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pfh1976/adGenPFH-dataset | pfh1976 | 2023-11-07T03:03:53Z | 0 | 0 | null | [
"region:us"
] | 2023-11-07T03:03:53Z | 2023-11-07T03:03:52.000Z | 2023-11-07T03:03:52 | ---
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dataset_size: 1243
configs:
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path: data/train-*
---
# Dataset Card for "adGenPFH-dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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Pablao0948/titioavo1 | Pablao0948 | 2023-11-07T04:58:09Z | 0 | 0 | null | [
"license:openrail",
"region:us"
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license: openrail
---
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"license:openrail",
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license: openrail
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JosephLee/science_textbook_elementary_kor_seed | JosephLee | 2023-11-07T07:42:12Z | 0 | 0 | null | [
"task_categories:question-answering",
"language:ko",
"region:us"
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task_categories:
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language:
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pretty_name: test dataset
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Sharka/DocVQA_for_LayoutLM | Sharka | 2023-11-07T09:37:35Z | 0 | 0 | null | [
"region:us"
] | 2023-11-07T09:37:35Z | 2023-11-07T09:29:08.000Z | 2023-11-07T09:29:08 | ---
dataset_info:
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---
# Dataset Card for "DocVQA_layoutLM_large"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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SerahAKojenu/Masakhane-news | SerahAKojenu | 2023-11-07T10:41:51Z | 0 | 0 | null | [
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"size_categories:n<1K",
"language:en",
"language:yo",
"biology",
"finance",
"region:us"
] | 2023-11-07T10:41:51Z | 2023-11-07T10:15:57.000Z | 2023-11-07T10:15:57 | ---
task_categories:
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language:
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- yo
tags:
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- finance
size_categories:
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---
TODO: Add YAML tags here. Copy-paste the tags obtained with the online tagging app: https://huggingface.co/spaces/huggingface/datasets-tagging
---
# Dataset Card for [Dataset Name]
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
[More Information Needed]
### 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
Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset. | [
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ShuoShuoShuo/parquet_test | ShuoShuoShuo | 2023-11-28T09:01:25Z | 0 | 0 | null | [
"region:us"
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danaroth/pavia | danaroth | 2023-11-09T17:57:39Z | 0 | 0 | null | [
"license:unknown",
"region:us"
] | 2023-11-09T17:57:39Z | 2023-11-07T10:58:25.000Z | 2023-11-07T10:58:25 | ---
license: unknown
---
# Description
The Pavia Centre and University are two scenes acquired by the [ROSIS](http://www.opairs.aero/rosis_en.html) sensor during a flight campaign over Pavia, nothern Italy. The number of spectral bands is 102 for Pavia Centre and 103 for Pavia University. Pavia Centre is a 1096 $\times$ 1096 pixels image, and Pavia University is 610 $\times$ 610 pixels, but some of the samples in both images contain no information and have to be discarded before the analysis. The geometric resolution is 1.3 meters. Both image groundtruths differenciate 9 classes each. It can be seen the discarded samples in the figures as abroad black strips.
# Characteristics
**Groundtruth classes for the Pavia centre scene and their respective samples number**
| # | Class | Samples |
|---|----------------------|---------|
| 1 | Water | 824 |
| 2 | Trees | 820 |
| 3 | Asphalt | 816 |
| 4 | Self-Blocking Bricks | 808 |
| 5 | Bitumen | 808 |
| 6 | Tiles | 1260 |
| 7 | Shadows | 476 |
| 8 | Meadows | 824 |
| 9 | Bare Soil | 820 |
**Groundtruth classes for the Pavia University scene and their respective samples number**
| # | Class | Samples |
|---|----------------------|---------|
| 1 | Asphalt | 6631 |
| 2 | Meadows | 18649 |
| 3 | Gravel | 2099 |
| 4 | Trees | 3064 |
| 5 | Painted metal sheets | 1345 |
| 6 | Bare Soil | 5029 |
| 7 | Bitumen | 1330 |
| 8 | Self-Blocking Bricks | 3682 |
| 9 | Shadows | 947 |
# Quick look
<figure>
<img src= "assets/Pavia_60.png" alt="Pavia" width="300" />
<figcaption>Sample band of Pavia Centre dataset.</figcaption>
</figure>
<figure>
<img src= "assets/Pavia_gt.png" alt="Pavia gt" width="300" />
<figcaption>Groundtruth of Pavia Centre dataset.</figcaption>
</figure>
<figure>
<img src= "assets/PaviaU_60.png" alt="PaviaU" width="300" />
<figcaption>Sample band of Pavia University dataset.</figcaption>
</figure>
<figure>
<img src= "assets/PaviaU_gt.png" alt="PaviaU gt" width="300" />
<figcaption>Groundtruth of Pavia University dataset.</figcaption>
</figure>
# Credits
Pavia scenes were provided by [Prof. Paolo Gamba](http://tlclab.unipv.it/sito_tlc/people.do?id=pgamba) from the [Telecommunications and Remote Sensing Laboratory](http://tlclab.unipv.it/), [Pavia university](http://www.unipv.eu/) (Italy).
This dataset was originally collected by Manuel Graña, Miguel-Angel Veganzones, Borja Ayerdi.
The original link for the dataset is available below:
https://www.ehu.eus/ccwintco/index.php/Hyperspectral_Remote_Sensing_Scenes | [
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danaroth/indian_pines | danaroth | 2023-11-09T12:22:21Z | 0 | 0 | null | [
"license:cc0-1.0",
"region:us"
] | 2023-11-09T12:22:21Z | 2023-11-07T11:06:13.000Z | 2023-11-07T11:06:13 | ---
license: cc0-1.0
---
# Description
This scene was gathered by [AVIRIS sensor](http://aviris.jpl.nasa.gov/) over the Indian Pines test site in North-western Indiana and consists of 145 $\times$ 145 pixels and 224 spectral reflectance bands in the wavelength range 0.4–2.5 10^(-6) meters. This scene is a subset of a larger one. The Indian Pines scene contains two-thirds agriculture, and one-third forest or other natural perennial vegetation. There are two major dual lane highways, a rail line, as well as some low density housing, other built structures, and smaller roads. Since the scene is taken in June some of the crops present, corn, soybeans, are in early stages of growth with less than 5% coverage. The ground truth available is designated into sixteen classes and is not all mutually exclusive. We have also reduced the number of bands to 200 by removing bands covering the region of water absorption: [104-108], [150-163], 220. Indian Pines data are available through [Pursue's univeristy MultiSpec site](https://engineering.purdue.edu/~biehl/MultiSpec/hyperspectral.html).
