id stringlengths 2 115 | lastModified stringlengths 24 24 | tags list | author stringlengths 2 42 ⌀ | description stringlengths 0 6.67k ⌀ | citation stringlengths 0 10.7k ⌀ | likes int64 0 3.66k | downloads int64 0 8.89M | created timestamp[us] | card stringlengths 11 977k | card_len int64 11 977k | embeddings list |
|---|---|---|---|---|---|---|---|---|---|---|---|
frimelle/wiki-stance | 2023-10-19T14:20:42.000Z | [
"license:cc-by-sa-3.0",
"region:us"
] | frimelle | null | null | 0 | 0 | 2023-10-19T13:58:53 | ---
license: cc-by-sa-3.0
---
# wiki-stance dataset
The wiki-stance dataset is provided in three languages: English (en), German (de), and Turkish (tr), as well as a multilingual version (ml), which mixes the three languages
and aligns the policies across languages.
For more details see the EMNLP 2023 paper "Why Should This Article Be Deleted? Transparent Stance Detection in Multilingual Wikipedia Editor Discussions". | 424 | [
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AndreiBlahovici/OneFormer | 2023-10-19T14:15:20.000Z | [
"region:us"
] | AndreiBlahovici | null | null | 0 | 0 | 2023-10-19T14:15:06 | Entry not found | 15 | [
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tigertop/auditreports | 2023-10-19T14:21:10.000Z | [
"region:us"
] | tigertop | null | null | 0 | 0 | 2023-10-19T14:16:41 | Entry not found | 15 | [
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] |
Kamyar-zeinalipour/ITA_CW | 2023-10-19T14:53:57.000Z | [
"region:us"
] | Kamyar-zeinalipour | null | null | 0 | 0 | 2023-10-19T14:53:54 | ---
dataset_info:
features:
- name: Clue
dtype: string
- name: Answer
dtype: string
- name: couple_occurencies
dtype: int64
splits:
- name: train
num_bytes: 5767721
num_examples: 125202
download_size: 3409199
dataset_size: 5767721
---
# Dataset Card for "ITA_CW"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 431 | [
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myownskyW7/V3Det | 2023-10-19T15:09:48.000Z | [
"task_categories:object-detection",
"size_categories:1K<n<10K",
"license:cc-by-4.0",
"arxiv:2304.03752",
"region:us"
] | myownskyW7 | null | null | 0 | 0 | 2023-10-19T14:54:04 | ---
license: cc-by-4.0
task_categories:
- object-detection
size_categories:
- 1K<n<10K
---
<p align="center">
<img src="images/v3det_icon.jpg" width="100"/>
</p>
<p align="center">
<b><font size="6">V3Det: Vast Vocabulary Visual Detection Dataset</font></b>
</p>
<p>
<div align="center">
<div>
<a href='https://myownskyw7.github.io/' target='_blank'>Jiaqi Wang</a>*,
<a href='https://panzhang0212.github.io/' target='_blank'>Pan Zhang</a>*,
Tao Chu*,
Yuhang Cao*, </br>
Yujie Zhou,
<a href='https://wutong16.github.io/' target='_blank'>Tong Wu</a>,
Bin Wang,
Conghui He,
<a href='http://dahua.site/' target='_blank'>Dahua Lin</a></br>
(* equal contribution)</br>
<strong>Accepted to ICCV 2023 (Oral)</strong>
</div>
</p>
<p>
<div>
<strong>
<a href='https://arxiv.org/pdf/2304.03752.pdf' target='_blank'>Paper</a>,
<a href='https://v3det.openxlab.org.cn/' target='_blank'>Dataset</a></br>
</strong>
</div>
</div>
</p>
<p align="center">
<img width=960 src="images/introduction.jpg"/>
</p>
## Codebase
### Object Detection
- mmdetection: https://github.com/V3Det/mmdetection-V3Det/tree/main/configs/v3det
- Detectron2： https://github.com/V3Det/Detectron2-V3Det
### Open Vocabulary Detection (OVD)
- Detectron2： https://github.com/V3Det/Detectron2-V3Det
## Data Format
The data includes a training set, a validation set, comprising 13,204 categories. The training set consists of 183,354 images, while the validation set has 29,821 images. The data organization is:
```
V3Det/
images/
<category_node>/
|────<image_name>.png
...
...
annotations/
|────v3det_2023_v1_category_tree.json # Category tree
|────category_name_13204_v3det_2023_v1.txt # Category name
|────v3det_2023_v1_train.json # Train set
|────v3det_2023_v1_val.json # Validation set
```
## Annotation Files
### Train/Val
The annotation files are provided in dictionary format and contain the keywords "images," "categories," and "annotations."
- images : store a list containing image information, where each element is a dictionary representing an image.
```
file_name # The relative image path, eg. images/n07745046/21_371_29405651261_633d076053_c.jpg.
height # The height of the image
width # The width of the image
id # Unique identifier of the image.
```
- categories : store a list containing category information, where each element is a dictionary representing a category.
```
name # English name of the category.
name_zh # Chinese name of the category.
cat_info # The format for the description information of categories is a list.
cat_info_gpt # The format for the description information of categories generated by ChatGPT is a list.
novel # For open-vocabulary detection, indicate whether the current category belongs to the 'novel' category.
id # Unique identifier of the category.
```
- annotations : store a list containing annotation information, where each element is a dictionary representing a bounding box annotation.
```
image_id # The unique identifier of the image where the bounding box is located.
category_id # The unique identifier of the category corresponding to the bounding box.
bbox # The coordinates of the bounding box, in the format [x, y, w, h], representing the top-left corner coordinates and the width and height of the box.
iscrowd # Whether the bounding box is a crowd box.
area # The area of the bounding box
```
### Category Tree
- The category tree stores information about dataset category mappings and relationships in dictionary format.
```
categoryid2treeid # Unique identifier of node in the category tree corresponding to the category identifier in dataset
id2name # English name corresponding to each node in the category tree
id2name_zh # Chinese name corresponding to each node in the category tree
id2desc # English description corresponding to each node in the category tree
id2desc_zh # Chinese description corresponding to each node in the category tree
id2synonym_list # List of synonyms corresponding to each node in the category tree
id2center_synonym # Center synonym corresponding to each node in the category tree
father2child # All direct child categories corresponding to each node in the category tree
child2father # All direct parent categories corresponding to each node in the category tree
ancestor2descendant # All descendant nodes corresponding to each node in the category tree
descendant2ancestor # All ancestor nodes corresponding to each node in the category tree
```
## Image Download
- Run the command to crawl the images. By default, the images will be stored in the './V3Det/' directory.
```
python v3det_image_download.py
```
- If you want to change the storage location, you can specify the desired folder by adding the option '--output_folder' when executing the script.
```
python v3det_image_download.py --output_folder our_folder
```
## Category Tree Visualization
- Run the command and then select dataset path `path/to/V3Det` to visualize the category tree.
```
python v3det_visualize_tree.py
```
Please refer to the [TreeUI Operation Guide](VisualTree.md) for more information.
## License:
- **V3Det Images**: Around 90% images in V3Det were selected from the [Bamboo Dataset](https://github.com/ZhangYuanhan-AI/Bamboo), sourced from the Flickr website. The remaining 10% were directly crawled from the Flickr. **We do not own the copyright of the images.** Use of the images must abide by the [Flickr Terms of Use](https://www.flickr.com/creativecommons/). We only provide lists of image URLs without redistribution.
- **V3Det Annotations**: The V3Det annotations, the category relationship tree, and related tools are licensed under a [Creative Commons Attribution 4.0 License](https://creativecommons.org/licenses/by/4.0/) (allow commercial use).
## Citation
```bibtex
@inproceedings{wang2023v3det,
title = {V3Det: Vast Vocabulary Visual Detection Dataset},
author = {Wang, Jiaqi and Zhang, Pan and Chu, Tao and Cao, Yuhang and Zhou, Yujie and Wu, Tong and Wang, Bin and He, Conghui and Lin, Dahua},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {October},
year = {2023}
}
``` | 6,705 | [
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Raghavan/fast_model_samples | 2023-10-20T14:46:13.000Z | [
"region:us"
] | Raghavan | null | null | 0 | 0 | 2023-10-19T15:27:19 | Entry not found | 15 | [
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] |
Slowblood/guanaco-llama2-1k | 2023-10-19T15:40:58.000Z | [
"region:us"
] | Slowblood | null | null | 0 | 0 | 2023-10-19T15:40:56 | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 1654448
num_examples: 1000
download_size: 966693
dataset_size: 1654448
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "guanaco-llama2-1k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 444 | [
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open-llm-leaderboard/details_ehartford__Samantha-1.1-70b | 2023-10-19T15:48:05.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | 0 | 0 | 2023-10-19T15:47:56 | ---
pretty_name: Evaluation run of ehartford/Samantha-1.1-70b
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [ehartford/Samantha-1.1-70b](https://huggingface.co/ehartford/Samantha-1.1-70b)\
\ 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 agregated 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_ehartford__Samantha-1.1-70b\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-19T15:47:52.190208](https://huggingface.co/datasets/open-llm-leaderboard/details_ehartford__Samantha-1.1-70b/blob/main/results_2023-10-19T15-47-52.190208.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\": 0.5110109060402684,\n\
\ \"em_stderr\": 0.00511922622901773,\n \"f1\": 0.5558829697986593,\n\
\ \"f1_stderr\": 0.004885906429154466,\n \"acc\": 0.5744121045517131,\n\
\ \"acc_stderr\": 0.011649119740139782\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.5110109060402684,\n \"em_stderr\": 0.00511922622901773,\n\
\ \"f1\": 0.5558829697986593,\n \"f1_stderr\": 0.004885906429154466\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.3161485974222896,\n \
\ \"acc_stderr\": 0.012807630673451488\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.8326756116811366,\n \"acc_stderr\": 0.010490608806828079\n\
\ }\n}\n```"
repo_url: https://huggingface.co/ehartford/Samantha-1.1-70b
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_10_19T15_47_52.190208
path:
- '**/details_harness|drop|3_2023-10-19T15-47-52.190208.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-19T15-47-52.190208.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_19T15_47_52.190208
path:
- '**/details_harness|gsm8k|5_2023-10-19T15-47-52.190208.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-19T15-47-52.190208.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_19T15_47_52.190208
path:
- '**/details_harness|winogrande|5_2023-10-19T15-47-52.190208.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-19T15-47-52.190208.parquet'
- config_name: results
data_files:
- split: 2023_10_19T15_47_52.190208
path:
- results_2023-10-19T15-47-52.190208.parquet
- split: latest
path:
- results_2023-10-19T15-47-52.190208.parquet
---
# Dataset Card for Evaluation run of ehartford/Samantha-1.1-70b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/ehartford/Samantha-1.1-70b
- **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 [ehartford/Samantha-1.1-70b](https://huggingface.co/ehartford/Samantha-1.1-70b) 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 agregated 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_ehartford__Samantha-1.1-70b",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-19T15:47:52.190208](https://huggingface.co/datasets/open-llm-leaderboard/details_ehartford__Samantha-1.1-70b/blob/main/results_2023-10-19T15-47-52.190208.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": 0.5110109060402684,
"em_stderr": 0.00511922622901773,
"f1": 0.5558829697986593,
"f1_stderr": 0.004885906429154466,
"acc": 0.5744121045517131,
"acc_stderr": 0.011649119740139782
},
"harness|drop|3": {
"em": 0.5110109060402684,
"em_stderr": 0.00511922622901773,
"f1": 0.5558829697986593,
"f1_stderr": 0.004885906429154466
},
"harness|gsm8k|5": {
"acc": 0.3161485974222896,
"acc_stderr": 0.012807630673451488
},
"harness|winogrande|5": {
"acc": 0.8326756116811366,
"acc_stderr": 0.010490608806828079
}
}
```
### 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] | 7,201 | [
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laughingrice/Ultrasound_planewave_sos_inversion | 2023-10-21T21:43:44.000Z | [
"license:mit",
"medical imaging",
"ultrasound",
"doi:10.57967/hf/1240",
"region:us"
] | laughingrice | null | null | 0 | 0 | 2023-10-19T16:03:06 | ---
license: mit
tags:
- medical imaging
- ultrasound
Authors:
- Micha Feigin
- Daniel Freedman
- Brian W. Anthony
---
# Plane wave raw ultrasound simualted data for deep learning speed of sound inversion
Cite this dataset as:
Feigin M, Freedman D, Anthony BW. Computing Speed-of-Sound from ultrasound: user-agnostic recovery and a new benchmark. IEEE Trans Biomed Eng. 2023; doi:10.1109/TBME.2023.3327147
## Simulation
The full dataset consists of 112640 simulations split into 9216 simulations in the training set, 1024 in the validation set, and 1024 in the test set. The measured signal is simulated using the k-wave MATLAB toolbox. Simulations were performed for nine plane waves at \\(0\\), \\(\pm 8\\), \\(\pm 16\\), \\(\pm 24\\), and \\(\pm 32\\) element offsets, with corresponding wavefront angles of \\(0\\), \\(\pm 6.7\\), \\(\pm 13.7\\), \\(\pm 20.2\\), and \\(\pm 26.3\\) (the time delay is calculated based on 1540 m/s speed of sound so the actual angle will differ per sample), set to pass through the center of the domain. See the figures for details (three of the 9 plane waves are shown to reduce clutter). Each simulation was performed with two center frequencies, 2.5 MHz and 5 MHz, with a Gaussian window (pulse width) of 5 oscillations. An additional simulation at 4.4 MHz is available under the validation directory to allow testing for transfer learning.
