datasetId
stringlengths 2
117
| author
stringlengths 2
42
⌀ | last_modified
timestamp[us, tz=UTC] | downloads
int64 0
9.36M
| likes
int64 0
3.89k
| tags
list | task_categories
list | createdAt
timestamp[us, tz=UTC] | card
stringlengths 19
1.01M
| embedding
list |
|---|---|---|---|---|---|---|---|---|---|
nlewins/cebuano-dictionary-examples-with-audio-noisy | nlewins | 2023-12-10T09:45:33Z | 3 | 0 | [
"region:us"
]
| null | 2023-12-10T09:41:38Z | ---
dataset_info:
features:
- name: en
dtype: string
- name: ceb
dtype: string
- name: en_audio
dtype:
audio:
sampling_rate: 16000
- name: ceb_audio
dtype:
audio:
sampling_rate: 16000
splits:
- name: train
num_bytes: 5296213073.0
num_examples: 23260
- name: test
num_bytes: 665202515.184
num_examples: 2908
- name: valid
num_bytes: 657789139.184
num_examples: 2908
download_size: 6139818854
dataset_size: 6619204727.368
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: valid
path: data/valid-*
---
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|
ChiragAI12/alpaca-gpt4 | ChiragAI12 | 2023-12-10T10:11:12Z | 3 | 0 | [
"region:us"
]
| null | 2023-12-10T10:11:11Z | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 1446184
num_examples: 1000
download_size: 843423
dataset_size: 1446184
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
marmofayezi/M3EditLandmark | marmofayezi | 2023-12-10T10:34:23Z | 3 | 0 | [
"region:us"
]
| null | 2023-12-10T10:34:16Z | ---
dataset_info:
features:
- name: id
dtype: string
- name: original_image
dtype: image
- name: prompt
dtype: string
- name: landmark
dtype: image
- name: edit_20_0.5
dtype: image
- name: edit_20_0.7
dtype: image
- name: edit_20_0.8
dtype: image
- name: edit_20_1.0
dtype: image
- name: edit_20_1.1
dtype: image
- name: edit_20_1.3
dtype: image
- name: edit_40_0.5
dtype: image
- name: edit_40_0.7
dtype: image
- name: edit_40_0.8
dtype: image
- name: edit_40_1.0
dtype: image
- name: edit_40_1.1
dtype: image
- name: edit_40_1.3
dtype: image
splits:
- name: train
num_bytes: 25887019.0
num_examples: 51
download_size: 24895686
dataset_size: 25887019.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
thudoann/finetuningllm2 | thudoann | 2023-12-10T13:35:17Z | 3 | 0 | [
"task_categories:table-question-answering",
"size_categories:10K<n<100K",
"language:en",
"region:us"
]
| [
"table-question-answering"
]
| 2023-12-10T13:33:14Z | ---
task_categories:
- table-question-answering
language:
- en
size_categories:
- 10K<n<100K
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|
Arthuerwang/finetune_lora_pikachu | Arthuerwang | 2023-12-10T16:36:18Z | 3 | 0 | [
"region:us"
]
| null | 2023-12-10T16:18:39Z | ---
dataset_info:
features:
- name: image
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 922
num_examples: 5
download_size: 2618
dataset_size: 922
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
kfahn/chest-xray-pneumonia | kfahn | 2023-12-10T17:39:52Z | 3 | 0 | [
"license:cc-by-4.0",
"region:us"
]
| null | 2023-12-10T17:21:01Z | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': NORMAL
'1': PNEUMONIA
splits:
- name: train
num_bytes: 3186635036.504
num_examples: 5216
- name: validation
num_bytes: 3030633
num_examples: 16
- name: test
num_bytes: 79062317
num_examples: 624
download_size: 1230487171
dataset_size: 3268727986.504
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
license: cc-by-4.0
---
**Dataset Summary**
* The dataset is organized into 3 folders (train, test, val) and contains subfolders for each image category (Pneumonia/Normal). There are 5,863 X-Ray images (JPEG) and 2 categories (Pneumonia/Normal).
* Chest X-ray images (anterior-posterior) were selected from retrospective cohorts of pediatric patients of one to five years old from Guangzhou Women and Children’s Medical Center, Guangzhou. All chest X-ray imaging was performed as part of patients’ routine clinical care.
* For the analysis of chest x-ray images, all chest radiographs were initially screened for quality control by removing all low quality or unreadable scans. The diagnoses for the images were then graded by two expert physicians before being cleared for training the AI system. In order to account for any grading errors, the evaluation set was also checked by a third expert.
* For more information, see [Application of the AI System for Pneumonia Detection Using Chest X-Ray Images](https://www.cell.com/cell/fulltext/S0092-8674(18)30154-5?_returnURL=https%3A%2F%2Flinkinghub.elsevier.com%2Fretrieve%2Fpii%2FS0092867418301545%3Fshowall%3Dtrue)
* [Dataset source](https://data.mendeley.com/datasets/rscbjbr9sj/2)
**Citation
Citation: Kermany, Daniel; Zhang, Kang; Goldbaum, Michael (2018), “Labeled Optical Coherence Tomography (OCT) and Chest X-Ray Images for Classification”, Mendeley Data, V2, doi: 10.17632/rscbjbr9sj.2 | [
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|
mrm8488/openhermes_conversations | mrm8488 | 2023-12-10T19:29:42Z | 3 | 0 | [
"region:us"
]
| null | 2023-12-10T19:29:26Z | ---
dataset_info:
features:
- name: output
dtype: string
- name: input
dtype: string
- name: instruction
dtype: string
- name: messages
list:
- name: content
dtype: string
- name: role
dtype: string
splits:
- name: train
num_bytes: 614634948
num_examples: 242831
download_size: 278855286
dataset_size: 614634948
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
harpreetsahota/modern-to-shakesperean-translation | harpreetsahota | 2023-12-10T21:09:14Z | 3 | 1 | [
"region:us"
]
| null | 2023-12-10T19:42:51Z | ---
dataset_info:
features:
- name: modern
dtype: string
- name: shakespearean
dtype: string
splits:
- name: train
num_bytes: 29477
num_examples: 274
download_size: 22809
dataset_size: 29477
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# 📜 Modern to Shakespearean-Style Phrases Dataset
Welcome to the Modern to Shakespearean-Style Phrases Dataset! This collection features contemporary phrases alongside their whimsical translations into a Shakespearean style. 🎭 It was ingeniously crafted by GPT-4 and is available for a variety of purposes, in accordance with the terms of service provided by OpenAI.
## 📖 Dataset Description
Behold, within this dataset, you shall find an array of modern phrases commonly spoken in our time, juxtaposed with their illustrious transformations into a more Middle English, Old English era, and Shakespearean style. 🗣️ Each entry presents an "input" phrase in modern vernacular, juxtaposed with an "output" phrase translated into a classical literary form. 📜 The dataset is designed to offer a playful linguistic metamorphosis of contemporary expressions into a traditional literary context.
## 🚀 Usage
This wondrous dataset of Modern to Shakespearean-Style Phrases can be harnessed for a myriad of natural language processing (NLP) quests, including but not limited to:
- 🌍 Language translation and style transmutation
- 📝 Creative writing and text generation
- 🧐 Profound linguistic and cultural analysis
- 📚 Enlightening educational endeavors
## 📚 Sources of Definitions
The definitions of these contemporary phrases were gleaned from esteemed sources dedicated to the exploration of modern slang:
- [Parade - Gen Z Slang Words](https://parade.com/1293898/marynliles/gen-z-slang-words/) 📢
- [Pangea Global - Generational Slang Words and Phrases (X, Y, and Z)](https://www.pangea.global/blog/a-list-of-generational-slang-words-and-phrases-x-y-and-z/) 🌏
- [FamilyEducation - A Guide to New Teen Slang and Gen Z Slang Trends of 2023](https://www.familyeducation.com/teens/social-development/communication/a-guide-to-new-teen-slang-and-gen-z-slang-trends-of-2023) 📆
- [Sassy Mama HK - Parenting Slang Terms for Tweens and Teens](https://www.sassymamahk.com/parenting-slang-terms-tweens-teens/) 👶
Feel free to explore these sources for further insights into the origins and contexts of these phrases.
## 📝 License
This dataset is offered under the terms of service provided by OpenAI. For detailed information on usage and licensing, please refer to OpenAI's terms of service.
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|
nilaytufek/OPC_UA_NER_BC | nilaytufek | 2023-12-05T09:49:52Z | 2 | 0 | [
"task_categories:text-classification",
"language:en",
"license:apache-2.0",
"doi:10.57967/hf/1424",
"region:us"
]
| [
"text-classification"
]
| 2023-04-27T11:11:16Z | ---
license: apache-2.0
task_categories:
- text-classification
language:
- en
--- | [
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|
deepghs/anime_rating | deepghs | 2023-12-07T01:10:42Z | 2 | 0 | [
"task_categories:image-classification",
"size_categories:10K<n<100K",
"license:mit",
"art",
"region:us"
]
| [
"image-classification"
]
| 2023-05-24T07:20:43Z | ---
license: mit
task_categories:
- image-classification
tags:
- art
size_categories:
- 10K<n<100K
---
Simple anime image rating prediction task. Data is randomly scraped from Sankaku Complex.
Please note that due to the often unclear boundaries between `safe`, `r15` and `r18` levels,
there is no objective ground truth for this task, and the data is scraped without any manual filtering.
Therefore, the models trained on this dataset can only provide rough checks.
**If you require an accurate solution for classifying `R18` images, it is recommended to consider a solution based on keypoint object detection.**
| Dataset | Safe Images | R15 Images | R18 Images | Description |
|:-------:|:-----------:|:----------:|:----------:|--------------------------------------|
| v1 | 5991 | 4960 | 5070 | Simply crawled from Sankaku Complex. |
| v2 | 50000 | 50000 | 50000 | Better Dataset from Sankaku Complex. |
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|
ccmusic-database/bel_canto | ccmusic-database | 2023-12-10T15:26:43Z | 2 | 1 | [
"task_categories:audio-classification",
"task_categories:image-classification",
"size_categories:1K<n<10K",
"language:zh",
"language:en",
"license:mit",
"music",
"art",
"region:us"
]
| [
"audio-classification",
"image-classification"
]
| 2023-05-26T08:53:43Z | ---
license: mit
task_categories:
- audio-classification
- image-classification
language:
- zh
- en
tags:
- music
- art
pretty_name: Bel Conto and Chinese Folk Song Singing Tech
size_categories:
- 1K<n<10K
viewer: false
---
# Dataset Card for Bel Conto and Chinese Folk Song Singing Tech
## Dataset Description
- **Homepage:** <https://ccmusic-database.github.io>
- **Repository:** <https://huggingface.co/datasets/ccmusic-database/bel_canto>
- **Paper:** <https://doi.org/10.5281/zenodo.5676893>
- **Leaderboard:** <https://ccmusic-database.github.io/team.html>
- **Point of Contact:** N/A
### Dataset Summary
This database contains hundreds of acapella singing clips that are sung in two styles, Bel Conto and Chinese national singing style by professional vocalists. All of them are sung by professional vocalists and were recorded in professional commercial recording studios.
### Supported Tasks and Leaderboards
Audio classification, Image classification, singing method classification, voice classification
### Languages
Chinese, English
## Dataset Structure
<style>
#belcanto td {
vertical-align: middle !important;
text-align: center;
}
#belcanto th {
text-align: center;
}
</style>
<table id="belcanto">
<tr>
<th>mel<br>(.jpg, 1.6s)</th>
<th>cqt<br>(.jpg, 1.6s)</th>
<th>chroma<br>(.jpg, 1.6s)</th>
<th>label<br>(4-class)</th>
<th>gender<br>(2-class)</th>
<th>singing_method<br>(2-class)</th>
</tr>
<tr>
<td><img src="https://cdn-uploads.huggingface.co/production/uploads/655e0a5b8c2d4379a71882a9/TSTXTg2s2j6gs3O8q_bpD.jpeg"></td>
<td><img src="https://cdn-uploads.huggingface.co/production/uploads/655e0a5b8c2d4379a71882a9/BiuWkk_rkYBfN2hqG60Iy.jpeg"></td>
<td><img src="https://cdn-uploads.huggingface.co/production/uploads/655e0a5b8c2d4379a71882a9/WmcP0UsMe_9lmLmNpAOzr.jpeg"></td>
<td>m_bel, f_bel, m_folk, f_folk</td>
<td>male, female</td>
<td>Folk_Singing, Bel_Canto</td>
</tr>
<tr>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
</tr>
</table>
### Data Instances
.zip(.wav, .jpg)
### Data Fields
m_bel, f_bel, m_folk, f_folk
### Data Splits
| total | 9603 |
| :-------------: | :---: |
| train(80%) | 7682 |
| validation(10%) | 960 |
| test(10%) | 961 |
## Dataset Creation
### Curation Rationale
Lack of a dataset for Bel Conto and Chinese folk song singing tech
### Source Data
#### Initial Data Collection and Normalization
Zhaorui Liu, Monan Zhou
#### Who are the source language producers?
Students from CCMUSIC
### Annotations
#### Annotation process
All of them are sung by professional vocalists and were recorded in professional commercial recording studios.
#### Who are the annotators?
professional vocalists
### Personal and Sensitive Information
None
## Considerations for Using the Data
### Social Impact of Dataset
Promoting the development of AI in the music industry
### Discussion of Biases
Only for Chinese songs
### Other Known Limitations
Some singers may not have enough professional training in classical or ethnic vocal techniques.
## Additional Information
### Dataset Curators
Zijin Li
### Evaluation
<https://huggingface.co/ccmusic-database/bel_canto>
### Licensing Information
```
MIT License
Copyright (c) CCMUSIC
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
```
### Citation Information
```
@dataset{zhaorui_liu_2021_5676893,
author = {Zhaorui Liu, Monan Zhou, Shenyang Xu, Yuan Wang, Zhaowen Wang, Wei Li and Zijin Li},
title = {CCMUSIC DATABASE: A Music Data Sharing Platform for Computational Musicology Research},
month = {nov},
year = {2021},
publisher = {Zenodo},
version = {1.1},
doi = {10.5281/zenodo.5676893},
url = {https://doi.org/10.5281/zenodo.5676893}
}
```
### Contributions
Provide a dataset for distinguishing Bel Conto and Chinese folk song singing tech | [
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|
higart/beginner | higart | 2023-12-07T09:42:05Z | 2 | 0 | [
"license:bigcode-openrail-m",
"region:us"
]
| null | 2023-06-09T00:32:03Z | ---
license: bigcode-openrail-m
---
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|
kjj0/parler | kjj0 | 2023-12-08T08:00:35Z | 2 | 0 | [
"arxiv:2101.03820",
"region:us"
]
| null | 2023-06-22T21:40:03Z | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 9158565661
num_examples: 77805831
download_size: 6693514578
dataset_size: 9158565661
---
# Dataset Card for "kjj0/parler"
This dataset contains 78M unique posts made between August 2018 and January 2021.
This is a variant of the dataset provided by [An Early Look at the Parler Online Social Network](https://arxiv.org/abs/2101.03820).
We have deduplicated posts and stripped metadata to create an easily accessible collection of unique texts.
Our purpose for creating this dataset, along with the OpenAI predictions (which are fairly accurate)
is to cheaply obtain a massive labeled text dataset (albeit with some unpleasant content) in order
to do research on data selection, active learning, label noise, and training curricula.
```
@article{aliapoulios2021early,
title={An early look at the parler online social network},
author={Aliapoulios, Max and Bevensee, Emmi and Blackburn, Jeremy and Bradlyn, Barry and De Cristofaro, Emiliano and Stringhini, Gianluca and Zannettou, Savvas},
journal={arXiv preprint arXiv:2101.03820},
year={2021}
}
```
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]
|
kjj0/4chanpol-openaimod | kjj0 | 2023-12-08T08:00:08Z | 2 | 2 | [
"arxiv:2001.07487",
"region:us"
]
| null | 2023-06-23T21:08:52Z | ---
dataset_info:
features:
- name: text
dtype: string
- name: sexual
dtype: float64
- name: hate
dtype: float64
- name: violence
dtype: float64
- name: self-harm
dtype: float64
- name: sexual/minors
dtype: float64
- name: hate/threatening
dtype: float64
- name: violence/graphic
dtype: float64
splits:
- name: train
num_bytes: 23614214277
num_examples: 114647404
download_size: 14061193653
dataset_size: 23614214277
---
# Dataset Card for "kjj0/4chanpol-openaimod"
This dataset contains 114M unique posts made between June 2016 and November 2019.
This is a variant of the dataset provided by [Raiders of the Lost Kek: 3.5 Years of Augmented 4chan Posts from the Politically Incorrect Board](https://arxiv.org/abs/2001.07487).
We have deduplicated posts and stripped metadata to create an easily accessible collection of unique texts.
We have also provided OpenAI moderation scores. A variant without these scores can be found at [kjj0/4chanpol](https://huggingface.co/datasets/kjj0/4chanpol).
Our purpose for creating this dataset, along with the OpenAI predictions (which are fairly accurate)
is to cheaply obtain a massive labeled text dataset (albeit with some unpleasant content) in order
to do research on data selection, active learning, label noise, and training curricula.
```
@inproceedings{papasavva2020raiders,
title={Raiders of the lost kek: 3.5 years of augmented 4chan posts from the politically incorrect board},
author={Papasavva, Antonis and Zannettou, Savvas and De Cristofaro, Emiliano and Stringhini, Gianluca and Blackburn, Jeremy},
booktitle={Proceedings of the International AAAI Conference on Web and Social Media},
volume={14},
pages={885--894},
year={2020}
}
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|
diplomaticvegetation/portuguese | diplomaticvegetation | 2023-12-10T20:51:35Z | 2 | 0 | [
"license:cc0-1.0",
"region:us"
]
| null | 2023-08-25T14:12:33Z | ---
license: cc0-1.0
---
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|
marketspace/real | marketspace | 2023-12-06T08:03:22Z | 2 | 0 | [
"task_categories:text-generation",
"task_categories:translation",
"size_categories:1K<n<10K",
"license:openrail",
"code",
"region:us"
]
| [
"text-generation",
"translation"
]
| 2023-09-18T02:50:35Z | ---
license: openrail
task_categories:
- text-generation
- translation
tags:
- code
size_categories:
- 1K<n<10K
---
## Real Language Model
---
license: other
--- | [
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|
kanoyo/req | kanoyo | 2023-12-10T08:29:03Z | 2 | 0 | [
"license:mit",
"region:us"
]
| null | 2023-09-30T17:15:50Z | ---
license: mit
---
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|
Reza8848/MUFFIN_68k | Reza8848 | 2023-12-06T18:26:09Z | 2 | 1 | [
"size_categories:10K<n<100K",
"language:en",
"arxiv:2312.02436",
"region:us"
]
| null | 2023-10-05T23:41:51Z | ---
language:
- en
size_categories:
- 10K<n<100K
---
<img src="https://cdn-uploads.huggingface.co/production/uploads/6434a6e8ea46c009904c617e/J_4FHXmtM6TuRnN3aL06y.png" width="38" height="38">
This is the training dataset of **MUFFIN** (**Mu**lti-**F**aceted **In**structions).
Please refer to our project website for more details: [Website](https://renzelou.github.io/Muffin/)
## JSON Format
The download data can be read as a Python list.
In this list, each elemental Python dictionary has one input text.
This input text has multiple task instructions and the corresponding outputs.
```json
[
{
"input": "XXX",
"instances": [
{ "instruction": "III", "output": "YYY" },
{ "instruction": "III", "output": "YYY" }
]
}
,
{
"input": "XXX",
"instances": [
{ "instruction": "III", "output": "YYY" }
]
}
]
```
## Data Statistics
There are a total of 1,463 input texts, where each input is equipped with multiple task instructions (~46.48 instructions per input), resulting in **68,014** training instances in total.
