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arieg/bw_spec_cls_80_37 | arieg | 2023-11-09T19:17:18Z | 0 | 0 | null | [
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# Dataset Card for "bw_spec_cls_80_37"
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# Dataset Card for "bw_spec_cls_80_38"
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pkboom/codegen | pkboom | 2023-11-09T20:12:42Z | 0 | 0 | null | [
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# Dataset Card for "codegen"
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AIQuest/House-Price-In_Multan | AIQuest | 2023-11-09T20:06:04Z | 0 | 0 | null | [
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arieg/bw_spec_cls_80_39 | arieg | 2023-11-09T20:09:06Z | 0 | 0 | null | [
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# Dataset Card for "bw_spec_cls_80_39"
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arieg/bw_spec_cls_80_40 | arieg | 2023-11-09T20:35:12Z | 0 | 0 | null | [
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# Dataset Card for "bw_spec_cls_80_40"
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arieg/bw_spec_cls_80_41 | arieg | 2023-11-09T21:01:34Z | 0 | 0 | null | [
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---
# Dataset Card for "bw_spec_cls_80_41"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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Paia2/raulbio2 | Paia2 | 2023-11-09T21:09:04Z | 0 | 0 | null | [
"license:openrail",
"region:us"
] | 2023-11-09T21:09:04Z | 2023-11-09T21:09:04.000Z | 2023-11-09T21:09:04 | ---
license: openrail
---
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pkboom/codegen-v2 | pkboom | 2023-11-09T21:12:32Z | 0 | 0 | null | [
"region:us"
] | 2023-11-09T21:12:32Z | 2023-11-09T21:12:30.000Z | 2023-11-09T21:12:30 | ---
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---
# Dataset Card for "codegen-v2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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Pablao0948/Tiffany_Wilson | Pablao0948 | 2023-11-10T19:39:32Z | 0 | 0 | null | [
"region:us"
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samxu29/Charlotte_Area_Traffic_Light_Dataset | samxu29 | 2023-11-10T19:47:19Z | 0 | 0 | null | [
"license:mit",
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license: mit
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mvkvc/stack_elixir | mvkvc | 2023-11-09T22:19:39Z | 0 | 0 | null | [
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] | 2023-11-09T22:19:39Z | 2023-11-09T21:56:42.000Z | 2023-11-09T21:56:42 | ---
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---
# Dataset Card for "stack_elixir"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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andrewatef/llama2-llm | andrewatef | 2023-11-09T21:59:33Z | 0 | 0 | null | [
"region:us"
] | 2023-11-09T21:59:33Z | 2023-11-09T21:59:32.000Z | 2023-11-09T21:59:32 | ---
dataset_info:
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---
# Dataset Card for "llama2-llm"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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h4/llama.cpp | h4 | 2023-11-09T22:10:08Z | 0 | 0 | null | [
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] | 2023-11-09T22:10:08Z | 2023-11-09T22:10:08.000Z | 2023-11-09T22:10:08 | ---
license: mit
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introspector/llama.cpp | introspector | 2023-11-09T22:37:56Z | 0 | 0 | null | [
"license:mit",
"region:us"
] | 2023-11-09T22:37:56Z | 2023-11-09T22:37:56.000Z | 2023-11-09T22:37:56 | ---
license: mit
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BubbleJoe/mscoco_augmented | BubbleJoe | 2023-11-14T05:09:32Z | 0 | 0 | null | [
"region:us"
] | 2023-11-14T05:09:32Z | 2023-11-09T23:17:50.000Z | 2023-11-09T23:17:50 | ---
configs:
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---
# Dataset Card for "mscoco_augmented"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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Tonic/medquad | Tonic | 2023-11-09T23:20:05Z | 0 | 0 | null | [
"region:us"
] | 2023-11-09T23:20:05Z | 2023-11-09T23:18:38.000Z | 2023-11-09T23:18:38 | ---
dataset_info:
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---
