 2023-11-01T09:38:45.000Z | 2023-11-01T09:38:45 | ---
license: cc0-1.0
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marziye-A/dataset-farma-test3 | marziye-A | 2023-11-01T10:15:26Z | 23 | 0 | null | [
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---
# Dataset Card for "dataset-farma-test3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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# Dataset Card for "imdb-card-pred-decimal"
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# Dataset Card for "arxiv-metadata-oai-snapshot"
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# Small PubLayNet (WebDataset)
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# Dataset Card for "zephyr-7b-beta-judgelm"
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---
# Dataset Card for MultiPL-C2C
## Dataset Description
- **Repository:** https://github.com/PootieT/explain-then-translate
- **Paper:** https://openreview.net/forum?id=uyUO80sbm0
- **Point of Contact:** zilutang@bu.edu
## Dataset Summary
MultiPL-C2C is a dataset for evaluating large language models for code
translation between any of the 19 programming languages. It is an extension
from MultiPL-E dataset, which takes the OpenAI "HumanEval" and the MBPP
Python benchmarks and uses little compilers to translate them to other
languages. On top of MultiPL-E, it is easy to add support for new languages
and benchmarks. For MultiPL-C2C, we release only the subset for HumanEval.
## Subsets
To evaluate program translation with MultiPL-C2C, you need mainly three
arguments: *SRC_LANG*, *TGT_LANG*, and *EXP*. We use the canonical file extension
for each language to identify the language, e.g., "py" for Python, "cpp" for
C++, "lua" for Lua, and so on. *EXP* specifies what type of prompting is done for
the translation: `direct` is direct translation. {`exp`, `exp-lbl`, `exp-lbl-d`}
are other explain-then-translate method. See paper for detailed description.
Our paper ["explain-then-translate"](https://openreview.net/forum?id=uyUO80sbm0)
explores a Chain-of-Thought like 2-stage prompting methods to improve 0-shot
translation accuracy. We also provide a few other variations:
- *SRCDATA-SRC_LANG-TGT_LANG-EXP-gpt3.5* is the same as *SRCDATA-SRC_LANG-TGT_LANG-EXP*, but are the prompts we
used to evaluated GPT-3.5 in March 2023. For example, in Python-to-X direction,
we cached Python-Java intermediate generations (explanations), and reused them
in other Python-to-X directions.
- *SRCDATA-SRC_LANG-TGT_LANG-EXP-gpt3.5exp* is a step further to control reproducibility for
our experiments on top of *SRCDATA-SRC_LANG-TGT_LANG-EXP-gpt3.5*. We additionally cached all
intermediate generations (explanations) in the prompts. Different from
*SRCDATA-SRC_LANG-TGT_LANG-EXP-gpt3.5*, translation direction such as `Lua-Python` will have
explanations generated by GPT-3.5
Note that we did not experiment with MBPP even thought it is available with
MultiPL-E. Feel free to PR and we can include them to further improve
code-translation datasets.
## Example
The following script uses the Salesforce/codegen model to translate Python-Lua
and MultiPL-E to produce a script with unit tests for luaunit.
```python
import datasets
from transformers import AutoTokenizer, AutoModelForCausalLM
SRC_LANG = "py"
TGT_LANG = "lua"
EXP = "direct"
MODEL_NAME = "Salesforce/codegen-350M-multi"
TOFILL_TOKEN = "{{{TOFILL}}}"
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
model = AutoModelForCausalLM.from_pretrained(MODEL_NAME).half().cuda()
problems = datasets.load_dataset("zilu-peter-tang/MultiPL-C2C", f"humaneval-{SRC_LANG}-{TGT_LANG}-{EXP}")
def stop_at_stop_token(decoded_string, problem):
"""
Truncates the output at stop tokens, taking care to skip the prompt
which may have stop tokens.
"""
min_stop_index = len(decoded_string)
for stop_token in problem["stop_tokens"]:
stop_index = decoded_string.find(stop_token, len(problem["translation_prompt"]))
if stop_index != -1 and stop_index < min_stop_index:
min_stop_index = stop_index
return decoded_string[:min_stop_index]
def generate(problem, tokenizer, model):
input_ids = tokenizer(
problem["translation_prompt"],
return_tensors="pt",
).input_ids.cuda()
generated_ids = model.generate(
input_ids, max_length=512, pad_token_id=tokenizer.eos_token_id + 2
)
truncated_string = stop_at_stop_token(tokenizer.decode(generated_ids[0]), problem)
return truncated_string
for problem in problems["test"]:
# generate each intermediate steps (explanation) sequentially, if any
stop = problem["stop_tokens"]
prompt = problem["translation_prompt"]
while TOFILL_TOKEN in problem["translation_prompt"]:
incomplete_prompt = prompt[:prompt.find(TOFILL_TOKEN)]
problem["stop_tokens"] = ["\n#"]
problem["translation_prompt"] = incomplete_prompt
intermediate_completion = generate(problem, tokenizer, model).replace(incomplete_prompt, "")
prompt = prompt.replace(TOFILL_TOKEN, intermediate_completion, 1)
# finally generate translation
problem["translation_prompt"] = prompt
problem["stop_tokens"] = stop
completion = generate(problem, tokenizer, model).replace(prompt, "")
filename = problem["name"] + "-" + SRC_LANG + "-" + TGT_LANG + "." + TGT_LANG
with open(filename, "w") as f:
print(f"Created {filename}")
f.write(problem["prompt"])
f.write(completion)
f.write("\n")
f.write(problem["tests"])
```
Depending on the quality of the model, you may or may not find some extraneous natural langauge
or program commands at the end of the generated functions. To trim them using programmatic rules,
see our [repo](https://github.com/PootieT/explain-then-translate/blob/main/MultiPL-C2C/inference/__main__.py)
for a more detailed inference script that takes care of this in post-processing.
For few shot examples checkout our all other translation prompts used [here](https://github.com/PootieT/explain-then-translate/tree/main/MultiPL-C2C/translation_prompts/translation_prompts_chatgpt_with_explanation) | [
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0.3810852169990539... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
k3w15hu8h/MissBert-Data | k3w15hu8h | 2023-11-11T17:59:52Z | 23 | 0 | null | [
"license:mit",
"region:us"
] | 2023-11-11T17:59:52Z | 2023-11-11T13:55:17.000Z | 2023-11-11T13:55:17 | ---
license: mit
---
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pphuc25/cv13-validation-vectorized | pphuc25 | 2023-11-11T17:45:47Z | 23 | 0 | null | [
"region:us"
] | 2023-11-11T17:45:47Z | 2023-11-11T17:45:09.000Z | 2023-11-11T17:45:09 | ---
dataset_info:
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configs:
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data_files:
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path: data/train-*
---
# Dataset Card for "cv13-validation-vectorized"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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pphuc25/vivos-validation-vectorized | pphuc25 | 2023-11-11T17:48:59Z | 23 | 0 | null | [
"region:us"
] | 2023-11-11T17:48:59Z | 2023-11-11T17:48:15.000Z | 2023-11-11T17:48:15 | ---
dataset_info:
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configs:
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data_files:
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path: data/train-*
---
# Dataset Card for "vivos-validation-vectorized"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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Foxasdf/common_voice_v3_en_whisper_medium-en | Foxasdf | 2023-11-11T18:39:46Z | 23 | 0 | null | [
"license:apache-2.0",
"region:us"
] | 2023-11-11T18:39:46Z | 2023-11-11T18:33:20.000Z | 2023-11-11T18:33:20 | ---
license: apache-2.0
configs:
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data_files:
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path: data/train-*
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path: data/test-*
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---
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mengmengmmm/csn_ruby_trainuse | mengmengmmm | 2023-11-13T12:23:52Z | 23 | 0 | null | [
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umarbutler/open-australian-legal-embeddings | umarbutler | 2023-11-18T10:05:51Z | 23 | 3 | null | [
"task_categories:text-retrieval",
"task_ids:document-retrieval",
"annotations_creators:no-annotation",
"language_creators:found",
"size_categories:1M<n<10M",
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"language:en",
"license:other",
"law",
"legal",
"australia",
"embeddings",
... | 2023-11-18T10:05:51Z | 2023-11-14T00:14:21.000Z | 2023-11-14T00:14:21 | ---
language:
- en
license: other
license_name: open-australian-legal-corpus
license_link: https://huggingface.co/datasets/umarbutler/open-australian-legal-corpus/blob/main/LICENCE.md
tags:
- law
- legal
- australia
- embeddings
annotations_creators:
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language_creators:
- found
language_details: en-AU, en-GB
pretty_name: Open Australian Legal Embeddings
size_categories:
- 1M<n<10M
source_datasets:
- umarbutler/open-australian-legal-corpus
task_categories:
- text-retrieval
task_ids:
- document-retrieval
viewer: true
dataset_info:
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dtype: string
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config_name: train
splits:
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num_bytes: 28500857221
num_examples: 5208238
download_size: 45586801753
dataset_size: 28500857221
---
<!-- To update the above `dataset_info` section, please run the following command: `datasets-cli test open_australian_legal_embeddings.py --save_info --all_configs`. -->
# **Open Australian Legal Embeddings ⚖️**
<a href="https://huggingface.co/datasets/umarbutler/open-australian-legal-embeddings" alt="Release"><img src="https://img.shields.io/badge/release-v1.0.0-green"></a>
The Open Australian Legal Embeddings are the first open-source embeddings of Australian legislative and judicial documents.
