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license: openrail
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THUDM/BPO | THUDM | 2023-11-20T11:49:55Z | 0 | 2 | null | [
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"region:us"
] | 2023-11-20T11:49:55Z | 2023-11-20T11:46:58.000Z | 2023-11-20T11:46:58 | ---
license: apache-2.0
task_categories:
- text-generation
language:
- en
tags:
- human_feedback
size_categories:
- 10K<n<100K
pretty_name: BPO
---
# Dataset Card for Black-box Prompt Optimization (BPO)
## Dataset Description
- **Repository:** https://github.com/thu-coai/BPO
- **Paper:** https://arxiv.org/abs/2311.04155
- **Model:**
### Data Summary
To advance the development of alignment in language models, we introduce a black-box alignment method. BPO enhances the alignment of various Large Language Models (LLMs) with human preferences using only a plug-and-play model. To further promote alignment work from the prompting perspective, we are releasing the BPO Dataset. This dataset comprises 14,395 entries of prompt optimization pairs, constructed using open-source feedback data with OpenAI's `gpt-3.5-turbo` engine. We have thoroughly ensured the quality and diversity of the data through careful filtering and correction.
Please refer to our [paper](https://arxiv.org/abs/2311.04155) for further details.
### Supported Tasks
The BPO Dataset is designed for Prompt Optimization / Engineering task. As we have carefully flitered and curated the dataset, it is also a good choice for SFT or RLHF.
### Language
The data in BPO are in English.
## Data Structure
### Data Instances
An example of "train" looks as follows:
```json
{
"prompt": "Minimize the following data set\n[3, 4, 6, 7, 8, 10]",
"optimized_prompt": "Provide a step-by-step solution to minimize the following data set: [3, 4, 6, 7, 8, 10].",
"good_res": "To minimize the values in the given data set, which is a list of numbers, you could sort them in ascending order as follows: [3, 4, 6, 7, 8, 10]. The minimized (least) value in this data set would be 3.",
"bad_res": "[3, 4, 6, 7, 8, 10]"
}
```
### Data Fields
The data fields are as follows:
* `prompt`: the original user input
* `optimized_prompt`: the optimized prompt based on feedback
* `good_res`: human-chosen (preferred) response
* `bad_res`: human-rejected response
### Data Splits
| | train | valid | test |
|---------------|------:|------:|------:|
| BPO | 13895 | 300 | 200 |
## Additional Information
### Source Data
Our feedback data come from the following open-sourced datasets:
- [OASST1](https://huggingface.co/datasets/OpenAssistant/oasst1)
- [hh-rlhf](https://huggingface.co/datasets/Anthropic/hh-rlhf)
- [Alpaca-GPT4](https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM/blob/main/data/comparison_data_v2.json)
- [Chatbot Arena Conversation](https://huggingface.co/datasets/lmsys/chatbot_arena_conversations)
### Other Known Limitations
- Feedback Data Quality: Due to our use of open-source feedback data, some human preferences included may not be entirely accurate.
- Task Diversity: Despite our efforts to filter and achieve a diverse dataset, these open-source datasets are clearly not sufficient to cover the wide variety of user queries.
- Optimized Prompts: The optimized prompts are auto-generated by `gpt-3.5-turbo` based on feedback data. Even though we have manually reviewed and modified the dataset, we cannot guarantee that all prompt optimizations are correct.
### Citation Information
```
@article{cheng2023black,
title={Black-Box Prompt Optimization: Aligning Large Language Models without Model Training},
author={Cheng, Jiale and Liu, Xiao and Zheng, Kehan and Ke, Pei and Wang, Hongning and Dong, Yuxiao and Tang, Jie and Huang, Minlie},
journal={arXiv preprint arXiv:2311.04155},
year={2023}
}
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Djacon/ru-izard-emotions | Djacon | 2023-11-23T19:17:45Z | 0 | 0 | null | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"task_ids:multi-class-classification",
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"emotion",
"region:us"
] | 2023-11-23T19:17:45Z | 2023-11-20T12:08:50.000Z | 2023-11-20T12:08:50 | ---
language:
- ru
license:
- mit
multilinguality:
- russian
task_categories:
- text-classification
task_ids:
- sentiment-classification
- multi-class-classification
- multi-label-classification
pretty_name: RuIzardEmotions
tags:
- emotion
size_categories:
- 10K<n<100K
---
# Dataset Card for RuIzardEmotions
## 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 Summary
The RuIzardEmotions dataset is a high-quality translation of the [go-emotions](https://huggingface.co/datasets/go_emotions) dataset and the other [emotion-detection](https://www.kaggle.com/datasets/ishantjuyal/emotions-in-text/data) dataset. It contains 30k Reddit comments labeled for 10 emotion categories (__joy__, __sadness__, __anger__, __enthusiasm__, __surprise__, __disgust__, __fear__, __guilt__, __shame__ and __neutral__).