# Dataset Card for "medquad"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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wagnergrangeiro/edsonlima | wagnergrangeiro | 2023-11-09T23:20:45Z | 0 | 0 | null | [
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license: openrail
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davozing223/xandeestudante1 | davozing223 | 2023-11-09T23:23:36Z | 0 | 0 | null | [
"license:openrail",
"region:us"
] | 2023-11-09T23:23:36Z | 2023-11-09T23:23:35.000Z | 2023-11-09T23:23:35 | ---
license: openrail
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davozing223/xandeestudante2 | davozing223 | 2023-11-09T23:28:52Z | 0 | 0 | null | [
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joelbrs/coco_dataset | joelbrs | 2023-11-09T23:34:04Z | 0 | 0 | null | [
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# Dataset Card for "recipes"
[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 "Text2Speech_LJSpeech"
[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 "admits_fyi"
[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 "zaloai-2023-nlp-test-vi"
[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 "Text2Speech_LibriTTS-TestOther"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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MLCommons/unsupervised_peoples_speech | MLCommons | 2023-11-28T17:57:53Z | 0 | 0 | null | [
"task_categories:automatic-speech-recognition",
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language:
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pretty_name: Unsupervised Peoples Speech
tags:
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- unsupervised
task_categories:
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- audio-classification
task_ids:
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viewer: false
---
# Dataset Card for Unsupervised Peoples Speech
## Table of Contents
- [Dataset Card for Unuspervised Peoples Speech](#dataset-card-for-unsupervised-peoples-speech)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Relevant Statistics](#relevant-statistics)
- [Dataset Structure](#dataset-structure)
- [Audio folders](#audio-folders)
- [Dataset Creation](#dataset-creation)
- [Source Data](#source-data)
- [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
- [Preprocessing](#preprocessing)
- [Annotations] (#annotations)
- [Annotation Process] (#annotation-process)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Discussion of Biases](#discussion-of-biases)
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Point of Contact:** [datasets@mlcommons.org](mailto:datasets@mlcommons.org)
### Dataset Summary
The Unsupervised Peoples Speech Dataset is a compilation of audiofiles extracted from Archive.org that is licensed for academic and commercial usage under CC-BY and CC-BY-SA licenses. It includes more than one million hours of audio with a diverse set of speakers.
### Relevant Statistics
#### Duration Distribution
Most of the audios range between 1 and 10 minutes in length, with only 14 of them exceeding the 100 hour mark.
#### Sample Rates
99% of the audio in the dataset has a 44.1Khz sample rate, and the remaining audio varies from the more common 16Khz, 24Khz and 48 Khz to custom sample rates.
## Dataset Structure
### Audio folders
Folders with the raw audio. We split this into two directories because Hugging Face does not support more than 10,000 files in a single directory.
## Dataset Creation
### Source Data
#### Initial Data Collection and Normalization
Data was downloaded via the archive.org API. No data inference was done.
#### Preprocessing
No preprocessing was done.
### Annotations
#### Annotation process
No manual annotation is done. We download only source audio.
In particular, there is no "forced alignment" or "segmentation" done on this dataset.
### Personal and Sensitive Information
Several of our sources are legal and government proceedings, spoken stories, speeches, and so on. Given that these were intended as public documents and licensed as such, it is natural that the involved individuals are aware of this.
## Considerations for Using the Data
### Discussion of Biases
Our data is downloaded from archive.org. As such, the data is biased towards whatever users decide to upload there.
Almost all of our data is American accented English.
## Additional Information
### Licensing Information
The source data contains data under CC-BY-SA and CC-BY licenses.