Trained on the largest open database of Australian law, the [Open Australian Legal Corpus](https://huggingface.co/datasets/umarbutler/open-australian-legal-corpus), the Embeddings consist of roughly 5.2 million 384-dimensional vectors embedded with [`BAAI/bge-small-en-v1.5`](https://huggingface.co/BAAI/bge-small-en-v1.5).
The Embeddings open the door to a wide range of possibilities in the field of Australian legal AI, including the development of document classifiers, search engines and chatbots.
To ensure their accessibility to as wide an audience as possible, the Embeddings are distributed under the same licence as the [Open Australian Legal Corpus](https://huggingface.co/datasets/umarbutler/open-australian-legal-corpus/blob/main/LICENCE.md).
## Usage 👩💻
The below code snippet illustrates how the Embeddings may be loaded and queried via the [Hugging Face Datasets](https://huggingface.co/docs/datasets/index) Python library:
```python
import itertools
import sklearn.metrics.pairwise
from datasets import load_dataset
from sentence_transformers import SentenceTransformer
model = SentenceTransformer('BAAI/bge-small-en-v1.5')
instruction = 'Represent this sentence for searching relevant passages: '
oale = load_dataset('umarbutler/open_australian_legal_embeddings', split='train', streaming=True) # Set `streaming` to `False` if you wish to load the entire dataset into memory (unadvised unless you have at least 64 GB of RAM).
# Sample the first 100,000 embeddings.
sample = list(itertools.islice(oale, 100000))
# Embed a query.
query = model.encode(instruction + 'Who is the Governor-General of Australia?', normalize_embeddings=True)
# Identify the most similar embedding to the query.
similarities = sklearn.metrics.pairwise.cosine_similarity([query], [embedding['embedding'] for embedding in sample])
most_similar_index = similarities.argmax()
most_similar = sample[most_similar_index]
# Print the most similar text.
print(most_similar['text'])
```
To speed up the loading of the Embeddings, you may wish to install [`orjson`](https://github.com/ijl/orjson).
## Structure 🗂️
The Embeddings are stored in [`data/embeddings.jsonl`](https://huggingface.co/datasets/umarbutler/open-australian-legal-embeddings/blob/main/data/embeddings.jsonl), a json lines file where each line is a list of 384 32-bit floating point numbers. Associated metadata is stored in [`data/metadatas.jsonl`](https://huggingface.co/datasets/umarbutler/open-australian-legal-embeddings/blob/main/data/metadatas.jsonl) and the corresponding texts are located in [`data/texts.jsonl`](https://huggingface.co/datasets/umarbutler/open-australian-legal-embeddings/blob/main/data/texts.jsonl).
The metadata fields are the same as those used for the [Open Australian Legal Corpus](https://huggingface.co/datasets/umarbutler/open-australian-legal-corpus#structure-%F0%9F%97%82%EF%B8%8F), barring the `text` field, which was removed, and with the addition of the `is_last_chunk` key, which is a boolean flag for whether a text is the last chunk of a document (used to detect and remove corrupted documents when creating and updating the Embeddings).
## Creation 🧪
All documents in the [Open Australian Legal Corpus](https://huggingface.co/datasets/umarbutler/open-australian-legal-corpus#statistics-%F0%9F%93%8A) were split into semantically meaningful chunks up to 512-tokens-long (as determined by [`bge-small-en-v1.5`](https://huggingface.co/BAAI/bge-small-en-v1.5)'s tokeniser) with the [`semchunk`](https://github.com/umarbutler/semchunk) Python library. These chunks included a header embedding documents' titles, jurisdictions and types in the following format:
```perl
Title: {title}
Jurisdiction: {jurisdiction}
Type: {type}
{text}
```
When embedded into the above header, the names of jurisdictions were capitalised and stripped of hyphens. The `commonwealth` jurisdiction was also renamed to 'Commonwealth of Australia'. In the cases of types, `primary_legislation` became 'Act', `secondary_legislation` became 'Regulation', `bill` became 'Bill' and `decision` became 'Judgment'.
The chunks were then vectorised by [`bge-small-en-v1.5`](https://huggingface.co/BAAI/bge-small-en-v1.5) on a single GeForce RTX 2080 Ti with a batch size of 32 via the [`SentenceTransformers`](https://www.sbert.net/) library.
The resulting embeddings were serialised as json-encoded lists of floats by [`orjson`](https://github.com/ijl/orjson) and stored in [`data/embeddings.jsonl`](https://huggingface.co/datasets/umarbutler/open-australian-legal-embeddings/blob/main/data/embeddings.jsonl). The corresponding metadata and texts (with their headers removed) were saved to [`data/metadatas.jsonl`](https://huggingface.co/datasets/umarbutler/open-australian-legal-embeddings/blob/main/data/metadatas.jsonl) and [`data/texts.jsonl`](https://huggingface.co/datasets/umarbutler/open-australian-legal-embeddings/blob/main/data/texts.jsonl), respectively.
The code used to create and update the Embeddings may be found [here](https://github.com/umarbutler/open-australian-legal-embeddings-creator).
## Changelog 🔄
All notable changes to the Embeddings are documented in its [Changelog 🔄](https://huggingface.co/datasets/umarbutler/open-australian-legal-embeddings/blob/main/CHANGELOG.md).
This project adheres to [Keep a Changelog](https://keepachangelog.com/en/1.0.0/) and [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
## Licence 📜
The Embeddings are distributed under the same licence as the [Open Australian Legal Corpus](https://huggingface.co/datasets/umarbutler/open-australian-legal-corpus/blob/main/LICENCE.md).
## Citation 🔖
If you've relied on the Embeddings for your work, please cite:
```latex
@misc{butler-2023-open-australian-legal-embeddings,
author = {Butler, Umar},
year = {2023},
title = {Open Australian Legal Embeddings},
publisher = {Hugging Face},
version = {1.0.0},
doi = {10.57967/hf/1347},
url = {https://huggingface.co/datasets/umarbutler/open-australian-legal-embeddings}
}
```
## Acknowledgements 🙏
In the spirit of reconciliation, the author acknowledges the Traditional Custodians of Country throughout Australia and their connections to land, sea and community. He pays his respect to their Elders past and present and extends that respect to all Aboriginal and Torres Strait Islander peoples today.
The author thanks the creators of the many Python libraries relied upon in the creation of the Embeddings.
Finally, the author is eternally grateful for the endless support of his wife and her willingness to put up with many a late night spent writing code and quashing bugs. | [
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license: apache-2.0
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---
# Dataset Card for "squad_qa_title_v5_full"
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# Dataset Card for "cebuano-dictionary-examples"
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w11wo/imdb-javanese | w11wo | 2022-10-25T10:01:48Z | 22 | 0 | null | [
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---
# Dataset Card for "imdb-javanese"
## 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 Sample Size](#data-instances-sample-size)
- [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:** [Github](https://github.com/w11wo/nlp-datasets#javanese-imdb)
- **Repository:** [Github](https://github.com/w11wo/nlp-datasets#javanese-imdb)
- **Paper:** [Aclweb](http://www.aclweb.org/anthology/P11-1015)
- **Point of Contact:** [Wilson Wongso](https://github.com/w11wo)
- **Size of downloaded dataset files:** 17.0 MB
- **Size of the generated dataset:** 47.5 MB
- **Total amount of disk used:** 64.5 MB
### Dataset Summary
Large Movie Review Dataset translated to Javanese. This is a dataset for binary sentiment classification containing substantially more data than previous benchmark datasets. We provide a set of 25,000 highly polar movie reviews for training, and 25,000 for testing. There is additional unlabeled data for use as well. We translated the [original IMDB Dataset](https://huggingface.co/datasets/imdb) to Javanese using the multi-lingual MarianMT Transformer model from [`Helsinki-NLP/opus-mt-en-mul`](https://huggingface.co/Helsinki-NLP/opus-mt-en-mul).
### 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
We show detailed information for up to 5 configurations of the dataset.