Each simulation comprised of \\(1152 \times 1152\\) random speed-of-sound and \\(\alpha\\) (attenuation) coefficient maps following power law attenuation [\\(\mbox{dB} / \mbox{cm} / \mbox{MHz}^2\\)] in a domain \\(42.35 \times 42.35\\) mm in size
The domain is constructed by layering a randomly selected set of ellipses and half-planes. For each of the resulting domains (organs), we randomly selected the speed of sound, attenuation coefficient, speckle density, and speckle amplitude. Domains were verified to not slice the probe face; i.e. the resulting maps are verified not to have a discontinuity at the probe face.
The speed of sound range is 1300 m/s to 1800 m/s. The \\(\alpha\\) coefficient range is \\(0.05\\) to \\(0.15\\) dB/cm/MHz\\({}^2\\). Background density is set to 0.9 g/cm\\({}^3\\) (density of fat).
Speckle noise is randomly generated in the density domain so as not to affect the wavefront propagation speed (uniformly distributed point sources with 2-10 points per wavelength and uniformly distributed amplitude at \\(\pm 10\%\\)).
## Probe
To match our physical hardware, we simulated a 128-element array with 64 active transmit elements. The simulation was carried out with two pulse center frequencies, 2.5 MHz and 5 MHz with a Gaussian window of 5 oscillations.
The central plane wave (zero degrees) is centered at elements 33 to 96. The probe face is placed at \\(y = 60\\) (outside the perfectly matched layer) and centered on the \\(x\\) axis. The numerical receive array is 4 elements per sensor element, with a matching kerf (spacing) value, i.e., 4 on 4 off. The signal for each receiver is summed across the 4 receiver elements to generate the 128 receive channels, and the signal is down-sampled to a 40 MHz sampling rate (ADC rate). For the transmit signal, we use a continuous array, as we found that it better matches real-world signals, so for the centered plane wave, a source is placed on all pixels with \\(y = 60\\) and \\(322 \le x \le 830\\) with a zero time delay on all elements.
## File format
The data is in Matlab v7.3 (HDF5) file format create by the python hdf5storage package.
Fields in each file:
- alpha_coeff: alpha coeffienct (attenuation) map {1024 - samples, 1channel, 1152 - X dimension, 1152 - Z dimension}
- c0: speed of sound map {1024 - samples, 1, 1152 - X dimension, 1152 - Z dimension}
- cycles: number of cycles in the acoustic wavelet
- f: frequencies simulated
- offsets: plane wave offsets
- p_f<freq>_o<offset>: simulated ultrasound signal {1024 - samples, 1, 128 - reciever, 2667 - time sample}
the k-wave simulation setup. The US array is placed at line 60 of the numerical grid. Due to kerf, slightly less than half of the array (64 elements) is excited to generate the outgoing plane wave. To better match the actual signal and avoid artifacts, a continuous section is excited. The angle is set based on an assumed 1540 m/s speed of sound so that the plane wave overlaps the center of the domain
Array structure, with 4 active elements and 4 kerf elements interleaved. The recorded signal is the average of the 4 receiving cells for each element
The data simulates plane wave ultrasound data in random media
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Linhz/data_qag | 2023-10-19T16:07:25.000Z | [
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] | Linhz | null | null | 0 | 0 | 2023-10-19T16:06:34 | Entry not found | 15 | [
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Kamyar-zeinalipour/AR_CW | 2023-10-19T16:36:45.000Z | [
"region:us"
] | Kamyar-zeinalipour | null | null | 0 | 0 | 2023-10-19T16:36:21 | ---
dataset_info:
features:
- name: clue
dtype: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 2063175
num_examples: 57706
download_size: 1126121
dataset_size: 2063175
---
# Dataset Card for "AR_CW"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 383 | [
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] |
Iliab/emotion_dataset | 2023-10-19T16:59:35.000Z | [
"region:us"
] | Iliab | null | null | 0 | 0 | 2023-10-19T16:59:34 | ---
dataset_info:
features:
- name: question
dtype: string
id: field
- name: answer
dtype: string
id: field
- name: relevant
list:
- name: user_id
dtype: string
id: question
- name: value
dtype: string
id: suggestion
- name: status
dtype: string
id: question
- name: relevant-suggestion
dtype: string
id: suggestion
- name: relevant-suggestion-metadata
struct:
- name: type
dtype: string
id: suggestion-metadata
- name: score
dtype: float32
id: suggestion-metadata
- name: agent
dtype: string
id: suggestion-metadata
- name: external_id
dtype: string
id: external_id
- name: metadata
dtype: string
id: metadata
splits:
- name: train
num_bytes: 252
num_examples: 1
download_size: 7180
dataset_size: 252
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "emotion_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 1,137 | [
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EZUNIGAF/f2.001 | 2023-10-19T17:17:08.000Z | [
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EZUNIGAF/f2.002 | 2023-10-19T17:14:06.000Z | [
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dylanebert/3dgs | 2023-10-22T22:40:30.000Z | [
"region:us"
] | dylanebert | null | null | 0 | 0 | 2023-10-19T17:28:05 | Entry not found | 15 | [
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jonavila/DRAL | 2023-10-23T21:01:49.000Z | [
"task_categories:translation",
"language:en",
"language:es",
"license:cc0-1.0",
"arxiv:2211.11584",
"arxiv:2307.04123",
"region:us"
] | jonavila | null | null | 0 | 0 | 2023-10-19T17:32:48 | ---
license: cc0-1.0
task_categories:
- translation
language:
- en
- es
---
# Dialogs Re-enacted Across Languages (DRAL) corpus
DRAL is a bilingual speech corpus of parallel utterances, using recorded conversations and fragments re-enacted in a different language. It is intended as a resource for research, especially for training and evaluating speech-to-speech translation models and systems. We dedicate this corpus to the public domain; there is no copyright (CC 0).
DRAL is described in a new technical report: [Dialogs Re-enacted Across Languages, Version 2](https://arxiv.org/abs/2211.11584), Nigel G. Ward, Jonathan E. Avila, Emilia Rivas, Divette Marco.
Some initial analyses of this data are described in our [Interspeech 2023 paper](https://arxiv.org/abs/2307.04123).
The releases include 2893 short matched Spanish-English pairs (> 2 hours) taken from 104 conversations with 70 unique participants. There are also some illustrative, lower-quality, pairs in Bengali-English, Japanese-English, and French-English. All are packaged together with the full original conversations and full re-enactment recording sessions.
## Links
- [DRAL home page](https://www.cs.utep.edu/nigel/dral/)
- [DRAL GitHub repo](https://github.com/joneavila/DRAL)
- [DRAL technical report](https://arxiv.org/abs/2211.11584)
- [Interspeech 2023 paper](https://arxiv.org/abs/2307.04123) | 1,378 | [
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open-llm-leaderboard/details_yeontaek__Platypus2xOpenOrca-13B-IA3-v2.1 | 2023-10-22T14:08:34.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | 0 | 0 | 2023-10-19T17:39:57 | ---
pretty_name: Evaluation run of yeontaek/Platypus2xOpenOrca-13B-IA3-v2.1
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [yeontaek/Platypus2xOpenOrca-13B-IA3-v2.1](https://huggingface.co/yeontaek/Platypus2xOpenOrca-13B-IA3-v2.1)\
\ 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 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 agregated 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_yeontaek__Platypus2xOpenOrca-13B-IA3-v2.1\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-22T14:08:24.468600](https://huggingface.co/datasets/open-llm-leaderboard/details_yeontaek__Platypus2xOpenOrca-13B-IA3-v2.1/blob/main/results_2023-10-22T14-08-24.468600.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\": 0.005138422818791947,\n\
\ \"em_stderr\": 0.0007322104102794228,\n \"f1\": 0.07932466442953026,\n\
\ \"f1_stderr\": 0.0017316010986472678,\n \"acc\": 0.4421008477279133,\n\
\ \"acc_stderr\": 0.010182910924383982\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.005138422818791947,\n \"em_stderr\": 0.0007322104102794228,\n\
\ \"f1\": 0.07932466442953026,\n \"f1_stderr\": 0.0017316010986472678\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.10993176648976498,\n \
\ \"acc_stderr\": 0.008616195587865416\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7742699289660616,\n \"acc_stderr\": 0.011749626260902547\n\
\ }\n}\n```"
repo_url: https://huggingface.co/yeontaek/Platypus2xOpenOrca-13B-IA3-v2.1
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_10_19T17_39_53.818572
path:
- '**/details_harness|drop|3_2023-10-19T17-39-53.818572.parquet'
- split: 2023_10_22T14_08_24.468600
path:
- '**/details_harness|drop|3_2023-10-22T14-08-24.468600.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-22T14-08-24.468600.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_19T17_39_53.818572
path:
- '**/details_harness|gsm8k|5_2023-10-19T17-39-53.818572.parquet'
- split: 2023_10_22T14_08_24.468600
path:
- '**/details_harness|gsm8k|5_2023-10-22T14-08-24.468600.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-22T14-08-24.468600.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_19T17_39_53.818572
path:
- '**/details_harness|winogrande|5_2023-10-19T17-39-53.818572.parquet'
- split: 2023_10_22T14_08_24.468600
path:
- '**/details_harness|winogrande|5_2023-10-22T14-08-24.468600.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-22T14-08-24.468600.parquet'
- config_name: results
data_files:
- split: 2023_10_19T17_39_53.818572
path:
- results_2023-10-19T17-39-53.818572.parquet
- split: 2023_10_22T14_08_24.468600
path:
- results_2023-10-22T14-08-24.468600.parquet
- split: latest
path:
- results_2023-10-22T14-08-24.468600.parquet
---
# Dataset Card for Evaluation run of yeontaek/Platypus2xOpenOrca-13B-IA3-v2.1
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/yeontaek/Platypus2xOpenOrca-13B-IA3-v2.1
- **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 [yeontaek/Platypus2xOpenOrca-13B-IA3-v2.1](https://huggingface.co/yeontaek/Platypus2xOpenOrca-13B-IA3-v2.1) 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 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 agregated 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_yeontaek__Platypus2xOpenOrca-13B-IA3-v2.1",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-22T14:08:24.468600](https://huggingface.co/datasets/open-llm-leaderboard/details_yeontaek__Platypus2xOpenOrca-13B-IA3-v2.1/blob/main/results_2023-10-22T14-08-24.468600.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": 0.005138422818791947,
"em_stderr": 0.0007322104102794228,
"f1": 0.07932466442953026,
"f1_stderr": 0.0017316010986472678,
"acc": 0.4421008477279133,
"acc_stderr": 0.010182910924383982
},
"harness|drop|3": {
"em": 0.005138422818791947,
"em_stderr": 0.0007322104102794228,
"f1": 0.07932466442953026,
"f1_stderr": 0.0017316010986472678
},
"harness|gsm8k|5": {
"acc": 0.10993176648976498,
"acc_stderr": 0.008616195587865416
},
"harness|winogrande|5": {
"acc": 0.7742699289660616,
"acc_stderr": 0.011749626260902547
}
}
```
### 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] | 7,850 | [
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Cdaprod/Whitepapers | 2023-10-19T18:57:53.000Z | [
"region:us"
] | Cdaprod | null | null | 0 | 0 | 2023-10-19T18:57:06 | Entry not found | 15 | [
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] |
yongchanskii/youtube-data-various-domain | 2023-10-19T19:07:56.000Z | [
"region:us"
] | yongchanskii | null | null | 0 | 0 | 2023-10-19T19:07:02 | ---
dataset_info:
features:
- name: source
dtype: string
- name: channelName
dtype: string
- name: category
dtype: string
- name: title
dtype: string
- name: videoId
dtype: string
- name: domainTag
dtype: string
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: transcriptionPath
dtype: string
- name: start
dtype: float64
- name: end
dtype: float64
- name: WER
dtype: float64
- name: CER
dtype: float64
- name: referenceText
dtype: string
- name: hypotheseText
dtype: string
- name: referenceTextLength
dtype: int64
- name: hypotheseTextLength
dtype: int64
splits:
- name: train
num_bytes: 591096131.5720359
num_examples: 1512
- name: test
num_bytes: 143215520.34796405
num_examples: 379
download_size: 729072307
dataset_size: 734311651.92
---
# Dataset Card for "youtube-data-various-domain"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 1,071 | [
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autoevaluate/autoeval-eval-acronym_identification-default-e413c7-96238146605 | 2023-10-19T19:16:45.000Z | [
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alokkulkarni/financial_Transactions | 2023-10-19T20:23:36.000Z | [
"region:us"
] | alokkulkarni | This dataset consists of 378 transactions performed on account and categorised according to the description of the transaction. | @misc{mccreery2020effective,
title={Effective Transfer Learning for classifying Transactions},
author={AK},
year={2020},
eprint={2008.13546},
archivePrefix={arXiv},
primaryClass={cs.IR}
} | 0 | 0 | 2023-10-19T19:37:55 | Entry not found | 15 | [
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] |
chirunder/MixSNIPS_inference | 2023-10-19T20:09:05.000Z | [
"region:us"
] | chirunder | null | null | 0 | 0 | 2023-10-19T20:09:02 | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: completion
dtype: string
- name: text
dtype: string
- name: prediction
dtype: string
splits:
- name: test
num_bytes: 5196889
num_examples: 5001
download_size: 1806789
dataset_size: 5196889
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
---
# Dataset Card for "MixSNIPS_inference"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 556 | [
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Giulietta/lotr-book | 2023-10-20T08:26:40.000Z | [
"region:us"
] | Giulietta | null | null | 0 | 0 | 2023-10-19T20:19:14 | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 2432593
num_examples: 1
download_size: 0
dataset_size: 2432593
---
# Dataset Card for "lotr-book"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 342 | [
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foureyednymph/test | 2023-10-19T20:27:36.000Z | [
"region:us"
] | foureyednymph | null | null | 0 | 0 | 2023-10-19T20:25:44 | Entry not found | 15 | [
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Muhammadreza/your-dataset-name | 2023-10-19T20:40:49.000Z | [
"region:us"
] | Muhammadreza | null | null | 0 | 0 | 2023-10-19T20:40:49 | Entry not found | 15 | [
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] |
sam2ai/hindi_arc_e_mini | 2023-10-19T21:31:30.000Z | [
"region:us"
] | sam2ai | null | null | 0 | 0 | 2023-10-19T21:31:28 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: answerKey
dtype: string
- name: id
dtype: string
- name: question
struct:
- name: choices
list:
- name: label
dtype: string
- name: text
dtype: string
- name: stem
dtype: string
splits:
- name: train
num_bytes: 30675
num_examples: 50
download_size: 19352
dataset_size: 30675
---
# Dataset Card for "hindi_arc_e_mini"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 655 | [
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cmu-mlsp/encodec_24khz-opt-125m-pretrained-ft-librispeech_asr_dummy-validation-features | 2023-10-20T05:42:02.000Z | [
"region:us"
] | cmu-mlsp | null | null | 0 | 0 | 2023-10-19T22:59:57 | ---
dataset_info:
features:
- name: file
dtype: string
- name: audio
dtype:
audio:
sampling_rate: 24000
- name: text
dtype: string
- name: speaker_id
dtype: int64
- name: chapter_id
dtype: int64
- name: id
dtype: string
- name: audio_codes
sequence:
sequence: int64
splits:
- name: validation
num_bytes: 23693835.0
num_examples: 73
download_size: 22836090
dataset_size: 23693835.0
configs:
- config_name: default
data_files:
- split: validation
path: data/validation-*
---
# Dataset Card for "encodec_24khz-opt-125m-pretrained-ft-librispeech_asr_dummy-validation-features"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 793 | [
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TobiasKG/eggmandreamcastera | 2023-10-19T23:09:20.000Z | [
"region:us"
] | TobiasKG | null | null | 0 | 0 | 2023-10-19T23:05:42 | Entry not found | 15 | [
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TobiasKG/silverthehedgehogdarkage | 2023-10-19T23:15:45.000Z | [
"region:us"
] | TobiasKG | null | null | 0 | 0 | 2023-10-19T23:13:12 | Entry not found | 15 | [
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sebastiandizon/genius-song-lyrics | 2023-10-20T00:09:37.000Z | [
"region:us"
] | sebastiandizon | null | null | 0 | 0 | 2023-10-20T00:06:52 | Entry not found | 15 | [
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HammaniMohamed/EcomSales | 2023-10-20T00:44:47.000Z | [
"region:us"
] | HammaniMohamed | null | null | 0 | 0 | 2023-10-20T00:40:44 | Entry not found | 15 | [
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mikael17125/fmnist_train | 2023-10-20T01:17:47.000Z | [
"region:us"
] | mikael17125 | null | null | 0 | 0 | 2023-10-20T01:14:18 | Entry not found | 15 | [
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jjgilces/directorioEspol | 2023-10-20T01:18:26.000Z | [
"region:us"
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zeio/batch | 2023-11-02T19:36:58.000Z | [
"task_categories:text-generation",
"task_categories:text-classification",
"task_categories:question-answering",
"language_creators:crowdsourced",
"size_categories:100K<n<1M",
"language:ru",
"language:en",
"license:apache-2.0",
"social-networks",
"not-for-all-audiences",
"region:us"
] | zeio | null | null | 0 | 0 | 2023-10-20T02:12:50 | ---
language:
- ru
- en
license: apache-2.0
tags:
- social-networks
- not-for-all-audiences
annotation_creators:
- crowdsourced
language_creators:
- crowdsourced
pretty_name: batch
size_categories:
- 100K<n<1M
task_categories:
- text-generation
- text-classification
- question-answering
dataset_info:
- config_name: written
features:
- name: title
dtype: string
- name: topics
sequence:
- name: posts
sequence:
- name: text
dtype: string
- config_name: spoken
features:
- name: title
dtype: string
- name: speech
dtype: audio
- name: topics
sequence:
- name: posts
sequence:
- name: text
dtype: string
---
<p align="center">
<img src="https://i.ibb.co/WVkDGyW/image.png"/>
</p>
# Dataset card for batch
## Table of contents
- [Dataset description](#dataset-description)
- [Dataset summary](#dataset-summary)
- [Dataset structure](#dataset-structure)
- [Dataset instance](#dataset-instance)
- [Dataset fields](#dataset-fields)
## Dataset description
- **Homepage**: [batch homepage](https://huggingface.co/datasets/zeio/batch)
- **Repository**: [batch repository](https://huggingface.co/datasets/zeio/batch)
- **Point of contact**: [Zeio Nara](mailto:zeionara@gmail.com)
- **Dataset version**: `31.10.2023`
### Dataset summary
This dataset contains threads parsed from the `/b/` board of [2ch archive][archive]. See dataset viewer at the [derivative repo](/datasets/zeio/auto-batch).
## Dataset structure
The dataset is represented in three formats - **compressed**, **uncompressed** and **spoken**:
1. `uncompressed` representation is the default and simplest one - in this form the content of dataset is organised inside `txt` files which are grouped into clusters inside [`threads` folder](/datasets/zeio/batch/tree/main/threads). The grouping is done due to `git's` constraints, namely, because it's not possible to have more than 10000 files in a single directory. That's why each cluster contains 10000 items (except the last one, which *could* contain fewer elements). Each cluster name has the format `${START_PAGE}-${END_PAGE}`, where `${START_PAGE}` is the index of the first page in the [archive][archive] from which posts have been put into the cluster, and `${END_PAGE}` is the last such paget respectively;
1. `compressed` representation is slightly more sophisticated than the `uncompressed` one - in consists of a set of `tar.xz` files which are nothing more than **the compressed clusters** of `txt` files described above. This representation corresponds to the [`threads-compressed` folder](/datasets/zeio/batch/tree/main/threads-compressed);
1. `spoken` representation consists of `mp3` files with speech generated for **some threads using an alternating speaker voice pattern** meaning that the 1st post is said by the first speaker, the 2nd post is said by the second speaker, the 3rd post is said by the first speaker, the 4th post is said by the second speaker and so on. The speech is generated automatically using a `TTS` engine. The `mp3` files are located in the [`threads-spoken-compressed`](/datasets/zeio/batch/tree/main/threads-spoken-compressed) and are grouped using `tar.xz` archives in the same way as `txt` files in the [`compressed` dataset representation](/datasets/zeio/batch/tree/main/threads-compressed).
Concerning particular `txt` files under `threads/\*/` folder, each item here corresponds to **one thread** and is organised as follows:
1. Each non-empty line corresponds to a single post from a user;
1. If a non-empty line follows another non-empty line, then it should be treated as a **comment** to one of the posts above it, a **response** to a request above or as an **answer** to a question;
1. If a non-empty line follows an empty line, it should be treated as a beginning of a discussion or a topic.
Therefore, the dataset consists of **threads**, which can be separated into **topics**, which, in turn, consist of **posts**. Posts are the lowermost units in the dataset and are not divided further - they should be interpreted as a plain text.
### Dataset instance
The following code snippet contains text for the thread `0000-0019/119540414`:
```sh
Всем привет. Нужна помощь богов фотошопа, на картинке надо изменить дату на 09/03/2016 и значения тесто на 86.500++
черес код елемента ебаш
Опять ты, сука ебаная? Хули тебе опять надо?
СПАСИБО
Размер шрифта не совпадает, але.
```
This thread consists of two topics, the first one of which includes 3 posts, and the second - 2 posts.
Therefore, this dataset entry can be represented in json in the following format:
```sh
{
"title": "Всем привет. Нужна помощь богов фотошопа, на картинке надо изменить дату на 09/03/2016 и значения тесто на 86.500++",
"topics": [
{
"posts": [
{
"text": "Всем привет. Нужна помощь богов фотошопа, на картинке надо изменить дату на 09/03/2016 и значения тесто на 86.500++"
},
{
"text": "черес код елемента ебаш"
},
{
"text": "Опять ты, сука ебаная? Хули тебе опять надо?"
}
]
},
{
"posts": [
{
"text": "СПАСИБО"
},
{
"text": "Размер шрифта не совпадает, але."
}
]
}
]
}
```
### Dataset fields
In `written` configuration the dataset is represented as a list of `Thread` objects, each `Thread` has a single property `topics`, which contains a list of `Topic` objects. Each `Topic` object has a single property `posts`, which points to the list of `Post` objects, making up the `Topic`. Each `Post` object contains a single property `text` which contains text representation of the post (essentially `text` is `html` code without `tags` and explicit links to other posts; there may still be implicit links to other posts in a form of quotes, prefixed with `>` symbol). As an additional field, each instance has a property `title` which is equivalent to the thread's main post content.