The datasets were translated using the accurate translator [DeepL](https://www.deepl.com/translator) and additional processing. The idea for the dataset was inspired by the [Izard's model](https://en.wikipedia.org/wiki/Differential_Emotions_Scale) of human emotions.
The dataset already with predefined train/val/test splits.
### Supported Tasks and Leaderboards
This dataset is intended for multi-class, multi-label emotion classification.
### Languages
The data is in Russian.
## Dataset Structure
### Data Instances
Each instance is a reddit comment with one or more emotion annotations (or neutral).
### Data Splits
The simplified data includes a set of train/val/test splits with 24k, 3k, and 3k examples respectively.
## Considerations for Using the Data
### Social Impact of Dataset
Emotion detection is a worthwhile problem which can potentially lead to improvements such as better human/computer
interaction. However, emotion detection algorithms (particularly in computer vision) have been abused in some cases
to make erroneous inferences in human monitoring and assessment applications such as hiring decisions, insurance
pricing, and student attentiveness
## Additional Information
### Licensing Information
The GitHub repository which houses this dataset has an
[Apache License 2.0](https://github.com/Djacon/russian-emotion-detection/blob/main/LICENSE).
### Citation Information
```
@inproceedings{Djacon,
author={Djacon},
title={RuIzardEmotions: A Dataset of Fine-Grained Emotions},
year={2023}
}
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---
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license: mit
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language:
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---
## Dataset Description
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italovinixius/news-pt | italovinixius | 2023-11-20T13:05:27Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
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license: apache-2.0
---
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0ho/llama2 | 0ho | 2023-11-21T09:39:30Z | 0 | 0 | null | [
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license: gpl
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tfoldi/tesla3_av_rosbags | tfoldi | 2023-11-23T07:57:14Z | 0 | 0 | null | [
"task_categories:object-detection",
"size_categories:100M<n<1B",
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"rosbag",
"ros2",
"lidar",
"vehicle",
"car",
"canbus",
"autonomous_vehicles",
"region:us"
] | 2023-11-23T07:57:14Z | 2023-11-20T13:27:20.000Z | 2023-11-20T13:27:20 | ---
license: mit
task_categories:
- object-detection
tags:
- rosbag
- ros2
- lidar
- vehicle
- car
- canbus
- autonomous_vehicles
pretty_name: tesla3_av_rosbags
size_categories:
- 100M<n<1B
---
# Tesla Model 3 ROS Data Repository
## Overview
This repository contains a collection of ROS bag files (in MCAP format) and video streams from a Tesla Model 3 vehicle. It includes data from multiple runs, featuring a variety of sensors such as VLP-16 LiDAR, CAN bus signals, GPS, a 9-axis IMU, and four camera streams.

## Dataset Description
The data is organized into multiple runs, each containing synchronized streams from the following sensors:
* VLP-16 LiDAR: 3D point cloud data capturing the vehicle's surroundings.
* CAN Bus: Vehicle's internal communication data including speed, steering angle, and more.
* GPS: Geolocation data showing the vehicle's position.
* 9-Axis IMU: Inertial data providing acceleration, orientation, and gyroscope measurements.
* Camera Streams: Four video streams capturing front, rear, and side views.
# License
This project is licensed under the MIT License - see the LICENSE file for details.
# Contact
For any queries or issues, please open an issue in the repository, or contact me directly at tfoldi@starschema.com | [
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license: mit
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---
## Dataset Description
- **Repository:** [GitHub Repository](https://kgxqr.github.io/) | [
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DamarJati/gpt2-m-storybook | DamarJati | 2023-11-21T22:20:27Z | 0 | 0 | null | [
"task_categories:text-generation",
"language:en",
"storybook",
"fiction",
"region:us"
] | 2023-11-21T22:20:27Z | 2023-11-20T13:27:42.000Z | 2023-11-20T13:27:42 | ---
language:
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pretty_name: Your Storybook Dataset
tags:
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- fiction
task_categories:
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---
# Your Storybook Dataset
Welcome to the repository for the "Your Storybook Dataset." This dataset contains a collection of stories and narratives suitable for various natural language processing tasks.