The detailed statistics are shown below:
<div style="text-align:center"><img src="https://cdn-uploads.huggingface.co/production/uploads/6434a6e8ea46c009904c617e/hcQjRr1TqX08C4tMnEQaZ.png" alt="statistics.png" width="500"/></div>
## 🥳 Citation
Please kindly cite our paper if you use our dataset:
```
@misc{Lou2023MUFFIN,
title={MUFFIN: Curating Multi-Faceted Instructions for Improving Instruction-Following},
author={Renze Lou and Kai Zhang and Jian Xie and Yuxuan Sun and Janice Ahn and Hanzi Xu and Yu su and Wenpeng Yin},
year={2023},
eprint={2312.02436},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
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|
recogna-nlp/FakeRecogna | recogna-nlp | 2023-12-07T19:36:39Z | 2 | 0 | [
"task_categories:text-classification",
"size_categories:10K<n<100K",
"language:pt",
"license:mit",
"FakeRecogna ",
"Fake News",
"Portuguese",
"Dataset",
"region:us"
]
| [
"text-classification"
]
| 2023-10-12T02:26:50Z | ---
task_categories:
- text-classification
language:
- pt
tags:
- 'FakeRecogna '
- Fake News
- Portuguese
- Dataset
license: mit
size_categories:
- 10K<n<100K
---
# FakeRecogna
FakeRecogna is a dataset comprised of real and fake news. The real news is not directly linked to fake news and vice-versa, which could lead to a biased classification. The news collection was performed by crawlers developed for mining pages of well-known and of great national importance agency news. The web crawlers were developed based on each analyzed webpage, where the extracted information is first separated into categories and then grouped by dates. The plurality of news on several pages and the different writing styles provide the dataset with great diversity for natural language processing analysis and machine learning algorithms.
## The Dataset
The news collection was performed by crawlers developed for mining pages of well-known and of great national importance agency news. The fake news mining was mainly focused on pages mentioned by the [Duke Reporters Lab](https://reporterslab.org/fact-checking/), which provides a list of pages that verify the veracity of news worldwide.There were 160 active fact-checking agencies in the world in 2019 and Brazil figures as a growing ecosystem with currently 9 initiatives and there were considered 6 out of the 9 pages during search with a great variation in the number of fake news extracted from each one, ending in 5,951 samples. Table 1 presents the current initiatives as well as the number of fake news collected from each source.
| Fact-Check Agency | Web address | # News |
| ------------------ | ------------------------------------ | ------ |
| Boatos.org | https://boatos.org | 2,605 |
| Fato ou Fake | https://oglobo.globo.com/fato-ou-fake| 1,055 |
| E-farsas | https://www.e-farsas.com | 812 |
| UOL Confere | https://noticias.uol.com.br/confere | 582 |
| AFP Checamos | https://checamos.afp.com/afp-brasil | 509 |
| Projeto Comprova | https://checamos.afp.com/afp-brasil | 388 |
| Total | -------------------------------------| 5,951 |
Concerning the real news, the crawlers searched portals such as [G1](https://g1.globo.com/), [UOL](https://www.uol.com.br/) and [Extra](https://extra.globo.com/), which are publicly recognized as reliable news outlets, besides the [Ministry of Health of Brazil](https://www.gov.br/saude/pt-br) home page, resulting in a collection of over 100,000 samples. From this set, there were filtered out 5,951 samples to keep the balance between classes and, thus, resulting in a dataset comprised of 11,902 samples.
## More informations
The FakeRecogna dataset is available at GitHub as a single XLSX file that contains 8 columns for the metadata, and each row stands for a sample (real or fake news), as described in Table 2.
| Columns | Description |
| ------------------------ | ------------------------------------------ |
| Title | Title of article |
| Sub-title (if available) | Brief description of news |
| News | Information about the article |
| Category | News grouped according to your information |
| Author | Publication author |
| Date | Publication date |
| URL | Article web address |
| Class | 0 for fake news and 1 for real news |
The collected texts are distributed into six categories in relation to their main subjects: Brazil, Entertainment, Health, Politics, Science, and World. These categories are defined based on the journal sections where the news were extracted. The distribution of news by category and its percentages are described in Table 3.
| Category | # News | % |
| -------------- | ---------- | ------ |
| Brazil | 904 | 7.6 |
| Entertainment | 1,409 | 12.00 |
| Health | 4,456 | 37.4 |
| Politics | 3.951 | 33.1 |
| Science | 602 | 5.1 |
| World | 580 | 4.9 |
| Total | 11,902 | 100.00 |
# Citation
@aInProceedings{garcia2022fakerecogna,
author="Garcia, Gabriel L and Afonso, Luis CS and Papa, Jo{\~a}o P}",
title="Fakerecogna: A new brazilian corpus for fake news detection",
booktitle="International Conference on Computational Processing of the Portuguese Language",
year="2022",
publisher="Springer International Publishing",
address="Cham",
pages="57--67",
isbn="978-3-030-98305-5"} | [
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|
Hypersniper/riddles_v1 | Hypersniper | 2023-12-05T02:24:43Z | 2 | 6 | [
"language:en",
"license:apache-2.0",
"region:us"
]
| null | 2023-10-18T03:53:09Z | ---
license: apache-2.0
language:
- en
---
# Riddle Processing with GPT-4
[Buy me Ko-fi](https://ko-fi.com/hypersniper)
## Credits
All credit for the original riddles goes to [crawsome's GitHub repository](https://github.com/crawsome/riddles).
## Project Overview
This project involves processing each riddle using GPT-4. The correct answers were provided to the model to generate a desirable output focused on reasoning and logical breakdown.
- `riddles.json` (`riddles_1`) — 386 samples, sourced from [crawsome's GitHub repository](https://github.com/crawsome/riddles).
- `riddles_2.json` — 83 samples, sourced from various Google searches.
### Prompt Structure
The following prompt was used to guide GPT-4 responses:
> "Answer the user's question using the answer. Do not mention you have the answer to the riddle. You are to answer it as if you never got the answer. Prioritize educational insights by breaking down the logic behind your decisions. Make sure to cover all relevant topics in a logical order."
### Goal
The primary goal of this dataset was to fine-tune a language model in outputting structured and logical reasoning.
## Performance
The performance of this small dataset is noteworthy. It demonstrates a proficient ability in breaking down a question logically and providing clear, reasoned answers.
## Example Outputs
### Fine-Tuning Details
- **Model:** Open Orca Mistral 7B
- **Fine-Tuning:** One epoch with the specified dataset
### Sample Questions and Outputs
#### Riddle 1
**Question:** _"I am easy to lift, but hard to throw. What am I?"_
**Output:**
The riddle describes an object that is lightweight and easy to lift, yet not suitable for throwing due to its properties, like lacking aerodynamics or a solid shape. A feather fits this description as it is lightweight, easy to lift, but not designed for throwing, being used instead for insulation, flight, and decoration.
**Correct Answer:** Feather
#### Riddle 2
**Question:** _"Rachel goes to the supermarket and buys 10 tomatoes. Unfortunately, on the way back home, all but 9 get ruined. How many tomatoes are left in a good condition?"_
**Output:**
Breaking down the problem: Rachel buys 10 tomatoes, but only 1 gets ruined, as 'all but 9' implies. Therefore, 9 tomatoes remain in good condition.
**Correct Answer:** 9
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|
TurcoLoko/satab | TurcoLoko | 2023-12-07T20:50:21Z | 2 | 0 | [
"license:apache-2.0",
"region:us"
]
| null | 2023-11-03T20:15:05Z | ---
license: apache-2.0
---
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|
pgurazada1/tesla-qna-feedback-logs | pgurazada1 | 2023-12-06T04:57:38Z | 2 | 0 | [
"region:us"
]
| null | 2023-11-04T03:16:02Z | ---
configs:
- config_name: default
data_files:
- split: train
path: data.csv
---
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[More Information Needed] | [
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|
DucHaiten/chibi-world | DucHaiten | 2023-12-06T09:22:46Z | 2 | 1 | [
"license:openrail",
"region:us"
]
| null | 2023-11-07T08:06:21Z | ---
license: openrail
---
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|
DmitrMakeev/Deforum-file | DmitrMakeev | 2023-12-08T11:17:02Z | 2 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-11-13T20:58:25Z | ---
license: openrail
---
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|
chungimungi/Colors | chungimungi | 2023-12-07T12:22:01Z | 2 | 0 | [
"task_categories:text-classification",
"task_categories:feature-extraction",
"task_categories:zero-shot-classification",
"task_categories:question-answering",
"task_categories:table-question-answering",
"task_categories:token-classification",
"task_categories:text-generation",
"size_categories:10K<n<100K",
"language:en",
"license:apache-2.0",
"art",
"arxiv:2311.06542",
"region:us"
]
| [
"text-classification",
"feature-extraction",
"zero-shot-classification",
"question-answering",
"table-question-answering",
"token-classification",
"text-generation"
]
| 2023-11-14T04:10:28Z | ---
license: apache-2.0
task_categories:
- text-classification
- feature-extraction
- zero-shot-classification
- question-answering
- table-question-answering
- token-classification
- text-generation
tags:
- art
size_categories:
- 10K<n<100K
language:
- en
pretty_name: t
---
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
A dataset that contains the color names and their relations with their respective RGB Values with over 40k rows.
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [https://github.com/chungimungi/Generation-of-Colors-using-BiLSTMs]
- **Citation:**
```
@misc{sinha2023generation,
title={Generation Of Colors using Bidirectional Long Short Term Memory Networks},
author={A. Sinha},
year={2023},
eprint={2311.06542},
archivePrefix={arXiv},
primaryClass={cs.CV}
``` | [
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AnilKamat/SongAlbumCover | AnilKamat | 2023-12-05T19:46:37Z | 2 | 0 | [
"license:mit",
"region:us"
]
| null | 2023-11-14T22:47:38Z | ---
license: mit
---
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|
v2ray/Tony-Chase-Transcripts | v2ray | 2023-12-08T15:53:11Z | 2 | 0 | [
"task_categories:text-generation",
"size_categories:1K<n<10K",
"language:en",
"license:mit",
"not-for-all-audiences",
"region:us"
]
| [
"text-generation"
]
| 2023-11-15T03:26:39Z | ---
license: mit
task_categories:
- text-generation
language:
- en
tags:
- not-for-all-audiences
size_categories:
- 1K<n<10K
---
# Tony Chase Transcripts
Around 3500 transcripts of videos from [Tony Chase](https://www.youtube.com/@tonychaseiamnotgaynotnever2043) captioned with GPT-3.5-Turbo. | [
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|
StephanAkkerman/financial-tweets-crypto | StephanAkkerman | 2023-12-07T15:32:44Z | 2 | 1 | [
"task_categories:text-classification",
"license:mit",
"tweet",
"tweets",
"finance",
"crypto",
"fintwit",
"region:us"
]
| [
"text-classification"
]
| 2023-11-15T19:56:31Z | ---
license: mit
task_categories:
- text-classification
tags:
- tweet
- tweets
- finance
- crypto
- fintwit
---
# Financial Tweets - Cryptocurrency
This dataset is part of the scraped financial tweets that I collected from a variety of financial influencers on Twitter, all the datasets can be found here:
- Crypto: https://huggingface.co/datasets/StephanAkkerman/financial-tweets-crypto
- Stocks (and forex): https://huggingface.co/datasets/StephanAkkerman/financial-tweets-stocks
- Other (Tweet without cash tags): https://huggingface.co/datasets/StephanAkkerman/financial-tweets-other
## Data Fields
The data fields are as follows:
* `timestap`: The time the tweet was sent.
* `tweet_text`: All of the text of the tweet, including quoted tweets (prefixed with `>`).
* `tweet_url`: The URL of the tweet.
* `tweet_type`: The type of tweet, this can be tweet, retweet, or quote tweet.
* `price_of_ticker`: The price of the tickers mentioned.
* `change_of_ticker`: The 24h price change of the tickers.
* `tickers_mentioned`: All the tickers that are mentioned in the tweet.
* `category`: What type of category the tweet has, the suffix `_images` means that the tweet included an image.
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|
StephanAkkerman/financial-tweets-stocks | StephanAkkerman | 2023-12-07T15:33:25Z | 2 | 0 | [
"license:mit",
"region:us"
]
| null | 2023-11-15T20:09:23Z | ---
license: mit
---
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|
doanhm/dataset_generate_music | doanhm | 2023-12-06T09:42:32Z | 2 | 0 | [
"size_categories:100B<n<1T",
"language:ae",
"license:mit",
"chemistry",
"code",
"region:us"
]
| null | 2023-11-16T16:21:26Z | ---
license: mit
language:
- ae
tags:
- chemistry
- code
size_categories:
- 100B<n<1T
---
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[More Information Needed] | [
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|
albertcc/test | albertcc | 2023-12-05T06:28:41Z | 2 | 0 | [
"license:mit",
"region:us"
]
| null | 2023-11-20T12:22:40Z | ---
license: mit
---
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|
Tamazight-NLP/AmaWar | Tamazight-NLP | 2023-12-06T12:00:19Z | 2 | 0 | [
"task_categories:translation",
"task_categories:text2text-generation",
"size_categories:1K<n<10K",
"language:ber",
"language:tzm",
"language:ar",
"region:us"
]
| [
"translation",
"text2text-generation"
]
| 2023-11-25T23:19:50Z | ---
task_categories:
- translation
- text2text-generation
language:
- ber
- tzm
- ar
pretty_name: Amawal Warayni
size_categories:
- 1K<n<10K
---
# Amawal Warayni
Bitext scraped from the online [AmaWar](https://amawalwarayni.com/) dictionary of the Tamazight dialect of Ait Warain spoken in northeastern Morocco.
Contains sentences, stories, and poems in Tamazight along with their translations into Modern Standard Arabic.
Big thanks to Dr. Noureddine Amhaoui for his amazing work.
# Citation
```
نور الدين أمهاوي. (2021). معجم محوسب لمعاني الأسماء والأفعال الأمازيغية الوارينية أمازيغي-عربي.
تاريخ الاسترداد 15 11، 2023، من https://amawalwarayni.com/
```
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|
open-llm-leaderboard/details_01-ai__Yi-34B-Chat | open-llm-leaderboard | 2023-12-05T03:50:56Z | 2 | 0 | [
"region:us"
]
| null | 2023-11-26T08:58:33Z | ---
pretty_name: Evaluation run of 01-ai/Yi-34B-Chat
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [01-ai/Yi-34B-Chat](https://huggingface.co/01-ai/Yi-34B-Chat) on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 64 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the aggregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_01-ai__Yi-34B-Chat\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-12-05T03:47:25.491369](https://huggingface.co/datasets/open-llm-leaderboard/details_01-ai__Yi-34B-Chat/blob/main/results_2023-12-05T03-47-25.491369.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.7393930299846158,\n\
\ \"acc_stderr\": 0.028807135333088364,\n \"acc_norm\": 0.7489434623723922,\n\
\ \"acc_norm_stderr\": 0.02935457295982731,\n \"mc1\": 0.3843329253365973,\n\
\ \"mc1_stderr\": 0.017028707301245203,\n \"mc2\": 0.5536831362008046,\n\
\ \"mc2_stderr\": 0.015524186394858242\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.6373720136518771,\n \"acc_stderr\": 0.014049106564955012,\n\
\ \"acc_norm\": 0.6544368600682594,\n \"acc_norm_stderr\": 0.013896938461145678\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6536546504680343,\n\
\ \"acc_stderr\": 0.004748324319714274,\n \"acc_norm\": 0.8415654252141008,\n\
\ \"acc_norm_stderr\": 0.003644017383711605\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.45,\n \"acc_stderr\": 0.05,\n \"acc_norm\"\
: 0.45,\n \"acc_norm_stderr\": 0.05\n },\n \"harness|hendrycksTest-anatomy|5\"\
: {\n \"acc\": 0.7111111111111111,\n \"acc_stderr\": 0.03915450630414251,\n\
\ \"acc_norm\": 0.7111111111111111,\n \"acc_norm_stderr\": 0.03915450630414251\n\
\ },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.8552631578947368,\n\
\ \"acc_stderr\": 0.028631951845930387,\n \"acc_norm\": 0.8552631578947368,\n\
\ \"acc_norm_stderr\": 0.028631951845930387\n },\n \"harness|hendrycksTest-business_ethics|5\"\
: {\n \"acc\": 0.79,\n \"acc_stderr\": 0.040936018074033256,\n \
\ \"acc_norm\": 0.79,\n \"acc_norm_stderr\": 0.040936018074033256\n \
\ },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\":\
\ 0.7924528301886793,\n \"acc_stderr\": 0.02495991802891127,\n \"\
acc_norm\": 0.7924528301886793,\n \"acc_norm_stderr\": 0.02495991802891127\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.8472222222222222,\n\
\ \"acc_stderr\": 0.030085743248565666,\n \"acc_norm\": 0.8472222222222222,\n\
\ \"acc_norm_stderr\": 0.030085743248565666\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.53,\n \"acc_stderr\": 0.05016135580465919,\n \
\ \"acc_norm\": 0.53,\n \"acc_norm_stderr\": 0.05016135580465919\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\
: 0.59,\n \"acc_stderr\": 0.04943110704237101,\n \"acc_norm\": 0.59,\n\
\ \"acc_norm_stderr\": 0.04943110704237101\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \
\ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695235\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6994219653179191,\n\
\ \"acc_stderr\": 0.034961014811911786,\n \"acc_norm\": 0.6994219653179191,\n\
\ \"acc_norm_stderr\": 0.034961014811911786\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.46078431372549017,\n \"acc_stderr\": 0.049598599663841815,\n\
\ \"acc_norm\": 0.46078431372549017,\n \"acc_norm_stderr\": 0.049598599663841815\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.82,\n \"acc_stderr\": 0.03861229196653695,\n \"acc_norm\": 0.82,\n\
\ \"acc_norm_stderr\": 0.03861229196653695\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.7659574468085106,\n \"acc_stderr\": 0.02767845257821239,\n\
\ \"acc_norm\": 0.7659574468085106,\n \"acc_norm_stderr\": 0.02767845257821239\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.5526315789473685,\n\
\ \"acc_stderr\": 0.046774730044911984,\n \"acc_norm\": 0.5526315789473685,\n\
\ \"acc_norm_stderr\": 0.046774730044911984\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.8,\n \"acc_stderr\": 0.0333333333333333,\n \
\ \"acc_norm\": 0.8,\n \"acc_norm_stderr\": 0.0333333333333333\n },\n\
\ \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.6349206349206349,\n\
\ \"acc_stderr\": 0.024796060602699965,\n \"acc_norm\": 0.6349206349206349,\n\
\ \"acc_norm_stderr\": 0.024796060602699965\n },\n \"harness|hendrycksTest-formal_logic|5\"\
: {\n \"acc\": 0.5396825396825397,\n \"acc_stderr\": 0.04458029125470973,\n\
\ \"acc_norm\": 0.5396825396825397,\n \"acc_norm_stderr\": 0.04458029125470973\n\
\ },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.56,\n\
\ \"acc_stderr\": 0.049888765156985884,\n \"acc_norm\": 0.56,\n \
\ \"acc_norm_stderr\": 0.049888765156985884\n },\n \"harness|hendrycksTest-high_school_biology|5\"\
: {\n \"acc\": 0.8709677419354839,\n \"acc_stderr\": 0.019070889254792767,\n\
\ \"acc_norm\": 0.8709677419354839,\n \"acc_norm_stderr\": 0.019070889254792767\n\
\ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\
: 0.6206896551724138,\n \"acc_stderr\": 0.03413963805906235,\n \"\
acc_norm\": 0.6206896551724138,\n \"acc_norm_stderr\": 0.03413963805906235\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.81,\n \"acc_stderr\": 0.03942772444036625,\n \"acc_norm\"\
: 0.81,\n \"acc_norm_stderr\": 0.03942772444036625\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.8545454545454545,\n \"acc_stderr\": 0.027530196355066573,\n\
\ \"acc_norm\": 0.8545454545454545,\n \"acc_norm_stderr\": 0.027530196355066573\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.898989898989899,\n \"acc_stderr\": 0.021469735576055343,\n \"\
acc_norm\": 0.898989898989899,\n \"acc_norm_stderr\": 0.021469735576055343\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.9533678756476683,\n \"acc_stderr\": 0.015216761819262585,\n\
\ \"acc_norm\": 0.9533678756476683,\n \"acc_norm_stderr\": 0.015216761819262585\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.7846153846153846,\n \"acc_stderr\": 0.020843034557462878,\n\
\ \"acc_norm\": 0.7846153846153846,\n \"acc_norm_stderr\": 0.020843034557462878\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.35555555555555557,\n \"acc_stderr\": 0.029185714949857403,\n \
\ \"acc_norm\": 0.35555555555555557,\n \"acc_norm_stderr\": 0.029185714949857403\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.8361344537815126,\n \"acc_stderr\": 0.024044054940440488,\n\
\ \"acc_norm\": 0.8361344537815126,\n \"acc_norm_stderr\": 0.024044054940440488\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.5033112582781457,\n \"acc_stderr\": 0.04082393379449654,\n \"\
acc_norm\": 0.5033112582781457,\n \"acc_norm_stderr\": 0.04082393379449654\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.908256880733945,\n \"acc_stderr\": 0.012376323409137123,\n \"\
acc_norm\": 0.908256880733945,\n \"acc_norm_stderr\": 0.012376323409137123\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.6342592592592593,\n \"acc_stderr\": 0.03284738857647206,\n \"\
acc_norm\": 0.6342592592592593,\n \"acc_norm_stderr\": 0.03284738857647206\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.9068627450980392,\n \"acc_stderr\": 0.020397853969426998,\n \"\
acc_norm\": 0.9068627450980392,\n \"acc_norm_stderr\": 0.020397853969426998\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.9029535864978903,\n \"acc_stderr\": 0.019269323025640255,\n \
\ \"acc_norm\": 0.9029535864978903,\n \"acc_norm_stderr\": 0.019269323025640255\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.8116591928251121,\n\
\ \"acc_stderr\": 0.026241132996407256,\n \"acc_norm\": 0.8116591928251121,\n\
\ \"acc_norm_stderr\": 0.026241132996407256\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.8931297709923665,\n \"acc_stderr\": 0.027096548624883733,\n\
\ \"acc_norm\": 0.8931297709923665,\n \"acc_norm_stderr\": 0.027096548624883733\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.8925619834710744,\n \"acc_stderr\": 0.028268812192540627,\n \"\
acc_norm\": 0.8925619834710744,\n \"acc_norm_stderr\": 0.028268812192540627\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8888888888888888,\n\
\ \"acc_stderr\": 0.03038159675665167,\n \"acc_norm\": 0.8888888888888888,\n\
\ \"acc_norm_stderr\": 0.03038159675665167\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.852760736196319,\n \"acc_stderr\": 0.027839915278339657,\n\
\ \"acc_norm\": 0.852760736196319,\n \"acc_norm_stderr\": 0.027839915278339657\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.6160714285714286,\n\
\ \"acc_stderr\": 0.04616143075028546,\n \"acc_norm\": 0.6160714285714286,\n\
\ \"acc_norm_stderr\": 0.04616143075028546\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.8737864077669902,\n \"acc_stderr\": 0.0328818027880863,\n\
\ \"acc_norm\": 0.8737864077669902,\n \"acc_norm_stderr\": 0.0328818027880863\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.9145299145299145,\n\
\ \"acc_stderr\": 0.01831589168562586,\n \"acc_norm\": 0.9145299145299145,\n\
\ \"acc_norm_stderr\": 0.01831589168562586\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.87,\n \"acc_stderr\": 0.033799766898963086,\n \
\ \"acc_norm\": 0.87,\n \"acc_norm_stderr\": 0.033799766898963086\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8991060025542784,\n\
\ \"acc_stderr\": 0.01077047201488672,\n \"acc_norm\": 0.8991060025542784,\n\
\ \"acc_norm_stderr\": 0.01077047201488672\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.8092485549132948,\n \"acc_stderr\": 0.021152676966575277,\n\
\ \"acc_norm\": 0.8092485549132948,\n \"acc_norm_stderr\": 0.021152676966575277\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.7016759776536313,\n\
\ \"acc_stderr\": 0.01530184004512928,\n \"acc_norm\": 0.7016759776536313,\n\
\ \"acc_norm_stderr\": 0.01530184004512928\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.8300653594771242,\n \"acc_stderr\": 0.02150538312123137,\n\
\ \"acc_norm\": 0.8300653594771242,\n \"acc_norm_stderr\": 0.02150538312123137\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.8102893890675241,\n\
\ \"acc_stderr\": 0.02226819625878322,\n \"acc_norm\": 0.8102893890675241,\n\
\ \"acc_norm_stderr\": 0.02226819625878322\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.8703703703703703,\n \"acc_stderr\": 0.018689725721062072,\n\
\ \"acc_norm\": 0.8703703703703703,\n \"acc_norm_stderr\": 0.018689725721062072\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.6170212765957447,\n \"acc_stderr\": 0.02899908090480618,\n \
\ \"acc_norm\": 0.6170212765957447,\n \"acc_norm_stderr\": 0.02899908090480618\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.5495436766623207,\n\
\ \"acc_stderr\": 0.012707390438502348,\n \"acc_norm\": 0.5495436766623207,\n\
\ \"acc_norm_stderr\": 0.012707390438502348\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.7830882352941176,\n \"acc_stderr\": 0.025035845227711274,\n\
\ \"acc_norm\": 0.7830882352941176,\n \"acc_norm_stderr\": 0.025035845227711274\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.826797385620915,\n \"acc_stderr\": 0.015309329266969138,\n \
\ \"acc_norm\": 0.826797385620915,\n \"acc_norm_stderr\": 0.015309329266969138\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.7272727272727273,\n\
\ \"acc_stderr\": 0.04265792110940589,\n \"acc_norm\": 0.7272727272727273,\n\
\ \"acc_norm_stderr\": 0.04265792110940589\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.8367346938775511,\n \"acc_stderr\": 0.02366169917709861,\n\
\ \"acc_norm\": 0.8367346938775511,\n \"acc_norm_stderr\": 0.02366169917709861\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8855721393034826,\n\
\ \"acc_stderr\": 0.022509345325101706,\n \"acc_norm\": 0.8855721393034826,\n\
\ \"acc_norm_stderr\": 0.022509345325101706\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.89,\n \"acc_stderr\": 0.03144660377352203,\n \