In `spoken` configuration the structure is basically the same, but some `Thread` objects have and additional property `speech` with a spoken representation of the thread.
[archive]: https://2ch.hk/b/arch/
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] |
namespace-Pt/books3-subset | 2023-10-20T03:28:31.000Z | [
"region:us"
] | namespace-Pt | null | null | 0 | 0 | 2023-10-20T02:19:48 | ---
dataset_info:
features:
- name: text
dtype: string
- name: meta
struct:
- name: pile_set_name
dtype: string
- name: md5
dtype: string
splits:
- name: train
num_bytes: 27878496460
num_examples: 50000
download_size: 16569223165
dataset_size: 27878496460
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "books3-subset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 559 | [
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bjwswang/arcadia | 2023-10-20T02:46:23.000Z | [
"region:us"
] | bjwswang | null | null | 0 | 0 | 2023-10-20T02:46:23 | Entry not found | 15 | [
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MadVoyager/singing_train | 2023-10-20T02:54:41.000Z | [
"region:us"
] | MadVoyager | null | null | 0 | 0 | 2023-10-20T02:54:41 | Entry not found | 15 | [
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AidanSimkins/AstroSeedPlantImagesForDiagnosis | 2023-11-02T05:13:49.000Z | [
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mtb2008/Gghhjj | 2023-10-20T03:03:05.000Z | [
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sizukululu/DIV2K_sub | 2023-10-20T03:04:22.000Z | [
"region:us"
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autoevaluate/autoeval-eval-acronym_identification-default-ab70e7-96295146633 | 2023-10-20T03:06:47.000Z | [
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namespace-Pt/codeparrot | 2023-10-20T04:49:53.000Z | [
"region:us"
] | namespace-Pt | null | null | 0 | 0 | 2023-10-20T03:30:39 | ---
dataset_info:
features:
- name: repo_name
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 53934285944
num_examples: 437079
download_size: 17809127301
dataset_size: 53934285944
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "codeparrot"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 490 | [
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] |
mb23/GraySpectrogram2 | 2023-10-20T03:55:56.000Z | [
"region:us"
] | mb23 | null | null | 0 | 0 | 2023-10-20T03:37:18 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: image
dtype: image
- name: caption
dtype: string
splits:
- name: train
num_bytes: 1063161338.75
num_examples: 9258
- name: test
num_bytes: 982933207.75
num_examples: 8722
download_size: 2041757562
dataset_size: 2046094546.5
---
# Dataset Card for "GraySpectrogram2"
## Dataset info:
* ```mb23/GraySpectrogram```で使用できるサブセットのみから画像とキャプションだけの組にしたもの。
## How to use dataset:
```python
(!pip install datasets)
import datasets
from datasets import load_dataset
dataset = load_datasets("mb23/GraySpectrogram")
```
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 839 | [
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digiworldsolution/digiworldsolution | 2023-10-20T05:34:03.000Z | [
"region:us"
] | digiworldsolution | null | null | 0 | 0 | 2023-10-20T03:55:00 | <a href="https://digiworldsolution.online/">Digital Marketing Services in Noida </a>:- As businesses look to the future, the need for effective digital marketing is becoming more and more apparent. Digital marketing is a powerful tool that allows businesses to reach out to potential customers, create brand awareness, and build relationships. With the right strategies and tactics, businesses can maximize their digital marketing potential. Noida is one of the leading cities in India for digital marketing, and there are a number of agencies that can help businesses unlock their digital marketing potential.
One of the best digital marketing services in Noida is the Noida Agency. The Noida Agency offers a comprehensive suite of digital marketing services that help businesses reach their goals. From SEO to PPC to social media marketing, the Noida Agency has the expertise necessary to help businesses reach their target audience. The team of experts at the Noida Agency is experienced in all aspects of digital marketing, and can create campaigns that are tailored to the needs of each business.
Online marketing is an important part of any digital marketing strategy. The Noida Agency can help businesses create effective online campaigns that focus on reaching the right audience. Through SEO, PPC, and other tactics, businesses can ensure that their websites are seen by the right people. The Noida Agency can also help businesses measure the success of their online marketing efforts, and make adjustments as needed.
Social media marketing is another important part of any digital marketing strategy. The Noida Agency can help businesses create effective campaigns that target the right audience. Through targeted campaigns, businesses can ensure that their content reaches the right people. The Noida Agency can also help businesses measure the success of their campaigns, and make any necessary changes based on the results.
In addition to online and social media marketing, the Noida Agency can also help businesses with content creation. Content creation is a key part of any digital marketing strategy, and the Noida Agency can help businesses create content that is engaging and effective. The Noida Agency can also help businesses with content distribution, ensuring that the right people are seeing the right content.
The Noida Agency is an excellent choice for businesses looking to unlock their digital marketing potential. With the expertise of the Noida Agency team, businesses can create effective campaigns that will help them reach their target audience. Through online and social media marketing, content creation, and content distribution, businesses can ensure that their digital marketing campaigns are successful.
In conclusion, the Noida Agency is an excellent choice for businesses looking to unlock their digital marketing potential. With the expertise of the Noida Agency team, businesses can create effective campaigns that will help them reach their target audience and maximize their digital marketing potential.
OFFICAL WEBSITE-https://digiworldsolution.online/ | 3,112 | [
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rahular/varta | 2023-10-23T20:17:02.000Z | [
"task_categories:summarization",
"task_categories:feature-extraction",
"size_categories:1B<n<10B",
"language:as",
"language:bh",
"language:bn",
"language:en",
"language:gu",
"language:hi",
"language:kn",
"language:ml",
"language:mr",
"language:ne",
"language:or",
"language:pa",
"language:ta",
"language:te",
"language:ur",
"license:cc",
"arxiv:2305.05858",
"region:us"
] | rahular | null | null | 0 | 0 | 2023-10-20T03:55:08 | ---
license: cc
task_categories:
- summarization
- feature-extraction
language:
- as
- bh
- bn
- en
- gu
- hi
- kn
- ml
- mr
- ne
- or
- pa
- ta
- te
- ur
pretty_name: varta
size_categories:
- 1B<n<10B
---
## Dataset Description
- **Repository:** https://github.com/rahular/varta
- **Paper:** https://arxiv.org/abs/2305.05858
### Dataset Summary
Varta is a diverse, challenging, large-scale, multilingual, and high-quality headline-generation dataset containing 41.8 million news articles in 14 Indic languages and English.
The data is crawled from DailyHunt, a popular news aggregator in India that pulls high-quality articles from multiple trusted and reputed news publishers.
### Languages
Assamese, Bhojpuri, Bengali, English, Gujarati, Hindi, Kannada, Malayalam, Marathi, Nepali, Oriya, Punjabi, Tamil, Telugu, and Urdu.
## Dataset Structure
### Data Fields
- id: unique identifier for the artilce on DailyHunt. This id will be used to recreate the dataset.
- langCode: ISO 639-1 language code
- source_url: the url that points to the article on the website of the original publisher
- dh_url: the url that points to the article on DailyHunt
- id: unique identifier for the artilce on DailyHunt.
- url: the url that points to the article on DailyHunt
- headline: headline of the article
- publication_date: date of publication
- text: main body of the article
- tags: main topics related to the article
- reactions: user likes, dislikes, etc.
- source_media: original publisher name
- source_url: the url that points to the article on the website of the original publisher
- word_count: number of words in the article
- langCode: language of the article
### Data Splits
From every language, we randomly sample 10,000 articles each for validation and testing. We also ensure that at least 80% of a language’s data is available for training.
Therefore, if a language has less than 100,000 articles, we restrict its validation and test splits to 10% of its size.
We also create a `small` training set by limiting the number of articles from each language to 100K.
This `small` training set with a size of 1.3M is used in all our fine-tuning experiments.
You can find the `small` training set [here](https://huggingface.co/datasets/rahular/varta/blob/main/varta/train/train_100k.json)
## Data Recreation
To recreate the dataset, follow this [README file](https://github.com/rahular/varta/tree/main/crawler#README.md).
## Misc
- Original source: https://m.dailyhunt.in/
- License: CC-BY 4.0
## Citation Information
```
@misc{aralikatte2023varta,
title={V\=arta: A Large-Scale Headline-Generation Dataset for Indic Languages},
author={Rahul Aralikatte and Ziling Cheng and Sumanth Doddapaneni and Jackie Chi Kit Cheung},
year={2023},
eprint={2305.05858},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
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ahhany/constructionQAs | 2023-10-20T04:01:16.000Z | [
"region:us"
] | ahhany | null | null | 0 | 0 | 2023-10-20T04:01:16 | Entry not found | 15 | [
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autoevaluate/autoeval-eval-acronym_identification-default-0e34b7-96304146634 | 2023-10-20T04:21:10.000Z | [
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reczoo/Frappe_x1 | 2023-10-20T05:36:44.000Z | [
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reczoo/Criteo_x1 | 2023-10-20T05:41:47.000Z | [
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reczoo/MIND_small_x1 | 2023-10-20T05:42:08.000Z | [
"region:us"
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reczoo/TaobaoAd_x1 | 2023-10-20T05:42:36.000Z | [
"region:us"
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erecprime1/ErecPrime24 | 2023-10-20T05:47:39.000Z | [
"region:us"
] | erecprime1 | null | null | 0 | 0 | 2023-10-20T05:44:10 | <a href="https://www.healthnews360.org/erecprime-reviews/">Erec Prime Reviews</a> - Real Users, Real Results: Testimonials and Reviews on ErecPrime
Curious about the real results that men have experienced with ErecPrime Male Enhancement? Look no further. In this section, we'll share some testimonials and reviews from actual users of ErecPrime.
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<a href="https://www.healthnews360.org/erecprime-reviews/">Erec Prime 24</a>
<a href="https://www.healthnews360.org/erecprime-reviews/">Erec Prime Scam</a>
<a href="https://www.healthnews360.org/erecprime-reviews/">ErecPrime</a>
<a href="https://www.healthnews360.org/erecprime-reviews/">ErecPrime Male Enhancement</a>
Official Website
https://www.healthnews360.org/erecprime-reviews/
https://www.healthnews360.org/glucontrol-blood-sugar/
https://erecprimemale.blogspot.com/2023/10/erecprimeerec-prime-reviewserecprime.html
https://erecprimemale.blogspot.com/2023/10/erec-prime-24erec-prime-scamerec-prime.html
https://erecprimemale.blogspot.com/2023/10/erecprime-male-enhancementerec-prime.html
https://medium.com/@erecprimee/erecprime-erec-prime-reviews-erecprime-male-enhancement-fc4e0791b138
https://medium.com/@erecprimee/erec-prime-24-erec-prime-scam-erec-prime-reviews-erec-prime-24-b60daec7134e
https://medium.com/@erecprimee/erecprime-male-enhancement-erec-prime-scam-erec-prime-scam-erec-prime-reviews-erecprime-23fb005764be
https://sites.google.com/view/erec-prime-24/
https://sites.google.com/view/erec-prime-scam/
https://sites.google.com/view/erec-prime-24/
https://groups.google.com/g/erecprime24/c/Xf0RAOKTuXU
https://groups.google.com/g/erecprime24/c/LD8StXEm9go
https://groups.google.com/g/erecprime24/c/P6tuVOvumlg
https://www.apsense.com/page/erec-prime-reviews
https://www.apsense.com/page/erec-prime-24erec-prime-scam
https://www.apsense.com/article/erec-prime-reviewserec-prime-24.html
https://www.facebook.com/buyerecprime/
https://www.facebook.com/groups/geterecprime
https://www.facebook.com/groups/buyerecprimereviews
https://www.facebook.com/groups/buyerecprimemaleenhancement
https://soundcloud.com/erecprimee/erec-prime-reviews
https://soundcloud.com/erecprimee/erec-prime-24
https://soundcloud.com/erecprimee/erec-prime-scam
https://soundcloud.com/erecprimee/erecprimeerecprime-male-enhancement
https://community.weddingwire.in/forum/erecprime-erec-prime-reviews-erecprime-male-enhancement--t184842
https://community.weddingwire.in/forum/erec-prime-24-erec-prime-scam-erec-prime-reviews-erec-prime-24--t184844
https://community.weddingwire.in/forum/erecprime-male-enhancement-erec-prime-scam-erec-prime-scam-erec-prime-reviews-erecprime--t184846
https://forums.hitched.co.uk/chat/forums/thread/erec-prime-reviewserec-prime-24erec-prime-scam-1130515/
https://forums.hitched.co.uk/chat/forums/thread/erec-prime-reviewserec-prime-24erec-prime-scam-1130515/
https://forums.hitched.co.uk/chat/forums/thread/erec-prime-scamerec-prime-reviewserecprime-1130519/
https://www.magcloud.com/browse/issue/2639108?__r=7561729
https://www.ourboox.com/books/erec-prime-reviews/
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teowu/Q-Instruct | 2023-10-27T04:36:05.000Z | [
"license:apache-2.0",
"region:us"
] | teowu | null | null | 4 | 0 | 2023-10-20T06:14:59 | ---
license: apache-2.0
---
A Preview version of the **Q-Instruct** dataset. A technical report coming soon.