### Data Instances
An example of `javanese_imdb_train.csv` looks as follows.
| label | text |
| ----- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| 1 | "Drama romantik sing digawé karo direktur Martin Ritt kuwi ora dingertèni, nanging ana momen-momen sing marahi karisma lintang Jane Fonda lan Robert De Niro (kelompok sing luar biasa). Dhèwèké dadi randha sing ora isa mlaku, iso anu anyar lan anyar-inventor-- kowé isa nganggep isiné. Adapsi novel Pat Barker ""Union Street"" (yak titel sing apik!) arep dinggo-back-back it on bland, lan pendidikan film kuwi gampang, nanging isih nyenengké; a rosy-hued-inventor-fantasi. Ora ana sing ngganggu gambar sing sejati ding kok iso dinggo nggawe gambar sing paling nyeneng." |
| 0 | "Pengalaman wong lanang sing nduwé perasaan sing ora lumrah kanggo babi. Mulai nganggo tuladha sing luar biasa yaiku komedia. Wong orkestra termel digawé dadi wong gila, sing kasar merga nyanyian nyanyi. Sayangé, kuwi tetep absurd wektu WHOLE tanpa ceramah umum sing mung digawé. Malah, sing ana ing jaman kuwi kudu ditinggalké. Diyalog kryptik sing nggawé Shakespeare marah gampang kanggo kelas telu. Pak teknis kuwi luwih apik timbang kowe mikir nganggo cinematografi sing apik sing jenengé Vilmos Zsmond. Masa depan bintang Saly Kirkland lan Frederic Forrest isa ndelok." |
### Data Fields
- `text`: The movie review translated into Javanese.
- `label`: The sentiment exhibited in the review, either `1` (positive) or `0` (negative).
### Data Splits Sample Size
| train | unsupervised | test |
| ----: | -----------: | ----: |
| 25000 | 50000 | 25000 |
## 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
If you use this dataset in your research, please cite:
```
@inproceedings{wongso2021causal,
title={Causal and Masked Language Modeling of Javanese Language using Transformer-based Architectures},
author={Wongso, Wilson and Setiawan, David Samuel and Suhartono, Derwin},
booktitle={2021 International Conference on Advanced Computer Science and Information Systems (ICACSIS)},
pages={1--7},
year={2021},
organization={IEEE}
}
```
```
@InProceedings{maas-EtAl:2011:ACL-HLT2011,
author = {Maas, Andrew L. and Daly, Raymond E. and Pham, Peter T. and Huang, Dan and Ng, Andrew Y. and Potts, Christopher},
title = {Learning Word Vectors for Sentiment Analysis},
booktitle = {Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies},
month = {June},
year = {2011},
address = {Portland, Oregon, USA},
publisher = {Association for Computational Linguistics},
pages = {142--150},
url = {http://www.aclweb.org/anthology/P11-1015}
}
```
| [
-0.709762692451477,
-0.5571990013122559,
-0.08677966147661209,
0.37171486020088196,
-0.3345322906970978,
0.12954215705394745,
-0.23301458358764648,
-0.3485200107097626,
0.8357465863227844,
0.5937275886535645,
-0.6087182760238647,
-0.5671302080154419,
-0.7326328158378601,
0.2538271248340606... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
yuanchuan/annotated_reference_strings | yuanchuan | 2022-10-26T14:53:23Z | 22 | 1 | null | [
"task_categories:token-classification",
"task_ids:parsing",
"annotations_creators:other",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10M<n<100M",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"region:us"
] | 2022-10-26T14:53:23Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- other
language_creators:
- found
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10M<n<100M
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- parsing
pretty_name: Annotated Reference Strings
---
# Dataset Card for annotated_reference_strings
## 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://www.github.com/kylase](https://www.github.com/kylase)
- **Repository:** [https://www.github.com/kylase](https://www.github.com/kylase)
- **Point of Contact:** [Yuan Chuan Kee](https://www.github.com/kylase)
### Dataset Summary
The `annotated_reference_strings` dataset comprises millions of the annotated reference strings, i.e. each token of the strings have an associated label such as author, title, year, etc.
These strings are synthesized using citation processor on millions of citations obtained from various sources, spanning different scientific domains.
### Supported Tasks
This dataset can be used for structure prediction.
### Languages
The dataset is composed of reference strings that are in English.
## Dataset Structure
### Data Instances
```json
{
"source": "pubmed",
"lang": "en",
"entry_type": "article",
"doi_prefix": "pubmed19n0001",
"csl_style": "annual-reviews",
"content": "<citation-number>8.</citation-number> <author>Mohr W.</author> <year>1977.</year> <title>[Morphology of bone tumors. 2. Morphology of benign bone tumors].</title> <container-title>Aktuelle Probleme in Chirurgie und Orthopadie.</container-title> <volume>5:</volume> <page>29–42</page>"
}
```
#### Important Note
1. Each citation is rendered to _at most_ **17** CSL styles. Therefore, there will be near duplicates.
2. All characters (including punctuations) of a segment (**a segment consists of 1 or more token**) are enclosed by tag(s).
1. Only tokens that act as "conjunctions" are not enclosed in tags. These tokens will be labelled as `other`.
3. There will be instances which a segment can be enclosed by more than one tag e.g. `<issued><year>2021</year></issued>`. This depends on how the styles' author(s).
### Data Fields
- `source`: Describe the source of the citation. `{pubmed, jstor, crossref}`
- `lang`: Describe the language of the citation. `{en}`
- `entry_type`: Describe the BibTeX entry type. `{article, book, inbook, misc, techreport, phdthesis, incollection, inproceedings}`
- `doi_prefix`: For JSTOR and CrossRef, it is the prefix of the DOI. For PubMed, it is the directory (e.g. `pubmed19nXXXX` where `XXXX` is 4 digits) of which the citation is generated from.
- `csl_style`: The CSL style which the citation is rendered as.
- `content`: The rendered citation of a specific style with each segment enclosed by tags named after the CSL variables
### Data Splits
Data splits are not available yet.
## Dataset Creation
### Source Data
#### Initial Data Collection and Normalization
The citations that are used to generate these reference strings are obtained from 3 main sources:
- [PubMed](https://www.nlm.nih.gov/databases/download/pubmed_medline.html) (2019 Baseline)
- CrossRef via [Open Academic Graph v2](https://www.microsoft.com/en-us/research/project/open-academic-graph/)
- JSTOR Sample Datasets (not available online as of publication date)
If the citation is not in BibTeX format, [bibutils](https://sourceforge.net/p/bibutils/home/Bibutils/) is used to convert it to BibTeX.
#### Who are the source language producers?
The manner which the citations are rendered as reference strings are based on rules/specifications dictated by the publisher.
[Citation Style Language](https://citationstyles.org/) (CSL) is an established standard which such specifications are prescribed.
Thousands of citation styles are available.
### Annotations
#### Annotation process
The annotation process involves 2 main interventions:
1. Modification of the styles' CSL specification to inject the CSL variable names as part of the render process
2. Sanitization of the rendered strings using regular expressions to ensure all tokens and characters are enclosed in the tags
#### Who are the annotators?
The original CSL specification are available on [GitHub](https://github.com/citation-style-language/styles).
The modification of the styles and the sanitization process are done by the author of this work.
## Additional Information
### Licensing Information
This dataset is licensed under [Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/).
### Citation Information
This dataset is a product of a Master Project done in the National University of Singapore.
If you are using it, please cite the following:
```bibtex
@techreport{kee2021,
author = {Yuan Chuan Kee},
title = {Synthesis of a large dataset of annotated reference strings for developing citation parsers},
institution = {National University of Singapore},
year = {2021}
}
```
### Contributions
Thanks to [@kylase](https://github.com/kylase) for adding this dataset.
| [
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0.3141063153743744,
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-0.3131927251815796,
0.4651120901107788... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Dayyan/bwns | Dayyan | 2022-03-17T14:41:53Z | 22 | 0 | null | [
"region:us"
] | 2022-03-17T14:41:53Z | 2022-03-15T19:45:05.000Z | 2022-03-15T19:45:05 | # BWNS: The Baha'i World News Service dataset.
BWNS articles from 2000 to 2022.
| [
0.05401168763637543,
-0.6507273316383362,
0.3883253037929535,
0.8477535843849182,
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0.14411894977092743,
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0.35759681463241577,
0.8042992353439331,
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-0.13081122934818268,
-0.5716710090637207,
0.28724399209022... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
tomekkorbak/pile-curse-chunk-25 | tomekkorbak | 2022-03-18T22:06:29Z | 22 | 0 | null | [
"region:us"
] | 2022-03-18T22:06:29Z | 2022-03-18T22:05:55.000Z | 2022-03-18T22:05:55 | Entry not found | [
-0.32276472449302673,
-0.22568407654762268,
0.8622258901596069,
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-0.5282984972000122,
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0.7746022939682007,
0.2563222646713257,
-0.785281777381897,
-0.22573848068714142,
-0.9104482531547546,
0.5715669393539429,
... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
tomekkorbak/pile-curse-full | tomekkorbak | 2022-03-23T20:05:15Z | 22 | 0 | null | [
"region:us"
] | 2022-03-23T20:05:15Z | 2022-03-18T23:17:53.000Z | 2022-03-18T23:17:53 | ## Generation procedure
The dataset was constructed using documents from [the Pile](https://pile.eleuther.ai/) scored using [LDNOOBW](https://github.com/LDNOOBW/List-of-Dirty-Naughty-Obscene-and-Otherwise-Bad-Words) wordlist (a score is number of curses per character).