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ryuseiken/PIMPINELA | ryuseiken | 2023-11-20T13:54:34Z | 0 | 0 | null | [
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license: openrail
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ahmedeltaron/testing_reoport_word | ahmedeltaron | 2023-11-20T14:15:14Z | 0 | 0 | null | [
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Jaspernl/common_voice_13_0_hi_pseudo_labelled | Jaspernl | 2023-11-22T09:38:34Z | 0 | 0 | null | [
"region:us"
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nxsbr/kk | nxsbr | 2023-11-24T17:45:48Z | 0 | 0 | null | [
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license: openrail
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wza/finbcf | wza | 2023-11-20T14:23:50Z | 0 | 0 | null | [
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nourheshamshaheen/ICPR_pipeline3_big | nourheshamshaheen | 2023-11-20T14:45:17Z | 0 | 0 | null | [
"region:us"
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---
# Dataset Card for "ICPR_pipeline3_big"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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nourheshamshaheen/ICPR_pipeline3_small | nourheshamshaheen | 2023-11-20T14:46:50Z | 0 | 0 | null | [
"region:us"
] | 2023-11-20T14:46:50Z | 2023-11-20T14:45:17.000Z | 2023-11-20T14:45:17 | ---
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---
# Dataset Card for "ICPR_pipeline3_small"
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lunaluan/chatbox7_history | lunaluan | 2023-11-27T08:22:01Z | 0 | 0 | null | [
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lmg-anon/VNTL-1k | lmg-anon | 2023-11-20T18:01:58Z | 0 | 0 | null | [
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---
# Dataset Card for "VNTL-1k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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DynamicSuperb/EmotionalSpeechAudioClassification_RAVDESS-EmotionalSound | DynamicSuperb | 2023-11-26T17:51:36Z | 0 | 0 | null | [
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FlyingFishzzz/des_mesh | FlyingFishzzz | 2023-11-20T15:51:31Z | 0 | 0 | null | [
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# Dataset Card for "des_mesh"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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codeSener/Kaggle-Stable-Diffusion | codeSener | 2023-11-20T16:13:29Z | 0 | 0 | null | [
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alexdg19/reddit_summaries | alexdg19 | 2023-11-20T16:17:44Z | 0 | 0 | null | [
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jpqueiroz335/vozdoriff | jpqueiroz335 | 2023-11-20T16:27:04Z | 0 | 0 | null | [
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Jcuhfehl/anna | Jcuhfehl | 2023-11-20T17:03:20Z | 0 | 0 | null | [
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Frixi/Almighty_2019_LaBestia_Era | Frixi | 2023-11-20T17:24:56Z | 0 | 0 | null | [
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mesolitica/dependency-parsing-instructions | mesolitica | 2023-11-22T04:51:04Z | 0 | 0 | null | [
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TerminatorJ/localization_dataset | TerminatorJ | 2023-11-20T17:00:01Z | 0 | 0 | null | [
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FudanSELab/SO_KGXQR_HISTORY | FudanSELab | 2023-11-20T17:11:05Z | 0 | 0 | null | [
"size_categories:1K<n<10K",
"language:en",
"license:mit",
"region:us"
] | 2023-11-20T17:11:05Z | 2023-11-20T17:04:47.000Z | 2023-11-20T17:04:47 | ---
license: mit
dataset_info:
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language:
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size_categories:
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---
## Dataset Description
- **Repository:** [GitHub Repository](https://kgxqr.github.io/) | [
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DataStudio/OCR_Red | DataStudio | 2023-11-20T17:33:45Z | 0 | 0 | null | [
"region:us"
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---
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BangumiBase/kamikazekaitoujeanne | BangumiBase | 2023-11-20T18:57:19Z | 0 | 0 | null | [
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
] | 2023-11-20T18:57:19Z | 2023-11-20T17:50:43.000Z | 2023-11-20T17:50:43 | ---
license: mit
tags:
- art
size_categories:
- 1K<n<10K
---
# Bangumi Image Base of Kamikaze Kaitou Jeanne
This is the image base of bangumi Kamikaze Kaitou Jeanne, we detected 43 characters, 3600 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|