# Characteristics
Groundtruth classes for the Indian Pines scene and their respective samples number
| # | Class | Samples |
|----|------------------------------|---------|
| 1 | Alfalfa | 46 |
| 2 | Corn-notill | 1428 |
| 3 | Corn-mintill | 830 |
| 4 | Corn | 237 |
| 5 | Grass-pasture | 483 |
| 6 | Grass-trees | 730 |
| 7 | Grass-pasture-mowed | 28 |
| 8 | Hay-windrowed | 478 |
| 9 | Oats | 20 |
| 10 | Soybean-notill | 972 |
| 11 | Soybean-mintill | 2455 |
| 12 | Soybean-clean | 593 |
| 13 | Wheat | 205 |
| 14 | Woods | 1265 |
| 15 | Buildings-Grass-Trees-Drives | 386 |
| 16 | Stone-Steel-Towers | 93 |
# Quick look
<figure>
<img src= "assets/Indian_pines_170.png" alt="Indian Pines" width="300" />
<figcaption>Sample band of Indian Pines dataset.</figcaption>
</figure>
<figure>
<img src= "assets/Indian_pines_gt.png" alt="Indian Pines gt" width="300" />
<figcaption>Groundtruth of Indian Pines dataset.</figcaption>
</figure>
# Credits
This dataset was originally collected by Manuel Graña, Miguel-Angel Veganzones, Borja Ayerdi.
The original link for the dataset is available below:
https://www.ehu.eus/ccwintco/index.php/Hyperspectral_Remote_Sensing_Scenes | [
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argilla/zephyr_ultrafeedback | argilla | 2023-11-07T11:07:20Z | 0 | 0 | null | [
"region:us"
] | 2023-11-07T11:07:20Z | 2023-11-07T11:07:10.000Z | 2023-11-07T11:07:10 | ---
dataset_info:
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configs:
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data_files:
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---
# Dataset Card for "zephyr_ultrafeedback"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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-0.3418572545051... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
danaroth/cuprite | danaroth | 2023-11-10T16:03:08Z | 0 | 0 | null | [
"license:unknown",
"region:us"
] | 2023-11-10T16:03:08Z | 2023-11-07T11:09:18.000Z | 2023-11-07T11:09:18 | ---
license: unknown
---
# Description
This data sets can be retrieved from [AVIRIS NASA](http://aviris.jpl.nasa.gov/) site. Among the many datasets available, the .mat archive posted here corresponds to the _f970619t01p02_r02_sc03.a.rfl_ reflectance file.
# Quick look
<figure>
<img src= "assets/Cuprite_false_greyscale.png" alt="Cuprite" width="300" />
<figcaption>False greyscale image of Cuprite sample.</figcaption>
</figure>
# Credits
This dataset was originally collected by Manuel Graña, Miguel-Angel Veganzones, Borja Ayerdi.
The original link for the dataset is available below:
https://www.ehu.eus/ccwintco/index.php/Hyperspectral_Remote_Sensing_Scenes | [
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1aurent/PatchCamelyon | 1aurent | 2023-11-07T13:30:20Z | 0 | 2 | null | [
"task_categories:image-classification",
"size_categories:100K<n<1M",
"license:cc0-1.0",
"camelyon",
"biology",
"Histopathology",
"Histology",
"Digital Pathology",
"region:us"
] | 2023-11-07T13:30:20Z | 2023-11-07T11:12:54.000Z | 2023-11-07T11:12:54 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: valid
path: data/valid-*
- split: test
path: data/test-*
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype: bool
splits:
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num_bytes: 6114271847
num_examples: 262144
- name: valid
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num_bytes: 750794905
num_examples: 32768
download_size: 7639680898
dataset_size: 7633585559
task_categories:
- image-classification
tags:
- camelyon
- biology
- Histopathology
- Histology
- Digital Pathology
size_categories:
- 100K<n<1M
license: cc0-1.0
---
[](https://doi.org/10.1007/978-3-030-00934-2_24)
# PatchCamelyon (PCam)
**Homepage**: https://github.com/basveeling/pcam \
**Publication Date**: 2018-09-26 \
**License**: [Creative Commons Zero v1.0 Universal](https://creativecommons.org/publicdomain/zero/1.0/legalcode) \
**Citation**:
```bibtex
@ARTICLE{Veeling2018-qh,
title = "Rotation Equivariant {CNNs} for Digital Pathology",
author = "Veeling, Bastiaan S and Linmans, Jasper and Winkens, Jim and Cohen, Taco and Welling, Max",
month = jun,
year = 2018,
archivePrefix = "arXiv",
primaryClass = "cs.CV",
eprint = "1806.03962"
}
```
## Description
The PatchCamelyon benchmark is a new and challenging image classification dataset. It consists of 327.680 color images (96 x 96px) extracted from histopathologic scans of lymph node sections. Each image is annoted with a binary label indicating presence of metastatic tissue. PCam provides a new benchmark for machine learning models: bigger than CIFAR10, smaller than imagenet, trainable on a single GPU
## Why PCam
Fundamental machine learning advancements are predominantly evaluated on straight-forward natural-image classification datasets. Think MNIST, CIFAR, SVHN. Medical imaging is becoming one of the major applications of ML and we believe it deserves a spot on the list of _go-to_ ML datasets. Both to challenge future work, and to steer developments into directions that are beneficial for this domain.
We think PCam can play a role in this. It packs the clinically-relevant task of metastasis detection into a straight-forward binary image classification task, akin to CIFAR-10 and MNIST. Models can easily be trained on a single GPU in a couple hours, and achieve competitive scores in the Camelyon16 tasks of tumor detection and WSI diagnosis. Furthermore, the balance between task-difficulty and tractability makes it a prime suspect for fundamental machine learning research on topics as active learning, model uncertainty and explainability.