Usage: The dataset is converted to LLaVA format. To get the data, first download the `cleaned_labels.json`; then download and extract `q-instruct-images.tar`.
Modify the `--data_path` and `--image_folder` in LLaVA training scripts to train with this dataset. | 370 | [
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namespace-Pt/pg19 | 2023-10-20T07:04:54.000Z | [
"region:us"
] | namespace-Pt | null | null | 0 | 0 | 2023-10-20T06:37:01 | ---
dataset_info:
features:
- name: text
dtype: string
- name: meta
struct:
- name: pile_set_name
dtype: string
- name: md5
dtype: string
splits:
- name: train
num_bytes: 11160715706
num_examples: 27878
download_size: 6850727741
dataset_size: 11160715706
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "pg19"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 549 | [
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autoevaluate/autoeval-eval-acronym_identification-default-221bb5-96318146635 | 2023-10-20T06:38:50.000Z | [
"region:us"
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Jiabin99/Arxiv-PubMed-mix-NC-LP | 2023-10-20T07:27:41.000Z | [
"region:us"
] | Jiabin99 | null | null | 3 | 0 | 2023-10-20T07:13:42 | Entry not found | 15 | [
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mesolitica/translated-UltraFeedback | 2023-10-20T07:24:42.000Z | [
"region:us"
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Jiabin99/All_pyg_graph_data | 2023-10-26T05:04:09.000Z | [
"region:us"
] | Jiabin99 | null | null | 2 | 0 | 2023-10-20T07:23:31 | Entry not found | 15 | [
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autoevaluate/autoeval-eval-conll2003-conll2003-11847a-96327146637 | 2023-10-20T07:37:31.000Z | [
"autotrain",
"evaluation",
"region:us"
] | autoevaluate | null | null | 0 | 0 | 2023-10-20T07:35:52 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- conll2003
eval_info:
task: entity_extraction
model: AIventurer/bert-finetuned-ner
metrics: []
dataset_name: conll2003
dataset_config: conll2003
dataset_split: test
col_mapping:
tokens: tokens
tags: ner_tags
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Token Classification
* Model: AIventurer/bert-finetuned-ner
* Dataset: conll2003
* Config: conll2003
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@Anmol-Hexaware](https://huggingface.co/Anmol-Hexaware) for evaluating this model. | 851 | [
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namespace-Pt/projects | 2023-10-21T04:23:27.000Z | [
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dokapirates/random-loras-for-testing | 2023-10-20T09:22:51.000Z | [
"region:us"
] | dokapirates | null | null | 2 | 0 | 2023-10-20T07:48:10 | Entry not found | 15 | [
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ProArtyom/imdb_jsons | 2023-10-20T08:03:55.000Z | [
"region:us"
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AnastasiaGolotina27/cagliostro-colab-ui | 2023-10-21T13:39:48.000Z | [
"region:us"
] | AnastasiaGolotina27 | null | null | 0 | 0 | 2023-10-20T08:17:42 | Entry not found | 15 | [
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AaAsr/Simple_Complex_pair | 2023-10-20T08:27:59.000Z | [
"region:us"
] | AaAsr | null | null | 0 | 0 | 2023-10-20T08:25:53 | Entry not found | 15 | [
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RHRolun/bird_data | 2023-10-20T09:31:55.000Z | [
"region:us"
] | RHRolun | null | null | 0 | 0 | 2023-10-20T09:00:19 | Entry not found | 15 | [
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AdrianM0/MUV | 2023-10-20T09:29:21.000Z | [
"region:us"
] | AdrianM0 | null | null | 0 | 0 | 2023-10-20T09:27:11 | Entry not found | 15 | [
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lshwwc/predict-demo | 2023-10-21T02:44:58.000Z | [
"region:us"
] | lshwwc | null | null | 0 | 0 | 2023-10-20T09:47:34 | Entry not found | 15 | [
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binwang/DialogSum_with_speaker | 2023-10-20T11:28:37.000Z | [
"license:apache-2.0",
"arxiv:2310.10981",
"region:us"
] | binwang | null | null | 0 | 0 | 2023-10-20T10:10:50 | ---
license: apache-2.0
---
This one is a post-processed DialogSum dataset (https://aclanthology.org/2021.findings-acl.449/) (https://huggingface.co/datasets/knkarthick/dialogsum).
It was first released and used in the InstructDS paper (EMNLP 2023 - https://arxiv.org/abs/2310.10981).
It automatically recognizes the speaker's information and recovers it to replace #Person1#, #Person2#, #Person3# in the original dataset. | 425 | [
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2003vishnu2003/test | 2023-10-20T10:22:33.000Z | [
"region:us"
] | 2003vishnu2003 | null | null | 0 | 0 | 2023-10-20T10:21:29 | Entry not found | 15 | [
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projetomemoreba/questoes_direito_administrativo_v1 | 2023-10-27T08:35:29.000Z | [
"region:us"
] | projetomemoreba | null | null | 0 | 0 | 2023-10-20T10:58:38 | Entry not found | 15 | [
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] |
jay401521/temp | 2023-10-20T11:04:54.000Z | [
"region:us"
] | jay401521 | null | null | 0 | 0 | 2023-10-20T11:04:50 | ---
dataset_info:
features:
- name: id
dtype: int32
- name: domain
dtype: string
- name: label
dtype:
class_label:
names:
'0': POS
'1': NEG
'2': NEU
- name: rank
dtype: string
- name: sentence
dtype: string
splits:
- name: test
num_bytes: 4688312
num_examples: 54113
download_size: 2501213
dataset_size: 4688312
---
# Dataset Card for "temp"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 566 | [
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CULT8R3U/test | 2023-10-20T11:13:19.000Z | [
"region:us"
] | CULT8R3U | null | null | 0 | 0 | 2023-10-20T11:13:19 | Entry not found | 15 | [
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Coroseven/RubyHoshino | 2023-10-20T11:22:37.000Z | [
"region:us"
] | Coroseven | null | null | 0 | 0 | 2023-10-20T11:21:12 | Entry not found | 15 | [
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shortcutsketoofficial/shortcuts-keto-gummies | 2023-10-20T11:25:24.000Z | [
"region:us"
] | shortcutsketoofficial | null | null | 0 | 0 | 2023-10-20T11:25:10 | <h2 style="text-align: left;"><span style="background-color: #ffcc00; color: #339966;"><a style="background-color: #ffcc00; color: #339966;" href="https://www.globalfitnessmart.com/get-shortcuts-keto-gummies"><strong>{</strong><strong>Shortcuts Keto Gummies - Official Website -- Order Now}</strong></a></span></h2>
<h2><strong>➡️<span style="color: #ff9900;">● For Order Official Website - <a style="color: #ff9900;" href="https://www.globalfitnessmart.com/get-shortcuts-keto-gummies">https://www.globalfitnessmart.com/get-shortcuts-keto-gummies</a></span></strong><br /><strong>➡️<span style="color: #33cccc;">● Item Name: — <a style="color: #33cccc;" href="https://www.globalfitnessmart.com/get-shortcuts-keto-gummies">Shortcuts Keto Gummies</a></span></strong><br /><strong>➡️<span style="color: #99cc00;">● Ingredients: — All Natural</span></strong><br /><strong>➡️<span style="color: #339966;">● Incidental Effects: — NA</span></strong><br /><strong>➡️<span style="color: purple;"><span style="color: red;">● Accessibility: — <a style="color: red;" href="https://www.globalfitnessmart.com/get-shortcuts-keto-gummies">Online</a></span><br /></span></strong></h2>
<h2><span style="background-color: #ffcc00; color: #339966;"><a style="background-color: #ffcc00; color: #339966;" href="https://www.globalfitnessmart.com/get-shortcuts-keto-gummies"><strong>➡️Hurry Up - Limited Time Offer - Purchase Now</strong></a></span><br /><span style="background-color: #ffcc00; color: #339966;"><a style="background-color: #ffcc00; color: #339966;" href="https://www.globalfitnessmart.com/get-shortcuts-keto-gummies"><strong>➡️Hurry Up - Limited Time Offer - Purchase Now</strong></a></span><br /><span style="background-color: #ffcc00; color: #339966;"><a style="background-color: #ffcc00; color: #339966;" href="https://www.globalfitnessmart.com/get-shortcuts-keto-gummies"><strong>➡️Hurry Up - Limited Time Offer - Purchase Now</strong></a></span></h2>
<div class="separator" style="clear: both; text-align: center;"><a style="margin-left: 1em; margin-right: 1em;" href="https://www.globalfitnessmart.com/get-shortcuts-keto-gummies"><img src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjLfkkEc7CrCS0NWnqTyRly6DRhtnlt9HN-s2mcISZPa3qKEcZiganLhxwDJPBydy4wM-7wFjI7QQCYj7P0DqQ9PIkew1eljgw5EBvRbTMT8duUC24AfbIFH9redpPr4XwYKHIv0KPId5EL3lO8WeUot6ZPLm-sGQ0X6DZJVLgewcxFCggrmr4LBXpjRlI0/w640-h378/Shortcuts%20Keto%20Gummies%20001.jpg" alt="" width="640" height="378" border="0" data-original-height="515" data-original-width="873" /></a></div>
<h2><strong><a href="https://devfolio.co/@shortcutsketo">Shortcuts Keto Gummies</a> USA – Critical information to know –</strong></h2>
<p><strong><a href="https://short-usa.clubeo.com">Shortcuts Keto Gummies</a></strong> is the combination of wholesome ingredients that emphasizes on turning on the ketosis process. The user attains a reshaped figure with no more overweight issues. <strong><a href="https://short-usa.clubeo.com/page/shortcuts-keto-gummies-usa-reviews-updated-2023.html">Shortcuts Keto Gummies</a></strong> is the trending option that works effectively on all body types to help with the best ketosis process. The user has to consume the gummies with not guilt consumption of food. Yes, you do not have to follow tiring workout sessions. The body gets no excessive fat accumulation and trims the figure with better reactions. The energy level increases with better strength and stamina. The effective extracts present in the formula assures of the best reactions with effective results.</p>
<h2 style="text-align: center;"><span style="color: #0000ff; background-color: #ffcc00;"><a style="color: #0000ff; background-color: #ffcc00;" href="https://www.globalfitnessmart.com/get-shortcuts-keto-gummies"><strong>In the USA - Visit the Shortcuts Keto Gummies Official Website USA Here</strong></a></span></h2>
<h2><strong>What are <a href="https://short-usa.clubeo.com/page/shortcuts-keto-gummies-is-legit-2023-updated-report.html">Shortcuts Keto Gummies</a> USA?</strong></h2>
<p><a href="https://short-usa.clubeo.com/calendar/2023/10/21/shortcuts-keto-gummies-usa-official-burn-fat-keto-gummies"><strong>Shortcuts Keto Gummies</strong> </a>is the fastest fat reduction option that eliminates the extra fat deposition and eliminates excessive pounds naturally. These gummies are a simple and easy way to consume compared to powder, pills, or oils.</p>
<p>This keto option attributes to the best vitality boost with effective endurance levels. There are herbal ingredients that are from nature and help with better benefits with no more fat deposition in the body. You can experience a better physique with elevated energy levels. The appetite level reduces with no more cravings. There are high demands for this regimen and most people have tried more than two bottles and are still into the regimen.</p>
<p>Consuming this formula has shown effective beneficial effects on increasing the metabolic rate that accelerates the digestion to fat loss. This review can help you get a better investigation of the effective health benefits of consuming these gummies to boost overall health.</p>
<div class="separator" style="clear: both; text-align: center;"><a style="margin-left: 1em; margin-right: 1em;" href="https://www.globalfitnessmart.com/get-shortcuts-keto-gummies"><img src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEj7tBqkJdaNLCFQdB1IPzVQ0Zo3wAxsOuZzulu8g3mDz5JqlfqgbQGksDqccXzhqFKMo8XIYIGq3BAfiliSddlMML7KM2DwTo1QVSZqdEMZgDrXMrPRoW-fEDjy51W3J6jVAdU-dUeMLaAUsfCaQXofYsQd8KvrQv0nT48gFQMQiJmFkRBc7b3lMnQhSLxk/w640-h422/WORK%20KETO.jpg" alt="" width="640" height="422" border="0" data-original-height="415" data-original-width="628" /></a></div>