We license this dataset under https://creativecommons.org/licenses/by-sa/4.0/
### Citation Information
Please cite
```
@article{USP,
author={Daniel Galvez and
Ryan Hileman and
Rafael Mosquera and
Juan Ciro and
Kurt Bollacker and
Peter Mattson and
David Kanter},
title = {Unsupervised People's Speech (The Million Hour Audio Dataset)},
year = {2023},
url = {https://huggingface.co/datasets/MLCommons/peoples_speech},
}
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cmu-mlsp/encodec_24khz-b24.0-librispeech_asr-features | cmu-mlsp | 2023-11-10T02:53:03Z | 0 | 0 | null | [
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# Dataset Card for "encodec_24khz-b24.0-librispeech_asr-features"
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# Dataset Card for "dataset_995_decompose_question"
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# Dataset Card for "test_ds"
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# Dataset Card for "test_ds_1"
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# Dataset Card for "health-100"
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# Dataset Card for "test-lcm"
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# Dataset Card for "food-100"
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# Dataset Card for "multy-choices-health-100"
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# Dataset Card for "airborne_general_qa"
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nlplabtdtu/logic-20 | nlplabtdtu | 2023-11-10T06:57:24Z | 0 | 0 | null | [
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# Dataset Card for "logic-20"
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ami-iit/paper_latella_2023_irim_muscle-force-estimation_dataset | ami-iit | 2023-11-10T08:28:09Z | 0 | 0 | null | [
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license: bsd-3-clause
---
<h1 align="center">
Real-time Lower Leg Muscle Forces Estimation using a Hill-type Model and Whole-body Wearable Sensors
</h1>
<div align="center">
Claudia Latella, Antonella Tatarelli, Lorenzo Fiori, Riccardo Grieco, Lorenzo Rapetti, Daniele Pucci <br> <br>
</div>
<br>
<div align="center">
5th Italian Conference in Robotics and Intelligent Machines (I-RIM), 2023
</div>
<section>
<h2>📂 Dataset</h2>
<p>
The dataset is related to a subject (S01) acquisition with 5 trials. Each trial folder is organized as follows:
</p>
<ul>
<li>
a <code>.tdf</code> file containing the EMG acquisition via BTS Engineering surface electromyography
</li>
<li>
a <code>.mat</code> file containing the kinematics data acquisition via <a href="https://ifeeltech.eu/"><b>iFeel technology</b></a>
</li>
</ul>
</section>
<p>
The dataset provides also the <code>.urdf</code> model for the subject S01.
</p> | [
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merchal/zhtext2emoji | merchal | 2023-11-13T06:41:21Z | 0 | 0 | null | [
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task_categories:
- translation
language:
- zh
- en
pretty_name: zhtext2emoji
size_categories:
- 1K<n<10K
--- | [
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danaroth/urban | danaroth | 2023-11-10T11:10:52Z | 0 | 0 | null | [
"license:unknown",
"arxiv:1708.05125",
"region:us"
] | 2023-11-10T11:10:52Z | 2023-11-10T08:07:56.000Z | 2023-11-10T08:07:56 | ---
license: unknown
---
# Description
Urban is one of the most widely used hyperspectral data used in the hyperspectral unmixing study. There are 307x307 pixels, each of which corresponds to a 2x2 m2 area. In this image, there are 210 wavelengths ranging from 400 nm to 2500 nm, resulting in a spectral resolution of 10 nm. After the channels 1-4, 76, 87, 101-111, 136-153 and 198-210 are removed (due to dense water vapor and atmospheric effects), 162 channels are left (this is a common preprocess for hyperspectral unmixing analyses). There are three versions of ground truth, which contain 4, 5 and 6 endmembers respectively, which are introduced in the ground truth.
# Quick look
<figure>
<img src= "assets/D9_1.png" alt="Urban" width="500" />
<figcaption>Urban and its ground truths.</figcaption>
</figure>
# Characteristics
Ground Truth: three versions, including 4, 5 and 6 endmembers respectively.
- 4 endmembers version: The 4 endmembers are "#1 Asphalt", "#2 Grass", "#3 Tree" and "#4 Roof" respectively.