\ \"acc_norm\": 0.89,\n \"acc_norm_stderr\": 0.03144660377352203\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5843373493975904,\n\
\ \"acc_stderr\": 0.03836722176598053,\n \"acc_norm\": 0.5843373493975904,\n\
\ \"acc_norm_stderr\": 0.03836722176598053\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8771929824561403,\n \"acc_stderr\": 0.02517298435015577,\n\
\ \"acc_norm\": 0.8771929824561403,\n \"acc_norm_stderr\": 0.02517298435015577\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.3843329253365973,\n\
\ \"mc1_stderr\": 0.017028707301245203,\n \"mc2\": 0.5536831362008046,\n\
\ \"mc2_stderr\": 0.015524186394858242\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.8011049723756906,\n \"acc_stderr\": 0.01121862997251531\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.3191811978771797,\n \
\ \"acc_stderr\": 0.012840345676251648\n }\n}\n```"
repo_url: https://huggingface.co/01-ai/Yi-34B-Chat
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_11_26T08_55_32.839765
path:
- '**/details_harness|arc:challenge|25_2023-11-26T08-55-32.839765.parquet'
- split: 2023_12_05T03_47_25.491369
path:
- '**/details_harness|arc:challenge|25_2023-12-05T03-47-25.491369.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-12-05T03-47-25.491369.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_11_26T08_55_32.839765
path:
- '**/details_harness|drop|3_2023-11-26T08-55-32.839765.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-11-26T08-55-32.839765.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_11_26T08_55_32.839765
path:
- '**/details_harness|gsm8k|5_2023-11-26T08-55-32.839765.parquet'
- split: 2023_12_05T03_47_25.491369
path:
- '**/details_harness|gsm8k|5_2023-12-05T03-47-25.491369.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-05T03-47-25.491369.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_11_26T08_55_32.839765
path:
- '**/details_harness|hellaswag|10_2023-11-26T08-55-32.839765.parquet'
- split: 2023_12_05T03_47_25.491369
path:
- '**/details_harness|hellaswag|10_2023-12-05T03-47-25.491369.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-12-05T03-47-25.491369.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_11_26T08_55_32.839765
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-11-26T08-55-32.839765.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-11-26T08-55-32.839765.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-11-26T08-55-32.839765.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-11-26T08-55-32.839765.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-11-26T08-55-32.839765.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-11-26T08-55-32.839765.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-11-26T08-55-32.839765.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-11-26T08-55-32.839765.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-11-26T08-55-32.839765.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-11-26T08-55-32.839765.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-11-26T08-55-32.839765.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-11-26T08-55-32.839765.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-11-26T08-55-32.839765.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-11-26T08-55-32.839765.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-11-26T08-55-32.839765.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-11-26T08-55-32.839765.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-11-26T08-55-32.839765.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-11-26T08-55-32.839765.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-11-26T08-55-32.839765.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-11-26T08-55-32.839765.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-11-26T08-55-32.839765.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-11-26T08-55-32.839765.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-11-26T08-55-32.839765.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-11-26T08-55-32.839765.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-11-26T08-55-32.839765.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-11-26T08-55-32.839765.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-11-26T08-55-32.839765.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-11-26T08-55-32.839765.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-11-26T08-55-32.839765.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-11-26T08-55-32.839765.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-11-26T08-55-32.839765.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-11-26T08-55-32.839765.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-11-26T08-55-32.839765.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-11-26T08-55-32.839765.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-11-26T08-55-32.839765.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-11-26T08-55-32.839765.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-11-26T08-55-32.839765.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-11-26T08-55-32.839765.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-11-26T08-55-32.839765.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-11-26T08-55-32.839765.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-11-26T08-55-32.839765.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-11-26T08-55-32.839765.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-11-26T08-55-32.839765.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-11-26T08-55-32.839765.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-11-26T08-55-32.839765.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-11-26T08-55-32.839765.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-11-26T08-55-32.839765.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-11-26T08-55-32.839765.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-11-26T08-55-32.839765.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-11-26T08-55-32.839765.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-11-26T08-55-32.839765.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-11-26T08-55-32.839765.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-11-26T08-55-32.839765.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-11-26T08-55-32.839765.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-11-26T08-55-32.839765.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-11-26T08-55-32.839765.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-11-26T08-55-32.839765.parquet'
- split: 2023_12_05T03_47_25.491369
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-05T03-47-25.491369.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-05T03-47-25.491369.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-05T03-47-25.491369.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-05T03-47-25.491369.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-05T03-47-25.491369.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-05T03-47-25.491369.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-05T03-47-25.491369.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-05T03-47-25.491369.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-05T03-47-25.491369.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-05T03-47-25.491369.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-05T03-47-25.491369.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-05T03-47-25.491369.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-05T03-47-25.491369.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-05T03-47-25.491369.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-05T03-47-25.491369.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-05T03-47-25.491369.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-05T03-47-25.491369.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-05T03-47-25.491369.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-05T03-47-25.491369.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-05T03-47-25.491369.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-05T03-47-25.491369.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-05T03-47-25.491369.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-05T03-47-25.491369.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-05T03-47-25.491369.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-05T03-47-25.491369.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-05T03-47-25.491369.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-05T03-47-25.491369.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-05T03-47-25.491369.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-05T03-47-25.491369.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-05T03-47-25.491369.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-05T03-47-25.491369.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-05T03-47-25.491369.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-05T03-47-25.491369.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-05T03-47-25.491369.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-05T03-47-25.491369.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-05T03-47-25.491369.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-05T03-47-25.491369.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-05T03-47-25.491369.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-05T03-47-25.491369.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-05T03-47-25.491369.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-05T03-47-25.491369.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-05T03-47-25.491369.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-05T03-47-25.491369.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-05T03-47-25.491369.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-05T03-47-25.491369.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-05T03-47-25.491369.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-05T03-47-25.491369.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-05T03-47-25.491369.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-05T03-47-25.491369.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-05T03-47-25.491369.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-05T03-47-25.491369.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-05T03-47-25.491369.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-05T03-47-25.491369.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-05T03-47-25.491369.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-05T03-47-25.491369.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-05T03-47-25.491369.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-05T03-47-25.491369.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-05T03-47-25.491369.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-05T03-47-25.491369.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-05T03-47-25.491369.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-05T03-47-25.491369.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-05T03-47-25.491369.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-05T03-47-25.491369.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-05T03-47-25.491369.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-05T03-47-25.491369.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-05T03-47-25.491369.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-05T03-47-25.491369.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-05T03-47-25.491369.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-05T03-47-25.491369.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-05T03-47-25.491369.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-05T03-47-25.491369.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-05T03-47-25.491369.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-05T03-47-25.491369.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-05T03-47-25.491369.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-05T03-47-25.491369.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-05T03-47-25.491369.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-05T03-47-25.491369.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-05T03-47-25.491369.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-05T03-47-25.491369.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-05T03-47-25.491369.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-05T03-47-25.491369.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-05T03-47-25.491369.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-05T03-47-25.491369.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-05T03-47-25.491369.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-05T03-47-25.491369.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-05T03-47-25.491369.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-05T03-47-25.491369.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-05T03-47-25.491369.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-05T03-47-25.491369.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-05T03-47-25.491369.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-05T03-47-25.491369.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-05T03-47-25.491369.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-05T03-47-25.491369.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-05T03-47-25.491369.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-05T03-47-25.491369.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-05T03-47-25.491369.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-05T03-47-25.491369.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-05T03-47-25.491369.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-05T03-47-25.491369.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-05T03-47-25.491369.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-05T03-47-25.491369.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-05T03-47-25.491369.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-05T03-47-25.491369.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-05T03-47-25.491369.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-05T03-47-25.491369.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-05T03-47-25.491369.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-05T03-47-25.491369.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-05T03-47-25.491369.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-05T03-47-25.491369.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-05T03-47-25.491369.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-05T03-47-25.491369.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-05T03-47-25.491369.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-05T03-47-25.491369.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-05T03-47-25.491369.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_11_26T08_55_32.839765
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-11-26T08-55-32.839765.parquet'
- split: 2023_12_05T03_47_25.491369
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-05T03-47-25.491369.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-05T03-47-25.491369.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_11_26T08_55_32.839765
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-11-26T08-55-32.839765.parquet'
- split: 2023_12_05T03_47_25.491369
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-05T03-47-25.491369.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-05T03-47-25.491369.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_11_26T08_55_32.839765
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-11-26T08-55-32.839765.parquet'
- split: 2023_12_05T03_47_25.491369
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-05T03-47-25.491369.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-05T03-47-25.491369.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_11_26T08_55_32.839765
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-11-26T08-55-32.839765.parquet'
- split: 2023_12_05T03_47_25.491369
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-05T03-47-25.491369.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-05T03-47-25.491369.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_11_26T08_55_32.839765
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-11-26T08-55-32.839765.parquet'
- split: 2023_12_05T03_47_25.491369
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-05T03-47-25.491369.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-05T03-47-25.491369.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_11_26T08_55_32.839765
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-11-26T08-55-32.839765.parquet'
- split: 2023_12_05T03_47_25.491369
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-05T03-47-25.491369.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-05T03-47-25.491369.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_11_26T08_55_32.839765
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-11-26T08-55-32.839765.parquet'
- split: 2023_12_05T03_47_25.491369
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-05T03-47-25.491369.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-05T03-47-25.491369.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_11_26T08_55_32.839765
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-11-26T08-55-32.839765.parquet'
- split: 2023_12_05T03_47_25.491369
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-05T03-47-25.491369.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-05T03-47-25.491369.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_11_26T08_55_32.839765
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-11-26T08-55-32.839765.parquet'
- split: 2023_12_05T03_47_25.491369
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-05T03-47-25.491369.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-05T03-47-25.491369.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_11_26T08_55_32.839765
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-11-26T08-55-32.839765.parquet'
- split: 2023_12_05T03_47_25.491369
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-05T03-47-25.491369.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-05T03-47-25.491369.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_11_26T08_55_32.839765
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-11-26T08-55-32.839765.parquet'
- split: 2023_12_05T03_47_25.491369
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-05T03-47-25.491369.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-05T03-47-25.491369.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_11_26T08_55_32.839765
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-11-26T08-55-32.839765.parquet'
- split: 2023_12_05T03_47_25.491369
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-05T03-47-25.491369.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-05T03-47-25.491369.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_11_26T08_55_32.839765
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-11-26T08-55-32.839765.parquet'
- split: 2023_12_05T03_47_25.491369
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-05T03-47-25.491369.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-05T03-47-25.491369.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_11_26T08_55_32.839765
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-11-26T08-55-32.839765.parquet'
- split: 2023_12_05T03_47_25.491369
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-05T03-47-25.491369.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-05T03-47-25.491369.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_11_26T08_55_32.839765
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-11-26T08-55-32.839765.parquet'
- split: 2023_12_05T03_47_25.491369
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-05T03-47-25.491369.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-05T03-47-25.491369.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_11_26T08_55_32.839765
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-11-26T08-55-32.839765.parquet'
- split: 2023_12_05T03_47_25.491369
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-05T03-47-25.491369.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-05T03-47-25.491369.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_11_26T08_55_32.839765
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-11-26T08-55-32.839765.parquet'
- split: 2023_12_05T03_47_25.491369
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-05T03-47-25.491369.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-05T03-47-25.491369.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_11_26T08_55_32.839765
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-11-26T08-55-32.839765.parquet'
- split: 2023_12_05T03_47_25.491369
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-05T03-47-25.491369.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-05T03-47-25.491369.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_11_26T08_55_32.839765
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-11-26T08-55-32.839765.parquet'
- split: 2023_12_05T03_47_25.491369
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-05T03-47-25.491369.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-05T03-47-25.491369.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_11_26T08_55_32.839765
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-11-26T08-55-32.839765.parquet'
- split: 2023_12_05T03_47_25.491369
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-05T03-47-25.491369.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-05T03-47-25.491369.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_11_26T08_55_32.839765
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-11-26T08-55-32.839765.parquet'
- split: 2023_12_05T03_47_25.491369
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-05T03-47-25.491369.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-05T03-47-25.491369.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_11_26T08_55_32.839765
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-11-26T08-55-32.839765.parquet'
- split: 2023_12_05T03_47_25.491369
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-05T03-47-25.491369.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-05T03-47-25.491369.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_11_26T08_55_32.839765
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-11-26T08-55-32.839765.parquet'
- split: 2023_12_05T03_47_25.491369
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-05T03-47-25.491369.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-05T03-47-25.491369.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_11_26T08_55_32.839765
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-11-26T08-55-32.839765.parquet'
- split: 2023_12_05T03_47_25.491369
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-05T03-47-25.491369.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-05T03-47-25.491369.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_11_26T08_55_32.839765
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-11-26T08-55-32.839765.parquet'
- split: 2023_12_05T03_47_25.491369
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-05T03-47-25.491369.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-05T03-47-25.491369.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_11_26T08_55_32.839765
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-11-26T08-55-32.839765.parquet'
- split: 2023_12_05T03_47_25.491369
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-05T03-47-25.491369.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-05T03-47-25.491369.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_11_26T08_55_32.839765
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-11-26T08-55-32.839765.parquet'
- split: 2023_12_05T03_47_25.491369
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-05T03-47-25.491369.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-05T03-47-25.491369.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_11_26T08_55_32.839765
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-11-26T08-55-32.839765.parquet'
- split: 2023_12_05T03_47_25.491369
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-05T03-47-25.491369.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-05T03-47-25.491369.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_11_26T08_55_32.839765
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-11-26T08-55-32.839765.parquet'
- split: 2023_12_05T03_47_25.491369
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-05T03-47-25.491369.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-05T03-47-25.491369.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_11_26T08_55_32.839765
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-11-26T08-55-32.839765.parquet'
- split: 2023_12_05T03_47_25.491369
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-05T03-47-25.491369.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-05T03-47-25.491369.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_11_26T08_55_32.839765
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-11-26T08-55-32.839765.parquet'
- split: 2023_12_05T03_47_25.491369
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-05T03-47-25.491369.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-05T03-47-25.491369.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_11_26T08_55_32.839765
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-11-26T08-55-32.839765.parquet'
- split: 2023_12_05T03_47_25.491369
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-05T03-47-25.491369.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-05T03-47-25.491369.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_11_26T08_55_32.839765
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-11-26T08-55-32.839765.parquet'
- split: 2023_12_05T03_47_25.491369
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-05T03-47-25.491369.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-05T03-47-25.491369.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_11_26T08_55_32.839765