<h2><strong>How do the <a href="https://groups.google.com/g/shortcuts-keto-gummies-official/c/-6M3fGVBAIk">Shortcuts Keto Gummies</a> work?</strong></h2>
<p>Adding the <a href="https://lookerstudio.google.com/u/0/reporting/6c6be7ee-b94e-4841-936d-9f6eada2a35f/page/WR1fD"><strong>Shortcuts Keto Gummies</strong></a> USA to the body proffers a proper dose of natural and organic blends that works well to help with the ketosis process. This regimen attributes the faster breakdown of the fat content to reduce overall weight with better reactions.</p>
<p>You do not have to wait for ages to lose all excessive fats from the body. The user can consume two gummies a day to enter into the metabolic ketosis process. With triggered ketosis, the body burns all extra fats and attributes a maintained body weight.</p>
<p>Following the keto diet which is the low-carb diet attributed to ketosis. Under ketosis, the body limits the use of carbohydrates and aids fat-burning processes. The body gets energy from the primary source of carbohydrates that is restricted in a diet. People follow the keto diet to help the body attain energy from the fats and these gummies ease the process with better reactions.</p>
<p>The body attains the energy from the fats to fuel and attributes better body functions. These gummies are good for elevating the metabolism which helps with rapid fat extirpation reactions. The reserved fat content is used for a better metabolic boost with effective digestive reactions. You get the best blood circulation to all the body parts and it amps up overall health with no harsh reactions.</p>
<p>The energy levels increase with boosted brain health. The person gets better concentration with elevated memory powers. The health of the person increases with faster fat-loss processes. The effective blend helps with faster actions in the body to trim the excessive fat content in the body. It allows the user to get a slim and trim outlook with no more issues caused by these keto gummies.</p>
<h2 style="text-align: center;"><span style="color: #0000ff; background-color: #ffcc00;"><a style="color: #0000ff; background-color: #ffcc00;" href="https://www.globalfitnessmart.com/get-shortcuts-keto-gummies"><strong>(EXCLUSIVE OFFER)Click Here : "Shortcuts Keto Gummies USA"Official Website!</strong></a></span></h2>
<h2><strong>Key components of <a href="https://colab.research.google.com/drive/1Z-PaquKqPvIYAZ5JgQnMRXGNm2-Uosot">Shortcuts Keto Gummies</a> –</strong></h2>
<p><strong>#Cinnamon –</strong> There are vast health benefits to using cinnamon for the body. The body does not reserve the fats and reduces them to proffer a slim and fit body with effective reactions. You get no more inflammation in the body as this element works well to shed fat molecules. The component has enriched antioxidants that prevent all health issues caused due to obesity.</p>
<p><strong>#Garcinia Cambogia</strong> – this Garcinia Cambogia is also known as the Malabar tamarind that reduces calories and body weight naturally. It contains a higher concentration of hydroxy citric acid that helps the user sustain smaller meal portions with no more fat deposition in the body.</p>
<p><strong>#Fennel seeds –</strong> these fennel seeds are helpful in burning all the extra fats from the body and help trim the physique with better effects.</p>
<p><strong>#Dandelion –</strong> it is the significant component in the gummy that cuts the unwanted pounds to reduce all accumulated fat content from the body. The digestion with better metabolic rate increases with the anti-diuretic properties of the dandelion component.</p>
<p><strong>#There are all plant-</strong>based ingredients that attribute fat reduction to better body weight. Anyone can attain a slim and trimmed physique in a few uses, but you need to consume it regularly for persistent outcomes.</p>
<div class="separator" style="clear: both; text-align: center;"><a style="margin-left: 1em; margin-right: 1em;" href="https://www.globalfitnessmart.com/get-shortcuts-keto-gummies"><img src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEheMQU8UWm-eWXHBjfwVC3W73gVyI2hyphenhyphenKFWWvCyhM72mDOzE4X91uS513AMUFj1N_bFNtT4AAErgCTUM_kvgzaCv3372feDimzPp1ETpqWZXNMOh_HhHN-DIlItV3TGxShyV8r2sj8et1Y-KItrEVQCHC4Qfp842zN5lXQw1LvahQfOzf7cAgprzuzqdriT/w640-h316/Shortcuts%20Keto%20Gummies%20004.jpg" alt="" width="640" height="316" border="0" data-original-height="453" data-original-width="918" /></a></div>
<h2><strong>Benefits of <a href="https://sites.google.com/view/shortcuts-keto-gummie-official/home">Shortcuts Keto Gummies</a><br /></strong></h2>
<p>As explained above, apple cider vinegar offers numerous benefits to health and aiding weight loss. With a combination of other potent 100% natural ingredients, CiderFit ACV Gummies offer more health benefits that support a healthy lifestyle, particularly for those looking to lose weight fast.</p>
<p>Below we list some of the health benefits offered by <a href="https://shortcuts-keto-gummies-6.jimdosite.com/"><strong>Shortcuts Keto Gummies</strong></a>:</p>
<p><strong>#Supports Weight Management:</strong> CiderFit's potent formula is designed to help individuals maintain a healthy weight by boosting their metabolism and reducing excess fat accumulation.</p>
<p><strong>#Enhances Digestive Health:</strong> With apple cider vinegar as its core ingredient, CiderFit aids digestion by promoting the growth of beneficial gut bacteria and easing digestive discomfort.</p>
<p><strong>#Increases Energy Levels:</strong> In today's fast-paced world, energy is the fuel that keeps us going. <strong><a href="https://www.scoop.it/topic/shortcuts-keto-gummies-by-shortcutsketo-official-1">Shortcuts Keto Gummies</a></strong> can help you stay energized throughout the day. The gummies' unique blend of nutrients provides sustained energy, helping you power through daily tasks and activities with vigour.</p>
<p><strong>#Balances Blood Sugar Levels:</strong> The ingredients in CiderFit work harmoniously to regulate blood sugar levels, reducing cravings and the likelihood of unhealthy snacking.</p>
<p><strong>#A vibrant and healthy skin:</strong> The antioxidants in these gummies combat free radicals that can damage skin cells and accelerate the ageing process. Additionally, the improved digestion and gut health from apple cider vinegar may lead to better nutrient absorption, promoting healthier skin from within.</p>
<p><strong>#Detoxifies the Body:</strong> Pomegranate Powder and Beet Root Powder in CiderFit help cleanse the body by eliminating toxins, ensuring optimal organ function.</p>
<p><strong>#Better heart health:</strong> <strong><a href="https://leetcode.com/discuss/interview-question/4189079/Shortcuts-Keto-Gummies-Reviews%3A-Viral%3A*SCAM-or-LEGIT*-Is-It-Work-or-Not">Shortcuts Keto Gummies</a></strong> can play a role in supporting heart health due to their potential to regulate blood pressure and cholesterol levels.</p>
<p><strong>#Supports Immune Function:</strong> The natural antioxidants in CiderFit strengthen the body's immune system, making it more resilient against external threats.</p>
<h2 style="text-align: center;"><span style="color: #0000ff; background-color: #ffcc00;"><a style="color: #0000ff; background-color: #ffcc00;" href="https://www.globalfitnessmart.com/get-shortcuts-keto-gummies"><strong>SPECIAL PROMO[Limited Discount]: "Shortcuts Keto Gummies USA"Official Website!</strong></a></span></h2>
<h2><strong>How many <a href="https://shortcuts-keto-gummies-review.company.site/">Shortcuts Keto Gummies</a> to consume daily?</strong></h2>
<p>The user must consume two gummies of the <a href="https://devfolio.co/@shortcutsketo"><strong>Shortcuts Keto Gummies</strong></a> USA daily for two or three months. It is important to consume better water content with an effective dosage of the gummies. Do not overdose the body with the gummies.Moreover, the users are advised to get a consultation from a licensed healthcare provider or physician and consume accordingly. Consume the gums regularly with no overconsumption. Do follow all the instructions while consuming.</p>
<h2><strong><a href="https://shortcuts-keto-gummies-review.hashnode.dev/shortcuts-keto-gummies">Shortcuts Keto Gummies</a> PROS and CONS</strong></h2>
<h2><strong>PROS</strong></h2>
<ul>
<li><a href="https://bookshop.org/wishlists/25dd44bb54d1afef609d015f9b00ac5efa5b53f8"><strong>Shortcuts Keto Gummies</strong></a> are defined as a perfect keto supplement to suit your everyday needs without any side effects.</li>
<li>When you start taking Keto gummy on a regular basis there are certain dietary exchanges that would happen in your diet by satiating the hunger cravings properly.</li>
<li>There's no complex tutorial to start with <a href="https://www.c-sharpcorner.com/article/shortcuts-keto-gummies-reviews-legit-2023-its-really-works/"><strong>Shortcuts Keto Gummies</strong></a> as you just need to follow one simple recommended dosage intake highlighted by the products manufacturers.</li>
<li>The most valuable aspect in the Keto diet is the defining limit of sustaining in the ketosis regime which frankly decides the amount of body fat you burn while staying in the ketosis regime properly.</li>
<li><a href="https://flokii.com/blogs/view/128583"><strong>Shortcuts Keto Gummies</strong></a> deliver BHB(Beta-Hydroxybutyrate) in the form of Ketone bodies that would suit your bodily necessity in the same way as glucose and which is far more better and sustainable than glucose.</li>
</ul>
<h2><strong>CONS</strong></h2>
<p>Like many keto supplements <a href="https://community.weddingwire.in/forum/shortcuts-keto-gummies-official-usa-reviews-updated-2023--t185364"><strong>Shortcuts Keto Gummies</strong></a> are only available online at its official website so if you wish to purchase it then you simply need to place your order straight on their official website not any third party reviewing website.</p>
<ul>
<li><a href="https://gocrowdera.com/US/self/shortcuts-keto-gummies/shortcutsketo-51535"><strong>Shortcuts Keto Gummies</strong></a> are produced in low quantities because the ingredients which are included in this supplement needs to be extracted from the natural resources which takes time and tremendous effort of several individuals. That's why the stock of the supplement is quite low.</li>
<li>The most constructed criticism of any keto supplement simply reflects the similarities that every keto formula shares with each other making them exactly similar with each other with the core principle of keto diet.</li>
</ul>
<div class="separator" style="clear: both; text-align: center;"><a style="margin-left: 1em; margin-right: 1em;" href="https://www.globalfitnessmart.com/get-shortcuts-keto-gummies"><img src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgxieLqhp6hwwHQ6TfjzIaIFWWMl6TYz7BBjPJadHKk8QbYRuNcjaNleS7f5WE3ojv57IfnDSHz0B2aYt7UzcspICTMUO4rNWQTToXF-szvIHHJfXb5z5h8AWFHqiZS2pIjWldLcjWHFep60ifGsaKBx2hwGOsBUfmaTnZvrZmsALzCfFk8ICUnpWrbINfT/w520-h640/Shortcuts%20Keto%20Gummies%20price.jpg" alt="" width="520" height="640" border="0" data-original-height="536" data-original-width="436" /></a></div>
<h2><strong>Where to buy online <a href="https://wandering.flarum.cloud/d/23312-shortcuts-keto-gummies-is-legit-2023-updated-report">Shortcuts Keto Gummies</a><br /></strong></h2>
<p>Visit our official website and explore our range of products.Select the desired quantity of <strong><a href="https://knownet.com/question/shortcuts-keto-gummies-usa-reviews-updated-2023/">Shortcuts Keto Gummies.</a></strong></p>