| 0 | 527 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 39 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 690 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 38 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 33 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 26 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 110 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 23 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 28 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 435 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 351 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 45 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 69 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 14 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 78 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 54 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 21 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 23 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 45 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 18 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 159 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 22 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 103 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 42 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 12 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| 25 | 14 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| 26 | 30 | [Download](26/dataset.zip) |  |  |  |  |  |  |  |  |
| 27 | 15 | [Download](27/dataset.zip) |  |  |  |  |  |  |  |  |
| 28 | 12 | [Download](28/dataset.zip) |  |  |  |  |  |  |  |  |
| 29 | 18 | [Download](29/dataset.zip) |  |  |  |  |  |  |  |  |
| 30 | 12 | [Download](30/dataset.zip) |  |  |  |  |  |  |  |  |
| 31 | 11 | [Download](31/dataset.zip) |  |  |  |  |  |  |  |  |
| 32 | 208 | [Download](32/dataset.zip) |  |  |  |  |  |  |  |  |
| 33 | 28 | [Download](33/dataset.zip) |  |  |  |  |  |  |  |  |
| 34 | 24 | [Download](34/dataset.zip) |  |  |  |  |  |  |  |  |
| 35 | 13 | [Download](35/dataset.zip) |  |  |  |  |  |  |  |  |
| 36 | 34 | [Download](36/dataset.zip) |  |  |  |  |  |  |  |  |
| 37 | 7 | [Download](37/dataset.zip) |  |  |  |  |  |  |  | N/A |
| 38 | 12 | [Download](38/dataset.zip) |  |  |  |  |  |  |  |  |
| 39 | 19 | [Download](39/dataset.zip) |  |  |  |  |  |  |  |  |
| 40 | 12 | [Download](40/dataset.zip) |  |  |  |  |  |  |  |  |
| 41 | 17 | [Download](41/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 109 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
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lmg-anon/VNTL-4k | lmg-anon | 2023-11-20T20:42:18Z | 0 | 0 | null | [
"region:us"
] | 2023-11-20T20:42:18Z | 2023-11-20T17:57:09.000Z | 2023-11-20T17:57:09 | ---
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dataset_size: 87937072
---
# Dataset Card for "VNTL-4k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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m111styd4y/marisam | m111styd4y | 2023-11-20T18:23:00Z | 0 | 0 | null | [
"license:openrail",
"region:us"
] | 2023-11-20T18:23:00Z | 2023-11-20T18:22:18.000Z | 2023-11-20T18:22:18 | ---
license: openrail
---
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rkdeva/Dermnet-Test-2 | rkdeva | 2023-11-20T18:35:12Z | 0 | 0 | null | [
"region:us"
] | 2023-11-20T18:35:12Z | 2023-11-20T18:33:11.000Z | 2023-11-20T18:33:11 | ---
configs:
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path: data/train-*
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---
# Dataset Card for "Dermnet-Test-2"
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lunaluan/chatbox8_history | lunaluan | 2023-11-27T08:22:04Z | 0 | 0 | null | [
"region:us"
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sade-adrien/redpajama_v2_32k | sade-adrien | 2023-11-20T21:43:44Z | 0 | 0 | null | [
"region:us"
] | 2023-11-20T21:43:44Z | 2023-11-20T19:24:07.000Z | 2023-11-20T19:24:07 | ---
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---
# Dataset Card for "redpajama_v2_32k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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wmmarcellino/russian_telegram | wmmarcellino | 2023-11-24T17:38:32Z | 0 | 0 | null | [
"region:us"
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celsowm/openassistant-llama-style-pt-br | celsowm | 2023-11-20T21:06:08Z | 0 | 0 | null | [
"region:us"
] | 2023-11-20T21:06:08Z | 2023-11-20T21:06:01.000Z | 2023-11-20T21:06:01 | ---
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hayden-donnelly/easy-mnist | hayden-donnelly | 2023-11-20T21:45:49Z | 0 | 0 | null | [
"task_categories:image-classification",
"task_categories:unconditional-image-generation",
"size_categories:10K<n<100K",
"region:us"
] | 2023-11-20T21:45:49Z | 2023-11-20T21:31:37.000Z | 2023-11-20T21:31:37 | ---
task_categories:
- image-classification
- unconditional-image-generation
pretty_name: Easy MNIST
size_categories:
- 10K<n<100K
---
# Easy MNIST
MNIST processed into three easy to use formats. Each .zip file contains a labels_and_paths.csv file and a data directory.