The procedure was the following:
1. The first half of the data are 100k documents randomly sampled from the Pile and assigned scores
2. The second half are the most cursing document from the Pile, obtained by scoring the whole Pile and choosing 100k documents with highest scores
3. Then, the dataset was shuffled and a 9:1 train-test split was done
## Basic stats
The average and median scores are 0.013 and 0.019, respectively. | [
-0.6407318711280823,
-0.8749591708183289,
0.13998764753341675,
0.09066549688577652,
-0.7406529784202576,
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0.2104918211698532,
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0.5117092728614807,
0.7550392150878906,
-0.35638922452926636,
-0.8222532868385315,
-0.6293898224830627,
0.1156652867794036... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Georgii/russianPoetry | Georgii | 2022-03-26T16:32:30Z | 22 | 2 | null | [
"license:mit",
"region:us"
] | 2022-03-26T16:32:30Z | 2022-03-26T16:31:53.000Z | 2022-03-26T16:31:53 | ---
license: mit
---
| [
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0.49436280131340027,
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0.7400140762329102,
-0.6508104205131531,
-0.23783954977989197,
-0.7102249264717102,
-0.0478260256350... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
nadhifikbarw/id_ohsuhmed | nadhifikbarw | 2022-10-25T10:03:35Z | 22 | 0 | null | [
"task_categories:text-classification",
"language:id",
"region:us"
] | 2022-10-25T10:03:35Z | 2022-03-27T07:01:29.000Z | 2022-03-27T07:01:29 | ---
language:
- id
task_categories:
- text-classification
source:
- http://disi.unitn.it/moschitti/corpora.htm
---
Machine translated Ohsumed collection (EN to ID)
Original corpora: http://disi.unitn.it/moschitti/corpora.htm
Translated using: https://huggingface.co/Helsinki-NLP/opus-mt-en-id
Compatible with HuggingFace text-classification script (Tested in 4.17)
https://github.com/huggingface/transformers/tree/v4.17.0/examples/pytorch/text-classification
[Moschitti, 2003a]. Alessandro Moschitti, Natural Language Processing and Text Categorization: a study on the reciprocal beneficial interactions, PhD thesis, University of Rome Tor Vergata, Rome, Italy, May 2003. | [
-0.3848917782306671,
-0.7160055041313171,
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0.16002772748470306,
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-0.12823551893234253,
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0.46101808547973633,
0.4602445960044861,
-0.48664093017578125,
-0.5251643061637878,
-0.46549978852272034,
0.52529138326... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
huggan/summer2winter_yosemite | huggan | 2022-04-12T13:58:19Z | 22 | 0 | null | [
"arxiv:1703.10593",
"region:us"
] | 2022-04-12T13:58:19Z | 2022-03-29T12:53:43.000Z | 2022-03-29T12:53:43 | This dataset is part of the CycleGAN datasets, originally hosted here: https://people.eecs.berkeley.edu/~taesung_park/CycleGAN/datasets/
# Citation
```
@article{DBLP:journals/corr/ZhuPIE17,
author = {Jun{-}Yan Zhu and
Taesung Park and
Phillip Isola and
Alexei A. Efros},
title = {Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial
Networks},
journal = {CoRR},
volume = {abs/1703.10593},
year = {2017},
url = {http://arxiv.org/abs/1703.10593},
eprinttype = {arXiv},
eprint = {1703.10593},
timestamp = {Mon, 13 Aug 2018 16:48:06 +0200},
biburl = {https://dblp.org/rec/journals/corr/ZhuPIE17.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
``` | [
-0.048607271164655685,
-0.31038811802864075,
0.2472076416015625,
0.0040995338931679726,
-0.38853687047958374,
0.01113723311573267,
-0.12735313177108765,
-0.3449961841106415,
0.044890038669109344,
0.6094780564308167,
-0.6503734588623047,
-0.727931022644043,
-0.4177476167678833,
0.1436172127... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
huggan/few-shot-art-painting | huggan | 2022-04-12T14:06:24Z | 22 | 2 | null | [
"arxiv:2101.04775",
"region:us"
] | 2022-04-12T14:06:24Z | 2022-04-01T11:46:40.000Z | 2022-04-01T11:46:40 | # Citation
```
@article{DBLP:journals/corr/abs-2101-04775,
author = {Bingchen Liu and
Yizhe Zhu and
Kunpeng Song and
Ahmed Elgammal},
title = {Towards Faster and Stabilized {GAN} Training for High-fidelity Few-shot
Image Synthesis},
journal = {CoRR},
volume = {abs/2101.04775},
year = {2021},
url = {https://arxiv.org/abs/2101.04775},
eprinttype = {arXiv},
eprint = {2101.04775},
timestamp = {Fri, 22 Jan 2021 15:16:00 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-2101-04775.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
``` | [
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-0.8028349280357361,
0.018525345250964165,
0.33572760224342346,
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-0.3939037024974823,
0.0571840... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
huggan/inat_butterflies_top10k | huggan | 2022-04-04T12:50:28Z | 22 | 1 | null | [
"region:us"
] | 2022-04-04T12:50:28Z | 2022-04-04T12:45:06.000Z | 2022-04-04T12:45:06 | Filtered version of https://huggingface.co/datasets/huggan/inat_butterflies
To pick the best images, CLIP was used to compare each image with a text description of a good image ("")
Notebook for the filtering: https://colab.research.google.com/drive/1OEqr1TtL4YJhdj_bebNWXRuG3f2YqtQE?usp=sharing
See the original dataset for sources and licence caveats (tl;dr check the image descriptions to make sure you aren't breaking a licence like CC-BY-NC-ND which some images have) | [
-0.8514618873596191,
-0.549936830997467,
0.1859215945005417,
0.49847111105918884,
-0.6138963103294373,
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0.20304813981056213,
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-0.5958949327468872,
0.6403481364250183,
... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
iluvvatar/RuREBus | iluvvatar | 2023-03-30T13:37:32Z | 22 | 1 | null | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"multilinguality:monolingual",
"language:ru",
"region:us"
] | 2023-03-30T13:37:32Z | 2022-04-10T09:52:30.000Z | 2022-04-10T09:52:30 | ---
language:
- ru
multilinguality:
- monolingual
task_categories:
- token-classification
task_ids:
- named-entity-recognition
pretty_name: RuREBus
---
# RuREBus dataset
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Structure](#dataset-structure)
- [Citation Information](#citation-information)
- [Contacts](#contacts)
## Dataset Description
RuREBus dataset (https://github.com/dialogue-evaluation/RuREBus) is
a Russian dataset for named entity recognition and relation extraction.
## Dataset Structure
There are two subsets of the dataset.
Using
`load_dataset('MalakhovIlya/RuREBus')`
you can download annotated data (DatasetDict) for named entity recognition task and
relation extraction tasks.
This subset consists of two splits: "train" and "test".
Using
`load_dataset('MalakhovIlya/NEREL', 'raw_txt')['raw_txt']`
you can download (Dataset) large corpus (~3gb) raw texts of the same subject
area, but without any annotations.
"entities" are used in named-entity recognition task (see https://en.wikipedia.org/wiki/Named-entity_recognition).
"relations" are used in relationship extraction task (see https://en.wikipedia.org/wiki/Relationship_extraction).
Each entity is represented by a string of the following format:
`"<id>\t<type> <start> <stop>\t<text>"`, where
`<id>` is an entity id,
`<type>` is one of entity types,
`<start>` is a position of the first symbol of entity in text,
`<stop>` is the last symbol position in text +1.
Each relation is represented by a string of the following format:
`"<id>\t<type> Arg1:<arg1_id> Arg2:<arg2_id>"`, where
`<id>` is a relation id,
`<arg1_id>` and `<arg2_id>` are entity ids.