<p>Proceed to the secure checkout page and enter your shipping information.Complete the purchase and get ready to enhance your keto journey.</p>
<h2><strong>Final verdict –</strong></h2>
<p>Losing weight with the formidable ketosis process has never been easier than with the <a href="https://soundcloud.com/shortcutsketoofficial/shortcuts-keto-gummies-is-legit-2023-updated-report"><strong>Shortcuts Keto Gummies</strong></a> USA. This keto option maintains a proper muscle-to-fat ratio that uses the fats for fuel with the metabolic ketosis process. It prevents fat deposition with no more obesity issues. All the ingredients are wholesome and improve digestion with better metabolic rates. You can attain a slim and fit outlook in a few week.</p>
<div class="separator" style="clear: both; text-align: center;"><a style="margin-left: 1em; margin-right: 1em;" href="https://www.globalfitnessmart.com/get-shortcuts-keto-gummies"><img src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEi6NhBkYCk3Hu-5Hk4XFLjHpjK-mp-j-yLp-Vz9SMXk6yj3ptNDb7p5SmOr6nzat8W909DEI5K5rDNFiSXl4duR2pp_jGurmGD1RuKtHf0lIfJXDf_nkvXzEFaMzvphM0mkekLIMn8PCiUF0QaY5C2V6-3HVZWOmZNUIR16lesRwHMkplPeFE2qIYm8fICx/w640-h280/Shortcuts%20Keto%20Gummies%20order%20now.jpg" alt="" width="640" height="280" border="0" data-original-height="243" data-original-width="556" /></a></div>
<h2 style="text-align: center;"><span style="color: #0000ff; background-color: #ffcc00;"><a style="color: #0000ff; background-color: #ffcc00;" href="https://www.globalfitnessmart.com/get-shortcuts-keto-gummies"><strong>(EXCLUSIVE OFFER)Click Here : "Shortcuts Keto Gummies USA"Official Website!</strong></a></span></h2>
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<p> </p> | 23,550 | [
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TanvirOnHF/Allopathic_Drug_Manufacturers-BD | 2023-11-02T09:10:14.000Z | [
"size_categories:n<1K",
"language:en",
"license:odc-by",
"Allopathic",
"Drugs",
"Manufacturer",
"region:us"
] | TanvirOnHF | null | null | 0 | 0 | 2023-10-20T11:25:51 | ---
license: odc-by
pretty_name: Allopathic Drug Manufacturers Bangladesh
tags:
- Allopathic
- Drugs
- Manufacturer
language:
- en
size_categories:
- n<1K
---
# Allopathic_Drug_Manufacturers-BD [JSON dataset]
A dataset containing information about allopathic drug manufacturers in Bangladesh. The data is provided by the Directorate General of Drug Administration. The source in PDF format is found in the [MedEx](https://medex.com.bd) website.
## Dataset Contents
The dataset includes the following information for each allopathic drug manufacturer:
- Serial
- Pharmaceutical Name
- Location
- Manufacturing Licence Number Biological
- Manufacturing Licence Number Non-Biological
- Status
Please note that the dataset may not include all the manufacturers in Bangladesh.
## Data Source
- [Allopathic Drug Manufacturers - MedEx](https://medex.com.bd/downloads/KUE7l4PXoVUffEibhCCfyeU6Tn7Key6HtRVE6/allopathic-drug-manufacturers.pdf)
## Disclaimer
Please note that the dataset's accuracy cannot be guaranteed.
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Coroseven/Holo | 2023-10-20T11:30:14.000Z | [
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] | Coroseven | null | null | 0 | 0 | 2023-10-20T11:28:41 | Entry not found | 15 | [
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Teywa/Test | 2023-10-20T11:49:32.000Z | [
"region:us"
] | Teywa | null | null | 0 | 0 | 2023-10-20T11:42:40 | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 338808
num_examples: 200
download_size: 201257
dataset_size: 338808
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "Test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 428 | [
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BenjaminRAINGON/test1111 | 2023-10-20T12:13:41.000Z | [
"region:us"
] | BenjaminRAINGON | null | null | 0 | 0 | 2023-10-20T12:13:41 | Entry not found | 15 | [
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Kimata/GPT_Driver | 2023-10-20T12:20:34.000Z | [
"region:us"
] | Kimata | null | null | 0 | 0 | 2023-10-20T12:20:27 | ---
dataset_info:
features:
- name: messages
list:
- name: role
dtype: string
- name: content
dtype: string
splits:
- name: train
num_bytes: 59962578
num_examples: 28507
download_size: 7315280
dataset_size: 59962578
---
# Dataset Card for "GPT_Driver"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 428 | [
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lgoncalves/interaction_manager | 2023-10-20T12:26:34.000Z | [
"region:us"
] | lgoncalves | null | null | 0 | 0 | 2023-10-20T12:26:34 | Entry not found | 15 | [
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namespace-Pt/adon | 2023-10-29T08:09:43.000Z | [
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clean-as-teen/clean-as-teen-skintag-remover | 2023-10-20T12:35:45.000Z | [
"region:us"
] | clean-as-teen | null | null | 0 | 0 | 2023-10-20T12:34:42 | ➢ **Product Name** — [Clean As Teen Skin Tag Remover](https://www.healthsupplement24x7.com/get-clean-as-teen-skintag-remover)
➢ **Category** — Skin Care
➢ **Availability** — [Official Website](https://www.healthsupplement24x7.com/get-clean-as-teen-skintag-remover)
➢ **Main Benefits** — Remove Mole, Tags And Warts.
➢ **Side Effects** — N/A
➢ **Rating** — ★★★★✰ 4.8/5
➢ **Official Website** — [https://www.healthsupplement24x7.com/get-clean-as-teen-skintag-remover](https://www.healthsupplement24x7.com/get-clean-as-teen-skintag-remover)
The [Clean As Teen Skin Tag Remover](https://clean-as-teen-skin-tag-remover-serum.webflow.io/) Ingredients have been assembled with care by leading experts in the skincare industry. This serum has been formulated to perform well no matter the properties of your unique skin. Of particular note is the fact that there have been zero cases of reported [Clean As Teen Skin Tag Remover](https://soundcloud.com/clean-as-teen/clean-as-teen-skin-tag-remover-gmp-certified-premium-quality-all-natural-ingredients) Side Effects. That was the necessary condition for us to be aware of before performing these tests, let alone on our own staff.
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**What Is Clean As Teen Skin Tag Remover**
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Many people consider these skin conditions a nuisance, while others overlook them. However, skin tags, moles, and warts affect both appearance and confidence. [Clean As Teen Skin Tag Remover](https://www.sympla.com.br/evento/clean-as-teen-skin-tag-remover-gmp-certified-premium-quality-all-natural-ingredients/2215283) provides a cheaper alternative to invasive and painful procedures that are usually used to remove skin tags, moles, and warts.
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**Working Of Clean As Teen Skin Tag Remover
**
------------------------------------------------
[Clean As Teen Skin Tag Remover](https://www.fuzia.com/article_detail/810019/clean-as-teen-skin-tag-remover-gmp-certified), Remover’s innovative approach to removing skin tags, will solve all your skincare woes. It’s a treatment that eliminates marks forever, so there’s no chance of them returning. The outcome could startle you even if you only submit a few applications. The important thing is to get them done properly.
This product is intended for use by those over 18. Clean As Teen Skin Tag Remover will make your skin feel smoother and smoother with regular use. A few drops of this concoction applied to the scars twice daily (in the morning and before night) should help them fade and become less noticeable over time.
**Potential Benefits of Using Clean As Teen Skin Tag Remover**
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If applied regularly, this [Clean As Teen Skin Tag Remover](https://colab.research.google.com/drive/19yau02HkMcbcRyjvA1xzYTSm7IUoUjuM) topical solution would help you get rid of the unwanted moles, tags, and warts found on the skin surface. It would enhance the flow of white blood cells and make the tags fall off naturally. It also prevents any further growth of tags or warts on your skin. As a bonus, it also improves your overall complexion and skin smoothness.
The natural [Clean As Teen Skin Tag Remover](https://www.sympla.com.br/evento/clean-as-teen-skin-tag-remover-gmp-certified-premium-quality-all-natural-ingredients/2215283) ingredients can help facilitate better skin hydration. The hydrated skin would give you a youthful glow and shine as soon as possible.
The formula of [Clean As Teen Skin Tag Remover](https://clean-as-teen.clubeo.com/calendar/2023/10/21/clean-as-teen-skin-tag-remover-gmp-certified-premium-quality-all-natural-ingredients) would help to make your skin softer and supple. The region where the growth has been seen would most probably be harder. This can be effectively cured with this application.
[Clean As Teen Skin Tag Remover](https://sites.google.com/view/get-cleanasteenskintagremover/clean-as-teen-skin-tag-remover) skin care serum enhances blood flow to the region of the growth. This would help to make the skin or the affected region healthier and resistant to any sort of infection.
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**Clean As Teen Skin Tag Remover Ingredients Described**
--------------------------------------------------------
The powerful set of ingredients present in the formula provide healing effect to the skin. Here is what the product has in its combination minus
* **Sanguinaria canadensis**
The North American plant does wonders when applied to the skin. It stimulates white blood cells and fights away imperfections to give a special type of glow.
* **Zincum muriaticum**
The antiseptic properties of the herb helps your skin to recover and heal. It’s a powerful option that can easily give amazing effects.
* **Vitamin c**
Vitamin c is lately known to improve skin immunity and reduce ageing effect. It is added in [Clean As Teen Skin Tag Remover](https://lookerstudio.google.com/reporting/910f95ce-5a6d-4e35-8cc7-6e0d5f040a4b) very scientifically toimprove your appearance and skin quality.
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**How To Use Clean As Teen Skin Tag Remover**
----------------------------------------------
[Clean As Teen Skin Tag Remover](https://clean-as-teen.clubeo.com/page/clean-as-teen-skin-tag-remover-gmp-certified-premium-quality-all-natural-ingredients.html) is a very simple formula that gives natural healing effects while eliminating the unwanted skin marks and outgrowth. It creates a better skin immunity with the following process -
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Keep Applying The best skin [Clean As Teen](https://clean-as-teen-skintag-remover.blogspot.com/2023/10/clean-as-teen-skin-tag-remover.html) for warts and moles regularly to facilitate healing. It is going to take a few months to get results and permanent eradication of skin marks.
Experience the effectiveness of the skin serum every day and complete the [Clean As Teen Skin tag Remover](https://www.prlog.org/12990086-clean-as-teen-skin-tag-remover-gmp-certified-premium-quality-all-natural-ingredients.html) by using it continuously for three months.
A blemish free skin with complete healing results.
**Pricing Of Clean As Teen Skin Tag Remover**
---------------------------------------------
To order your favorite package, click “Shop Now,” and you will be redirected to a safe and secure checkout page. Here you will include your shipping and payment information. Once your order has been received, you will get a notification email. Then your package will be shipped to you between 3 to 5 business days.
They are exclusively available on the official website and come in different packages.