## Details
### Numbers
The dataset is divided into a training set of 262.144 (2^18) examples, and a validation and test set both of 32.768 (2^15) examples. There is no overlap in WSIs between the splits, and all splits have a 50/50 balance between positive and negative examples.
### Labeling
A positive label indicates that the center 32x32px region of a patch contains at least one pixel of tumor tissue. Tumor tissue in the outer region of the patch does not influence the label. This outer region is provided to enable the design of fully-convolutional models that do not use any zero-padding, to ensure consistent behavior when applied to a whole-slide image. This is however not a requirement for the PCam benchmark.
### Patch selection
PCam is derived from the Camelyon16 Challenge, which contains 400 H\&E stained WSIs of sentinel lymph node sections. The slides were acquired and digitized at 2 different centers using a 40x objective (resultant pixel resolution of 0.243 microns). We undersample this at 10x to increase the field of view.
We follow the train/test split from the Camelyon16 challenge, and further hold-out 20% of the train WSIs for the validation set. To prevent selecting background patches, slides are converted to HSV, blurred, and patches filtered out if maximum pixel saturation lies below 0.07 (which was validated to not throw out tumor data in the training set).
The patch-based dataset is sampled by iteratively choosing a WSI and selecting a positive or negative patch with probability _p_. Patches are rejected following a stochastic hard-negative mining scheme with a small CNN, and _p_ is adjusted to retain a balance close to 50/50.
| [
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NickKolok/regs-epicphotogasm-conv | NickKolok | 2023-11-08T16:48:46Z | 0 | 0 | null | [
"license:agpl-3.0",
"region:us"
] | 2023-11-08T16:48:46Z | 2023-11-07T11:15:53.000Z | 2023-11-07T11:15:53 | ---
license: agpl-3.0
---
| [
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thiyaneshnlp/po_layoutlm | thiyaneshnlp | 2023-11-28T13:09:48Z | 0 | 0 | null | [
"license:mit",
"region:us"
] | 2023-11-28T13:09:48Z | 2023-11-07T11:21:37.000Z | 2023-11-07T11:21:37 | ---
license: mit
---
| [
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danaroth/kennedy_space_center | danaroth | 2023-11-09T15:54:59Z | 0 | 0 | null | [
"license:unknown",
"region:us"
] | 2023-11-09T15:54:59Z | 2023-11-07T11:34:02.000Z | 2023-11-07T11:34:02 | ---
license: unknown
---
# Description
The NASA AVIRIS (Airborne Visible/Infrared Imaging Spectrometer) instrument acquired data over the Kennedy Space Center (KSC), Florida, on March 23, 1996. AVIRIS acquires data in 224 bands of 10 nm width with center wavelengths from 400 - 2500 nm. The KSC data, acquired from an altitude of approximately 20 km, have a spatial resolution of 18 m. After removing water absorption and low SNR bands, 176 bands were used for the analysis. Training data were selected using land cover maps derived from color infrared photography provided by the Kennedy Space Center and Landsat Thematic Mapper (TM) imagery. The vegetation classification scheme was developed by KSC personnel in an effort to define functional types that are discernable at the spatial resolution of Landsat and these AVIRIS data. Discrimination of land cover for this environment is difficult due to the similarity of spectral signatures for certain vegetation types. For classification purposes, 13 classes representing the various land cover types that occur in this environment were defined for the site.
# Quick look
<figure>
<img src= "assets/KSC.gif" alt="KSC" width="300" />
<figcaption>Bands visualization of the Kennedy Space Center dataset.</figcaption>
</figure>
# Credits
Corrected version offered by Ilya Kavalerov (email address: <ilyak@terpmail.umd.edu>). The corrected version was obtained by porting the original dataset to ENVI data (downloadable [here](http://www.csr.utexas.edu/hyperspectral/data/KSC/KSC_data.bin)) and using the code to process it available [here](https://gist.githubusercontent.com/ilyakava/1a2a6ef254603495379db6335573c506/raw/1a485c99a7987f1b62a1e4296bb736a9bf4f3f32/KSC_hsi_fix.m).
This dataset was originally collected by Manuel Graña, Miguel-Angel Veganzones, Borja Ayerdi.
The original link for the dataset is available below:
https://www.ehu.eus/ccwintco/index.php/Hyperspectral_Remote_Sensing_Scenes | [
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danaroth/salinas | danaroth | 2023-11-09T15:55:22Z | 0 | 0 | null | [
"license:unknown",
"region:us"
] | 2023-11-09T15:55:22Z | 2023-11-07T11:40:08.000Z | 2023-11-07T11:40:08 | ---
license: unknown
---
# Description
This scene was collected by the 224-band [AVIRIS sensor](http://aviris.jpl.nasa.gov/) over Salinas Valley, California, and is characterized by high spatial resolution (3.7-meter pixels). The area covered comprises 512 lines by 217 samples. As with Indian Pines scene, we discarded the 20 water absorption bands, in this case bands: [108-112], [154-167], 224. This image was available only as at-sensor radiance data. It includes vegetables, bare soils, and vineyard fields. Salinas groundtruth contains 16 classes.
A small subscene of Salinas image, denoted Salinas-A, is usually used too. It comprises 86*83 pixels located within the same scene at [samples, lines] = [591-676, 158-240] and includes six classes.