- 5 endmembers version: The 5 endmembers are "#1 Asphalt", "#2 Grass", "#3 Tree", "#4 Roof" and "#5 Dirt" respectively.
- 6 endmembers version: The 6 endmembers are "#1 Asphalt", "#2 Grass", "#3 Tree", "#4 Roof", "#5 Metal", and "#6 Dirt" respectively.
# Credits
Dataset originally collected by Feiyun Zhu and originally available at: http://www.escience.cn/people/feiyunZHU/Dataset_GT.html
To use this dataset, cite the associated paper:
```
@misc{zhu2017hyperspectral,
title={Hyperspectral Unmixing: Ground Truth Labeling, Datasets, Benchmark Performances and Survey},
author={Feiyun Zhu},
year={2017},
eprint={1708.05125},
archivePrefix={arXiv},
primaryClass={cs.CV}
} | [
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roszcz/maestro-sustain-v2 | roszcz | 2023-11-10T08:27:15Z | 0 | 0 | null | [
"region:us"
] | 2023-11-10T08:27:15Z | 2023-11-10T08:13:34.000Z | 2023-11-10T08:13:34 | ---
dataset_info:
features:
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sequence: float64
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num_examples: 962
download_size: 87293150
dataset_size: 281935362
---
# Dataset Card for "maestro-sustain-v2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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kwaikeg/Kuaipedia | kwaikeg | 2023-11-10T09:13:34Z | 0 | 0 | null | [
"language:zh",
"license:cc-by-nc-sa-4.0",
"arxiv:2211.00732",
"region:us"
] | 2023-11-10T09:13:34Z | 2023-11-10T08:26:14.000Z | 2023-11-10T08:26:14 | ---
license: cc-by-nc-sa-4.0
language:
- zh
---
[**Kuaipedia**](https://github.com/KwaiKEG/Kuaipedia) is developed by [KwaiKEG](https://github.com/KwaiKEG), collaborating with HIT and HKUST. It is the world's first large-scale multi-modal short-video encyclopedia where the primitive units are items, aspects, and short videos.
* **Items** is a set of entities and concepts, such as [Shiba Inu](https://en.wikipedia.org/wiki/Shiba_Inu), [Moon](https://en.wikipedia.org/wiki/Moon) and [Galileo Galilei](https://en.wikipedia.org/wiki/Galileo_Galilei), which can be edited at one Wikipedia page. An item may have a title, a subtitle, a summary, attributes, and other detailed information of the item.
* **Aspects** is a set of keywords or keyphrases attached to items. Those keywords are used to describe specific aspects of the item. For example, "selection", "food-protecting", "color" of item [Shiba Inu](https://en.wikipedia.org/wiki/Shiba_Inu), or "formation", "surface conditions", "how-to-draw" of item [Moon](https://en.wikipedia.org/wiki/Moon).
* **Videos** is a set of short-videos whose duration may not exceed 5 minutes. In this paper, we only focus on knowledge videos we detected, Where we follow OECD to define knowledge as:
* *Know-what* refers to knowledge about facts. E.g. How many people live in New York?
* *Know-why* refers to scientific knowledge of the principles and laws of nature. E.g. Why does the earth revolve around the sun?
* *Know-how* refers to skills or the capability to do something. E.g. How to cook bacon in the oven.
Please refer to the paper for more details.
Kuaipedia: a Large-scale Multi-modal Short-video Encyclopedia [[Manuscript]](https://arxiv.org/abs/2211.00732)
## Data
**Statistics**
| | Full Dump | Subset Dump |
|------------|-----------------|-------------|
| #Items | > 26 million | 51,702 |
| #Aspects | > 2.5 million | 1,074,539 |
| #Videos | > 200 million | 769,096 |
The comparative results with the baseline models are as follows:
| Model | Item P | Item R | Item-Aspect P | Item-Aspect R |
| ---- | ---- | ---- | ---- | ---- |
| Random | 87.7 | 49.8 | 36.4 | 49.6 |
| LR | 90.4 | 68.3 | 55.1 | 2.7 |
| T5-small | 93.7 | 76.1 | 79.3 | 58.5 |
| BERT-base | 94.3 | 77.8 | 81.5 | 62.7 |
| GPT-3.5 | 90.5 | 86.4 | 41.8 | 95.7 |
| Ours | 94.7 | 79.7 | 83.0 | 65.7 |
Feel free to explore and utilize this valuable dataset for your research and projects.