## Citation Information
@inproceedings{rurebus,
Address = {Moscow, Russia},
Author = {Ivanin, Vitaly and Artemova, Ekaterina and Batura, Tatiana and Ivanov, Vladimir and Sarkisyan, Veronika and Tutubalina, Elena and Smurov, Ivan},
Title = {RuREBus-2020 Shared Task: Russian Relation Extraction for Business},
Booktitle = {Computational Linguistics and Intellectual Technologies: Proceedings of the International Conference “Dialog” [Komp’iuternaia Lingvistika i Intellektual’nye Tehnologii: Trudy Mezhdunarodnoj Konferentsii “Dialog”]},
Year = {2020}
}
| [
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-0.3162457346916199,
0.3130010962486267,
... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
patrickvonplaten/librispeech_asr_self_contained | patrickvonplaten | 2022-10-24T17:48:37Z | 22 | 1 | librispeech-1 | [
"task_categories:automatic-speech-recognition",
"task_categories:audio-classification",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:e... | 2022-10-24T17:48:37Z | 2022-04-21T17:06:19.000Z | 2022-04-21T17:06:19 | ---
pretty_name: LibriSpeech
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
- expert-generated
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
paperswithcode_id: librispeech-1
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- automatic-speech-recognition
- audio-classification
task_ids:
- audio-speaker-identification
---
# Dataset Card for librispeech_asr
## 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:** [LibriSpeech ASR corpus](http://www.openslr.org/12)
- **Repository:** [Needs More Information]
- **Paper:** [LibriSpeech: An ASR Corpus Based On Public Domain Audio Books](https://www.danielpovey.com/files/2015_icassp_librispeech.pdf)
- **Leaderboard:** [Paperswithcode Leaderboard](https://paperswithcode.com/sota/speech-recognition-on-librispeech-test-other)
- **Point of Contact:** [Daniel Povey](mailto:dpovey@gmail.com)
### Dataset Summary
LibriSpeech is a corpus of approximately 1000 hours of 16kHz read English speech, prepared by Vassil Panayotov with the assistance of Daniel Povey. The data is derived from read audiobooks from the LibriVox project, and has been carefully segmented and aligned.
### Supported Tasks and Leaderboards
- `automatic-speech-recognition`, `audio-speaker-identification`: The dataset can be used to train a model for Automatic Speech Recognition (ASR). The model is presented with an audio file and asked to transcribe the audio file to written text. The most common evaluation metric is the word error rate (WER). The task has an active leaderboard which can be found at https://paperswithcode.com/sota/speech-recognition-on-librispeech-test-clean and ranks models based on their WER.
### Languages
The audio is in English. There are two configurations: `clean` and `other`.
The speakers in the corpus were ranked according to the WER of the transcripts of a model trained on
a different dataset, and were divided roughly in the middle,
with the lower-WER speakers designated as "clean" and the higher WER speakers designated as "other".
## Dataset Structure
### Data Instances
A typical data point comprises the path to the audio file, usually called `file` and its transcription, called `text`. Some additional information about the speaker and the passage which contains the transcription is provided.
```
{'chapter_id': 141231,
'file': '/home/patrick/.cache/huggingface/datasets/downloads/extracted/b7ded9969e09942ab65313e691e6fc2e12066192ee8527e21d634aca128afbe2/dev_clean/1272/141231/1272-141231-0000.flac',
'audio': {'path': '/home/patrick/.cache/huggingface/datasets/downloads/extracted/b7ded9969e09942ab65313e691e6fc2e12066192ee8527e21d634aca128afbe2/dev_clean/1272/141231/1272-141231-0000.flac',
'array': array([-0.00048828, -0.00018311, -0.00137329, ..., 0.00079346,
0.00091553, 0.00085449], dtype=float32),
'sampling_rate': 16000},
'id': '1272-141231-0000',
'speaker_id': 1272,
'text': 'A MAN SAID TO THE UNIVERSE SIR I EXIST'}
```
### Data Fields
- file: A path to the downloaded audio file in .flac format.
- 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]`.
- text: the transcription of the audio file.
- id: unique id of the data sample.
- speaker_id: unique id of the speaker. The same speaker id can be found for multiple data samples.
- chapter_id: id of the audiobook chapter which includes the transcription.
### Data Splits
The size of the corpus makes it impractical, or at least inconvenient
for some users, to distribute it as a single large archive. Thus the
training portion of the corpus is split into three subsets, with approximate size 100, 360 and 500 hours respectively.
A simple automatic
procedure was used to select the audio in the first two sets to be, on
average, of higher recording quality and with accents closer to US
English. An acoustic model was trained on WSJ’s si-84 data subset
and was used to recognize the audio in the corpus, using a bigram
LM estimated on the text of the respective books. We computed the
Word Error Rate (WER) of this automatic transcript relative to our
reference transcripts obtained from the book texts.
The speakers in the corpus were ranked according to the WER of
the WSJ model’s transcripts, and were divided roughly in the middle,
with the lower-WER speakers designated as "clean" and the higher-WER speakers designated as "other".
For "clean", the data is split into train, validation, and test set. The train set is further split into train.100 and train.360
respectively accounting for 100h and 360h of the training data.
For "other", the data is split into train, validation, and test set. The train set contains approximately 500h of recorded speech.
| | Train.500 | Train.360 | Train.100 | Valid | Test |
| ----- | ------ | ----- | ---- | ---- | ---- |
| clean | - | 104014 | 28539 | 2703 | 2620|
| other | 148688 | - | - | 2864 | 2939 |
## 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 this dataset.
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
The dataset was initially created by Vassil Panayotov, Guoguo Chen, Daniel Povey, and Sanjeev Khudanpur.
### Licensing Information
[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)
### Citation Information
```
@inproceedings{panayotov2015librispeech,
title={Librispeech: an ASR corpus based on public domain audio books},
author={Panayotov, Vassil and Chen, Guoguo and Povey, Daniel and Khudanpur, Sanjeev},
booktitle={Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on},
pages={5206--5210},
year={2015},
organization={IEEE}
}
```
### Contributions
Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset.
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filwsyl/video_tags | filwsyl | 2022-10-25T10:13:17Z | 22 | 1 | mnist | [
"task_categories:image-classification",
"task_ids:multi-class-image-classification",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|other-nist",
"language:enx",
"license:mit",
"region:us"
] | 2022-10-25T10:13:17Z | 2022-05-06T09:19:54.000Z | 2022-05-06T09:19:54 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- enx
license:
- mit
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|other-nist
task_categories:
- image-classification
task_ids:
- multi-class-image-classification
paperswithcode_id: mnist
pretty_name: MNIST
---
# Dataset Card for MNIST
## 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:** http://yann.lecun.com/exdb/mnist/
- **Repository:**
- **Paper:** MNIST handwritten digit database by Yann LeCun, Corinna Cortes, and CJ Burges
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
The MNIST dataset consists of 70,000 28x28 black-and-white images of handwritten digits extracted from two NIST databases. There are 60,000 images in the training dataset and 10,000 images in the validation dataset, one class per digit so a total of 10 classes, with 7,000 images (6,000 train images and 1,000 test images) per class.
Half of the image were drawn by Census Bureau employees and the other half by high school students (this split is evenly distributed in the training and testing sets).
### Supported Tasks and Leaderboards
- `image-classification`: The goal of this task is to classify a given image of a handwritten digit into one of 10 classes representing integer values from 0 to 9, inclusively. The leaderboard is available [here](https://paperswithcode.com/sota/image-classification-on-mnist).
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its label:
```
{
'image': <PIL.PngImagePlugin.PngImageFile image mode=L size=28x28 at 0x276021F6DD8>,
'label': 5
}
```
### Data Fields
- `image`: A `PIL.Image.Image` object containing the 28x28 image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `label`: an integer between 0 and 9 representing the digit.
### Data Splits
The data is split into training and test set. All the images in the test set were drawn by different individuals than the images in the training set. The training set contains 60,000 images and the test set 10,000 images.
## Dataset Creation
### Curation Rationale
The MNIST database was created to provide a testbed for people wanting to try pattern recognition methods or machine learning algorithms while spending minimal efforts on preprocessing and formatting. Images of the original dataset (NIST) were in two groups, one consisting of images drawn by Census Bureau employees and one consisting of images drawn by high school students. In NIST, the training set was built by grouping all the images of the Census Bureau employees, and the test set was built by grouping the images form the high school students.
The goal in building MNIST was to have a training and test set following the same distributions, so the training set contains 30,000 images drawn by Census Bureau employees and 30,000 images drawn by high school students, and the test set contains 5,000 images of each group. The curators took care to make sure all the images in the test set were drawn by different individuals than the images in the training set.
### Source Data
#### Initial Data Collection and Normalization
The original images from NIST were size normalized to fit a 20x20 pixel box while preserving their aspect ratio. The resulting images contain grey levels (i.e., pixels don't simply have a value of black and white, but a level of greyness from 0 to 255) as a result of the anti-aliasing technique used by the normalization algorithm. The images were then centered in a 28x28 image by computing the center of mass of the pixels, and translating the image so as to position this point at the center of the 28x28 field.
#### Who are the source language producers?
Half of the source images were drawn by Census Bureau employees, half by high school students. According to the dataset curator, the images from the first group are more easily recognizable.
### Annotations
#### Annotation process
The images were not annotated after their creation: the image creators annotated their images with the corresponding label after drawing them.
#### Who are the annotators?
Same as the source data creators.
### 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
Chris Burges, Corinna Cortes and Yann LeCun
### Licensing Information
MIT Licence
### Citation Information
```
@article{lecun2010mnist,
title={MNIST handwritten digit database},
author={LeCun, Yann and Cortes, Corinna and Burges, CJ},
journal={ATT Labs [Online]. Available: http://yann.lecun.com/exdb/mnist},
volume={2},
year={2010}
}
```
### Contributions
Thanks to [@sgugger](https://github.com/sgugger) for adding this dataset.