# Characteristics
Groundtruth classes for the Salinas scene and their respective samples number.
| # | Class | Samples |
|----|----------------------------|---------|
| 1 | Broccoli_green_weeds_1 | 2009 |
| 2 | Broccoli_green_weeds_2 | 3726 |
| 3 | Fallow | 1976 |
| 4 | Fallow_rough_plow | 1394 |
| 5 | Fallow_smooth | 2678 |
| 6 | Stubble | 3959 |
| 7 | Celery | 3579 |
| 8 | Grapes_untrained | 11271 |
| 9 | Soil_vinyard_develop | 6203 |
| 10 | Corn_senesced_green_weeds | 3278 |
| 11 | Lettuce_romaine_4wk | 1068 |
| 12 | Lettuce_romaine_5wk | 1927 |
| 13 | Lettuce_romaine_6wk | 916 |
| 14 | Lettuce_romaine_7wk | 1070 |
| 15 | Vinyard_untrained | 7268 |
| 16 | Vinyard_vertical_trellis | 1807 |
Groundtruth classes for the Salinas-A scene and their respective samples number
| # | Class | Samples |
|---|---------------------------|---------|
| 1 | Broccoli_green_weeds_1 | 391 |
| 2 | Corn_senesced_green_weeds | 1343 |
| 3 | Lettuce_romaine_4wk | 616 |
| 4 | Lettuce_romaine_5wk | 1525 |
| 5 | Lettuce_romaine_6wk | 674 |
| 6 | Lettuce_romaine_7wk | 799 |
# Quick look
<figure>
<img src= "assets/Salinas_170.png" alt="Salinas" width="300" />
<figcaption>Sample band of Salinas dataset.</figcaption>
</figure>
<figure>
<img src= "assets/Salinas_gt.png" alt="Salinas gt" width="300" />
<figcaption>Groundtruth of Salinas dataset.</figcaption>
</figure>
<figure>
<img src= "assets/SalinasA_170.png" alt="SalinasA" width="300" />
<figcaption>Sample band of Salinas-A dataset.</figcaption>
</figure>
<figure>
<img src= "assets/SalinasA_gt.png" alt="SalinasA gt" width="300" />
<figcaption>Groundtruth of Salinas-A dataset.</figcaption>
</figure>
# Credits
This dataset was originally collected by Manuel Graña, Miguel-Angel Veganzones, Borja Ayerdi.
The original link for the dataset is available below:
https://www.ehu.eus/ccwintco/index.php/Hyperspectral_Remote_Sensing_Scenes | [
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astvito/Polachek | astvito | 2023-11-07T12:52:55Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | 2023-11-07T12:52:55Z | 2023-11-07T12:49:44.000Z | 2023-11-07T12:49:44 | ---
license: apache-2.0
---
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damilola2104/Nigeria_Audio_Dataset | damilola2104 | 2023-11-07T13:06:21Z | 0 | 0 | null | [
"license:cc-by-nc-sa-4.0",
"region:us"
] | 2023-11-07T13:06:21Z | 2023-11-07T13:02:28.000Z | 2023-11-07T13:02:28 | ---
license: cc-by-nc-sa-4.0
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argilla/zephyr_ultrafeedback-new | argilla | 2023-11-07T13:13:13Z | 0 | 0 | null | [
"region:us"
] | 2023-11-07T13:13:13Z | 2023-11-07T13:13:11.000Z | 2023-11-07T13:13:11 | ---
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---
# Dataset Card for "zephyr_ultrafeedback-new"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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sergioudi/rvc | sergioudi | 2023-11-07T15:30:46Z | 0 | 0 | null | [
"region:us"
] | 2023-11-07T15:30:46Z | 2023-11-07T13:18:50.000Z | 2023-11-07T13:18:50 | Entry not found | [
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argilla/zephyr_ultrafeedback-new-3 | argilla | 2023-11-07T13:26:45Z | 0 | 1 | null | [
"region:us"
] | 2023-11-07T13:26:45Z | 2023-11-07T13:26:41.000Z | 2023-11-07T13:26:41 | ---
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configs:
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---
# Dataset Card for "zephyr_ultrafeedback-new-3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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vira-chatbot/vira-intents-mod-gpt4-data-aug | vira-chatbot | 2023-11-07T14:48:07Z | 0 | 0 | null | [
"region:us"
] | 2023-11-07T14:48:07Z | 2023-11-07T13:35:13.000Z | 2023-11-07T13:35:13 | ---
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download_size: 0
dataset_size: 1305072
---
# Dataset Card for "vira-intents-mod-gpt4-data-aug"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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tillschwoerer/restaurant-reviews | tillschwoerer | 2023-11-07T13:59:53Z | 0 | 0 | null | [
"region:us"
] | 2023-11-07T13:59:53Z | 2023-11-07T13:39:53.000Z | 2023-11-07T13:39:53 | ---
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dataset_size: 2594848
configs:
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data_files:
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---
# Dataset Card for "restaurant-reviews"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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shossain/merged-no-pad-text-65536 | shossain | 2023-11-07T13:45:33Z | 0 | 0 | null | [
"region:us"
] | 2023-11-07T13:45:33Z | 2023-11-07T13:45:05.000Z | 2023-11-07T13:45:05 | ---
dataset_info:
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---
# Dataset Card for "merged-no-pad-text-65536"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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gengyuanmax/WikiTiLo | gengyuanmax | 2023-11-08T00:49:05Z | 0 | 0 | null | [
"license:mit",
"region:us"
] | 2023-11-08T00:49:05Z | 2023-11-07T14:11:52.000Z | 2023-11-07T14:11:52 | ---
license: mit
---
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polinaeterna/my_dataset | polinaeterna | 2023-11-07T14:18:41Z | 0 | 0 | null | [
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ytnjnh11/ytnjnh11 | ytnjnh11 | 2023-11-07T14:40:59Z | 0 | 0 | null | [
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license: openrail
---
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joshswartz/old_hellaswag_validation_challenge_subset_llama_wiki | joshswartz | 2023-11-07T14:42:45Z | 0 | 0 | null | [
"region:us"
] | 2023-11-07T14:42:45Z | 2023-11-07T14:42:40.000Z | 2023-11-07T14:42:40 | ---
configs:
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---