## Reference
```
@article{Kuaipedia22,
author = {Haojie Pan and
Zepeng Zhai and
Yuzhou Zhang and
Ruiji Fu and
Ming Liu and
Yangqiu Song and
Zhongyuan Wang and
Bing Qin
},
title = {{Kuaipedia:} a Large-scale Multi-modal Short-video Encyclopedia},
journal = {CoRR},
volume = {abs/2211.00732},
year = {2022}
}
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license: mit
---
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Dong237/Aceso-onboarding-5k | Dong237 | 2023-11-10T08:43:01Z | 0 | 0 | null | [
"region:us"
] | 2023-11-10T08:43:01Z | 2023-11-10T08:37:46.000Z | 2023-11-10T08:37:46 | ---
configs:
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data_files:
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path: data/train-*
dataset_info:
features:
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dtype: string
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splits:
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download_size: 308983
dataset_size: 1407210
---
# Dataset Card for "Aceso-onboarding-5k"
A ChatGPT-generated synthetic dataset that contains 5000 onboarding conversations between patients and doctors.
| [
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license: openrail
---
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hsiehpinghan/test | hsiehpinghan | 2023-11-10T09:48:12Z | 0 | 0 | null | [
"region:us"
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configs:
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data_files:
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path: data/train-*
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dataset_info:
features:
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---
# Dataset Card for "test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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yfyeung/medical | yfyeung | 2023-11-10T12:25:43Z | 0 | 0 | null | [
"license:cc-by-4.0",
"region:us"
] | 2023-11-10T12:25:43Z | 2023-11-10T10:06:34.000Z | 2023-11-10T10:06:34 | ---
license: cc-by-4.0
---
# A dataset of simulated patient-physician medical interviews with a focus on respiratory cases
Paper link: https://www.nature.com/articles/s41597-022-01423-1
## Dataset Description
The simulated medical conversation dataset is available on figshare.com.
The dataset is divided into two sets of files: audio files of the simulated conversations in mp3 format, and the transcripts of the audio files as text files.
There are 272 mp3 audio files and 272 corresponding transcript text files.
Each file is titled with three characters and four digits. RES stands for respiratory, GAS represents gastrointestinal, CAR is cardiovascular, MSK is musculoskeletal, DER is dermatological, and the four following digits represent the case number of the respective disease category. | [
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VINH123/tiengviet | VINH123 | 2023-11-10T10:39:28Z | 0 | 0 | null | [
"task_categories:automatic-speech-recognition",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:vi",
"license:cc-by-nc-sa-4.0",
"regio... | 2023-11-10T10:39:28Z | 2023-11-10T10:22:42.000Z | 2023-11-10T10:22:42 | ---
pretty_name: VIVOS
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
- expert-generated
language:
- vi
license:
- cc-by-nc-sa-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- automatic-speech-recognition
task_ids: []
dataset_info:
features:
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dtype: string
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dtype: string
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dtype:
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sampling_rate: 16000
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splits:
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num_examples: 11660
- name: test
num_bytes: 86120227
num_examples: 760
download_size: 1475540500
dataset_size: 1808122360
---
# Dataset Card for VIVOS
## Table of Contents
- [Dataset Card for VIVOS](#dataset-card-for-vivos)
- [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)
- [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
- [Who are the source language producers?](#who-are-the-source-language-producers)
- [Annotations](#annotations)
- [Annotation process](#annotation-process)
- [Who are the annotators?](#who-are-the-annotators)
- [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://doi.org/10.5281/zenodo.7068130
- **Repository:** [Needs More Information]
- **Paper:** [A non-expert Kaldi recipe for Vietnamese Speech Recognition System](https://aclanthology.org/W16-5207/)
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [AILAB](mailto:ailab@hcmus.edu.vn)
### Dataset Summary
VIVOS is a free Vietnamese speech corpus consisting of 15 hours of recording speech prepared for Vietnamese Automatic Speech Recognition task.