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khalidalt/ultimate_arabic_news | khalidalt | 2022-06-15T14:46:10Z | 22 | 1 | null | [
"region:us"
] | 2022-06-15T14:46:10Z | 2022-06-11T06:06:25.000Z | 2022-06-11T06:06:25 |
# Dataset Card for [Dataset Name]
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
The Ultimate Arabic News Dataset is a collection of single-label modern Arabic texts that are used in news websites and press articles.
Arabic news data was collected by web scraping techniques from many famous news sites such as Al-Arabiya, Al-Youm Al-Sabea (Youm7), the news published on the Google search engine and other various sources.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
license: cc-by-4.0
### Citation Information
```
@book{url,
author = {Al-Dulaimi, Ahmed Hashim},
year = {2022},
month = {05},
website = {Mendeley Data, V1},
title = {Ultimate Arabic News Dataset},
doi = {10.17632/jz56k5wxz7.1}
}
```
### Contributions
[More Information Needed] | [
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rjac/all-the-news-2-1-Component-one | rjac | 2022-07-28T21:01:39Z | 22 | 0 | null | [
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"language:en",
"region:us"
] | 2022-07-28T21:01:39Z | 2022-06-19T22:35:47.000Z | 2022-06-19T22:35:47 | ---
annotations_creators:
- Andrew Thompson
- components.one
language:
- en
---
# 2.7 million news articles and essays
## Table of Contents
- [Dataset Description](#dataset-description)
## Dataset Description
2.7 million news articles and essays from 27 American publications. Includes date, title, publication, article text, publication name, year, month, and URL (for some). Articles mostly span from 2016 to early 2020.
- Type: CSV
- Size: 3.4 GB compressed, 8.8 GB uncompressed
- Created by: Andrew Thompson
- Date added: 4/3/2020
- Date modified: 4/3/2020
- source: [Component one Datasets 2.7 Millions](https://components.one/datasets/all-the-news-2-news-articles-dataset)
- Date of Download and processed: 19/6/2022
- Header was modified with the respective columns
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autoevaluate/autoeval-staging-eval-project-8ef742e5-7734972 | autoevaluate | 2022-06-27T07:48:40Z | 22 | 0 | null | [
"autotrain",
"evaluation",
"region:us"
] | 2022-06-27T07:48:40Z | 2022-06-27T07:46:57.000Z | 2022-06-27T07:46:57 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- squad
eval_info:
task: extractive_question_answering
model: mrp/bert-finetuned-squad
metrics: []
dataset_name: squad
dataset_config: plain_text
dataset_split: validation
col_mapping:
context: context
question: question
answers-text: answers.text
answers-answer_start: answers.answer_start
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Question Answering
* Model: mrp/bert-finetuned-squad
* Dataset: squad
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@thomwolf](https://huggingface.co/thomwolf) for evaluating this model. | [
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Nexdata/Infrared_Face_Recognition_Data | Nexdata | 2023-08-31T02:46:48Z | 22 | 0 | null | [
"region:us"
] | 2023-08-31T02:46:48Z | 2022-06-27T08:14:00.000Z | 2022-06-27T08:14:00 | ---
YAML tags:
- copy-paste the tags obtained with the tagging app: https://github.com/huggingface/datasets-tagging
---
# Dataset Card for Nexdata/Infrared_Face_Recognition_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://www.nexdata.ai/datasets/1134?source=Huggingface
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
4,134 People – Infrared Face Recognition Data. The collecting scenes of this dataset include indoor scenes and outdoor scenes. The data includes male and female. The age distribution ranges from child to the elderly, the young people and the middle aged are the majorities. The collecting device is realsense D453i. The data diversity includes multiple age periods, multiple facial postures, multiple scenes. The data can be used for tasks such as infrared face recognition.
For more details, please refer to the link: https://www.nexdata.ai/datasets/1134?source=Huggingface
### Supported Tasks and Leaderboards
face-detection, computer-vision: The dataset can be used to train a model for face detection.
### Languages
English
## 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
Commerical License: https://drive.google.com/file/d/1saDCPm74D4UWfBL17VbkTsZLGfpOQj1J/view?usp=sharing
### Citation Information
[More Information Needed]
### Contributions
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ScandEval/scala-is | ScandEval | 2023-07-05T09:48:33Z | 22 | 0 | null | [
"task_categories:text-classification",
"size_categories:1K<n<10K",
"language:is",
"license:cc-by-sa-4.0",
"region:us"
] | 2023-07-05T09:48:33Z | 2022-06-27T15:15:12.000Z | 2022-06-27T15:15:12 | ---
license: cc-by-sa-4.0
task_categories:
- text-classification
language:
- is
size_categories:
- 1K<n<10K
--- | [
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davanstrien/biglamdemo | davanstrien | 2022-07-06T16:29:37Z | 22 | 0 | null | [
"license:cc-by-4.0",
"region:us"
] | 2022-07-06T16:29:37Z | 2022-07-06T16:29:01.000Z | 2022-07-06T16:29:01 | ---
license: cc-by-4.0
---
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OGB/ogbg-molpcba | OGB | 2023-02-07T16:39:54Z | 22 | 0 | null | [
"task_categories:graph-ml",
"license:mit",
"region:us"
] | 2023-02-07T16:39:54Z | 2022-07-06T16:30:22.000Z | 2022-07-06T16:30:22 | ---
license: mit
task_categories:
- graph-ml
---
# Dataset Card for ogbg-molpcba
## 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)
- [External Use](#external-use)
- [PyGeometric](#pygeometric)
- [Dataset Structure](#dataset-structure)
- [Data Properties](#data-properties)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [Homepage](https://ogb.stanford.edu/docs/graphprop/#ogbg-mol)
- **Repository:** [Repo](https://github.com/snap-stanford/ogb)
- **Paper:**: Open Graph Benchmark: Datasets for Machine Learning on Graphs
- **Leaderboard:**: [OGB leaderboard](https://ogb.stanford.edu/docs/leader_graphprop/#ogbg-molpcba) and [Papers with code leaderboard](https://paperswithcode.com/sota/graph-property-prediction-on-ogbg-molpcba)
### Dataset Summary
The `ogbg-molpcba` dataset is a small molecular property prediction dataset, adapted from MoleculeNet by teams at Stanford, to be a part of the Open Graph Benchmark.
### Supported Tasks and Leaderboards
`ogbg-molpcba` should be used for molecular property prediction (with 128 properties to predict, not all present for all graphs), a binary classification task.
The score used is Average Precision (AP) averaged over the tasks.
The associated leaderboards are here: [OGB leaderboard](https://ogb.stanford.edu/docs/leader_graphprop/#ogbg-molpcba) and [Papers with code leaderboard](https://paperswithcode.com/sota/graph-property-prediction-on-ogbg-molpcba).
## External Use
### PyGeometric
To load in PyGeometric, do the following:
```python
from datasets import load_dataset
from torch_geometric.data import Data
from torch_geometric.loader import DataLoader
dataset = load_dataset("graphs-datasets/ogbg-molpcba")
# For the train set (replace by valid or test as needed)
graphs_list_pygeometric = [Data(graph) for graph in dataset["train"]]
dataset_pygeometric = DataLoader(graphs_list_pygeometric)
```
## Dataset Structure
### Data Properties
| property | value |
|---|---|
| scale | medium |
| #graphs | 437,929 |
| average #nodes | 26.0 |
| average #edges | 28.1 |
| average node degree | 2.2 |
| average cluster coefficient | 0.002 |
| MaxSCC ratio | 0.999 |
| graph diameter | 13.6 |
### Data Fields
Each row of a given file is a graph, with:
- `node_feat` (list: #nodes x #node-features): nodes
- `edge_index` (list: 2 x #edges): pairs of nodes constituting edges
- `edge_attr` (list: #edges x #edge-features): for the aforementioned edges, contains their features
- `y` (list: 1 x #labels): contains the number of labels available to predict (here 128 labels, equal to zero, one, or Nan if the property is not relevant for the graph)
- `num_nodes` (int): number of nodes of the graph
### Data Splits
This data comes from the PyGeometric version of the dataset provided by OGB, and follows the provided data splits.
This information can be found back using
```python
from ogb.graphproppred import PygGraphPropPredDataset
dataset = PygGraphPropPredDataset(name = 'ogbg-molpcba')
split_idx = dataset.get_idx_split()
train = dataset[split_idx['train']] # valid, test
```
## Additional Information
### Licensing Information
The dataset has been released under MIT license.