# Dataset Card for "hellaswag_validation_challenge_subset_llama_wiki"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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Sesamoo/for-pdf-jsonl | Sesamoo | 2023-11-07T14:46:11Z | 0 | 0 | null | [
"license:unknown",
"region:us"
] | 2023-11-07T14:46:11Z | 2023-11-07T14:45:59.000Z | 2023-11-07T14:45:59 | ---
license: unknown
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hippocrates/UMLS_train | hippocrates | 2023-11-07T15:13:01Z | 0 | 0 | null | [
"region:us"
] | 2023-11-07T15:13:01Z | 2023-11-07T15:12:59.000Z | 2023-11-07T15:12:59 | ---
configs:
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download_size: 6360200
dataset_size: 31344818
---
# Dataset Card for "UMLS_train"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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tillwenke/cwa | tillwenke | 2023-11-07T15:40:00Z | 0 | 0 | null | [
"region:us"
] | 2023-11-07T15:40:00Z | 2023-11-07T15:23:49.000Z | 2023-11-07T15:23:49 | ---
configs:
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path: data/d.arrow
--- | [
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Kitare/wilydataset | Kitare | 2023-11-07T15:26:17Z | 0 | 0 | null | [
"license:openrail",
"region:us"
] | 2023-11-07T15:26:17Z | 2023-11-07T15:26:01.000Z | 2023-11-07T15:26:01 | ---
license: openrail
---
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notoriousdto/marvin-scheme | notoriousdto | 2023-11-10T04:13:40Z | 0 | 0 | null | [
"license:mit",
"region:us"
] | 2023-11-10T04:13:40Z | 2023-11-07T15:32:35.000Z | 2023-11-07T15:32:35 | ---
license: mit
---
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Kitare/WilyBR | Kitare | 2023-11-07T15:39:47Z | 0 | 0 | null | [
"license:openrail",
"region:us"
] | 2023-11-07T15:39:47Z | 2023-11-07T15:36:02.000Z | 2023-11-07T15:36:02 | ---
license: openrail
---
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urielpalo/voiceshadrielawhiteproducts | urielpalo | 2023-11-07T16:06:02Z | 0 | 0 | null | [
"region:us"
] | 2023-11-07T16:06:02Z | 2023-11-07T15:58:42.000Z | 2023-11-07T15:58:42 | Entry not found | [
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tubasid/toy-car-annotation-YOLO | tubasid | 2023-11-07T16:19:37Z | 0 | 1 | null | [
"task_categories:image-classification",
"size_categories:n<1K",
"language:en",
"license:apache-2.0",
"yolo",
"opensource",
"computervision",
"imageprocessing",
"yolov3",
"yplov4",
"labelimg",
"region:us"
] | 2023-11-07T16:19:37Z | 2023-11-07T16:07:24.000Z | 2023-11-07T16:07:24 | ---
license: apache-2.0
task_categories:
- image-classification
language:
- en
tags:
- yolo
- opensource
- computervision
- imageprocessing
- yolov3
- yplov4
- labelimg
pretty_name: ToyCarAnnotation
size_categories:
- n<1K
---
Hey everyone,
In my final year project, I created **Smart Traffic Management System**.
The project was to manage traffic lights' delays based on the number of vehicles on road.
I made everything worked using Raspberry Pi and pre-recorded videos but it was a "final year project", it was needed to be tested by changing videos frequently which was a kind of hustle. Collecting tons of videos and loading them in Pi was not too hard but it would have cost time, by every time changing names of videos in the code. Also, it was not possible to implement it in real *(unless govt. would have permitted me, hehe)*. So I chose to showcase my work by making a beautiful prototype.
[](https://postimg.cc/jDBySvRP)
I know, the image isn't so appealing, I apologise for that, but you got the idea, right.
I placed my cars on tracks and took real-time video of the lanes from the two cameras attached to two big sticks.
***Why only two cameras when there are four roads?***
Raspberry Pi supports only two cameras. In my case, the indexes were 0 and 2.
But to make things work as I have planned, I cropped images for each lane.
***What does it mean?***
Let us take one camera and the respective two roads as an example.
I took real-time video, performed image framing on it. Since the roads beneath the cars were supposed to be still *(obvio, cars move, not roads :>)*, I performed image framing after every 2 seconds of the video. The images were first cropped and then saved in the Pi. I resized the images, found the coordinates on which the two roads were separating, cropped the image till those coordinates and got 2 images of 2 separate roads from 1 camera.
Finally, I ran my code and I found it could only detect a few cars. I thought real and toy ones looked quite similar, but the model didn't think the same. My YOLO weight file was trained on original cars and now I had to do training, again.
I looked for datasets already available but couldn't find any. So I decided to make one.
I collected images from different web sources and performed the most important task on each of them. ***ANNOTATION***, using LabelImg.
I separately annotated around 1000 images, in YOLO format, did all the processing and created this dataset. Usually, for YOLO especially, you get pictures on the internet but not text files. You have to individually perform annotation on all of them. It takes time and there isn't any tool to do it in bulk because you have to properly tell how many cars are there in the picture. Maybe in the future, LableImg gets updated with some machine learning algorithm for detecting and annotating images automatically (who knows).
So here it is for your help.
I will be adding the notebook as well in some time.
Any questions? drop down below. Do like if it’s helpful.
***You can find me on:***
[https://www.github.com/tubasid](url)
[https://www.linkedin.com/in/tubasid](url)
[https://www.twitter.com/in/tubaasid](url)
[https://www.discord.com/channels/@tubasid](url)
Until next post.