The corpus was prepared by AILAB, a computer science lab of VNUHCM - University of Science, with Prof. Vu Hai Quan is the head of.
We publish this corpus in hope to attract more scientists to solve Vietnamese speech recognition problems.
### Supported Tasks and Leaderboards
[Needs More Information]
### Languages
Vietnamese
## Dataset Structure
### Data Instances
A typical data point comprises the path to the audio file, called `path` and its transcription, called `sentence`. Some additional information about the speaker and the passage which contains the transcription is provided.
```
{'speaker_id': 'VIVOSSPK01',
'path': '/home/admin/.cache/huggingface/datasets/downloads/extracted/b7ded9969e09942ab65313e691e6fc2e12066192ee8527e21d634aca128afbe2/vivos/train/waves/VIVOSSPK01/VIVOSSPK01_R001.wav',
'audio': {'path': '/home/admin/.cache/huggingface/datasets/downloads/extracted/b7ded9969e09942ab65313e691e6fc2e12066192ee8527e21d634aca128afbe2/vivos/train/waves/VIVOSSPK01/VIVOSSPK01_R001.wav',
'array': array([-0.00048828, -0.00018311, -0.00137329, ..., 0.00079346, 0.00091553, 0.00085449], dtype=float32),
'sampling_rate': 16000},
'sentence': 'KHÁCH SẠN'}
```
### Data Fields
- speaker_id: An id for which speaker (voice) made the recording
- path: The path to the audio file
- audio: A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, *i.e.* `dataset[0]["audio"]` should **always** be preferred over `dataset["audio"][0]`.
- sentence: The sentence the user was prompted to speak
### Data Splits
The speech material has been subdivided into portions for train and test.
Speech was recorded in a quiet environment with high quality microphone, speakers were asked to read one sentence at a time.
| | Train | Test |
| ---------------- | ----- | ----- |
| Speakers | 46 | 19 |
| Utterances | 11660 | 760 |
| Duration | 14:55 | 00:45 |
| Unique Syllables | 4617 | 1692 |
## 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
Dataset provided for research purposes only. Please check dataset license for additional information.
## Additional Information
### Dataset Curators
The dataset was initially prepared by AILAB, a computer science lab of VNUHCM - University of Science.