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-11-26T08-55-32.839765.parquet'
- split: 2023_12_05T03_47_25.491369
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-05T03-47-25.491369.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-05T03-47-25.491369.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_11_26T08_55_32.839765
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-11-26T08-55-32.839765.parquet'
- split: 2023_12_05T03_47_25.491369
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-05T03-47-25.491369.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-05T03-47-25.491369.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_11_26T08_55_32.839765
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-11-26T08-55-32.839765.parquet'
- split: 2023_12_05T03_47_25.491369
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-05T03-47-25.491369.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-05T03-47-25.491369.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_11_26T08_55_32.839765
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-11-26T08-55-32.839765.parquet'
- split: 2023_12_05T03_47_25.491369
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-05T03-47-25.491369.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-05T03-47-25.491369.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_11_26T08_55_32.839765
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-11-26T08-55-32.839765.parquet'
- split: 2023_12_05T03_47_25.491369
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-05T03-47-25.491369.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-05T03-47-25.491369.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_11_26T08_55_32.839765
path:
- '**/details_harness|hendrycksTest-management|5_2023-11-26T08-55-32.839765.parquet'
- split: 2023_12_05T03_47_25.491369
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-05T03-47-25.491369.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-05T03-47-25.491369.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_11_26T08_55_32.839765
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-11-26T08-55-32.839765.parquet'
- split: 2023_12_05T03_47_25.491369
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-05T03-47-25.491369.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-05T03-47-25.491369.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_11_26T08_55_32.839765
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-11-26T08-55-32.839765.parquet'
- split: 2023_12_05T03_47_25.491369
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-05T03-47-25.491369.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-05T03-47-25.491369.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_11_26T08_55_32.839765
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-11-26T08-55-32.839765.parquet'
- split: 2023_12_05T03_47_25.491369
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-05T03-47-25.491369.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-05T03-47-25.491369.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_11_26T08_55_32.839765
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-11-26T08-55-32.839765.parquet'
- split: 2023_12_05T03_47_25.491369
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-05T03-47-25.491369.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-05T03-47-25.491369.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_11_26T08_55_32.839765
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-11-26T08-55-32.839765.parquet'
- split: 2023_12_05T03_47_25.491369
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-05T03-47-25.491369.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-05T03-47-25.491369.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_11_26T08_55_32.839765
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-11-26T08-55-32.839765.parquet'
- split: 2023_12_05T03_47_25.491369
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-05T03-47-25.491369.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-05T03-47-25.491369.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_11_26T08_55_32.839765
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-11-26T08-55-32.839765.parquet'
- split: 2023_12_05T03_47_25.491369
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-05T03-47-25.491369.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-05T03-47-25.491369.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_11_26T08_55_32.839765
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-11-26T08-55-32.839765.parquet'
- split: 2023_12_05T03_47_25.491369
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-05T03-47-25.491369.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-05T03-47-25.491369.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_11_26T08_55_32.839765
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-11-26T08-55-32.839765.parquet'
- split: 2023_12_05T03_47_25.491369
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-05T03-47-25.491369.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-05T03-47-25.491369.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_11_26T08_55_32.839765
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-11-26T08-55-32.839765.parquet'
- split: 2023_12_05T03_47_25.491369
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-05T03-47-25.491369.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-05T03-47-25.491369.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_11_26T08_55_32.839765
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-11-26T08-55-32.839765.parquet'
- split: 2023_12_05T03_47_25.491369
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-05T03-47-25.491369.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-05T03-47-25.491369.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_11_26T08_55_32.839765
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-11-26T08-55-32.839765.parquet'
- split: 2023_12_05T03_47_25.491369
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-05T03-47-25.491369.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-05T03-47-25.491369.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_11_26T08_55_32.839765
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-11-26T08-55-32.839765.parquet'
- split: 2023_12_05T03_47_25.491369
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-05T03-47-25.491369.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-05T03-47-25.491369.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_11_26T08_55_32.839765
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-11-26T08-55-32.839765.parquet'
- split: 2023_12_05T03_47_25.491369
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-05T03-47-25.491369.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-05T03-47-25.491369.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_11_26T08_55_32.839765
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-11-26T08-55-32.839765.parquet'
- split: 2023_12_05T03_47_25.491369
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-05T03-47-25.491369.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-05T03-47-25.491369.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_11_26T08_55_32.839765
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-11-26T08-55-32.839765.parquet'
- split: 2023_12_05T03_47_25.491369
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-05T03-47-25.491369.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-05T03-47-25.491369.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_11_26T08_55_32.839765
path:
- '**/details_harness|hendrycksTest-virology|5_2023-11-26T08-55-32.839765.parquet'
- split: 2023_12_05T03_47_25.491369
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-05T03-47-25.491369.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-05T03-47-25.491369.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_11_26T08_55_32.839765
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-11-26T08-55-32.839765.parquet'
- split: 2023_12_05T03_47_25.491369
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-05T03-47-25.491369.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-05T03-47-25.491369.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_11_26T08_55_32.839765
path:
- '**/details_harness|truthfulqa:mc|0_2023-11-26T08-55-32.839765.parquet'
- split: 2023_12_05T03_47_25.491369
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-05T03-47-25.491369.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-05T03-47-25.491369.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_11_26T08_55_32.839765
path:
- '**/details_harness|winogrande|5_2023-11-26T08-55-32.839765.parquet'
- split: 2023_12_05T03_47_25.491369
path:
- '**/details_harness|winogrande|5_2023-12-05T03-47-25.491369.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-12-05T03-47-25.491369.parquet'
- config_name: results
data_files:
- split: 2023_11_26T08_55_32.839765
path:
- results_2023-11-26T08-55-32.839765.parquet
- split: 2023_12_05T03_47_25.491369
path:
- results_2023-12-05T03-47-25.491369.parquet
- split: latest
path:
- results_2023-12-05T03-47-25.491369.parquet
---
# Dataset Card for Evaluation run of 01-ai/Yi-34B-Chat
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/01-ai/Yi-34B-Chat
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [01-ai/Yi-34B-Chat](https://huggingface.co/01-ai/Yi-34B-Chat) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_01-ai__Yi-34B-Chat",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-05T03:47:25.491369](https://huggingface.co/datasets/open-llm-leaderboard/details_01-ai__Yi-34B-Chat/blob/main/results_2023-12-05T03-47-25.491369.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.7393930299846158,
"acc_stderr": 0.028807135333088364,
"acc_norm": 0.7489434623723922,
"acc_norm_stderr": 0.02935457295982731,
"mc1": 0.3843329253365973,
"mc1_stderr": 0.017028707301245203,
"mc2": 0.5536831362008046,
"mc2_stderr": 0.015524186394858242
},
"harness|arc:challenge|25": {
"acc": 0.6373720136518771,
"acc_stderr": 0.014049106564955012,
"acc_norm": 0.6544368600682594,
"acc_norm_stderr": 0.013896938461145678
},
"harness|hellaswag|10": {
"acc": 0.6536546504680343,
"acc_stderr": 0.004748324319714274,
"acc_norm": 0.8415654252141008,
"acc_norm_stderr": 0.003644017383711605
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.45,
"acc_stderr": 0.05,
"acc_norm": 0.45,
"acc_norm_stderr": 0.05
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.7111111111111111,
"acc_stderr": 0.03915450630414251,
"acc_norm": 0.7111111111111111,
"acc_norm_stderr": 0.03915450630414251
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.8552631578947368,
"acc_stderr": 0.028631951845930387,
"acc_norm": 0.8552631578947368,
"acc_norm_stderr": 0.028631951845930387
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.79,
"acc_stderr": 0.040936018074033256,
"acc_norm": 0.79,
"acc_norm_stderr": 0.040936018074033256
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.7924528301886793,
"acc_stderr": 0.02495991802891127,
"acc_norm": 0.7924528301886793,
"acc_norm_stderr": 0.02495991802891127
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.8472222222222222,
"acc_stderr": 0.030085743248565666,
"acc_norm": 0.8472222222222222,
"acc_norm_stderr": 0.030085743248565666
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.53,
"acc_stderr": 0.05016135580465919,
"acc_norm": 0.53,
"acc_norm_stderr": 0.05016135580465919
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.59,
"acc_stderr": 0.04943110704237101,
"acc_norm": 0.59,
"acc_norm_stderr": 0.04943110704237101
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.34,
"acc_stderr": 0.04760952285695235,
"acc_norm": 0.34,
"acc_norm_stderr": 0.04760952285695235
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6994219653179191,
"acc_stderr": 0.034961014811911786,
"acc_norm": 0.6994219653179191,
"acc_norm_stderr": 0.034961014811911786
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.46078431372549017,
"acc_stderr": 0.049598599663841815,
"acc_norm": 0.46078431372549017,
"acc_norm_stderr": 0.049598599663841815
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.82,
"acc_stderr": 0.03861229196653695,
"acc_norm": 0.82,
"acc_norm_stderr": 0.03861229196653695
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.7659574468085106,
"acc_stderr": 0.02767845257821239,
"acc_norm": 0.7659574468085106,
"acc_norm_stderr": 0.02767845257821239
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.5526315789473685,
"acc_stderr": 0.046774730044911984,
"acc_norm": 0.5526315789473685,
"acc_norm_stderr": 0.046774730044911984
},
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```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] | [
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|
shachardon/midjourney-threads | shachardon | 2023-12-10T11:26:11Z | 2 | 2 | [
"task_categories:text-to-image",
"size_categories:100K<n<1M",
"language:en",
"region:us"
]
| [
"text-to-image"
]
| 2023-11-27T11:22:44Z | ---
task_categories:
- text-to-image
language:
- en
pretty_name: Midjourney-Threads
size_categories:
- 100K<n<1M
configs:
- config_name: default
data_files:
- split: train
path:
- "threads_0.csv"
- "threads_20000.csv"
- "threads_40000.csv"
- "threads_60000.csv"
- "threads_80000.csv"
- "threads_100000.csv"
- "threads_120000.csv"
- "threads_140000.csv"
- "threads_160000.csv"
---
# Dataset Card for Midjourney-Threads 🧵💬
<!-- Provide a quick summary of the dataset. -->
This dataset contains users prompts from the Midjourney discord channel, organized into "threads of interaction".
Each thread contains a user’s trails to create one target image.
The dataset was introduced as part of the paper: [Human Learning by Model Feedback: The Dynamics of Iterative Prompting with Midjourney][ourpaper].
[ourpaper]: https://aclanthology.org/2023.emnlp-main.253/ "markdown our paper"
### Dataset Sources
<!-- Provide the basic links for the dataset. -->
- **Repository:** https://github.com/shachardon/Mid-Journey-to-alignment
- **Paper:** https://aclanthology.org/2023.emnlp-main.253/
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
Main Columns:
- 'text' - the original prompt
- 'args' - predefined parameters (such as the aspect ratio, chaos and [more][myexample])
- 'channel_id' - the discord channel
- 'userid' - an anonymous user id
- 'timestamp' - a timestamp of the prompt creation
- 'label' - Ture whether an image that was generated based on that prompt was upscaled, otherwise False.
- 'id' - unique id of the prompt
- 'url_png' - link to the generated images (a 4-grid version)
- 'main_content' - prefix of the prompt, without trailing magic-words
- 'concreteness' - concreteness score, based on the [this paper][concpaper]
- 'word_len' - the number of words
- 'repeat_words' - the occurrences of each word that appears more than once in the prompt, excluding stop words.
- 'reapeat_words_ratio' - repeat_words / word_len
- 'perplexity' - the perplexity GPT-2 assigns to each prompt.
- 'caption_0-3' - captions that were generated by the BLIP-2 model, with the 4 created images as its inputs.
- 'phase' - train/test split, as was used to train image/text classifiers
- 'magic_ratio' - the percentage of words that were recognized as magic words in the prompt
- 'thread_id' - the id of the thread
- 'depth' - the max depth of a constituency parse tree of the prompt.
- 'num_sent_parser' - the number of sentences in the prompt.
- 'num_sent_parser_ratio' - num_sent_parser / word_len
- 'words_per_sent' - word_len / num_sent_parser
[myexample]: https://docs.midjourney.com/docs/parameter-list "markdown more"
[concpaper]: https://link.springer.com/article/10.3758/s13428-013-0403-5 "markdown this paper"
## Dataset Creation
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
We construct the dataset by scraping user-generated prompts from the Midjourney Discord server.
The server contains channels in which a user can type a prompt and arguments, and then the Midjourney bot would reply with 4 generated images, combined together into a grid. Then, if the user is satisfied with one of the 4 images, they can send an 'upscale' command to the bot, to get an upscaled version of the desired image.
We randomly choose one of the 'newbies' channels, where both new and experienced users are experimenting with general domain prompts. We collect 693,528 prompts (From 23 January to 1 March 2023), together with their matching images and meta-data such as timestamps and user ids (which we anonymize).
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
We split the prompts into threads automatically, see the paper for more details.
In addition, we extract features (perplexity, sentence length, and more).
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
We fully anonymize the data by removing user names and other user-specific meta-data. If you recognize your prompts here and want to remove them, please send us an [email](mailto:[email protected]).
The Midjourney Discord is an open community that allows others to use images and prompts whenever they are posted in a public setting.
Paying users do own all assets they create, and therefore we do not include the image files in our dataset, but only links to them.
### Recommendations, Risks, and Limitations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
We split the prompts into threads automatically, and therefore there are some mistakes. For more about our annotations method, please see the paper.
Our manual sample did not find any offensive content in the prompts.
## Citation
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
```
@inproceedings{don-yehiya-etal-2023-human,
title = "Human Learning by Model Feedback: The Dynamics of Iterative Prompting with Midjourney",
author = "Don-Yehiya, Shachar and
Choshen, Leshem and
Abend, Omri",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.emnlp-main.253",
pages = "4146--4161",
abstract = "Generating images with a Text-to-Image model often requires multiple trials, where human users iteratively update their prompt based on feedback, namely the output image. Taking inspiration from cognitive work on reference games and dialogue alignment, this paper analyzes the dynamics of the user prompts along such iterations. We compile a dataset of iterative interactions of human users with Midjourney. Our analysis then reveals that prompts predictably converge toward specific traits along these iterations. We further study whether this convergence is due to human users, realizing they missed important details, or due to adaptation to the model{'}s {``}preferences{''}, producing better images for a specific language style. We show initial evidence that both possibilities are at play. The possibility that users adapt to the model{'}s preference raises concerns about reusing user data for further training. The prompts may be biased towards the preferences of a specific model, rather than align with human intentions and natural manner of expression.",
}
```
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|
tarudesu/ViOCD | tarudesu | 2023-12-06T16:27:34Z | 2 | 0 | [
"task_categories:text-classification",
"size_categories:1K<n<10K",
"language:vi",
"code",
"arxiv:2103.10069",
"arxiv:2104.11969",
"region:us"
]
| [
"text-classification"
]
| 2023-11-28T07:13:26Z | ---
task_categories:
- text-classification
language:
- vi
tags:
- code
pretty_name: Vietnamese Open-Domain Complaint Detection in E-commerce Websites
size_categories:
- 1K<n<10K
---
# Vietnamese Open-Domain Complaint Detection in E-commerce Websites
This is the official repository for the ViOCD dataset from the paper [Vietnamese Open-Domain Complaint Detection in E-commerce Websites](https://arxiv.org/pdf/2103.10069.pdf), which was accepted at the [SoMeT 2021](https://dblp.org/db/conf/somet/somet2021.html).
# Citation Information
The provided dataset is only used for research purposes!
```
@misc{nguyen2021vietnamese,
title={Vietnamese Complaint Detection on E-Commerce Websites},
author={Nhung Thi-Hong Nguyen and Phuong Phan-Dieu Ha and Luan Thanh Nguyen and Kiet Van Nguyen and Ngan Luu-Thuy Nguyen},
year={2021},
eprint={2104.11969},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
## Abstract
Customer product reviews play a role in improving the quality of products and services for business organizations or their brands. Complaining is an attitude that expresses dissatisfaction with an event or a product not meeting customer expectations. In this paper, we build a Open-domain Complaint Detection dataset (ViOCD), including 5,485 human-annotated reviews on four categories about product reviews on e-commerce sites. After the data collection phase, we proceed to the annotation task and achieve the inter-annotator agreement Am of 87%. Then, we present an extensive methodology for the research purposes and achieve 92.16% by F1-score for identifying complaints. With the results, in the future, we aim to build a system for open-domain complaint detection in E-commerce websites.
## Dataset
The ViOCD dataset is consist of 5,485 reviews on four categories about product reviews on e-commerce sites.
The dataset is divided into three parts as below:
1. Train set: 4.39K reviews
2. Valid set: 548 reviews
3. Test set: 549 reviews
## Contact
Please feel free to contact us by email [email protected] if you have any further information! | [
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|
alvarobartt/HelpSteer-AIF-raw | alvarobartt | 2023-12-05T12:22:53Z | 2 | 0 | [
"size_categories:n<1K",
"language:en",
"license:cc-by-4.0",
"distilabel",
"helpsteer",
"ai-feedback",
"preference",
"arxiv:2311.09528",
"region:us"
]
| null | 2023-11-28T15:09:48Z | ---
language:
- en
license: cc-by-4.0
size_categories:
- n<1K
pretty_name: HelpSteer with AIF
dataset_info:
features:
- name: prompt
dtype: string
- name: response
dtype: string
- name: labelling_model
dtype: string
- name: labelling_prompt
list:
- name: content
dtype: string
- name: role
dtype: string
- name: raw_labelling_response
dtype: string
- name: correctness
dtype: int64
- name: coherence
dtype: int64
- name: complexity
dtype: int64
- name: verbosity
dtype: int64
- name: helpfulness
dtype: int64
splits:
- name: train
num_bytes: 6324068
num_examples: 1000
download_size: 1484409
dataset_size: 6324068
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
tags:
- distilabel
- helpsteer
- ai-feedback
- preference
---
# HelpSteer: Helpfulness SteerLM Dataset
HelpSteer is an open-source Helpfulness Dataset (CC-BY-4.0) that supports aligning models to become more helpful, factually correct and coherent, while being adjustable in terms of the complexity and verbosity of its responses.
[HelpSteer: Multi-attribute Helpfulness Dataset for SteerLM](http://arxiv.org/abs/2311.09528)
## Disclaimer
This is only a subset created with `distilabel` to evaluate the first 1000 rows using AI Feedback (AIF) coming from GPT-4, only created for experimenting / research purposes, please refer to [nvidia/HelpSteer](https://hf.co/nvidia/HelpSteer) if you want more information about the HelpSteer dataset.
## Dataset Description
HelpSteer contains 37120 samples, while this subset only contains the first 1000, each only containing a prompt and a response, even though the same prompt may appear up to 4 times with different responses generated by their in-house LLM of 43B params.
In this case, the annotations of the attributes have been discarded while just keeping the prompt and the response, to generate the annotations using AIF via `distilabel`.
## Attributes
1. **Helpfulness**: Overall helpfulness of the response to the prompt.
2. **Correctness**: Inclusion of all pertinent facts without errors.
3. **Coherence**: Consistency and clarity of expression.
4. **Complexity**: Intellectual depth required to write response (i.e. whether the response can be written by anyone with basic language competency or requires deep domain expertise).
5. **Verbosity**: Amount of detail included in the response, relative to what is asked for in the prompt.
## Source
1. (original) Prompts are collected based on a mixture of template-generated (mainly for prompt involving long reference text) and human generated by Scale AI. These prompts relate to the tasks of Rewrite, Summarization, Classification, Extraction, Closed Question Answering, Open Question Answering, Generation and Brainstorming.
2. (original) Responses are generated by an early version of an inhouse LLM. We generate up to 4 responses per prompts using sample techniques to give diverse yet reasonable responses.
3. (distilabel) Annotations of various attributes were done using OpenAI's GPT-4 via `distilabel`, following the same Likert 5 scale (0-4) that Scale AI used with human annotators, but this time asking GPT-4 to provide those, via AI Feedback (AIF).
## Citation
If you find this dataset useful, make sure to cite the original work, as the prompt and the responses have been reused from them, while only the annotations have been modified.
```bibtex
@misc{wang2023helpsteer,
title={HelpSteer: Multi-attribute Helpfulness Dataset for SteerLM},
author={Zhilin Wang and Yi Dong and Jiaqi Zeng and Virginia Adams and Makesh Narsimhan Sreedhar and Daniel Egert and Olivier Delalleau and Jane Polak Scowcroft and Neel Kant and Aidan Swope and Oleksii Kuchaiev},
year={2023},
eprint={2311.09528},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
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|
FanjiaYan/CS182-DreamBooth-dataset | FanjiaYan | 2023-12-06T01:40:04Z | 2 | 0 | [
"license:apache-2.0",
"region:us"
]
| null | 2023-11-28T19:24:29Z | ---
license: apache-2.0
---
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|
thanhdath/vietnamese-mathcoder | thanhdath | 2023-12-06T16:39:26Z | 2 | 0 | [
"region:us"
]
| null | 2023-11-29T07:45:53Z | ---
dataset_info:
features:
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 21151768
num_examples: 22825
download_size: 7937515
dataset_size: 21151768
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
### Dataset description
This dataset contains both English and Vietnamese samples.
Each sample has 3 solutions including a coding solution, an explanation solution and a direct solution. They are distinguished by the tokens \<sol\>, \<exp\> and \<dir\>, respectively.