***TubaSid***
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ddrg/math_text | ddrg | 2023-11-15T21:47:33Z | 0 | 0 | null | [
"region:us"
] | 2023-11-15T21:47:33Z | 2023-11-07T16:27:37.000Z | 2023-11-07T16:27:37 | ---
dataset_info:
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download_size: 3381871008
dataset_size: 6072931933.0
---
# Dataset Card for "math_text"
Mathematical dataset containing mathematical texts, i.e. texts containing LaTeX formulas, based on the [AMPS](https://drive.google.com/file/d/1hQsua3TkpEmcJD_UWQx8dmNdEZPyxw23) Khan dataset and the [ARQMath](https://drive.google.com/drive/folders/1YekTVvfmYKZ8I5uiUMbs21G2mKwF9IAm) dataset V1.3. Based on the retrieved LaTeX texts, more mathematically equivalent versions have been generated by applying randomized LaTeX printing with this [SymPy fork](https://drive.google.com/drive/folders/1YekTVvfmYKZ8I5uiUMbs21G2mKwF9IAm). A positive `id` corresponds to the ARQMath post id of the generated text version, a negative `id` indicates an AMPS text. | [
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satpalsr/generalchat | satpalsr | 2023-11-07T16:51:49Z | 0 | 0 | null | [
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mikeion/dissertation_project-dissertation-all-help-channels | mikeion | 2023-11-07T18:31:42Z | 0 | 0 | null | [
"region:us"
] | 2023-11-07T18:31:42Z | 2023-11-07T18:30:07.000Z | 2023-11-07T18:30:07 | Found. Redirecting to https://cdn-lfs-us-1.huggingface.co/repos/9a/38/9a38714f997737fcf180d5cacf070925407a167389f72a7df90c22db6eef9c4e/89d393b6f883b66b9fba5ff49e024c95cd3c8590b4106ba33d75616cdd71f0e8?response-content-disposition=attachment%3B+filename*%3DUTF-8%27%27README.md%3B+filename%3D%22README.md%22%3B&response-content-type=text%2Fmarkdown&Expires=1701481737&Policy=eyJTdGF0ZW1lbnQiOlt7IkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTcwMTQ4MTczN319LCJSZXNvdXJjZSI6Imh0dHBzOi8vY2RuLWxmcy11cy0xLmh1Z2dpbmdmYWNlLmNvL3JlcG9zLzlhLzM4LzlhMzg3MTRmOTk3NzM3ZmNmMTgwZDVjYWNmMDcwOTI1NDA3YTE2NzM4OWY3MmE3ZGY5MGMyMmRiNmVlZjljNGUvODlkMzkzYjZmODgzYjY2YjlmYmE1ZmY0OWUwMjRjOTVjZDNjODU5MGI0MTA2YmEzM2Q3NTYxNmNkZDcxZjBlOD9yZXNwb25zZS1jb250ZW50LWRpc3Bvc2l0aW9uPSomcmVzcG9uc2UtY29udGVudC10eXBlPSoifV19&Signature=aDiiDD%7EjX8KSdSj3Qsm2IaD09ciRmTuWAUk7RrwRfQxC8Uu9eE72jF%7EMjUNbzom33fkvv-xvKTsIeCKZPicj%7EqhJQAakjj7LS92yPiDa0lSQ2%7EI2gh%7EYwhCPZcV5JTPoBtN6hOojvamFuOFfclnYhRj5%7EZdkRdGDcNzYq-gbGebdldpM1CvQtBOmikZtPpLYFRLTnNHceurirAVWKNpdHdhXEG6xgZ2auPsktV64oO6X0S15YzXrAPoxjKC8oyYdGnHn4yoZOrgfdIaeJdECeXZilcFwObjYbKortG-FWiIMwqin6jdAXagHnflspt5kZIUDl%7EigAeqiADFtLsA7FQ__&Key-Pair-Id=KCD77M1F0VK2B | [
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hippocrates/sharegpt_train | hippocrates | 2023-11-07T19:00:06Z | 0 | 0 | null | [
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# Dataset Card for "sharegpt_train"
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# Dataset Card for "Test"
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] | 2023-11-07T20:04:40Z | 2023-11-07T20:01:10.000Z | 2023-11-07T20:01:10 | I don't know why would you need that, but this exist. You welcome.
(photos contains in 'selfies' folder)
---
license: mit
---
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# Dataset Card for "flan2022"
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Tural/stanford_alpaca | Tural | 2023-11-07T21:57:10Z | 0 | 0 | null | [
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---
# Dataset Card for "stanford_alpaca"
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kheder/quran_hasanat_hadith_datasets | kheder | 2023-11-07T22:03:20Z | 0 | 0 | null | [
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mlfoundations/open_lm_test_data | mlfoundations | 2023-11-28T23:39:13Z | 0 | 0 | null | [
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joshswartz/old_hellaswag_validation_challenge_subset_falcon_wiki | joshswartz | 2023-11-07T23:16:04Z | 0 | 0 | null | [
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# Dataset Card for "hellaswag_validation_challenge_subset_falcon_wiki"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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cogumelo22/cogumelo22 | cogumelo22 | 2023-11-07T22:58:49Z | 0 | 0 | null | [
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license: openrail
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xingkunliuxtracta/processed_demo | xingkunliuxtracta | 2023-11-07T23:10:43Z | 0 | 0 | null | [
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# Dataset Card for "processed_demo"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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jjiguaran/tweets_emotions_elections_colombia | jjiguaran | 2023-11-07T23:42:06Z | 0 | 0 | null | [
"license:mit",
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license: mit
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joaofreitas/Club57 | joaofreitas | 2023-11-07T23:50:57Z | 0 | 0 | null | [
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krishi/testing | krishi | 2023-11-08T00:24:53Z | 0 | 0 | null | [
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---
# Dataset Card for "testing"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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JLB-JLB/seizure_eeg_iirFilter_greyscale_224x224_6secWindow | JLB-JLB | 2023-11-08T03:15:04Z | 0 | 0 | null | [
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---
# Dataset Card for "seizure_eeg_iirFilter_greyscale_224x224_6secWindow"
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bill141477/b1ue | bill141477 | 2023-11-08T01:25:47Z | 0 | 0 | null | [
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---
# Dataset Card for "emrqaQA_risk_train"
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---
# Dataset Card for "emrqaQA_relation_train"
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---
# Dataset Card for "emrqaQA_risk_test"
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---
# Dataset Card for "emrqaQA_relation_test"
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---
# Dataset Card for "emrqaQA_medication_test"
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---
# Dataset Card for "meta_math_meta_math_qa_50k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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open-llm-leaderboard/details_TheBloke__guanaco-33B-GPTQ_public | open-llm-leaderboard | 2023-11-08T02:15:06Z | 0 | 0 | null | [
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pretty_name: Evaluation run of TheBloke/guanaco-33B-GPTQ
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [TheBloke/guanaco-33B-GPTQ](https://huggingface.co/TheBloke/guanaco-33B-GPTQ)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 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_TheBloke__guanaco-33B-GPTQ_public\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-11-08T02:14:39.195892](https://huggingface.co/datasets/open-llm-leaderboard/details_TheBloke__guanaco-33B-GPTQ_public/blob/main/results_2023-11-08T02-14-39.195892.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 \"em\": 0.0035654362416107383,\n\