### Licensing Information
Public Domain, Creative Commons Attribution NonCommercial ShareAlike v4.0 ([CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode))
### Citation Information
```
@inproceedings{luong-vu-2016-non,
title = "A non-expert {K}aldi recipe for {V}ietnamese Speech Recognition System",
author = "Luong, Hieu-Thi and
Vu, Hai-Quan",
booktitle = "Proceedings of the Third International Workshop on Worldwide Language Service Infrastructure and Second Workshop on Open Infrastructures and Analysis Frameworks for Human Language Technologies ({WLSI}/{OIAF}4{HLT}2016)",
month = dec,
year = "2016",
address = "Osaka, Japan",
publisher = "The COLING 2016 Organizing Committee",
url = "https://aclanthology.org/W16-5207",
pages = "51--55",
}
```
### Contributions
Thanks to [@binh234](https://github.com/binh234) for adding this dataset. | [
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license: openrail
---
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"language:aa",
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"license:mit",
"region:us"
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license: mit
language:
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--- | [
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antolin/tlc_func_interduplication | antolin | 2023-11-10T12:07:24Z | 0 | 0 | null | [
"region:us"
] | 2023-11-10T12:07:24Z | 2023-11-10T12:07:12.000Z | 2023-11-10T12:07:12 | ---
dataset_info:
features:
- name: id_within_dataset
dtype: int64
- name: snippet
dtype: string
- name: tokens
sequence: string
- name: nl
dtype: string
- name: split_within_dataset
dtype: string
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dtype: bool
- name: func_name
dtype: string
splits:
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num_bytes: 72309213.0
num_examples: 53327
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num_examples: 6642
- name: valid
num_bytes: 9062779.0
num_examples: 6666
download_size: 33769098
dataset_size: 90354364.0
---
# Dataset Card for "tlc_func_interduplication"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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"region:us"
] | 2023-11-10T12:24:09Z | 2023-11-10T12:20:57.000Z | 2023-11-10T12:20:57 | ---
dataset_info:
features:
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dtype: string
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list:
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splits:
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num_bytes: 151032
num_examples: 870
download_size: 76110
dataset_size: 151032
source: Bộ 870 Câu Hỏi & Đáp Án "Ai Là Triệu Phú" By Thaotac.com
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "general-multi-choices-ailatrieuphu-870"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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license: apache-2.0
---
| [
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"region:us"
] | 2023-11-10T13:04:36Z | 2023-11-10T13:04:30.000Z | 2023-11-10T13:04:30 | ---
dataset_info:
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num_examples: 12
download_size: 30674
dataset_size: 57588
configs:
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data_files:
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path: data/test-*
---
# Dataset Card for "capstone_fromgpt_without_gold"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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] | 2023-11-10T13:57:27Z | 2023-11-10T13:57:27.000Z | 2023-11-10T13:57:27 | ---
license: apache-2.0
---
| [
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shakeman/cyberpunk | shakeman | 2023-11-10T14:37:28Z | 0 | 0 | null | [
"region:us"
] | 2023-11-10T14:37:28Z | 2023-11-10T14:37:28.000Z | 2023-11-10T14:37:28 | Entry not found | [
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BeastyZ/LLM-Verified-Retrieval | BeastyZ | 2023-11-10T15:37:24Z | 0 | 1 | null | [
"language:en",
"license:apache-2.0",
"region:us"
] | 2023-11-10T15:37:24Z | 2023-11-10T14:45:57.000Z | 2023-11-10T14:45:57 | ---
license: apache-2.0
language:
- en
---
# Dataset Card for LLM-Verified-Retrieval
There are three types of datasets, namely ASQA, QAMPARI, and ELI5. We provide their raw data and the data with summay and answer generated by the model.
## Dataset Information
- raw data: We put the raw data in the origin directory. You can also find them and get more information in the repo of [ALCE](https://github.com/princeton-nlp/ALCE).
- summary-answer data: We put the data with summay and answer generated by the model(gpt-3.5-turbo-0301)in the summay-answer directory. You can also generate your own summary or answer if you can access to gpt-3.5-turbo-0301.
For brevity, we only introduce the field we add in the summary-answer data. Please refer to [ALCE](https://github.com/princeton-nlp/ALCE) for additional information.
**ASQA**
- summay_use_sub: For each sample, we use its sub-questions to summarize each document it encompasses.
**QAMPARI**
- summary: For each sample, we use its question to summarize each document it encompasses.
**ELI5**
- answer: For each sample, we use its question and each document it encompasses to answer the question. | [
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0.146508499979972... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
lbourdois/nativenames_iso639-3 | lbourdois | 2023-11-10T15:32:55Z | 0 | 0 | null | [
"region:us"
] | 2023-11-10T15:32:55Z | 2023-11-10T15:05:18.000Z | 2023-11-10T15:05:18 | ---
configs:
- config_name: semicolon
data_files: "native_names.csv"
sep: ";"
--- | [
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