For coding solution, execute the Python code wrapped inside ``` for the output result. DON'T TRUST the choice generated by model after coding, just execute the code and map it to the choice.
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|
KGBrain/visual_files | KGBrain | 2023-12-06T17:24:09Z | 2 | 0 | [
"size_categories:n<1K",
"region:us"
]
| null | 2023-12-01T03:51:05Z | ---
pretty_name: classification
size_categories:
- n<1K
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|
jahb57/gpt2_embeddings_BATCH_3 | jahb57 | 2023-12-08T16:45:56Z | 2 | 0 | [
"region:us"
]
| null | 2023-12-03T22:40:15Z | ---
dataset_info:
features:
- name: sentence
dtype: string
- name: last_hidden_state
sequence:
sequence: float32
splits:
- name: train
num_bytes: 18590683446
num_examples: 100000
download_size: 18639362250
dataset_size: 18590683446
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
playgroundai/MJHQ-30K | playgroundai | 2023-12-06T19:04:08Z | 2 | 7 | [
"task_categories:text-to-image",
"size_categories:10K<n<100K",
"language:en",
"text-to-image",
"playground",
"region:us"
]
| [
"text-to-image"
]
| 2023-12-03T23:25:06Z | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': animals
'1': art
'2': fashion
'3': food
'4': indoor
'5': landscape
'6': logo
'7': people
'8': plants
'9': vehicles
splits:
- name: test
num_bytes: 9764107710
num_examples: 30000
download_size: 8838465412
dataset_size: 9764107710
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
task_categories:
- text-to-image
language:
- en
size_categories:
- 10K<n<100K
tags:
- text-to-image
- playground
---
# MJHQ-30K Benchmark
| Model | Overall FID |
| ------------------------------------- | ----- |
| SDXL-1-0-refiner | 9.55 |
| [playground-v2-1024px-aesthetic](https://huggingface.co/playgroundai/playground-v2-1024px-aesthetic) | **7.07** |
We introduce a new benchmark, **MJHQ-30K**, for automatic evaluation of a model’s aesthetic quality. The benchmark computes FID on a high-quality dataset to gauge aesthetic quality.
We curate the high-quality dataset from Midjourney with 10 common categories, each category with 3K samples. Following common practice, we use aesthetic score and CLIP score to ensure high image quality and high image-text alignment. Furthermore, we take extra care to make the data diverse within each category.
For Playground v2, we report both the overall FID and per-category FID. (All FID metrics are computed at resolution 1024x1024.) From the results, our model outperforms SDXL-1-0-refiner in overall FID and all the categories FID, especially in people and fashion categories. This is inline with the results of the user study, which indicates the correlation between human preferences and the FID score of the MJHQ30K benchmark.
We release this benchmark to the public and encourage the community to adopt it for benchmarking their models’ aesthetic quality.
Please see our [blog](https://blog.playgroundai.com/playground-v2/) for more details.
### Dataset Download
First, download `mjhq30k_imgs.zip`
```python
from huggingface_hub import hf_hub_download
hf_hub_download(
repo_id="playgroundai/MJHQ-30K",
filename="mjhq30k_imgs.zip",
local_dir="path/to/folder",
repo_type="dataset"
)
```
Unzip `mjhq30k_imgs.zip` into its per-category folder structure.
```
root
├── animals
├── art
├── fashion
├── food
├── indoor
├── landscape
├── logo
├── people
├── plants
└── vehicles
```
`meta_data.json` contains metadata including its category and the promp for all the image.
Here is one example. Note that the key is the name of the image file.
```json
"126c23ae9a879fdc05f355f9a72b418d589f3926": {
"category": "plants",
"prompt": "beautiful British garden5, clear sky, unreal engine, detailed, tropical plants ,strong ambient lighting, volumetric lighting, forest plants and leaves, strong light, a lot of leaves in font, 3D effect, 8k render, bright color rendering "
}
```
### Measure FID
To benchmark your model's performance, you need to first generate images using the same prompts in `meta_data.json`.
We calculate our FID using [clean-fid](https://github.com/GaParmar/clean-fid). You can measure the FID between the generated images and the reference images using
```python
from cleanfid import fid
score = fid.compute_fid(ref_dir, gen_dir)
```
### Contributor
Dataset curated by: [Playground](https://playground.com/) Research Team
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|
breno30/AlexHunt | breno30 | 2023-12-06T00:14:25Z | 2 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-12-04T01:32:45Z | ---
license: openrail
---
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]
|
gagan3012/fin_instruct | gagan3012 | 2023-12-05T03:24:41Z | 2 | 0 | [
"region:us"
]
| null | 2023-12-04T02:05:29Z | ---
dataset_info:
features:
- name: messages
list:
- name: content
dtype: string
- name: role
dtype: string
- name: prompt
dtype: string
- name: prompt_id
dtype: string
splits:
- name: train
num_bytes: 684832310.8001372
num_examples: 306030
- name: test
num_bytes: 6919261.199862838
num_examples: 3092
download_size: 343942467
dataset_size: 691751572.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
# Dataset Card for "fin_instruct"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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|
qgyd2021/spam_detect | qgyd2021 | 2023-12-05T06:45:21Z | 2 | 0 | [
"license:apache-2.0",
"region:us"
]
| null | 2023-12-04T08:09:48Z | ---
license: apache-2.0
---
## 垃圾信息检测
垃圾邮件, 垃圾短信息, 广告识别.
### 数据来源
数据集从网上收集整理如下:
| 数据 | 语言 | 任务类型 | 原始数据/项目地址 | 样本个数 | 原始数据描述 | 替代数据下载地址 |
| :--- | :---: | :---: | :---: | :---: | :---: | :---: |
| enron_spam | 英语 | 垃圾邮件分类 | [enron_spam_data](https://github.com/MWiechmann/enron_spam_data); [Enron-Spam](https://www2.aueb.gr/users/ion/data/enron-spam/); [spam-mails-dataset](https://www.kaggle.com/datasets/venky73/spam-mails-dataset) | ham: 16545; spam: 17171 | Enron-Spam 数据集是 V. Metsis、I. Androutsopoulos 和 G. Paliouras 收集的绝佳资源 | [SetFit/enron_spam](https://huggingface.co/datasets/SetFit/enron_spam); [enron-spam](https://www.kaggle.com/datasets/wanderfj/enron-spam) |
| enron_spam_subset | 英语 | 垃圾邮件分类 | [email-spam-dataset](https://www.kaggle.com/datasets/nitishabharathi/email-spam-dataset) | ham: 5000; spam: 5000 | | |
| ling_spam | 英语 | 垃圾邮件分类 | [lingspam-dataset](https://www.kaggle.com/datasets/mandygu/lingspam-dataset); [email-spam-dataset](https://www.kaggle.com/datasets/nitishabharathi/email-spam-dataset) | ham: 2172; spam: 433 | Ling-Spam 数据集是从语言学家列表中整理的 2,893 条垃圾邮件和非垃圾邮件消息的集合。 | |
| sms_spam | 英语 | 垃圾短信分类 | [SMS Spam Collection](https://archive.ics.uci.edu/dataset/228/sms+spam+collection); [SMS Spam Collection Dataset](https://www.kaggle.com/datasets/uciml/sms-spam-collection-dataset) | ham: 4827; spam: 747 | SMS 垃圾邮件集合是一组公开的 SMS 标记消息，为移动电话垃圾邮件研究而收集。 | [sms_spam](https://huggingface.co/datasets/sms_spam) |
| sms_spam_collection | 英语 | 垃圾短信分类 | [spam-emails](https://www.kaggle.com/datasets/abdallahwagih/spam-emails) | ham: 4825; spam: 747 | 该数据集包含电子邮件的集合 | [email-spam-detection-dataset-classification](https://www.kaggle.com/datasets/shantanudhakadd/email-spam-detection-dataset-classification); [spam-identification](https://www.kaggle.com/datasets/amirdhavarshinis/spam-identification); [sms-spam-collection](https://www.kaggle.com/datasets/thedevastator/sms-spam-collection-a-more-diverse-dataset); [spam-or-ham](https://www.kaggle.com/datasets/arunasivapragasam/spam-or-ham) |
| spam_assassin | 英语 | 垃圾邮件分类 | [datasets-spam-assassin](https://github.com/stdlib-js/datasets-spam-assassin); [Apache SpamAssassin’s public datasets](https://spamassassin.apache.org/old/publiccorpus/); [Spam or Not Spam Dataset](https://www.kaggle.com/datasets/ozlerhakan/spam-or-not-spam-dataset) | ham: 4150; spam: 1896 | 数据集从[email-spam-dataset](https://www.kaggle.com/datasets/nitishabharathi/email-spam-dataset)的completeSpamAssassin.csv文件而来。 | [email-spam-dataset](https://www.kaggle.com/datasets/nitishabharathi/email-spam-dataset); [talby/SpamAssassin](https://huggingface.co/datasets/talby/spamassassin); [spamassassin-2002](https://www.kaggle.com/datasets/cesaber/spam-email-data-spamassassin-2002) |
| spam_base | 英语 | 垃圾邮件分类 | [spambase](https://archive.ics.uci.edu/dataset/94/spambase) | | 将电子邮件分类为垃圾邮件或非垃圾邮件 | [spam-email-data-uci](https://www.kaggle.com/datasets/kaggleprollc/spam-email-data-uci) |
| spam_detection | 英语 | 垃圾短信分类 | [Deysi/spam-detection-dataset](https://huggingface.co/datasets/Deysi/spam-detection-dataset) | ham: 5400; spam: 5500 | | |
| spam_message | 汉语 | 垃圾短信分类 | [SpamMessage](https://github.com/hrwhisper/SpamMessage) | ham: 720000; spam: 80000 | 其中spam的数据是正确的数据，但是做了脱敏处理（招生电话：xxxxxxxxxxx），这里的 x 可能会成为显著特征。而ham样本像是从普通文本中截断出来充作样本的，建议不要用这些数据。 | |
| spam_message_lr | 汉语 | 垃圾短信分类 | [SpamMessagesLR](https://github.com/x-hacker/SpamMessagesLR) | ham: 3983; spam: 6990 | | |
| trec07p | 英语 | 垃圾邮件分类 | [2007 TREC Public Spam Corpus](https://plg.uwaterloo.ca/~gvcormac/treccorpus07/); [Spam Track](https://trec.nist.gov/data/spam.html) | ham: 25220; spam: 50199 | 2007 TREC Public Spam Corpus | [trec07p.tar.gz](https://pan.baidu.com/s/1jC9CxVaxwizFCvGtI1JvJA?pwd=g72z) |
| trec06c | 汉语 | 垃圾邮件分类 | [2006 TREC Public Spam Corpora](https://plg.uwaterloo.ca/~gvcormac/treccorpus06/); | | 2006 TREC Public Spam Corpora | |
| youtube_spam_collection | 英语 | 垃圾评论分类 | [youtube+spam+collection](https://archive.ics.uci.edu/dataset/380/youtube+spam+collection); [YouTube Spam Collection Data Set](https://www.kaggle.com/datasets/lakshmi25npathi/images) | ham: 951; spam: 1005 | 它是为垃圾邮件研究而收集的公共评论集。 | |
### 样本示例
<details>
<summary>enron_spam 样本示例</summary>
<pre><code>------------
wanted to try ci 4 lis but thought it was way too expensive for you ?
<br>
viagra at $ 1 . 12 per dose
ready to boost your sex life ? positive ?
time to do it right now . order viagra at incredibly low prices
$ 1 . 12 per dose . unbelivable
remove
<br>
spam
------------
enron / hpl actuals for december 11 , 2000
<br>
teco tap 30 . 000 / enron ; 120 . 000 / hpl gas daily
ls hpl lsk ic 30 . 000 / enron
ham
------------
looking for cheap high - quality software ? rotated napoleonizes
<br>
water past also , burn , course . gave country , mass lot . act north
good . from , learn form most brother vary . when more for . up
stick , century put , song be . test , describe , plain , against wood
star . began dress ever group . here oh , most world stay .
<br>
spam
------------
ideabank website
<br>
please read the attached document for information about an exciting new
website for ets employees !
ham
------------
</code></pre>
</details>
<details>
<summary>enron_spam_subset 样本示例</summary>
<pre><code>------------
Subject: edrugs online
viagra - proven step to start something all over again .
nothing is more useful than silence .
teachers open the door . you enter by yourself .
how sharper than a serpent ' s tooth it isto have a thankless child !
spam
------------
Subject: start date : 12 / 13 / 01 ; hourahead hour : 5 ;
start date : 12 / 13 / 01 ; hourahead hour : 5 ; no ancillary schedules awarded . no variances detected .
log messages :
parsing file - - > > o : \ portland \ westdesk \ california scheduling \ iso final schedules \ 2001121305 . txt
ham
------------
Subject: cheapestt medss !
mediccationns at lowesst pricess everyy !
over 80 . % offf , pricess wontt get lowerr
we selll vic ' od ( in v , ia . gra x , ana . x
http : / / www . pr 3 sdlugs . com / ? refid = 87
<br>
spam
------------
Subject: fw : picture
>
>
> the following is an aerial photo of the wtc area . it kinda brings on
> vertigo , but is a phenomenal shot .
>
> http : / / userwww . service . emory . edu / ~ rdgarr / wtc . htm
ham
------------
</code></pre>
</details>
<details>
<summary>ling_spam 样本示例</summary>
<pre><code>------------
Subject: internet specialist 007 - the spy
<br>
internet specialist 007 - the spy learn everything about your friends , neighbors , enemies , employees or anyone else ! - - even your boss ! - - even yourself ! this mammoth snoop collection of internet sites will provide you the newest and most current addresses available on the net today . . . = 20 * track down an old debt , or anyone else that has done you wrong ! it 's incredible , and so many new data sites have come online in the past 90 days . . . * over 300 giant resources to look up people , credit , social security , current or past employment , mail order purchases , = 20 addresses , phone numbers , maps to city locations . . . * investigate your family history ! check birth , death , adoption or social security records check service records or army , navy , air force or = 20 marine corps . * locate an old friend ( or an enemy who is hiding ) or a lost = 20 love - - find e-mail , telephone or address information on anyone ! = 20 even look up * unlisted * phone numbers ! * find work by searching classified ads all over the world ! * screen prospective employees - - check credit , driving or criminal records verify income or educational accomplishments = 20 * check out your daughter 's new boyfriend ! * find trial transcripts and court orders ! * enjoy the enchantment of finding out a juicy tid-bit about a co-worker . the internet is a powerful megasource of information , = 20 if you only know where to look . i tell you how to find = 20 out nearly anything about anybody , and tell you exactly where to find it ! you will be amazed to find out what personal information = 20 other people can find out about you ! check your credit = 20 report so you can correct wrong information that may be = 20 used to deny you credit . research yourself first ! you ' ll be horrified , as i was , = 20 at how much data has been accumulated about you . any my huge collection is only the beginning ! once you = 20 locate these free private , college and government web sites , you ' ll find even more links to even more = 20 information search engines ! = 20 if you believe ( like i do ) that the information that is stored about each one of us should be freely accessible , you ' ll want to see the snoop collection i ' ve compiled . verify your own records , or find out what you need to = 20 know about others . i ' m telling you , it 's incredible what you can find out using the internet ! we will accept checks by fax at 813-269 - 9651 or > > > send $ 14 . 95 cash , check or money order to : > > > the coldwell group > > > p . o . box 3787 > > > dept 1007 > > > petersburg , va 23805 i will rush back to you my snoop information for fastest service include your * e-mail * address . = 20 * what information is available - - and exact url to get there ! * exactly where to look for - - and the clever way to use - - = 20 the above search engines , and tons more ! * my easy-to - browse categorized megacenter of information has my own description of how to use each site , and what you ' ll find when you get there - - and tricky tips on how to = 20 extract the best data ! you can know everything about everybody with this internet specialist collection ! * * soon to be available - - the most complete international internet spy = 20 sites available on the web today * * don ' t miss this one or you ' ll be sorry = 20 to be removed from our list please fax your address to 813-269 - 9651 . l = e3 = 01 @ u = 0b
<br>
spam
------------
Subject: usage - based models - symposium
<br>
announcing the sixth biennial symposium of the rice university department of linguistics usage-based models of language rice university march 15-18 , 1995 invited speakers : mira ariel tel aviv university joan bybee university of new mexico john du bois university of california , santa barbara michael israel university of california , san diego sydney lamb rice university ronald langacker university of california , san diego tom givon university of oregon brian macwhinney carnegie - mellon university janet pierrehumbert northwestern university john sinclair university of birmingham ( u . k . ) arie verhagen university of utrecht description : the goal of this symposium is to explore approaches to linguistic theory that have in common the aim of accounting for linguistic usage . the empirical data for such theories is not restricted to linguistic intuitions about acceptibility , but comes from usage events of varied types . the focus is on the patterns found in the various sorts of usage data examined , and how those patterns can be extracted , represented , and used by the human mind . research from a variety of traditions will be represented , including corpus-based analyses , discourse studies , experimental studies of language processing and language acquisition , and instrumental phonetics . the approaches taken can be called data-driven , rather than model-driven , in that the fewest possible prior assumptions are made about what types of data are relevant , and that large sets of usage events are observed so that the detailed patterns found in actual usage can emerge . moreover , the various approaches taken show signs of converging toward a view of language as a dynamic system in which linguistic knowledge is not separate from its processing in language use . the linguistic models representing this view are usage-based by virtue of three factors : ( 1 ) the importance placed on usage data for theory construction ; ( 2 ) the direct incorporation of processing ( production and comprehension ) into linguistic theory ; and ( 3 ) the requirement that the models arrived at , whatever the direct source of evidence , must be testable with reference to language use . registration : no charge . symposium attendance on a space-available basis . for further information , contact suzanne kemmer ( kemmer @ ruf . rice . edu ) or michael barlow ( barlow @ ruf . rice . edu ) snailmail : dept . of linguistics , rice university , houston tx 77251-1892 .