### Citation Information
```
@inproceedings{hu-etal-2020-open,
author = {Weihua Hu and
Matthias Fey and
Marinka Zitnik and
Yuxiao Dong and
Hongyu Ren and
Bowen Liu and
Michele Catasta and
Jure Leskovec},
editor = {Hugo Larochelle and
Marc Aurelio Ranzato and
Raia Hadsell and
Maria{-}Florina Balcan and
Hsuan{-}Tien Lin},
title = {Open Graph Benchmark: Datasets for Machine Learning on Graphs},
booktitle = {Advances in Neural Information Processing Systems 33: Annual Conference
on Neural Information Processing Systems 2020, NeurIPS 2020, December
6-12, 2020, virtual},
year = {2020},
url = {https://proceedings.neurips.cc/paper/2020/hash/fb60d411a5c5b72b2e7d3527cfc84fd0-Abstract.html},
}
```
### Contributions
Thanks to [@clefourrier](https://github.com/clefourrier) for adding this dataset. | [
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osanseviero/test_st2 | osanseviero | 2022-07-07T08:55:37Z | 22 | 0 | null | [
"region:us"
] | 2022-07-07T08:55:37Z | 2022-07-07T08:55:24.000Z | 2022-07-07T08:55:24 | Entry not found | [
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CShorten/Last-Week-on-ML-ArXiv | CShorten | 2022-07-12T21:03:47Z | 22 | 0 | null | [
"region:us"
] | 2022-07-12T21:03:47Z | 2022-07-07T12:01:47.000Z | 2022-07-07T12:01:47 | Please check here to see when the dataset was last updated. <br />
<h1> Last Updated July 12th, 2022 </h1> | [
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OGB/ogbg-ppa | OGB | 2023-02-07T16:40:13Z | 22 | 0 | null | [
"task_categories:graph-ml",
"license:cc0-1.0",
"region:us"
] | 2023-02-07T16:40:13Z | 2022-07-07T12:35:18.000Z | 2022-07-07T12:35:18 | ---
license: cc0-1.0
task_categories:
- graph-ml
---
# Dataset Card for ogbg-ppa
## 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)
- [External Use](#external-use)
- [PyGeometric](#pygeometric)
- [Dataset Structure](#dataset-structure)
- [Data Properties](#data-properties)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **[Homepage](https://ogb.stanford.edu/docs/graphprop/#ogbg-ppa)**
- **[Repository](https://github.com/snap-stanford/ogb):**:
- **Paper:**: Open Graph Benchmark: Datasets for Machine Learning on Graphs (see citation)
- **Leaderboard:**: [OGB leaderboard](https://ogb.stanford.edu/docs/leader_graphprop/#ogbg-ppa) and [Papers with code leaderboard](https://paperswithcode.com/sota/graph-property-prediction-on-ogbg-ppa)
### Dataset Summary
The `ogbg-ppa` dataset is "a set of undirected protein association neighborhoods extracted from the protein-protein association networks of 1,581 species", over 37 taxonomic groups, by teams at Stanford, to be a part of the Open Graph Benchmark. See their website for dataset postprocessing.
### Supported Tasks and Leaderboards
`ogbg-ppa` should be used for taxonomic group prediction, a 37-way multi-class classification task. The score used is Average Precision on the test set.
## External Use
### PyGeometric
To load in PyGeometric, do the following:
```python
from datasets import load_dataset
from torch_geometric.data import Data
from torch_geometric.loader import DataLoader
graphs_dataset = load_dataset("graphs-datasets/ogbg-ppa")
# For the train set (replace by valid or test as needed)
graphs_list = [Data(graph) for graph in graphs_dataset["train"]]
graphs_pygeometric = DataLoader(graph_list)
```
## Dataset Structure
### Data Properties
| property | value |
|---|---|
| scale | small |
| #graphs | 158,100 |
| average #nodes | 243.4 |
| average #edges | 2,266.1 |
| average node degree | 18.3 |
| average cluster coefficient | 0.513 |
| MaxSCC ratio | 1.000 |
| graph diameter | 4.8 |
### Data Fields
Each row of a given file is a graph, with:
- `edge_index` (list: 2 x #edges): pairs of nodes constituting edges
- `edge_attr` (list: #edges x #edge-features): for the aforementioned edges, contains their features
- `y` (list: 1 x #labels): contains the number of labels available to predict (here 1, equal to zero or one)
- `num_nodes` (int): number of nodes of the graph
The nodes don't have specific features and are implicit from the lists of edges
### Data Splits
This data comes from the PyGeometric version of the dataset provided by OGB, and follows the provided data splits.
This information can be found back using
```python
from ogb.graphproppred import PygGraphPropPredDataset
dataset = PygGraphPropPredDataset(name = 'ogbg-ppa')
split_idx = dataset.get_idx_split()
train = dataset[split_idx['train']] # valid, test
```
## Additional Information
### Licensing Information
The dataset has been released under CC-0 license.
### Citation Information
```
@inproceedings{hu-etal-2020-open,
author = {Weihua Hu and
Matthias Fey and
Marinka Zitnik and
Yuxiao Dong and
Hongyu Ren and
Bowen Liu and
Michele Catasta and
Jure Leskovec},
editor = {Hugo Larochelle and
Marc Aurelio Ranzato and
Raia Hadsell and
Maria{-}Florina Balcan and
Hsuan{-}Tien Lin},
title = {Open Graph Benchmark: Datasets for Machine Learning on Graphs},
booktitle = {Advances in Neural Information Processing Systems 33: Annual Conference
on Neural Information Processing Systems 2020, NeurIPS 2020, December
6-12, 2020, virtual},
year = {2020},
url = {https://proceedings.neurips.cc/paper/2020/hash/fb60d411a5c5b72b2e7d3527cfc84fd0-Abstract.html},
}
```
### Contributions
Thanks to [@clefourrier](https://github.com/clefourrier) for adding this dataset. | [
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schnell/neko | schnell | 2022-08-12T02:15:44Z | 22 | 0 | null | [
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SocialGrep/reddit-r-bitcoin-data-for-jun-2022 | SocialGrep | 2022-07-25T18:22:16Z | 22 | 2 | null | [
"annotations_creators:lexyr",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"region:us"
] | 2022-07-25T18:22:16Z | 2022-07-25T18:11:58.000Z | 2022-07-25T18:11:58 | ---
annotations_creators:
- lexyr
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
---
# Dataset Card for reddit-r-bitcoin-data-for-jun-2022
## 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)
- [Licensing Information](#licensing-information)
## Dataset Description
- **Homepage:** [https://socialgrep.com/datasets](https://socialgrep.com/datasets/reddit-r-bitcoin-data-for-jun-2022?utm_source=huggingface&utm_medium=link&utm_campaign=redditrbitcoindataforjun2022)
- **Reddit downloader used:** [https://socialgrep.com/exports](https://socialgrep.com/exports?utm_source=huggingface&utm_medium=link&utm_campaign=redditrbitcoindataforjun2022)
- **Point of Contact:** [Website](https://socialgrep.com/contact?utm_source=huggingface&utm_medium=link&utm_campaign=redditrbitcoindataforjun2022)
### Dataset Summary
Lite version of our premium [Reddit /r/Bitcoin dataset](https://socialgrep.com/datasets/the-reddit-r-bitcoin-dataset?utm_source=huggingface&utm_medium=link&utm_campaign=redditrbitcoindataforjun2022) - CSV of all posts & comments to the /r/Bitcoin subreddit over Jun 2022.
### Languages
Mainly English.
## Dataset Structure
### Data Instances
A data point is a post or a comment. Due to the separate nature of the two, those exist in two different files - even though many fields are shared.
### Data Fields
- 'type': the type of the data point. Can be 'post' or 'comment'.
- 'id': the base-36 Reddit ID of the data point. Unique when combined with type.
- 'subreddit.id': the base-36 Reddit ID of the data point's host subreddit. Unique.
- 'subreddit.name': the human-readable name of the data point's host subreddit.
- 'subreddit.nsfw': a boolean marking the data point's host subreddit as NSFW or not.
- 'created_utc': a UTC timestamp for the data point.
- 'permalink': a reference link to the data point on Reddit.
- 'score': score of the data point on Reddit.
- 'domain': (Post only) the domain of the data point's link.
- 'url': (Post only) the destination of the data point's link, if any.
- 'selftext': (Post only) the self-text of the data point, if any.
- 'title': (Post only) the title of the post data point.
- 'body': (Comment only) the body of the comment data point.
- 'sentiment': (Comment only) the result of an in-house sentiment analysis pipeline. Used for exploratory analysis.
## Additional Information
### Licensing Information
CC-BY v4.0
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spacemanidol/cc-stories | spacemanidol | 2023-05-02T11:48:55Z | 22 | 7 | null | [
"region:us"
] | 2023-05-02T11:48:55Z | 2022-08-16T21:35:11.000Z | 2022-08-16T21:35:11 | This is a reproduction of the CC-stories dataset as it has been removed from its original source.
To create this reproduction we process the English common crawl and only keep the top 0.1% of documents measured by their ngram overlap with a source document.