\ \"em_stderr\": 0.0006104082299890425,\n \"f1\": 0.05981019295302028,\n\
\ \"f1_stderr\": 0.0013198104887474009,\n \"acc\": 0.5132679261811913,\n\
\ \"acc_stderr\": 0.011604513216322042\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.0035654362416107383,\n \"em_stderr\": 0.0006104082299890425,\n\
\ \"f1\": 0.05981019295302028,\n \"f1_stderr\": 0.0013198104887474009\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.23805913570887036,\n \
\ \"acc_stderr\": 0.011731278748420901\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7884767166535123,\n \"acc_stderr\": 0.011477747684223183\n\
\ }\n}\n```"
repo_url: https://huggingface.co/TheBloke/guanaco-33B-GPTQ
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_11_08T02_14_39.195892
path:
- '**/details_harness|drop|3_2023-11-08T02-14-39.195892.parquet'
- split: latest
path:
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- config_name: harness_gsm8k_5
data_files:
- split: 2023_11_08T02_14_39.195892
path:
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- split: latest
path:
- '**/details_harness|gsm8k|5_2023-11-08T02-14-39.195892.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_11_08T02_14_39.195892
path:
- '**/details_harness|winogrande|5_2023-11-08T02-14-39.195892.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-11-08T02-14-39.195892.parquet'
- config_name: results
data_files:
- split: 2023_11_08T02_14_39.195892
path:
- results_2023-11-08T02-14-39.195892.parquet
- split: latest
path:
- results_2023-11-08T02-14-39.195892.parquet
---
# Dataset Card for Evaluation run of TheBloke/guanaco-33B-GPTQ
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/TheBloke/guanaco-33B-GPTQ
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** clementine@hf.co
### Dataset Summary
Dataset automatically created during the evaluation run of model [TheBloke/guanaco-33B-GPTQ](https://huggingface.co/TheBloke/guanaco-33B-GPTQ) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 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_TheBloke__guanaco-33B-GPTQ_public",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-11-08T02:14:39.195892](https://huggingface.co/datasets/open-llm-leaderboard/details_TheBloke__guanaco-33B-GPTQ_public/blob/main/results_2023-11-08T02-14-39.195892.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": {
"em": 0.0035654362416107383,
"em_stderr": 0.0006104082299890425,
"f1": 0.05981019295302028,
"f1_stderr": 0.0013198104887474009,
"acc": 0.5132679261811913,
"acc_stderr": 0.011604513216322042
},
"harness|drop|3": {
"em": 0.0035654362416107383,
"em_stderr": 0.0006104082299890425,
"f1": 0.05981019295302028,
"f1_stderr": 0.0013198104887474009
},
"harness|gsm8k|5": {
"acc": 0.23805913570887036,
"acc_stderr": 0.011731278748420901
},
"harness|winogrande|5": {
"acc": 0.7884767166535123,
"acc_stderr": 0.011477747684223183
}
}
```
### 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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path: data/train-*
---
# Dataset Card for "lmsys_chatbot_arena_conversations"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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open-llm-leaderboard/details_TheBloke__Airoboros-L2-70B-2.1-GPTQ_public | open-llm-leaderboard | 2023-11-08T02:27:14Z | 0 | 0 | null | [
"region:us"
] | 2023-11-08T02:27:14Z | 2023-11-08T02:27:05.000Z | 2023-11-08T02:27:05 | ---
pretty_name: Evaluation run of TheBloke/Airoboros-L2-70B-2.1-GPTQ
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [TheBloke/Airoboros-L2-70B-2.1-GPTQ](https://huggingface.co/TheBloke/Airoboros-L2-70B-2.1-GPTQ)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 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_TheBloke__Airoboros-L2-70B-2.1-GPTQ_public\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-11-08T02:26:46.433766](https://huggingface.co/datasets/open-llm-leaderboard/details_TheBloke__Airoboros-L2-70B-2.1-GPTQ_public/blob/main/results_2023-11-08T02-26-46.433766.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 \"em\": 0.4241820469798658,\n\
\ \"em_stderr\": 0.0050612570385902955,\n \"f1\": 0.5410476090604083,\n\
\ \"f1_stderr\": 0.004613044422574753,\n \"acc\": 0.48424459945200166,\n\
\ \"acc_stderr\": 0.010393744134050047\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.4241820469798658,\n \"em_stderr\": 0.0050612570385902955,\n\
\ \"f1\": 0.5410476090604083,\n \"f1_stderr\": 0.004613044422574753\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.15238817285822592,\n \
\ \"acc_stderr\": 0.009899572254794198\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.8161010260457774,\n \"acc_stderr\": 0.010887916013305896\n\
\ }\n}\n```"
repo_url: https://huggingface.co/TheBloke/Airoboros-L2-70B-2.1-GPTQ
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_11_08T02_26_46.433766
path:
- '**/details_harness|drop|3_2023-11-08T02-26-46.433766.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-11-08T02-26-46.433766.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_11_08T02_26_46.433766
path:
- '**/details_harness|gsm8k|5_2023-11-08T02-26-46.433766.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-11-08T02-26-46.433766.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_11_08T02_26_46.433766
path:
- '**/details_harness|winogrande|5_2023-11-08T02-26-46.433766.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-11-08T02-26-46.433766.parquet'
- config_name: results
data_files:
- split: 2023_11_08T02_26_46.433766
path:
- results_2023-11-08T02-26-46.433766.parquet
- split: latest
path:
- results_2023-11-08T02-26-46.433766.parquet
---
# Dataset Card for Evaluation run of TheBloke/Airoboros-L2-70B-2.1-GPTQ
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/TheBloke/Airoboros-L2-70B-2.1-GPTQ
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** clementine@hf.co
### Dataset Summary
Dataset automatically created during the evaluation run of model [TheBloke/Airoboros-L2-70B-2.1-GPTQ](https://huggingface.co/TheBloke/Airoboros-L2-70B-2.1-GPTQ) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 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_TheBloke__Airoboros-L2-70B-2.1-GPTQ_public",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-11-08T02:26:46.433766](https://huggingface.co/datasets/open-llm-leaderboard/details_TheBloke__Airoboros-L2-70B-2.1-GPTQ_public/blob/main/results_2023-11-08T02-26-46.433766.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": {
"em": 0.4241820469798658,
"em_stderr": 0.0050612570385902955,
"f1": 0.5410476090604083,
"f1_stderr": 0.004613044422574753,
"acc": 0.48424459945200166,
"acc_stderr": 0.010393744134050047
},
"harness|drop|3": {
"em": 0.4241820469798658,
"em_stderr": 0.0050612570385902955,
"f1": 0.5410476090604083,
"f1_stderr": 0.004613044422574753
},
"harness|gsm8k|5": {
"acc": 0.15238817285822592,
"acc_stderr": 0.009899572254794198
},
"harness|winogrande|5": {
"acc": 0.8161010260457774,
"acc_stderr": 0.010887916013305896
}
}
```