<br>
ham
------------
Subject: domani
<br>
new improved with free software , free bulk e mail system , free web site = to do what you wish , ongoing support ( optional ) , and a lot more ! all = included . . . . . . . . . . . this is a one time mailing . . . . . . . . . . . . . . . \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ $ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ = \ \ \ \ \ you are about to make at least $ 50 , 000 in less than 90 days read the enclosed program . . . then read it again . . . / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / = / / / / / / / dear friend , the enclosed information is something i almost let slip through my fingers . fortunately , sometime later i re-read everything and gave some thought and study to it . my name is christopher erickson . two years ago , the corporation i worked at for the past twelve years down-sized and my position was eliminated . after unproductive job interviews , i decided to open my own business . over the past year , i incurred many unforeseen financial problems . i owed my family , friends , and creditors over $ 35 , 000 . the economy was taking a toll on my business and i just could n't seem to make ends meet . i had to refinance and borrow against my home to support my family and struggling business . i truly believe it was wrong for me to be in debt like this . at that moment something significant happened in my life and i am writing to share my experience in hopes that this will change your life forever . . . . financially ! ! ! in mid - december , i received this program via email . six months prior to receiving this program i had been sending away for information on various business opportunities . all of the programs i received , in my opinion , were not cost effective . they were either too difficult for me to comprehend or the initial investment was too much for me to risk to see if they worked or not . one claimed i 'd make a million dollars in one year . . . it did n't tell me i 'd have to write a book to make it . but like i was saying , in december of ' 92 i received this program . i did n't send for it , or ask for it , they just got my name off a mailing list . thank goodness for that ! ! ! after reading it several times , to = make sure i was reading it correctly , i could n't believe my eyes . = 20 here was a money-making phenomenon . i could invest as much as i wanted = to start , without putting me further in debt . after i got a pencil and paper and figured it out , i would at least get my money back . after determining that the program is legal and not a chain letter , i decided " why not " . initially i sent out 10 , 000 emails . it only cost me about $ 15 . 00 for my time on-line . the great thing about email is that i did n't need any money for printing to send out the program , only the cost to fulfill my orders . i am telling you like it is , i hope it does n't turn you off , but i promised myself that i would not " rip-off " anyone , no matter how much money it cost me ! . in less than one week , i was starting to receive orders for report # 1 . by january 13th , i had received 26 orders for report # 1 . when you read the guarantee in the program , you will see that " you must receive = 15 to 20 orders for report # 1 within two weeks . if you don ' t , send out = more programs until you do ! " my first step in making $ 50 , 000 in 20 to = 90 days was done . by january 30th , i had received 196 orders for report = # 2 . if you go back to the guarantee , " you must receive 100 or more orders for report # 2 within two weeks . if not , send out more = programs until you do . once you have 100 orders , the rest is easy , = relax , you will make your $ 50 , 000 goal . " well , i had 196 orders for = report # 2 , 96 more than i needed . so i sat back and relaxed . by march = 19th , of my emailing of 10 , 000 , i received $ 58 , 000 with more coming in = every day . i paid off all my debts and bought a much needed new car . please take time to read the attached program , it will change your life forever ! remember , it wont work if you do n't try it . this program does work , but you must follow it exactly ! especially the rules of not trying to place your name in a different place . it does n't work , you ' ll lose out on a lot of money ! report # 2 explains this . = 20 always follow the guarantee , 15 to 20 orders for report # 1 , and 100 or more orders for report # 2 and you will make $ 50 , 000 or more in 20 to 90 days . i am living proof that it works ! ! ! if you choose not to participate in this program , i ' m sorry . it really is a great opportunity with little cost or risk to you . if you choose to participate , follow the program and you will be on your way to financial security . if you are a fellow business owner and you are in financial trouble like i was , or you want to start your own business , consider this a sign . i did ! sincerely , christopher erickson ps do you have any idea what 11 , 700 $ 5 bills ( $ 58 , 000 ) look like piled up on a kitchen table ? it ' s awesome ! " threw it away " " i had received this program before . i threw it away , but later wondered if i should n't have given it a try . of course , i had no idea who to contact to get a copy , so i had to wait until i was emailed another copy of the program . eleven months passed , then it came . i didn ' t throw this one away . i made $ 41 , 000 on the first try . " dawn w . , evansville , in " no free lunch " " my late father always told me , ' remember , alan , there is no free lunch in life . you get out of life what you put into it . ' through trial and error and a somewhat slow frustrating start , i finally figured it out . the program works very well , i just had to find the right target group of people to email it to . so far this year , i have made over $ 63 , 000 using this program . i know my dad would have been very proud of me . " alan b . , philadelphia , pa a personal note from the originator of this program by the time you have read the enclosed information and looked over the enclosed program and reports , you should have concluded that such a program , and one that is legal , could not have been created by an amateur . let me tell you a little about myself . i had a profitable business for ten years . then in 1979 my business began falling off . i was doing the same things that were previously successful for me , but it was n't working . finally , i figured it out . it was n't me , it was the economy . inflation and recession had replaced the stable economy that had been with us since 1945 . i do n't have to tell you what happened to the unemployment rate . . . because many of you know from first hand experience . there were more failures and bankruptcies than ever before . the middle class was vanishing . those who knew what they were doing = invested wisely and moved up . those who did not , including those who = never had anything to save or invest , were moving down into the ranks of = the poor . as the saying goes , " the rich get richer and the poor get = poorer . " the traditional methods of making money will never allow you = to " move up " or " get rich " , inflation will see to that . you have just received information that can give you financial freedom for the rest of your life , with " no risk " and " just a little bit of effort . " you can make more money in the next few months than you have = ever imagined . i should also point out that i will not see a penny of your money , nor anyone else who has provided a testimonial for this program . i have already made over four million dollars ! i have retired from the program after sending out over 16 , 000 programs . now i have several offices which market this and several other programs here in the us and overseas . by the spring , we wish to market the ' internet ' by a partnership with america on line . follow the program exactly as instructed . do not change it in any way . = it works exceedingly well as it is now . remember to email a copy of = this exciting program to everyone that you can think of . one of the people you send this to may send out 50 , 000 . . . and your name will be on every one of them ! . remember though , the more you send out , the = more potential customers you will reach . so my friend , i have given you the ideas , information , materials and opportunity to become financially independent , it is up to you now ! " think about it " before you delete this program from your mailbox , as i almost did , take a little time to read it and really think about it . get a pencil and figure out what could happen when you participate . figure out the worst possible response and no matter how you calculate it , you will still make a lot of money ! definitely get back what you invested . = 20 any doubts you have will vanish when your first orders come in . it works ! paul johnson , raleigh , nc here ' s how this amazing program will make you $ $ $ $ $ $ let 's say that you decide to start small , just to see how it goes , and we ' ll assume you and all those involved send out 2 , 000 programs each . let 's also assume that the mailing receives a . 5 % response . using a good list the response could be much better . also many people will send out hundreds of thousands of programs instead of 2 , 000 . but continuing with this example , you send out only 2 , 000 programs . with a . 5 % response , that is only 10 orders for report # 1 . those 10 people respond by sending out 2 , 000 programs each for a total of 20 , 000 . out of those . 5 % , 100 people respond and order report # 2 . those 100 mail out 2 , 000 programs each for a total of 200 , 000 . the . 5 % response to that is 1 , 000 orders for report # 3 . those 1 , 000 send out 2 , 000 programs each for a 2 , 000 , 000 total . the . 5 % response to that is 10 , 000 orders for report # 4 . that 's 10 , 000 five dollar bills for you . cash ! ! ! ! your total income in this example is $ 50 + $ 500 + $ 5000 + $ 50 , 000 for a total of $ 55 , 550 ! ! ! ! remember friend , this is assuming 1 , 990 out of 2 , 000 people you mail to = will do absolutely nothing . . . and trash this program ! dare to think for = a moment what would happen if everyone or half sent out 100 , 000 programs instead of only 2 , 000 . believe me , many people will do = that and more ! by the way , your cost to participate in this is = practically nothing . you obviously already have an internet connection and email is free ! ! ! report # 3 will show you the best methods for bulk emailing and purchasing email lists . this is a legitimate , legal , money making opportunity . it does not require you to come in contact with people , do any hard work , and best of all , you never have to leave the house except to get the mail . if you believe that someday you ' ll get that big break that you ' ve been waiting for , this is it ! simply follow the instructions , and your dream will come true . this multi-level email order marketing program works perfectly . . . 100 % every time . email is the sales tool of the future . take advantage of this non-commercialized method of advertising now ! ! the longer you wait , the more people will be doing business using email . get your piece of this action ! ! multi-level marketing ( mlm ) has finally gained respectability . it is = being taught in the harvard business school , and both stanford research and the wall street journal have stated that between 50 % and = 65 % of all goods and services will be sold throughout multi - level methods by the mid to late 1990 's . this is a multi - billion dollar industry and of the 500 , 000 millionaires in the us , 20 % ( 100 , 000 ) made their fortune in the last several years in mlm . moreover , statistics show 45 people become millionaires everyday through multi - level marketing . instructions we at erris mail order marketing business , have a method of raising capital that really works 100 % every time . i am sure that you could use = $ 50 , 000 to $ 125 , 000 in the next 20 to 90 days . before you say " bull " , please read the program carefully . this is not a chain letter , but a perfectly legal money making opportunity . basically , this is what we do : as with all multi-level business , we build our business by recruiting new partners and selling our products . every state in the usa allows you to recruit new multi - level business partners , and we offer a product for every dollar sent . your orders come and are filled through the mail , so you are not = involved in personal selling . you do it privately in your own home , = store or office . this is the greatest multi - level mail order marketing anywhere : step ( 1 ) order all four 4 reports listed by name and number . dothis by ordering the report from each of the four 4 names listed on the next page . for each report , send $ 5 cash and a self - addressed , stamped envelope ( business size # 10 ) = to the person listed for the specific report . international = = 20 orders should also include $ 2 extra for postage . it is essential that you specify the name and number of the report requested to the person you are ordering from . you will need all four 4 reports because you will be reprinting and reselling them . do not alter the names or sequence other than what the instructions say . important : always provide same-day service on all orders . step ( 2 ) replace the name and address under report # 1 with yours , moving the one that was there down to report # 2 . drop the name and address under report # 2 to report # 3 , moving the one that was there to report # 4 . the name and address that was under report # 4 is dropped from the list and this party is no doubt on the way to the bank . when doing this , make certain you type the names and addresses accurately ! do not mix up moving product / report positions ! ! ! step ( 3 ) having made the required changes in the name list , save it as a text ( . txt ) file in it 's own directory to be used with whatever email program you like . again , report # 3 will tell you the best methods of bulk emailing and acquiring email lists . step ( 4 ) email a copy of the entire program ( all of this is very important ) to everyone whose address you can get your hands on . start with friends and relatives since you can encourage them to take advantage of this fabulous = 20 money-making opportunity . that 's what i did . and they love me now , more than ever . then , email to anyone and everyone ! use your imagination ! you can get email addresses from companies on the internet who specialize in email mailing lists . these are very cheap , 100 , 000 addresses for around $ 35 . 00 . important : you won't get a good response if you use an old list , so always request a fresh , new list . you will find out where to purchase these lists when you order the four 4 reports . always provide same-day service on all orders ! ! ! required reports * * * order each report by number and name * * * always send a self-addressed , stamped envelope and $ 5 usd cash for each order requesting the specific report by name and number ( international orders should also include $ 2 usd extra for postage ) = 20 add you e amil address when sending in for your report this is for = updated information and continueing support ( optional ) that will be = handed down by you sponcers . _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ report # 1 " how to make $ 250 , 000 through multi-level sales " order report # 1 from : a . siegmund # 57 trakehnenstr . 13 53332 bornheim , germany _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ report # 2 " major corporations and multi-level sales " j . maz 15774 s . lagrange rd suite # 312 orland pk , il 60462 usa _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ order report # 2 from : a . siegmund # 57 trakehnenstr . 13 53332 bornheim , germany _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ report # 3 " sources for the best mailing lists " order report # 3 from : b . thompson 13504 greencaslte ridge tr . 404 burtonsville md . 20866 _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ report # 4 " evaluating multi-level sales plans " order report # 4 from : muw # 2 po box 71442 salt lake city , ut 84171-0442 _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ conclusion . i am enjoying my fortune that i made by sending out this program . you too , will be making money in 20 to 90 days , if you follow the simple steps outlined in this mailing . to be financially independent is to be free . free to make financial decisions as never before . go into business , get into investments , retire or take a vacation . = 20 = = = = = = 20
<br>
spam
------------
Subject: linguistic datasources
<br>
at the request of subscribers , we ' ve been collecting the addresses of linguistic datasources which can be reached through world wide web . these addresses are now available to any of you who have web access on the linguist web server at the following url : http : / / engserve . tamu . edu / files / linguistics / linguist / datasources . html this file is also available , to those of you who read web - linguist , through the " linguistic datasources " link . we 'd be delighted to hear any comments anyone would care to make . and if there ' re any addresses we need to add , please let us know what they are . we 'd like to emphasize that we 'd be happy to include sites where individual linguists keep data they would like to make available to their colleagues . since the web allows us to share not merely text , but pictures and sound-recordings , we can now begin an interchange of linguistic information that is of a very different nature from that which was possible in the past . anthony & helen
<br>
ham
------------
</code></pre>
</details>
<details>
<summary>sms_spam 样本示例</summary>
<pre><code>------------
She's borderline but yeah whatever.
ham
------------
URGENT! You have won a 1 week FREE membership in our £100,000 Prize Jackpot! Txt the word: CLAIM to No: 81010 T&C www.dbuk.net LCCLTD POBOX 4403LDNW1A7RW18
spam
------------
I got a call from a landline number. . . I am asked to come to anna nagar . . . I will go in the afternoon
ham
------------
Free Msg: get Gnarls Barkleys "Crazy" ringtone TOTALLY FREE just reply GO to this message right now!
spam
------------
I will be gentle princess! We will make sweet gentle love...
ham
------------
Refused a loan? Secured or Unsecured? Can't get credit? Call free now 0800 195 6669 or text back 'help' & we will!
spam
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Sorry, went to bed early, nightnight
ham
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Free entry in 2 a wkly comp to win FA Cup final tkts 21st May 2005. Text FA to 87121 to receive entry question(std txt rate)T&C's apply 08452810075over18's
spam
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HMM yeah if your not too grooved out! And im looking forward to my pound special :)
ham
------------
FreeMsg Hey there darling it's been 3 week's now and no word back! I'd like some fun you up for it still? Tb ok! XxX std chgs to send, £1.50 to rcv
spam
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Hi dude hw r u da realy mising u today
ham
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WINNER!! As a valued network customer you have been selected to receivea £900 prize reward! To claim call 09061701461. Claim code KL341. Valid 12 hours only.
spam
------------
</code></pre>
</details>
<details>
<summary>spam_assassin 样本示例</summary>
<pre><code>------------
"\n>>>Robert Elz said:\n > Date: Wed, 28 Aug 2002 09:22:34 -0500\n > From: Chris Garrigues <[email protected]\nm>\n > Message-ID: <[email protected]>\n > \n > \n > | so I'll probably poke around at the sequences performance issues,\n > \n > Well, there's this wonderful piece of code in MhSeqExpand ...\n > \n > # Hack to weed out sequence numbers for messages that don't exist\n > foreach m $rseq {\n > if ![file exists $mhProfile(path)/$folder/$m] {\n > Exmh_Debug $mhProfile(path)/$folder/$m not found\n > set ix [lsearch $seq $m]\n > set seq [lreplace $seq $ix $ix]\n > } else {\n > # Real hack\n\nAt least I'm up-front about my hacks :-)\n\n > break\n > }\n > }\n > \n > which is going to run slow if a sequence happens to start with a bunch\n > of messages that don't exist. I'm not sure why it is important that the\n > first message in the sequence returned exists, but not necessarily any\n > of the others, but I'm sure glad it is, as MhSeqExpand gets called lots,\n > and I don't know if I could cope if it were checking every file in the\n > sequences it is looking at, all the time...\n\nThat was my thinking. My recollection about the first message being valid\nis that the ftoc code wants to find that message to start its highlighting,\nfor example, or you are selecting a message to display.\n\n > It may help to keep a list of the valid message numbers for the current\n > folder (though that would then need to be verified against changes to the\n > directory). Does tcl have a directory read function? I assume so...\n\nglob -nocomplain $mhProfile(path)/$folder *\nwill return an unsorted list of the directory's contents.\nBut the thought of keeping an in memory list of valid messages is not fun.\nExmh already maintains in-core lists of messages in sequences, which is\nalready pretty tricky\n\n > Mh_Sequence also goes and rereads the files (.mh_sequences and the\n > context file) but I'm not sure how frequently that one is called.\n\nIn some places I maintain caches of files by checking their modify time,\nbut the sequence files are soo small that by the time you stat them to\ncheck their date stamp, you could just read them again. Also, now that\nwe checkpoint message state on every message view, that file will change\nevery time. In the old days exmh used to cache a bunch of state about\nthe folder.\n\n--\nBrent Welch\nSoftware Architect, Panasas Inc\nPioneering the World's Most Scalable and Agile Storage Network\nwww.panasas.com\[email protected]\n\n\n\n\n_______________________________________________\nExmh-workers mailing list\[email protected]\nhttps://listman.redhat.com/mailman/listinfo/exmh-workers\n\n"
spam
------------
"Whiter teeth and a brighter smile are just a click away! \nhttp://www.newnamedns.com/dental/\n\nHave you considered professional teeth whitening? If so, you know it usually costs between $300 and $500 from your local dentist!\n\nVisit our site to learn how to professionally whiten your teeth, using the exact same whitening system your dentist uses, at a fraction of the cost!\n\nWe know our product is the best on the market, and we back it with a 30 day money back guarantee!\n\nClick here to find out more!\nhttp://www.newnamedns.com/dental/\n \n \n \n \n \n---------------------------------------\nTo easily remove your address from the list, go to: \nhttp://www.newnamedns.com/stopthemailplease/\nPlease allow 48-72 hours for removal.\n\n"
ham
------------
"> Hi,\n> \n> On Sun, 01 Sep 2002 00:05:03 MDT Reg Clemens wrote: \n> \n> [...]\n> > in messages with GnuPG signatures. But punching the line ALWAYS\n> > gives\n> > \n> > Signature made Thu Aug 29 00:27:17 2002 MDT using DSA key ID BDDF997A\n> > Can't check signature: public key not found\n> > \n> > So, something else is missing.\n> \n> Yes, the public key of the signature you want to check :-).\n> \n> Are you really sure that you have the public key of the message's\n> signature? If not, try downloading it or try to check a signature from\n> which you know you have the public key.\n> \n> \n> \n\nAh, sorry for not making that clearer.\nBut no.\nPreviously (v1.0.6 of GnuPG) there would be a slight pause at this point while\nit went out to get the public key from a keyserver.\nNow, whether I have the key or NOT, I get the failure message.\n\nIts as if it cant find gpg to execute it (but I fixed that path), so there\nmust be something else that I am missing...\n\n\n-- \n Reg.Clemens\n [email protected]\n\n\n\n\n_______________________________________________\nExmh-users mailing list\[email protected]\nhttps://listman.redhat.com/mailman/listinfo/exmh-users\n\n"
spam
------------
"Below is the result of your feedback form. It was submitted by\n ([email protected]) on Tuesday, August 27, 2002 at 22:26:56\n---------------------------------------------------------------------------\n\n:: <A HREF=\"http://www.pornhere.net\">click here</A>Don't want to pay for Porn? Would you like to get it for FREE? The Honest, No Risk 100% Free Way? If you take a couple of minutes and read through our simple guide you will be able to get Free Passes to the top Paysites online!<A HREF=\"http://www.pornhere.net\">click here</A>\n\n---------------------------------------------------------------------------\n\n\n"
ham
------------
</code></pre>
</details>
<details>
<summary>spam_detection 样本示例</summary>
<pre><code>------------
Looking for an epic way to boost your followers? Want to increase your likes? Well, listen up! Our site has the ultimate solution for you! With our automated system, you can get hundreds of followers in seconds! Not only that, but our team of social media experts will also provide you with the latest and greatest tips and tricks to make your page go viral! Don't wait, join the millions of successful users who have already transformed their social media game with our help!
<br>
spam
------------
I'm working on a bayesian network to genetare synthetic WIM observations such as gross vehicle weight, total vehicle length, individual axle load and inter axle distances. It will be very helpful to have data sets for different sites. Thanks
ham
------------
Hey, check out these amazing deals just for you! 🎁🎉
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spam
------------
Looking for a dataset with the latest Omicron covid variant data and confirmed locations. Any links would be appreciated, thanks!
ham
------------
</code></pre>
</details>
<details>
<summary>spam_message 样本示例</summary>
<pre><code>------------
欣彩印刷厂主营彩盒.吊牌.产品说明书及提供四色印刷对外加工.电话xxxxxxxxxxx米先生
spam
------------
微软称Windows10是“有史以来最安全的Windows版本”
ham
------------
您好！欢迎致电川中意货运有限公司，本公司承接成都至恩施，武汉至恩施及周边县市，覆射全国各地的整车、零担往返运输业务。我们以“信誉第一，
spam
------------
我用了9个月时间学会在医院的时候自己照顾自己这件事情
ham
------------
在元宵节到来之际
spam
------------
丰县大队要求各中队加强日常执勤
ham
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欢迎致电开锁王。本公司公安备案，工商注册。经营范围：汽车锁，保险柜，防盗门及各种民用锁芯升级！联系电话xxxx-xxxxxxx
spam
------------
扬州天姿现在基本也留不住高端顾客群体了
ham
------------
</code></pre>
</details>
<details>
<summary>spam_message_lr 样本示例</summary>
<pre><code>------------
3G小贴士提醒您可不要让您的流量白白浪费了哦，快来唤醒吧！与您分享杨子黎2013全新单曲《爱人好累》MV 详情点击：http://yuny.com.cn:3042/tpo/SU/NjiYby
spam
------------
巫倩云:来周总办公室现在
ham
------------
结婚娶亲本是一件高兴事，新郎却因一辆加长林肯车而当场落泪！这是为什么？详情请点击 http://10006.co/lbJ5
spam
------------
PP提醒你来认证啦！在电脑上登录PP租车官方网站（www.ppzuche.com）或下载PP租车手机客户端（www.ppzuche.com/get-app）上传身份证和驾驶证照片，即可完成租客身份认证。600余款车型，低于市场价30%，随时随地取车，开启便捷用车新时代！【PP租车】
ham
------------
【联通飞影】对美女自作多情的后果… http://fql.cc/pub/view/iid-48305
spam
------------
您已成功添加王然(13811083077)为好友，可以接收对方的飞信消息。回复本短信可直接与对方聊天。
ham
------------
棕盛商业广场一期5月18号火爆认筹，孟庭苇邀您亲见世界500强签约蕲春，VIP卡全城免费热办中。0713-7300000【棕盛商业地产】
spam
------------
信用卡1707于10月23日16:19消费人民币360.07,记账金额以账单显示为准。加“浦发银行信用卡中心”官方微信查询/待续【浦发银行】
ham
------------
</code></pre>
</details>
### 参考来源
<details>
<summary>参考的数据来源,展开查看</summary>
<pre><code>
https://huggingface.co/datasets/dbarbedillo/SMS_Spam_Multilingual_Collection_Dataset
https://huggingface.co/datasets/FredZhang7/all-scam-spam
https://huggingface.co/datasets/Deysi/spam-detection-dataset
https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-sms_spam-216c1ded-12215630
https://huggingface.co/datasets/NotShrirang/email-spam-filter
https://huggingface.co/datasets/sms_spam
</code></pre>
</details>
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|
mesolitica/chatgpt4-code-instruct | mesolitica | 2023-12-10T19:55:53Z | 2 | 0 | [
"task_categories:conversational",
"language:ms",
"region:us"
]
| [
"conversational"
]
| 2023-12-04T08:18:52Z | ---
task_categories:
- conversational
language:
- ms
---
# ChatGPT4 Code Instruct
Originally from https://huggingface.co/datasets/theblackcat102/evol-codealpaca-v1, translate and answer using ChatGPT4.