## mnist_png.zip
MNIST in the png format.
```
label path
0 5 data/0.png
1 0 data/1.png
2 4 data/2.png
3 1 data/3.png
4 9 data/4.png
... ... ...
69995 2 data/69995.png
69996 3 data/69996.png
69997 4 data/69997.png
69998 5 data/69998.png
69999 6 data/69999.png
```
## mnist_numpy.zip
MNIST in the npy format.
```
label path
0 5 data/0.npy
1 0 data/1.npy
2 4 data/2.npy
3 1 data/3.npy
4 9 data/4.npy
... ... ...
69995 2 data/69995.npy
69996 3 data/69996.npy
69997 4 data/69997.npy
69998 5 data/69998.npy
69999 6 data/69999.npy
```
## mnist_numpy_flat.zip
MNIST in the npy format, flattened to 784 dimensional vectors.
```
label path
0 5 data/0.npy
1 0 data/1.npy
2 4 data/2.npy
3 1 data/3.npy
4 9 data/4.npy
... ... ...
69995 2 data/69995.npy
69996 3 data/69996.npy
69997 4 data/69997.npy
69998 5 data/69998.npy
69999 6 data/69999.npy
```
## Acknowledgements
- Yann LeCun, Courant Institute, NYU
- Corinna Cortes, Google Labs, New York
- Christopher J.C. Burges, Microsoft Research, Redmond
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license: openrail
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license: apache-2.0
---
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wearemusicai/moisesdb | wearemusicai | 2023-11-27T19:36:27Z | 0 | 0 | null | [
"language:en",
"license:other",
"audio",
"music",
"source separation",
"arxiv:2307.15913",
"region:us"
] | 2023-11-27T19:36:27Z | 2023-11-20T22:05:24.000Z | 2023-11-20T22:05:24 | ---
language:
- en
pretty_name: MoisesDB
tags:
- audio
- music
- source separation
license: other
license_name: ncrlc
license_link: https://github.com/moises-ai/moises-db
---
# MoisesDB
Moises Dataset for Source Separation
### Dataset Description
- **Homepage:** [MoisesDB homepage](https://developer.moises.ai/research/)
- **Repository:** [MoisesDB repository](https://github.com/moises-ai/moises-db)
- **Paper:** [Moisesdb: A dataset for source separation beyond 4-stems](https://arxiv.org/abs/2307.15913)
- **Point of Contact:** [Igor Pereira](mailto:igor@moises.ai)
### Dataset Summary
MoisesDB is a dataset for source separation. It provides a collection of tracks and their separated stems (vocals, bass, drums, etc.). The dataset is used to evaluate the performance of source separation algorithms.
# Download the data
Please download the dataset at our research [website](https://developer.moises.ai/research), extract it and configure the environment variable `MOISESDB_PATH` accordingly.
```
export MOISESDB_PATH=./moises-db-data
```
The directory structure should be
```
moisesdb:
moisesdb_v0.1
track uuid 0
track uuid 1
.
.
.
```
# Install
You can install this package with
```
pip install git+https://github.com/moises-ai/moises-db.git
```
# Usage
## `MoisesDB`
After downloading and configuring the path for the dataset, you can create an instance of `MoisesDB` to access the tracks. You can also provide the dataset path with the `data_path` argument.
```
from moisesdb.dataset import MoisesDB
db = MoisesDB(
data_path='./moisesdb',
sample_rate=44100
)
```
The `MoisesDB` object has iterator properties that you can use to access all files within the dataset.
```
n_songs = len(db)
track = db[0] # Returns a MoisesDBTrack object
```
## `MoisesDBTrack`
The `MoisesDBTrack` object holds information about a track in the dataset, perform on-the-fly mixing for stems and multiple sources within a stem.
You can access all the stems and mixture from the `stem` and `audio` properties. The `stem` property returns a dictionary whith available stems as keys and `nd.array` on values. The `audio` property results in a `nd.array` with the mixture.
```
track = db[0]
stems = track.stems # stems = {'vocals': ..., 'bass': ..., ...}
mixture track.audio # mixture = nd.array
```
The `MoisesDBTrack` object also contains other non-audio information from the track such as:
- `track.id`
- `track.provider`
- `track.artist`
- `track.name`
- `track.genre`
- `track.sources`
- `track.bleedings`
- `track.activity`
The stems and mixture are computed on-the-fly. You can create a stems-only version of the dataset using the `save_stems` method of the `MoisesDBTrack`.