### 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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thewall/PLBS | thewall | 2023-11-08T02:42:08Z | 0 | 0 | null | [
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license: apache-2.0
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Shinhati2023/Power_Golden | Shinhati2023 | 2023-11-08T02:45:30Z | 0 | 0 | null | [
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license: artistic-2.0
task_categories:
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pretty_name: Power_Golden
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kheder/quran_hasanat_hadith_datasets0 | kheder | 2023-11-08T02:50:33Z | 0 | 0 | null | [
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dataset_size: 44047246
configs:
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data_files:
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path: data/train-*
---
# Dataset Card for "quran_hasanat_hadith_datasets0"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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---
# Dataset Card for "validation_2000_cutoff_llama_first10results"
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---
# Dataset Card for "dataset_010"
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imageomics/Cambridge_butterfly | imageomics | 2023-11-08T04:32:49Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | 2023-11-08T04:32:49Z | 2023-11-08T04:30:52.000Z | 2023-11-08T04:30:52 | ---
license: apache-2.0
---
| [
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leeclark/decision-making-user-scenario | leeclark | 2023-11-08T04:44:22Z | 0 | 0 | null | [
"region:us"
] | 2023-11-08T04:44:22Z | 2023-11-08T04:43:23.000Z | 2023-11-08T04:43:23 | ---
# For reference on dataset card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/datasetcard.md?plain=1
# Doc / guide: https://huggingface.co/docs/hub/datasets-cards
{}
---
# Dataset Card for Dataset Name
<!-- Provide a quick summary of the dataset. -->
This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## 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. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### 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. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### 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. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Dataset Card Authors [optional]
[More Information Needed]
## Dataset Card Contact
[More Information Needed] | [
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yourAssistance/data-v1 | yourAssistance | 2023-11-08T04:50:41Z | 0 | 0 | null | [
"license:mit",
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] | 2023-11-08T04:50:41Z | 2023-11-08T04:50:41.000Z | 2023-11-08T04:50:41 | ---
license: mit
---
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davoodwadi/rvc | davoodwadi | 2023-11-08T04:58:02Z | 0 | 0 | null | [
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rebe121314/Protease_Hiv_drug | rebe121314 | 2023-11-08T05:05:09Z | 0 | 0 | null | [
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] | 2023-11-08T05:05:09Z | 2023-11-08T05:03:46.000Z | 2023-11-08T05:03:46 | Entry not found | [
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rebe121314/NRTIS_HIV_drug | rebe121314 | 2023-11-08T05:06:37Z | 0 | 0 | null | [
"region:us"
] | 2023-11-08T05:06:37Z | 2023-11-08T05:06:11.000Z | 2023-11-08T05:06:11 | Entry not found | [
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chunping-hf/hatmoc | chunping-hf | 2023-11-08T05:07:10Z | 0 | 0 | null | [
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] | 2023-11-08T05:07:10Z | 2023-11-08T05:06:37.000Z | 2023-11-08T05:06:37 | Entry not found | [
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Sulamit/ProjekAI | Sulamit | 2023-11-08T05:38:35Z | 0 | 0 | null | [
"region:us"
] | 2023-11-08T05:38:35Z | 2023-11-08T05:28:22.000Z | 2023-11-08T05:28:22 | Entry not found | [
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linxin020826/DINO-IR | linxin020826 | 2023-11-08T06:06:48Z | 0 | 0 | null | [
"region:us"
] | 2023-11-08T06:06:48Z | 2023-11-08T05:34:35.000Z | 2023-11-08T05:34:35 | Entry not found | [
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ItsMeBell/Yuzuki_Roa | ItsMeBell | 2023-11-14T16:15:05Z | 0 | 0 | null | [
"region:us"
] | 2023-11-14T16:15:05Z | 2023-11-08T06:01:29.000Z | 2023-11-08T06:01:29 | Entry not found | [
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tyzhu/find_second_sent_train_50_eval_20 | tyzhu | 2023-11-08T06:31:06Z | 0 | 0 | null | [
"region:us"
] | 2023-11-08T06:31:06Z | 2023-11-08T06:28:55.000Z | 2023-11-08T06:28:55 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
dataset_info:
features:
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dtype: string
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dtype: string
- name: title
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splits:
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num_bytes: 148379
num_examples: 120
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num_bytes: 18682
num_examples: 20
download_size: 0
dataset_size: 167061
---
# Dataset Card for "find_second_sent_train_50_eval_20"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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tyzhu/find_last_sent_train_50_eval_20 | tyzhu | 2023-11-08T06:31:10Z | 0 | 0 | null | [
"region:us"
] | 2023-11-08T06:31:10Z | 2023-11-08T06:29:04.000Z | 2023-11-08T06:29:04 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
dataset_info:
features:
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dtype: string
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dtype: string
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splits:
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num_bytes: 148655
num_examples: 120
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num_bytes: 18835
num_examples: 20
download_size: 0
dataset_size: 167490
---
# Dataset Card for "find_last_sent_train_50_eval_20"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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tyzhu/find_second_sent_train_50_eval_40 | tyzhu | 2023-11-08T06:31:35Z | 0 | 0 | null | [
"region:us"
] | 2023-11-08T06:31:35Z | 2023-11-08T06:29:35.000Z | 2023-11-08T06:29:35 | ---
configs:
- config_name: default
data_files:
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path: data/train-*
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path: data/validation-*
dataset_info:
features:
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dtype: string
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splits:
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num_bytes: 180317
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num_bytes: 39419
num_examples: 40
download_size: 0
dataset_size: 219736
---
# Dataset Card for "find_second_sent_train_50_eval_40"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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