Notebooks at https://github.com/mesolitica/malaysian-dataset/tree/master/chatbot/chatgpt4-code-instruct | [
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|
pythainlp/thai-wiktionary-prompt | pythainlp | 2023-12-06T07:51:22Z | 2 | 0 | [
"task_categories:text2text-generation",
"task_categories:text-generation",
"size_categories:10K<n<100K",
"language:th",
"license:cc-by-sa-3.0",
"region:us"
]
| [
"text2text-generation",
"text-generation"
]
| 2023-12-04T13:33:04Z | ---
language:
- th
license: cc-by-sa-3.0
size_categories:
- 10K<n<100K
task_categories:
- text2text-generation
- text-generation
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: inputs
dtype: string
- name: targets
dtype: string
splits:
- name: train
num_bytes: 10265752
num_examples: 19729
download_size: 2491006
dataset_size: 10265752
---
# Dataset Card for "thai-wiktionary-prompt"
This dataset made to create prompt for get the meaning of word.
Dataset from [Thai wiktionary](https://th.wiktionary.org/).
## Template
```
Inputs: จงบอกความหมายของคำต่อไปนี้: {word}
Targets: คำว่า {word} มีความหมายตามชนิดของคำได้ดังต่อไปนี้\n
- POS มีความหมายได้ว่า the meaning of word (split with "/")
```
Example:
```
Inputs: จงบอกความหมายของคำต่อไปนี้: ศึกษา
Targets: คำว่า ศึกษา มีความหมายตามชนิดของคำได้ดังต่อไปนี้
- คำนาม มีความหมายได้ว่า การเล่าเรียน การฝึกฝน และการอบรม
- คำกริยา มีความหมายได้ว่า เล่าเรียน ฝึกฝน และอบรม
```
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|
blanchon/FAIR1M | blanchon | 2023-12-06T12:38:09Z | 2 | 0 | [
"task_categories:object-detection",
"language:en",
"license:unknown",
"remote-sensing",
"earth-observation",
"geospatial",
"satellite-imagery",
"object-detection",
"arxiv:2103.05569",
"region:us"
]
| [
"object-detection"
]
| 2023-12-04T14:22:26Z | ---
language: en
license: unknown
task_categories:
- object-detection
paperswithcode_id: FAIR1M
pretty_name: FAIR1M
tags:
- remote-sensing
- earth-observation
- geospatial
- satellite-imagery
- object-detection
---
# FAIR1M
<!-- Dataset thumbnail -->
<!-- Provide a quick summary of the dataset. -->
The FAIR1M dataset is a fine-grained object recognition and detection dataset that focuses on high-resolution (0.3-0.8m) RGB images taken by the Gaogen (GF) satellites and extracted from Google Earth. It consists of a collection of 15,000 high-resolution images that cover various objects and scenes. The dataset provides annotations in the form of rotated bounding boxes for objects belonging to 5 main categories (ships, vehicles, airplanes, courts, and roads), further divided into 37 sub-categories.
- **Paper:** https://arxiv.org/abs/2103.05569
- **Homepage:** https://www.gaofen-challenge.com/benchmark
## Description
<!-- Provide a longer summary of what this dataset is. -->
FAIR1M is a part of the ISPRS Benchmark on Object Detection in High-Resolution Satellite Images. Please note that, as of now, only a portion of the training dataset (1,732/15,000 images) has been released for the challenge.
- **1 million object instances**
- **Number of Samples**: 15000
- **Bands**: 3 (RGB)
- **Image Size**: 1024x1024
- **Image Resolution**: 0.3–0.8m
- **Land Cover Classes**: 37
- **Classes**: 5 object categories, 37 object sub-categories.
- **Scene Categories**: Passenger Ship, Motorboat, Fishing Boat, Tugboat, other-ship, Engineering Ship, Liquid Cargo Ship, Dry Cargo Ship, Warship, Small Car, Bus, Cargo Truck, Dump Truck, other-vehicle, Van, Trailer, Tractor, Excavator, Truck Tractor, Boeing737, Boeing747, Boeing777, Boeing787, ARJ21, C919, A220, A321, A330, A350, other-airplane, Baseball Field, Basketball Court, Football Field, Tennis Court, Roundabout, Intersection, Bridge
- **Source**: Gaofen/Google Earth
## Usage
To use this dataset, simply use `datasets.load_dataset("blanchon/FAIR1M")`.
<!-- Provide any additional information on how to use this dataset. -->
```python
from datasets import load_dataset
FAIR1M = load_dataset("blanchon/FAIR1M")
```
## Citation
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
If you use the FAIR1M dataset in your research, please consider citing the following publication:
```bibtex
@article{sun2021fair1m,
title = {FAIR1M: A Benchmark Dataset for Fine-grained Object Recognition in High-Resolution Remote Sensing Imagery},
author = {Xian Sun and Peijin Wang and Zhiyuan Yan and F. Xu and Ruiping Wang and W. Diao and Jin Chen and Jihao Li and Yingchao Feng and Tao Xu and M. Weinmann and S. Hinz and Cheng Wang and K. Fu},
journal = {Isprs Journal of Photogrammetry and Remote Sensing},
year = {2021},
doi = {10.1016/j.isprsjprs.2021.12.004},
bibSource = {Semantic Scholar https://www.semanticscholar.org/paper/6d3c2dc63ff0deec10f60e5a515c93af4f8676f2}
}
```
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|
dumyy/token-classification-brand | dumyy | 2023-12-07T16:55:43Z | 2 | 0 | [
"region:us"
]
| null | 2023-12-04T14:35:05Z | ---
dataset_info:
features:
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': '0'
'1': B-BRAND
'2': I-BRAND
- name: spans
dtype: string
splits:
- name: train
num_bytes: 462
num_examples: 3
- name: val
num_bytes: 462
num_examples: 3
download_size: 4670
dataset_size: 924
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: val
path: data/val-*
---
# Dataset Card for "token-classification-brand"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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|
TIGER-Lab/I2V-Test | TIGER-Lab | 2023-12-05T04:00:31Z | 2 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-12-04T21:11:38Z | ---
license: openrail
---
This repo contains the testing data for image-to-video generation evaluation set. | [
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|
wilsonorozco/porfiriotorres | wilsonorozco | 2023-12-05T11:57:00Z | 2 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-12-04T22:49:17Z | ---
license: openrail
---
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|
KETI-AIR/kor_ai2_arc | KETI-AIR | 2023-12-05T02:37:22Z | 2 | 0 | [
"license:cc-by-sa-4.0",
"region:us"
]
| null | 2023-12-05T01:35:37Z | ---
license: cc-by-sa-4.0
configs:
- config_name: ARC-Challenge
data_files:
- split: train
path: ARC-Challenge/train-*
- split: validation
path: ARC-Challenge/validation-*
- split: test
path: ARC-Challenge/test-*
- config_name: ARC-Easy
data_files:
- split: train
path: ARC-Easy/train-*
- split: validation
path: ARC-Easy/validation-*
- split: test
path: ARC-Easy/test-*
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
dataset_info:
- config_name: ARC-Challenge
features:
- name: data_index_by_user
dtype: int32
- name: id
dtype: string
- name: question
dtype: string
- name: choices
struct:
- name: text
sequence: string
- name: label
sequence: string
- name: answerKey
dtype: string
splits:
- name: train
num_bytes: 396164
num_examples: 1119
- name: validation
num_bytes: 108314
num_examples: 299
- name: test
num_bytes: 425252
num_examples: 1172
download_size: 516331
dataset_size: 929730
- config_name: ARC-Easy
features:
- name: data_index_by_user
dtype: int32
- name: id
dtype: string
- name: question
dtype: string
- name: choices
struct:
- name: text
sequence: string
- name: label
sequence: string
- name: answerKey
dtype: string
splits:
- name: train
num_bytes: 694289
num_examples: 2251
- name: validation
num_bytes: 175983
num_examples: 570
- name: test
num_bytes: 735067
num_examples: 2376
download_size: 861121
dataset_size: 1605339
- config_name: default
features:
- name: data_index_by_user
dtype: int32
- name: id
dtype: string
- name: question
dtype: string
- name: choices
struct:
- name: text
sequence: string
- name: label
sequence: string
- name: answerKey
dtype: string
splits:
- name: train
num_bytes: 694289
num_examples: 2251
- name: validation
num_bytes: 175983
num_examples: 570
- name: test
num_bytes: 735067
num_examples: 2376
download_size: 861121
dataset_size: 1605339
---
# Dataset Card for "kor_ai2_arc"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
# Source Data Citation Information
```
@article{allenai:arc,
author = {Peter Clark and Isaac Cowhey and Oren Etzioni and Tushar Khot and
Ashish Sabharwal and Carissa Schoenick and Oyvind Tafjord},
title = {Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge},
journal = {arXiv:1803.05457v1},
year = {2018},
}
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|
asrar7787/dataset_mistral_instruct_commerce_operations-performance_best_practices_v1 | asrar7787 | 2023-12-05T05:51:03Z | 2 | 0 | [
"license:mit",
"region:us"
]
| null | 2023-12-05T05:48:52Z | ---
license: mit
---
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|
gagan3012/fin_instruct_dpo | gagan3012 | 2023-12-05T09:27:13Z | 2 | 0 | [
"region:us"
]
| null | 2023-12-05T06:51:29Z | ---
dataset_info:
features:
- name: messages
list:
- name: content
dtype: string
- name: role
dtype: string
- name: chosen
list:
- name: content
dtype: string
- name: role
dtype: string
- name: prompt
dtype: string
- name: prompt_id
dtype: string
- name: rejected
list:
- name: content
dtype: string
- name: role
dtype: string
splits:
- name: train
num_bytes: 135251054.04366517
num_examples: 42601
- name: test
num_bytes: 1368352.9563348205
num_examples: 431
download_size: 76818308
dataset_size: 136619407.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
# Dataset Card for "fin_instruct_dpo"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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|
IsaNLP/RuSRL | IsaNLP | 2023-12-05T11:01:32Z | 2 | 0 | [
"task_categories:token-classification",
"annotations_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"language:ru",
"license:cc-by-nc-4.0",
"semantic-role-labeling",
"syntax-parsing",
"tokenization",
"region:us"
]
| [
"token-classification"
]
| 2023-12-05T09:58:39Z | ---
language:
- ru
multilinguality:
- monolingual
annotations_creators:
- expert-generated
pretty_name: RuSRL
license: cc-by-nc-4.0
task_categories:
- token-classification
subtasks:
- semantic-role-labeling
- parsing
tags:
- semantic-role-labeling
- syntax-parsing
- tokenization
size_categories:
- 1K<n<10K
---
# Dataset Card for RuSRL
## Dataset Summary
This dataset contains annotations of semantic frames and intra-frame syntax for 1500 Russian sentences.
### Dataset Description
Each sentence is annotated with predicate-argument structures. Syntactic information is also provided for each frame.
```
{
"sent_id": 1404,
"tokens": ["в", "такой", "ситуации", "основные", "метеоэлементы",
"-", "температура", ",", "влажность", ",", "давление", "-",
"претерпевают", "малые", "суточные", "изменения", "."],
"synt_head": [12, 2, 0, 4, 12, -1, 4, -1, 6, -1, 8, -1, -1, 15, 15, 12, -1],
"sem_head": [-1, -1, -1, -1, 12, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 12, -1],
"sem_role": ["_", "_", "_", "_", "субъект", "_", "_", "_", "_", "_", "_", "_", "_", "_", "_", "предикат", "_"]
}
```
- **Language:** Russian
- **Size:** 1500 sentences
## Citation
```
@inproceedings{shelmanov2014methods,
title={Methods for semantic role labeling of Russian texts},
author={Shelmanov, AO and Smirnov, IV},
booktitle={Computational Linguistics and Intellectual Technologies: Papers from the Annual International Conference Dialogue},
volume={13},
number={20},
pages={607--620},
year={2014}
}
``` | [
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|
omkar56/text_category_classification | omkar56 | 2023-12-05T12:35:21Z | 2 | 0 | [
"license:apache-2.0",
"region:us"
]
| null | 2023-12-05T11:03:56Z | ---
license: apache-2.0
---
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|
HydraIndicLM/bengali_alpaca_dolly_67k | HydraIndicLM | 2023-12-05T11:11:02Z | 2 | 0 | [
"region:us"
]
| null | 2023-12-05T11:10:58Z | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: id
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 116369973
num_examples: 67017
download_size: 44110061
dataset_size: 116369973
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
HydraIndicLM/telgu_alpaca_dolly_67k | HydraIndicLM | 2023-12-05T11:12:51Z | 2 | 0 | [
"region:us"
]
| null | 2023-12-05T11:12:48Z | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: id
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 109797855
num_examples: 67017
download_size: 43050214
dataset_size: 109797855
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
HydraIndicLM/odia_alpaca_dolly_67k | HydraIndicLM | 2023-12-05T11:14:04Z | 2 | 0 | [
"region:us"
]
| null | 2023-12-05T11:14:01Z | ---
dataset_info:
features:
- name: output
dtype: string
- name: instruction
dtype: string
- name: input
dtype: string
splits:
- name: train
num_bytes: 117352849
num_examples: 64389
download_size: 44003356
dataset_size: 117352849
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
DianaJin/dataset2 | DianaJin | 2023-12-05T11:25:02Z | 2 | 0 | [
"region:us"
]
| null | 2023-12-05T11:21:13Z | ---
dataset_info:
features:
- name: input_features
sequence:
sequence: float32
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 31704216
num_examples: 33
- name: test
num_bytes: 4803832
num_examples: 5
- name: valid
num_bytes: 3842376
num_examples: 4
download_size: 13773361
dataset_size: 40350424
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: valid
path: data/valid-*
---
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|
aghent/copiapoas-final-ds | aghent | 2023-12-08T15:43:48Z | 2 | 0 | [
"region:us"
]
| null | 2023-12-05T12:52:58Z | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype: image
splits:
- name: train
num_bytes: 154401846.0
num_examples: 10000
download_size: 133707722
dataset_size: 154401846.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
blanchon/ADVANCE | blanchon | 2023-12-05T14:14:32Z | 2 | 0 | [
"task_categories:image-classification",
"size_categories:1K<n<10K",
"language:en",
"license:unknown",
"remote-sensing",
"earth-observation",
"geospatial",
"satellite-imagery",
"audiovisual-aerial-scene-recognition",
"sentinel-2",
"arxiv:2005.08449",
"region:us"
]
| [
"image-classification"
]
| 2023-12-05T13:38:06Z | ---
language: en
license: unknown
size_categories:
- 1K<n<10K
task_categories:
- image-classification
paperswithcode_id: advance
pretty_name: ADVANCE
tags:
- remote-sensing
- earth-observation
- geospatial
- satellite-imagery
- audiovisual-aerial-scene-recognition
- sentinel-2
dataset_info:
features:
- name: image
dtype: image
- name: audio
dtype: audio
- name: label
dtype:
class_label:
names:
'0': airport
'1': beach
'2': bridge
'3': farmland
'4': forest
'5': grassland
'6': harbour
'7': lake
'8': orchard
'9': residential
'10': sparse shrub land
'11': sports land
'12': train station
splits:
- name: train
num_bytes: 6698580359.05
num_examples: 5075
download_size: 6688165513
dataset_size: 6698580359.05
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# ADVANCE
<!-- Dataset thumbnail -->
<!-- Provide a quick summary of the dataset. -->
Audiovisual Aerial Scene Recognition Dataset (ADVANCE) is a comprehensive resource designed for audiovisual aerial scene recognition tasks. It consists of 5,075 pairs of geotagged audio recordings and high-resolution 512x512 RGB images extracted from FreeSound and Google Earth. These images are then labeled into 13 scene categories using OpenStreetMap.
- **Paper:** https://arxiv.org/abs/2005.08449
- **Homepage:** https://akchen.github.io/ADVANCE-DATASET/
## Description
<!-- Provide a longer summary of what this dataset is. -->
The **Audiovisual Aerial Scene Recognition Dataset** is a comprehensive resource designed for audiovisual aerial scene recognition tasks. It consists of 5,075 pairs of geotagged audio recordings and high-resolution 512x512 RGB images extracted from [FreeSound](https://freesound.org/browse/geotags/?c_lat=24&c_lon=20&z=2) and [Google Earth](https://earth.google.com/web/). These images are then labeled into 13 scene categories using OpenStreetMap
The dataset serves as a valuable benchmark for research and development in audiovisual aerial scene recognition, enabling researchers to explore cross-task transfer learning techniques and geotagged data analysis.
- **Total Number of Images**: 5075
- **Bands**: 3 (RGB)
- **Image Resolution**: 10mm
- **Image size**: 512x512
- **Land Cover Classes**: 13
- **Classes**: airport, beach, bridge, farmland, forest, grassland, harbour, lake, orchard, residential, sparse shrub land, sports land, train station
- **Source**: Sentinel-2
- **Dataset features**: 5,075 pairs of geotagged audio recordings and images, three spectral bands - RGB (512x512 px), 10-second audio recordings
- **Dataset format**:, images are three-channel jpgs, audio files are in wav format
## Usage
To use this dataset, simply use `datasets.load_dataset("blanchon/ADVANCE")`.
<!-- Provide any additional information on how to use this dataset. -->
```python
from datasets import load_dataset
ADVANCE = load_dataset("blanchon/ADVANCE")
```
## Citation
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
If you use the EuroSAT dataset in your research, please consider citing the following publication:
```bibtex
@article{hu2020crosstask,
title = {Cross-Task Transfer for Geotagged Audiovisual Aerial Scene Recognition},
author = {Di Hu and Xuhong Li and Lichao Mou and P. Jin and Dong Chen and L. Jing and Xiaoxiang Zhu and D. Dou},
journal = {European Conference on Computer Vision},
year = {2020},
doi = {10.1007/978-3-030-58586-0_5},
bibSource = {Semantic Scholar https://www.semanticscholar.org/paper/7fabb1ef96d2840834cfaf384408309bafc588d5}
}
```
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|
ineoApp/ds_01 | ineoApp | 2023-12-05T13:49:34Z | 2 | 0 | [
"region:us"
]
| null | 2023-12-05T13:44:03Z | ---
dataset_info:
features:
- name: id
dtype: string
- name: image
dtype: image
- name: bboxes
sequence:
sequence: int64
- name: ner_tags
sequence:
class_label:
names:
'0': O
'1': numero facture
'2': fournisseur
'3': date facture
'4': date limite
'5': montant ht
'6': montant ttc
'7': tva
'8': prix tva
'9': addresse
'10': reference
'11': art1 designation
'12': art1 quantite
'13': art1 prix unit
'14': art1 tva
'15': art1 montant ht
'16': art2 designation
'17': art2 quantite
'18': art2 prix unit
'19': art2 tva
'20': art2 montant ht
- name: tokens
sequence: string
splits:
- name: train
num_bytes: 19296675.80952381
num_examples: 16
- name: test
num_bytes: 6030211.19047619
num_examples: 5
download_size: 25277003
dataset_size: 25326887.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
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|
gggggggg123/sber-golos | gggggggg123 | 2023-12-05T15:51:12Z | 2 | 0 | [
"region:us"
]
| null | 2023-12-05T15:46:29Z | ---
dataset_info:
features:
- name: text
dtype: string
- name: input_features
sequence:
sequence: float32
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 7677527180
num_examples: 7993
- name: validation
num_bytes: 761700288
num_examples: 793
- name: test
num_bytes: 9599558230
num_examples: 9994
download_size: 3068066823
dataset_size: 18038785698
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
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|
Vishnu-add/indian_food_images | Vishnu-add | 2023-12-05T15:49:28Z | 2 | 0 | [
"region:us"
]
| null | 2023-12-05T15:48:18Z | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': burger
'1': butter_naan
'2': chai
'3': chapati
'4': chole_bhature
'5': dal_makhani
'6': dhokla
'7': fried_rice
'8': idli
'9': jalebi
'10': kaathi_rolls
'11': kadai_paneer
'12': kulfi
'13': masala_dosa
'14': momos
'15': paani_puri
'16': pakode
'17': pav_bhaji
'18': pizza
'19': samosa
splits:
- name: train
num_bytes: 1366445660.3034334
num_examples: 5328
- name: test
num_bytes: 228682771.3925666
num_examples: 941
download_size: 1601484619
dataset_size: 1595128431.696
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
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|
gymprathap/Breast-Cancer-Ultrasound-Images-Dataset | gymprathap | 2023-12-05T18:11:08Z | 2 | 0 | [
"size_categories:1K<n<10K",
"language:en",
"license:cc-by-4.0",
"medical",
"region:us"
]
| null | 2023-12-05T18:05:59Z | ---
license: cc-by-4.0
language:
- en
tags:
- medical
pretty_name: Breast Cancer Ultrasound Images Detection
size_categories:
- 1K<n<10K
---
Worldwide, breast cancer ranks high among women's leading causes of death. Reducing the number of premature deaths can be achieved through early detection. The information is based on medical ultrasound scans that show signs of breast cancer. There are three types of images included in the Breast Ultrasound Dataset: normal, benign, and malignant. Incorporating machine learning into breast ultrasound images improves their ability to detect, classify, and segment breast cancer.