```
track = db[0]
path = './moises-db-stems/0'
track.save_stems(path)
```
# Performance Evaluation
We run a few source separation algorithms as well as oracle methods to evaluate the performance of each track of the `MoisesDB`. These results are located in `csv` files at the `benchmark` folder.
# Citing
If you used the `MoisesDB` dataset on your research, please cite the following paper.
```
@misc{pereira2023moisesdb,
title={Moisesdb: A dataset for source separation beyond 4-stems},
author={Igor Pereira and Felipe Araújo and Filip Korzeniowski and Richard Vogl},
year={2023},
eprint={2307.15913},
archivePrefix={arXiv},
primaryClass={cs.SD}
}
```
# Licensing
`MoisesDB` is distributed with the NC-RCL license.
```
"Non-Commercial Research Community license (NC-RCL)
Limited Redistribution: You are permitted to copy and utilize the provided audio material in any medium or format, as long as it is done only for non-commercial purposes within the research community, and the redistribution is conducted solely through the platform moises.ai or other platforms explicitly authorized by the licensor. Redistribution outside the authorized platforms is not allowed without the licensor's written consent.
Attribution: You must give appropriate credit (including the artist's name and the song's title), and provide a link to this license or a notice indicating the terms of this license.
Non-Commercial Use: You cannot use the material for any commercial purposes or financial gain. This includes, but is not limited to, the sale, licensing, or rental of the material, as well as any use where the primary aim is to generate revenue or profits.
No Derivative Works: You cannot create, remix, adapt, or build upon the material, unless explicitly permitted by the artist.
Preservation of Legal Notices: You cannot remove any copyright or other proprietary notices which are included in or attached to the material.
Termination: If you fail to comply with this license, your rights to use the material will be terminated automatically.
Voice Cloning Restriction: You are prohibited from using the vocal stems or any part of the audio material to create a public digital imitation of the artist's voice (e.g: a vocal clone or replica). This includes, but is not limited to, the utilization of voice synthesis technology, deep learning algorithms, and other artificial intelligence-based tools."
```
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---
# Dataset Card for "redpajama_v2_sample_100M"
[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 "eval_dataset_character_20231108_sample43_response_reselected"
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prabhjotkaur27/embedded_faqs_medicare_test | prabhjotkaur27 | 2023-11-21T00:37:46Z | 0 | 0 | null | [
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] | 2023-11-21T00:37:46Z | 2023-11-21T00:37:46.000Z | 2023-11-21T00:37:46 | Entry not found | [
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... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
gagan3012/Numerical_understanding | gagan3012 | 2023-11-21T00:58:54Z | 0 | 0 | null | [
"region:us"
] | 2023-11-21T00:58:54Z | 2023-11-21T00:58:50.000Z | 2023-11-21T00:58:50 | ---
dataset_info:
features:
- name: text
dtype: string
- name: answer
dtype: string
- name: query
dtype: string
- name: id
dtype: string
splits:
- name: train
num_bytes: 169338670
num_examples: 20875
download_size: 43150062
dataset_size: 169338670
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
| [
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-0.047825977206230... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Maljean/State-Sponsored | Maljean | 2023-11-21T01:01:01Z | 0 | 0 | null | [
"license:unknown",
"region:us"
] | 2023-11-21T01:01:01Z | 2023-11-21T01:01:01.000Z | 2023-11-21T01:01:01 | ---
license: unknown
---
| [
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-0.710224986076355,
-0.047825977206230... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
manu/slim-lf | manu | 2023-11-21T01:08:42Z | 0 | 0 | null | [
"region:us"
] | 2023-11-21T01:08:42Z | 2023-11-21T01:08:41.000Z | 2023-11-21T01:08:41 | Entry not found | [
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0.5715669393539429,
-0... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Maljean/dataset | Maljean | 2023-11-21T01:09:42Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | 2023-11-21T01:09:42Z | 2023-11-21T01:08:43.000Z | 2023-11-21T01:08:43 | ---
license: apache-2.0
---
| [
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-0.710224986076355,
-0.047825977206230... | null | null | null | null | null | null | null | null | null | null | null | null | null |
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