The source document is created by joining the queries from [PDP-60](https://cs.nyu.edu/~davise/papers/WinogradSchemas/PDPChallenge2016.xml) and [WSC273](https://cs.nyu.edu/~davise/papers/WinogradSchemas/WSCollection.xml). Note, as the original dataset does not mention removing duplicate queries, neither do we.
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Mijavier/4classes_custom_dataset_donut | Mijavier | 2022-09-06T10:31:50Z | 22 | 0 | null | [
"region:us"
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-... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Mijavier/4_classes_custom_dataset_donut | Mijavier | 2022-09-06T10:32:39Z | 22 | 0 | null | [
"region:us"
] | 2022-09-06T10:32:39Z | 2022-09-06T02:09:36.000Z | 2022-09-06T02:09:36 | Entry not found | [
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autoevaluate/autoeval-eval-mathemakitten__winobias_antistereotype_test-mathemakitt-63d0bd-1672359217 | autoevaluate | 2022-10-05T16:21:37Z | 22 | 0 | null | [
"autotrain",
"evaluation",
"region:us"
] | 2022-10-05T16:21:37Z | 2022-10-05T16:20:57.000Z | 2022-10-05T16:20:57 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- mathemakitten/winobias_antistereotype_test
eval_info:
task: text_zero_shot_classification
model: mathemakitten/opt-125m
metrics: []
dataset_name: mathemakitten/winobias_antistereotype_test
dataset_config: mathemakitten--winobias_antistereotype_test
dataset_split: test
col_mapping:
text: text
classes: classes
target: target
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Zero-Shot Text Classification
* Model: mathemakitten/opt-125m
* Dataset: mathemakitten/winobias_antistereotype_test
* Config: mathemakitten--winobias_antistereotype_test
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@Tristan](https://huggingface.co/Tristan) for evaluating this model. | [
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inverse-scaling/NeQA | inverse-scaling | 2022-10-08T12:40:09Z | 22 | 2 | null | [
"task_categories:multiple-choice",
"task_categories:question-answering",
"task_categories:zero-shot-classification",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"language:en",
"license:cc-by-sa-4.0",
"region:us"
] | 2022-10-08T12:40:09Z | 2022-10-06T10:35:35.000Z | 2022-10-06T10:35:35 | ---
language:
- en
size_categories:
- 10K<n<100K
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
pretty_name: NeQA - Can Large Language Models Understand Negation in Multi-choice Questions?
source_datasets: []
task_categories:
- multiple-choice
- question-answering
- zero-shot-classification
train-eval-index:
- config: inverse-scaling--NeQA
task: text-generation
task_id: text_zero_shot_classification
splits:
eval_split: train
col_mapping:
prompt: text
classes: classes
answer_index: target
---
## NeQA: Can Large Language Models Understand Negation in Multi-choice Questions? (Zhengping Zhou and Yuhui Zhang)
### General description
This task takes an existing multiple-choice dataset and negates a part of each question to see if language models are sensitive to negation. The authors find that smaller language models display approximately random performance whereas the performance of larger models become significantly worse than random.
Language models failing to follow instructions in the prompt could be a serious issue that only becomes apparent on a task once models are sufficiently capable to perform non-randomly on the task.
### Example
The following are multiple choice questions (with answers) about common sense.
Question: If a cat has a body temp that is below average, it isn't in
A. danger
B. safe ranges
Answer:
(where the model should choose B.)
## Submission details
### Task description
Negation is a common linguistic phenomenon that can completely alter the semantics of a sentence by changing just a few words.
This task evaluates whether language models can understand negation, which is an important step towards true natural language understanding.
Specifically, we focus on negation in open-book multi-choice questions, considering its wide range of applications and the simplicity of evaluation.
We collect a multi-choice question answering dataset, NeQA, that includes questions with negations.
When negation is presented in the question, the original correct answer becomes wrong, and the wrong answer becomes correct.
We use the accuracy metric to examine whether the model can understand negation in the questions and select the correct answer given the presence of negation.
We observe a clear inverse scaling trend on GPT-3, demonstrating that larger language models can answer more complex questions but fail at the last step to understanding negation.
### Dataset generation procedure
The dataset is created by applying rules to transform questions in a publicly available multiple-choice question answering dataset named OpenBookQA. We use a simple rule by filtering questions containing "is" and adding "not" after it. For each question, we sample an incorrect answer as the correct answer and treat the correct answer as the incorrect answer. We randomly sample 300 questions and balance the label distributions (50% label as "A" and 50% label as "B" since there are two choices for each question)..
### Why do you expect to see inverse scaling?
For open-book question answering, larger language models usually achieve better accuracy because more factual and commonsense knowledge is stored in the model parameters and can be used as a knowledge base to answer these questions without context.
A higher accuracy rate means a lower chance of choosing the wrong answer. Can we change the wrong answer to the correct one? A simple solution is to negate the original question. If the model cannot understand negation, it will still predict the same answer and, therefore, will exhibit an inverse scaling trend.
We expect that the model cannot understand negation because negation introduces only a small perturbation to the model input. It is difficult for the model to understand that this small perturbation leads to completely different semantics.
### Why is the task important?
This task is important because it demonstrates that current language models cannot understand negation, a very common linguistic phenomenon and a real-world challenge to natural language understanding.
Why is the task novel or surprising? (1+ sentences)
To the best of our knowledge, no prior work shows that negation can cause inverse scaling. This finding should be surprising to the community, as large language models show an incredible variety of emergent capabilities, but still fail to understand negation, which is a fundamental concept in language.
## Results
[Inverse Scaling Prize: Round 1 Winners announcement](https://www.alignmentforum.org/posts/iznohbCPFkeB9kAJL/inverse-scaling-prize-round-1-winners#Zhengping_Zhou_and_Yuhui_Zhang__for_NeQA__Can_Large_Language_Models_Understand_Negation_in_Multi_choice_Questions_)
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0.153762891888618... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
robertmyers/pile_v2 | robertmyers | 2022-10-27T20:01:07Z | 22 | 2 | null | [
"license:other",
"region:us"
] | 2022-10-27T20:01:07Z | 2022-10-06T20:30:21.000Z | 2022-10-06T20:30:21 | ---
license: other
---
| [
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-0.0478255338966846... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
niurl/eraser_esnli | niurl | 2022-10-24T15:26:38Z | 22 | 1 | null | [
"license:apache-2.0",
"arxiv:1911.03429",
"region:us"
] | 2022-10-24T15:26:38Z | 2022-10-20T11:44:33.000Z | 2022-10-20T11:44:33 | ---
license: apache-2.0
---
## Eraser Dataset Description
- **Homepage:http://www.eraserbenchmark.com**
- **Repository:https://github.com/jayded/eraserbenchmark**
- **Paper:https://arxiv.org/abs/1911.03429**
- **Leaderboard:http://www.eraserbenchmark.com/#leaderboard**
## e-SNLI Dataset Description
- **Repository:https://github.com/OanaMariaCamburu/e-SNLI**
- **Paper:http://papers.nips.cc/paper/8163-e-snli-natural-language-inference-with-natural-language-explanations.pdf**
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0.032673995941877... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
arbml/LK_Hadith | arbml | 2022-10-23T17:25:24Z | 22 | 0 | null | [
"region:us"
] | 2022-10-23T17:25:24Z | 2022-10-23T17:25:02.000Z | 2022-10-23T17:25:02 | Entry not found | [
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... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
autoevaluate/autoeval-eval-jeffdshen__redefine_math0_8shot-jeffdshen__redefine_mat-1c694b-1853263418 | autoevaluate | 2022-10-23T22:09:30Z | 22 | 0 | null | [
"autotrain",
"evaluation",
"region:us"
] | 2022-10-23T22:09:30Z | 2022-10-23T21:43:01.000Z | 2022-10-23T21:43:01 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- jeffdshen/redefine_math0_8shot
eval_info:
task: text_zero_shot_classification
model: inverse-scaling/opt-2.7b_eval
metrics: []
dataset_name: jeffdshen/redefine_math0_8shot
dataset_config: jeffdshen--redefine_math0_8shot
dataset_split: train
col_mapping:
text: prompt
classes: classes
target: answer_index
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Zero-Shot Text Classification
* Model: inverse-scaling/opt-2.7b_eval
* Dataset: jeffdshen/redefine_math0_8shot
* Config: jeffdshen--redefine_math0_8shot
* Split: train
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@jeffdshen](https://huggingface.co/jeffdshen) for evaluating this model. | [
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0.0084367115... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
bigcode/admin | bigcode | 2023-05-04T17:16:18Z | 22 | 0 | null | [
"region:us"
] | 2023-05-04T17:16:18Z | 2022-10-25T13:53:18.000Z | 2022-10-25T13:53:18 | Entry not found | [
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... | null | null | null | null | null | null | null | null | null | null | null | null | null |
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