Data
Image data from breast ultrasounds taken of women aged 25–75 years old make up the baseline data set. Last year, this information was gathered. A total of 600 female patients are being treated. On average, each of the 780 images in the dataset has dimensions of 500 by 500 pixels. Pictures are saved as PNG files. Presenting the ground truth images alongside original images. Normal, benign, and cancerous are the three types of pictures that are classified.
<a href="http://projectcentersinchennai.co.in/Final-Year-Projects-for-CSE/Final-Year-Projects-for-CSE-Deep-learning-Domain" title="Deep Learning Projects for Final Year">Deep Learning Projects for Final Year</a>
Attribution is required when using this dataset:
Writers: Al-Dhabyani, Gomaa, Khaled, and Fahmy. Breast ultrasound image dataset. Summary of Data. The source is 2020 February; the article number is 28. The published version of this article is 10.1016/j.dib.2019.104863. | [
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|
wid4soe/182_2 | wid4soe | 2023-12-05T19:24:49Z | 2 | 0 | [
"region:us"
]
| null | 2023-12-05T18:10:01Z | ---
dataset_info:
features:
- name: original_image
dtype: image
- name: edit_prompt
dtype: string
- name: new_image
dtype: image
splits:
- name: train
num_bytes: 15287553.0
num_examples: 550
download_size: 15227528
dataset_size: 15287553.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
3una/Fer2013 | 3una | 2023-12-05T19:39:05Z | 2 | 0 | [
"task_categories:image-classification",
"size_categories:10K<n<100K",
"region:us"
]
| [
"image-classification"
]
| 2023-12-05T19:08:58Z | ---
task_categories:
- image-classification
pretty_name: FER2013
size_categories:
- 10K<n<100K
---
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[More Information Needed] | [
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|
adamjweintraut/eli5_lfqa_best | adamjweintraut | 2023-12-06T01:07:19Z | 2 | 1 | [
"region:us"
]
| null | 2023-12-06T00:37:02Z | ---
dataset_info:
features:
- name: index
dtype: int64
- name: q_id
dtype: string
- name: question
dtype: string
- name: best_answer
dtype: string
- name: all_answers
sequence: string
- name: num_answers
dtype: int64
- name: top_answers
sequence: string
- name: num_top_answers
dtype: int64
- name: context
dtype: string
- name: orig
dtype: string
- name: target
dtype: string
splits:
- name: train
num_bytes: 2557146936.7402635
num_examples: 183333
- name: test
num_bytes: 319648597.62986815
num_examples: 22917
- name: validation
num_bytes: 319648597.62986815
num_examples: 22917
download_size: 1932532942
dataset_size: 3196444131.9999995
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: validation
path: data/validation-*
---
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|
DianaJin/logi | DianaJin | 2023-12-06T08:13:10Z | 2 | 0 | [
"region:us"
]
| null | 2023-12-06T08:00:00Z | ---
dataset_info:
features:
- name: input_features
sequence:
sequence: float32
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 31703680
num_examples: 33
- name: test
num_bytes: 4804216
num_examples: 5
- name: valid
num_bytes: 3842528
num_examples: 4
download_size: 14124383
dataset_size: 40350424
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: valid
path: data/valid-*
---
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|
lotusbro/ipc_decisions_summarized | lotusbro | 2023-12-06T09:23:42Z | 2 | 1 | [
"license:gpl-3.0",
"region:us"
]
| null | 2023-12-06T09:14:58Z | ---
license: gpl-3.0
---
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|
leyanpan/sat-solver | leyanpan | 2023-12-06T21:10:53Z | 2 | 0 | [
"license:mit",
"region:us"
]
| null | 2023-12-06T16:28:56Z | ---
license: mit
---
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|
JayeQuay/llama2-TaxDataAssistant-v-schema-prompt | JayeQuay | 2023-12-06T17:30:54Z | 2 | 1 | [
"license:afl-3.0",
"region:us"
]
| null | 2023-12-06T17:29:51Z | ---
license: afl-3.0
---
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|
dana2002/asr_goodreviews | dana2002 | 2023-12-06T19:42:55Z | 2 | 0 | [
"language:ar",
"region:us"
]
| null | 2023-12-06T19:27:30Z | ---
language:
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|
KK1mo/tedigan_1 | KK1mo | 2023-12-07T03:53:40Z | 2 | 0 | [
"region:us"
]
| null | 2023-12-06T20:30:42Z | ---
dataset_info:
features:
- name: id
dtype: string
- name: caption
dtype: string
- name: generated_image
dtype: image
splits:
- name: train
num_bytes: 58927475.0
num_examples: 500
download_size: 58912429
dataset_size: 58927475.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
phamnam-misa/tts | phamnam-misa | 2023-12-07T02:39:01Z | 2 | 0 | [
"language:vi",
"license:apache-2.0",
"region:us"
]
| null | 2023-12-07T02:34:41Z | ---
license: apache-2.0
language:
- vi
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|
thanaphatt1/semi-training_set | thanaphatt1 | 2023-12-07T04:35:46Z | 2 | 0 | [
"region:us"
]
| null | 2023-12-07T04:35:34Z | ---
dataset_info:
features:
- name: tokens
sequence: string
- name: ner_tags
sequence: int64
splits:
- name: train
num_bytes: 21002777
num_examples: 18564
download_size: 2712321
dataset_size: 21002777
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
Sabrina1763/reuters_articles | Sabrina1763 | 2023-12-07T10:27:57Z | 2 | 0 | [
"region:us"
]
| null | 2023-12-07T10:27:54Z | ---
dataset_info:
features:
- name: title
dtype: string
- name: body
dtype: string
splits:
- name: train
num_bytes: 13792576
num_examples: 17262
- name: validation
num_bytes: 1870389
num_examples: 2158
- name: test
num_bytes: 1379190
num_examples: 2158
download_size: 10073411
dataset_size: 17042155
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
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|
ineoApp/ds_facture | ineoApp | 2023-12-07T12:07:50Z | 2 | 0 | [
"region:us"
]
| null | 2023-12-07T11:59:11Z | ---
dataset_info:
features:
- name: id
dtype: string
- name: image
dtype: image
- name: bboxes
sequence:
sequence: int64
- name: ner_tags
sequence:
class_label:
names:
'0': O
'1': numero facture
'2': fournisseur
'3': date facture
'4': date limite
'5': montant ht
'6': montant ttc
'7': tva
'8': prix tva
'9': addresse
'10': reference
'11': art1 designation
'12': art1 quantite
'13': art1 prix unit
'14': art1 tva
'15': art1 montant ht
'16': art2 designation
'17': art2 quantite
'18': art2 prix unit
'19': art2 tva
'20': art2 montant ht
- name: tokens
sequence: string
splits:
- name: train
num_bytes: 31122194.75
num_examples: 19
- name: test
num_bytes: 8190051.25
num_examples: 5
download_size: 12317157
dataset_size: 39312246.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
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|
ineoApp/facture_ds_01 | ineoApp | 2023-12-07T14:19:56Z | 2 | 0 | [
"region:us"
]
| null | 2023-12-07T12:09:21Z | ---
dataset_info:
features:
- name: id
dtype: string
- name: image
dtype: image
- name: bboxes
sequence:
sequence: int64
- name: ner_tags
sequence:
class_label:
names:
'0': O
'1': numero facture
'2': fournisseur
'3': date facture
'4': date limite
'5': montant ht
'6': montant ttc
'7': tva
'8': prix tva
'9': addresse
'10': reference
'11': art1 designation
'12': art1 quantite
'13': art1 prix unit
'14': art1 tva
'15': art1 montant ht
'16': art2 designation
'17': art2 quantite
'18': art2 prix unit
'19': art2 tva
'20': art2 montant ht
- name: tokens
sequence: string
splits:
- name: train
num_bytes: 14736563.333333334
num_examples: 14
- name: test
num_bytes: 4210446.666666667
num_examples: 4
download_size: 6308297
dataset_size: 18947010.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
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|
adxtya/mplug_test | adxtya | 2023-12-07T12:22:48Z | 2 | 0 | [
"license:mit",
"region:us"
]
| null | 2023-12-07T12:17:43Z | ---
license: mit
---
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|
Honcel/SPSVID00230 | Honcel | 2023-12-07T14:25:56Z | 2 | 0 | [
"region:us"
]
| null | 2023-12-07T12:51:05Z | ---
dataset_info:
features:
- name: audio
dtype: audio
- name: file
dtype: string
splits:
- name: test
num_bytes: 22930428.0
num_examples: 127
download_size: 22931731
dataset_size: 22930428.0
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
---
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|
KK1mo/tedigan_mask_2 | KK1mo | 2023-12-07T19:18:06Z | 2 | 0 | [
"region:us"
]
| null | 2023-12-07T13:05:57Z | ---
dataset_info:
features:
- name: id
dtype: string
- name: caption
dtype: string
- name: mask
dtype: image
- name: non_edited_image
dtype: image
- name: generated_image
dtype: image
splits:
- name: train
num_bytes: 652173233.0
num_examples: 500
download_size: 652195046
dataset_size: 652173233.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
KK1mo/tedigan_mask_3 | KK1mo | 2023-12-07T19:19:33Z | 2 | 0 | [
"region:us"
]
| null | 2023-12-07T13:07:03Z | ---
dataset_info:
features:
- name: id
dtype: string
- name: caption
dtype: string
- name: mask
dtype: image
- name: non_edited_image
dtype: image
- name: generated_image
dtype: image
splits:
- name: train
num_bytes: 651542205.0
num_examples: 498
download_size: 651562588
dataset_size: 651542205.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
crodri/multilingual_qa | crodri | 2023-12-07T14:05:14Z | 2 | 0 | [
"task_categories:question-answering",
"size_categories:10K<n<100K",
"language:ca",
"language:es",
"language:en",
"license:mit",
"region:us"
]
| [
"question-answering"
]
| 2023-12-07T14:00:37Z | ---
license: mit
task_categories:
- question-answering
language:
- ca
- es
- en
pretty_name: multilingual_qa
size_categories:
- 10K<n<100K
---
Multilingual instructional dataset for extractive QA finetunning for Catalan, Spanish and English, using SQAD, SQAC, CatalanQA and COQCAT datasets.
Contains almost 55K questions, answers and contexts, with development and train splits. | [
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|
lyon-nlp/mteb-fr-reranking-syntec-s2p | lyon-nlp | 2023-12-08T08:35:42Z | 2 | 0 | [
"region:us"
]
| null | 2023-12-07T15:27:12Z | ---
dataset_info:
features:
- name: query
dtype: string
- name: negative
sequence: string
- name: positive
sequence: string
splits:
- name: test
num_bytes: 683409
num_examples: 100
download_size: 107153
dataset_size: 683409
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
---
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|
CJWeiss/multitiny_rename | CJWeiss | 2023-12-07T16:56:44Z | 2 | 0 | [
"region:us"
]
| null | 2023-12-07T16:56:04Z | ---
dataset_info:
features:
- name: id
dtype: int64
- name: input
sequence: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 784230112
num_examples: 1202
- name: test
num_bytes: 118646528
num_examples: 240
- name: valid
num_bytes: 116992208
num_examples: 161
download_size: 460324193
dataset_size: 1019868848
---
# Dataset Card for "multitiny_rename"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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|
JayChauhan99/llama2-political-guanaco-small | JayChauhan99 | 2023-12-07T19:23:03Z | 2 | 0 | [
"region:us"
]
| null | 2023-12-07T18:50:41Z | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 988182
num_examples: 750
download_size: 550394
dataset_size: 988182
---
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|
textminr/mn-ds | textminr | 2023-12-07T19:30:31Z | 2 | 0 | [
"task_categories:text-classification",
"size_categories:1K<n<10K",
"language:en",
"license:cc",
"region:us"
]
| [
"text-classification"
]
| 2023-12-07T19:22:14Z | ---
license: cc
task_categories:
- text-classification
language:
- en
pretty_name: >-
MN-DS: A Multilabeled News Dataset for News Articles Hierarchical
Classification
size_categories:
- 1K<n<10K
--- | [
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|
shelvin94/Arabic_Darija | shelvin94 | 2023-12-08T18:52:18Z | 2 | 0 | [
"task_categories:translation",
"language:en",
"language:ar",
"region:us"
]
| [
"translation"
]
| 2023-12-07T20:46:31Z | ---
task_categories:
- translation
language:
- en
- ar
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|
theohlong/bis-project | theohlong | 2023-12-08T02:16:42Z | 2 | 0 | [
"license:mit",
"region:us"
]
| null | 2023-12-08T02:16:42Z | ---
license: mit
---
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|
imone/OpenOrca_FLAN | imone | 2023-12-08T04:37:54Z | 2 | 5 | [
"license:mit",
"region:us"
]
| null | 2023-12-08T04:28:50Z | ---
license: mit
---
This is the [OpenOrca](https://huggingface.co/datasets/Open-Orca/OpenOrca) GPT4 subset with the original FLAN answers. Each even row (indexed starting from 0) contains the OpenOrca GPT4 answer, while each odd row contains the corresponding FLAN answer. | [
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|
thanaphatt1/semi-training_set-v2 | thanaphatt1 | 2023-12-08T09:18:36Z | 2 | 0 | [
"region:us"
]
| null | 2023-12-08T05:14:30Z | ---
dataset_info:
features:
- name: tokens
sequence: string
- name: word_ids
sequence: int64
- name: ner_tags
sequence: int64
- name: id
dtype: string
- name: fname
dtype: string
- name: pos_tags
sequence: int64
- name: clause_tags
sequence: int64
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 27456882
num_examples: 18571
download_size: 2918093
dataset_size: 27456882
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
nlplabtdtu/law_edu_2023 | nlplabtdtu | 2023-12-08T07:14:08Z | 2 | 0 | [
"region:us"
]
| null | 2023-12-08T07:14:07Z | ---
dataset_info:
features:
- name: data
dtype: string
- name: meta_data
struct:
- name: source
dtype: string
- name: tag
dtype: string
- name: url
dtype: string
- name: time
dtype: string
- name: content
dtype: string
splits:
- name: train
num_bytes: 32462436
num_examples: 9717
download_size: 9668327
dataset_size: 32462436
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
nlplabtdtu/review_edu_data | nlplabtdtu | 2023-12-08T07:19:37Z | 2 | 0 | [
"region:us"
]
| null | 2023-12-08T07:19:36Z | ---
dataset_info:
features:
- name: id
dtype: int64
- name: date
dtype: string
- name: alias
dtype: string
- name: university
dtype: string
- name: content
dtype: string
splits:
- name: train
num_bytes: 9075720
num_examples: 684
download_size: 3649707
dataset_size: 9075720
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
nlplabtdtu/edu_data_with_tag | nlplabtdtu | 2023-12-08T08:21:04Z | 2 | 0 | [
"region:us"
]
| null | 2023-12-08T08:20:14Z | ---
dataset_info:
features:
- name: title
dtype: string
- name: body
dtype: string
- name: url
dtype: string
- name: uni
dtype: string
- name: content
dtype: string
splits:
- name: train
num_bytes: 2035056554
num_examples: 213847
download_size: 775920816
dataset_size: 2035056554
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
SandPD/CPatMiner_buggy_and_fixed_annotated_small | SandPD | 2023-12-08T12:47:20Z | 2 | 0 | [
"license:apache-2.0",
"region:us"
]
| null | 2023-12-08T12:42:45Z | ---
license: apache-2.0
dataset_info:
features:
- name: id
dtype: int64
- name: buggy
dtype: string
- name: fixed
dtype: string
splits:
- name: train
num_bytes: 49365434
num_examples: 60000
- name: validation
num_bytes: 3997274
num_examples: 5000
- name: test
num_bytes: 4105285
num_examples: 5000
download_size: 16654760
dataset_size: 57467993
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
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|
mwitiderrick/OpenPlatypus | mwitiderrick | 2023-12-08T13:13:59Z | 2 | 0 | [
"task_categories:text-generation",
"task_categories:question-answering",
"size_categories:10K<n<100K",
"language:en",
"license:apache-2.0",
"datasets",
"region:us"
]
| [
"text-generation",
"question-answering"
]
| 2023-12-08T13:13:21Z | ---
license: apache-2.0
task_categories:
- text-generation
- question-answering
language:
- en
created_by: mwitiderrick
size_categories:
- 10K<n<100K
tags:
- datasets
pretty_name: Open Platypus
---
# Open-Platypus
This is a modified version of the [Open-Platypus](https://huggingface.co/datasets/garage-bAInd/Open-Platypus) dataset for instruction fine-tuning of large language models using this
prompt template:
```
### Instruction:
{prompt}
### Response:
<leave a newline for the model to answer>
```
Check out a sample Open-Llama model [here](https://huggingface.co/mwitiderrick/open_llama_3b_chat_v_0.1)
## Usage
```python
from datasets import load_dataset
dataset = load_dataset("mwitiderrick/Open-Platypus", split="train[0:5000]") # to load the first 5000 samples
``` | [
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|
Scalable-ML/common_voice | Scalable-ML | 2023-12-08T17:10:31Z | 2 | 0 | [
"region:us"
]
| null | 2023-12-08T17:06:17Z | ---
dataset_info:
features:
- name: input_features
sequence:
sequence: float32
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 11871603408
num_examples: 12360
- name: test
num_bytes: 4868697560
num_examples: 5069
download_size: 2459668752
dataset_size: 16740300968
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
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|
ekolasky/InCntxConsolForLED | ekolasky | 2023-12-08T18:35:38Z | 2 | 0 | [
"region:us"
]
| null | 2023-12-08T18:35:36Z | ---
dataset_info:
features:
- name: input_ids
sequence: int32
- name: grouping_vector
sequence:
sequence: int64
- name: result_labels
sequence: int64
- name: attention_mask
sequence: int8
splits:
- name: train
num_bytes: 305693
num_examples: 18
download_size: 54541
dataset_size: 305693
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
jpodivin/plantorgans | jpodivin | 2023-12-10T15:22:27Z | 2 | 0 | [
"task_categories:image-segmentation",
"size_categories:1K<n<10K",
"license:cdla-permissive-2.0",
"biology",
"region:us"
]
| [
"image-segmentation"
]
| 2023-12-08T20:37:03Z | ---
license: cdla-permissive-2.0
task_categories:
- image-segmentation
tags:
- biology
size_categories:
- 1K<n<10K
---
# PLANT ORGANS - WIP
Photos of various plants with their major, above ground organs labeled.
Includes labels for stem, leafs, fruits and flowers.
Note, that categories listed above do not necessarily correspond to a correct
botanical term for the given part of the plant photographed.
Instead they correspond to the conventional understanding of them.
## Warning:
Not all files in the `sourcedata` directory have been labeled.
Files without annotations will be eventually separated in their own dir. | [
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|
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