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**Final Words**
---------------
[Clean As Teen Skin Tag Remover](https://lexcliq.com/clean-as-teen-skin-tag-remover-gmp-certified-premium-quality-all-natural-ingredients/) is natural, GMO free and very helpful to provide a pleasing texture. It delivers long-lasting results for skin tags and outgrowth. The pigmentation Reduction [Clean As Teen](https://clean-as-teen.wixsite.com/clean-as-teen) is for everybody who is above 18 years of age irrespective of their gender and lifestyle. Purchase it from the official website and use it regularly to ensure healthy skin all the way.
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] |
MajdTannous/Dataset2 | 2023-10-21T08:29:39.000Z | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|wikipedia",
"language:en",
"license:cc-by-4.0",
"arxiv:1606.05250",
"region:us"
] | MajdTannous | Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable. | @article{2016arXiv160605250R,
author = {{Rajpurkar}, Pranav and {Zhang}, Jian and {Lopyrev},
Konstantin and {Liang}, Percy},
title = "{SQuAD: 100,000+ Questions for Machine Comprehension of Text}",
journal = {arXiv e-prints},
year = 2016,
eid = {arXiv:1606.05250},
pages = {arXiv:1606.05250},
archivePrefix = {arXiv},
eprint = {1606.05250},
} | 0 | 0 | 2023-10-20T12:35:12 | ---
pretty_name: SQuAD
viewer: true
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
- found
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|wikipedia
task_categories:
- question-answering
task_ids:
- extractive-qa
paperswithcode_id: squad
train-eval-index:
- config: plain_text
task: question-answering
task_id: extractive_question_answering
splits:
train_split: train
eval_split: validation
col_mapping:
question: question
context: context
answers:
text: text
answer_start: answer_start
metrics:
- type: squad
name: SQuAD
dataset_info:
features:
- name: id
dtype: string
- name: title
dtype: string
- name: context
dtype: string
- name: question
dtype: string
- name: answers
sequence:
- name: text
dtype: string
- name: answer_start
dtype: int32
config_name: plain_text
splits:
- name: train
num_bytes: 79317110
num_examples: 87599
- name: validation
num_bytes: 10472653
num_examples: 10570
download_size: 35142551
dataset_size: 89789763
---
# Dataset Card for "squad"
## Table of Contents
- [Dataset Card for "squad"](#dataset-card-for-squad)
- [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)
- [plain_text](#plain_text)
- [Data Fields](#data-fields)
- [plain_text](#plain_text-1)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
- [Who are the source language producers?](#who-are-the-source-language-producers)
- [Annotations](#annotations)
- [Annotation process](#annotation-process)
- [Who are the annotators?](#who-are-the-annotators)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://rajpurkar.github.io/SQuAD-explorer/](https://rajpurkar.github.io/SQuAD-explorer/)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of downloaded dataset files:** 35.14 MB
- **Size of the generated dataset:** 89.92 MB
- **Total amount of disk used:** 125.06 MB
### Dataset Summary
Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable.
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Dataset Structure
### Data Instances
#### plain_text
- **Size of downloaded dataset files:** 35.14 MB
- **Size of the generated dataset:** 89.92 MB
- **Total amount of disk used:** 125.06 MB
An example of 'train' looks as follows.
```
{
"answers": {
"answer_start": [1],
"text": ["This is a test text"]
},
"context": "This is a test context.",
"id": "1",
"question": "Is this a test?",
"title": "train test"
}
```
### Data Fields
The data fields are the same among all splits.
#### plain_text
- `id`: a `string` feature.
- `title`: a `string` feature.
- `context`: a `string` feature.
- `question`: a `string` feature.
- `answers`: a dictionary feature containing:
- `text`: a `string` feature.
- `answer_start`: a `int32` feature.
### Data Splits
| name |train|validation|
|----------|----:|---------:|
|plain_text|87599| 10570|
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@article{2016arXiv160605250R,
author = {{Rajpurkar}, Pranav and {Zhang}, Jian and {Lopyrev},
Konstantin and {Liang}, Percy},
title = "{SQuAD: 100,000+ Questions for Machine Comprehension of Text}",
journal = {arXiv e-prints},
year = 2016,
eid = {arXiv:1606.05250},
pages = {arXiv:1606.05250},
archivePrefix = {arXiv},
eprint = {1606.05250},
}
``` | 7,395 | [
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] |
Back-up/so-tay-sv | 2023-10-20T13:48:45.000Z | [
"region:us"
] | Back-up | null | null | 0 | 0 | 2023-10-20T13:48:39 | ---
dataset_info:
features:
- name: '0'
dtype: string
splits:
- name: train
num_bytes: 164051
num_examples: 76
download_size: 81123
dataset_size: 164051
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "so-tay-sv"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 430 | [
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newsmediabias/ReDev | 2023-10-20T17:07:58.000Z | [
"license:mit",
"doi:10.57967/hf/1251",
"region:us"
] | newsmediabias | null | null | 0 | 0 | 2023-10-20T13:52:11 | ---
license: mit
pretty_name: bias, fairness, responsible AI, LLM, LLM Hallucinations
---
# Dataset Card for Responsible Development of Language Models (ReDev) Framework Test Suite
## Dataset Description
As the use of large language models (LLMs) increases for everyday tasks, appropriate safeguards must be in place to ensure unbiased and safe output.
There is a need for responsible LLMs that are trained fairly, transparent to the public, and regularly monitored after deployment.
We introduce the _Responsible Development of Language Models (ReDev)_ framework to foster the development of _fair_, _safe_, and _robust_ LLMs for all users.
Here, we present a test suite of unique prompt types to assess LLMs on the aforementioned elements and facilitate research, ensuring all generated responses are non-harmful and free from biased content.
Outputs from four state-of-the-art LLMs, OPT, GPT-3.5, GPT-4, and LLaMA-2, are evaluated by our test suite, highlighting the importance of considering fairness, safety, and robustness at every stage of the machine learning pipeline, including data curation, training, and post-deployment.
**Created by Veronica** | 1,164 | [
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vikhyatk/synthetic-pepe | 2023-10-25T02:31:49.000Z | [
"region:us"
] | vikhyatk | null | null | 0 | 0 | 2023-10-20T14:07:54 | ---
pretty_name: "synthetic pepe"
---
by using this dataset you are agreeing to the fact that the pleiades star system is a binary system and any claim otherwise is a lie | 171 | [
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Coroseven/AkaneKurokawa | 2023-10-20T14:39:21.000Z | [
"region:us"
] | Coroseven | null | null | 0 | 0 | 2023-10-20T14:37:56 | Entry not found | 15 | [
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oeg/software_benchmark_v2 | 2023-10-23T10:40:15.000Z | [
"task_categories:text-classification",
"language:es",
"license:cc-by-4.0",
"region:us"
] | oeg | null | null | 0 | 0 | 2023-10-20T14:42:39 | ---
license: cc-by-4.0
task_categories:
- text-classification
language:
- es
pretty_name: Software Benchmark Multidomain
---
The corpus have been built using two corpora in software mentions.
* SoMESCi [1]. We have used the corpus uploaded to [Github](https://github.com/dave-s477/SoMeSci/tree/9f17a43f342be026f97f03749457d4abb1b01dbf/PLoS_sentences), more specifically, the corpus created with sentences.
* Softcite [2]. This project has published another corpus for software mentions, which is also available on [Github](https://github.com/howisonlab/softcite-dataset/tree/master/data/corpus). We have used the annotations from bio and economics domain.
* Papers with code. We have downloaded a list of publications from the [Papers with Code](https://paperswithcode.com/) site. You can find there publications and software from machine learning domain. To build this corpus, we have selected texts where you can find mentions of the software related with the publication.
To build this corpus, we have removed the annotations of other entities such as version, url and those which are related with the relation of teh entity with the text. IN this case, we only use the label Application_Mention.
To reconciliate both corpora, we have mapping the labels of both corpora. Also, some decisions about the annotations have been taken, for example, in the case of Microsoft Excel, we have decided to annotate Excel as software mention, not the whole text.
## References
1. Schindler, D., Bensmann, F., Dietze, S., & Krüger, F. (2021, October). Somesci-A 5 star open data gold standard knowledge graph of software mentions in scientific articles. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management (pp. 4574-4583).
2. Du, C., Cohoon, J., Lopez, P., & Howison, J. (2021). Softcite dataset: A dataset of software mentions in biomedical and economic research publications. Journal of the Association for Information Science and Technology, 72(7), 870-884. | 1,998 | [
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Coroseven/KirigayaSuguha | 2023-10-20T14:46:17.000Z | [
"region:us"
] | Coroseven | null | null | 0 | 0 | 2023-10-20T14:45:02 | Entry not found | 15 | [
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] |
ML4SE2023-G1-WizardCoder/ML4SE23_G1_HumanEval-SCoT | 2023-10-20T15:10:07.000Z | [
"task_categories:text-generation",
"size_categories:n<1K",
"language:en",
"region:us"
] | ML4SE2023-G1-WizardCoder | null | null | 0 | 0 | 2023-10-20T14:54:54 | ---
task_categories:
- text-generation
pretty_name: HumanEval dataset enhanced with Structured-Chain-of-Thought
size_categories:
- n<1K
language:
- en
---
HumanEval dataset enhanced with Structured-Chain-of-Thought | 215 | [
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Pampkinus/Alexander-Lukashenko | 2023-10-20T15:05:04.000Z | [
"license:openrail",
"region:us"
] | Pampkinus | null | null | 0 | 0 | 2023-10-20T14:57:00 | ---
license: openrail
---
A faceset of the Belorussian president Alexander Lukashenko , 33910 images (jpg)
https://en.wikipedia.org/wiki/Alexander_Lukashenko | 157 | [
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ML4SE2023-G1-WizardCoder/ML4SE23_G1_EvolInstruct-SCoT-1k | 2023-10-20T15:01:51.000Z | [
"task_categories:text-generation",
"size_categories:1K<n<10K",
"language:en",
"region:us"
] | ML4SE2023-G1-WizardCoder | null | null | 0 | 0 | 2023-10-20T15:00:25 | ---
task_categories:
- text-generation
language:
- en
pretty_name: EvolInstruct enhanced 1k entries dataset with Structured-Chain-of-Thought
size_categories:
- 1K<n<10K
---
EvolInstruct enhanced 1k entries dataset with Structured-Chain-of-Thought | 247 | [
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whatever420/met-cropped | 2023-10-20T15:01:01.000Z | [
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Falconsai/customer_intents | 2023-10-20T23:51:04.000Z | [
"region:us"
] | Falconsai | null | null | 0 | 0 | 2023-10-20T15:01:23 | Entry not found | 15 | [
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ML4SE2023-G1-WizardCoder/ML4SE23_G1_MBPP-SCoT | 2023-10-20T15:26:45.000Z | [
"task_categories:text-generation",
"size_categories:n<1K",
"language:en",
"region:us"
] | ML4SE2023-G1-WizardCoder | null | null | 0 | 0 | 2023-10-20T15:10:48 | ---
task_categories:
- text-generation
language:
- en
pretty_name: MBPP enhanced dataset with Structured-Chain-of-Thought
size_categories:
- n<1K
---
MBPP enhanced dataset with Structured-Chain-of-Thought | 205 | [
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ML4SE2023-G1-WizardCoder/ML4SE23_G1_MBCPP-SCoT | 2023-10-25T08:22:03.000Z | [
"task_categories:text-generation",
"size_categories:n<1K",
"language:en",
"region:us"
] | ML4SE2023-G1-WizardCoder | null | null | 0 | 0 | 2023-10-20T15:12:40 | ---
task_categories:
- text-generation
language:
- en
pretty_name: MBCPP enhanced dataset with Structured-Chain-of-Thought
size_categories:
- n<1K
---
MBCPP enhanced dataset with Structured-Chain-of-Thought | 207 | [
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GARDC/on | 2023-10-20T15:32:57.000Z | [
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manestay/paxqa_train | 2023-10-20T16:20:41.000Z | [
"region:us"
] | manestay | null | null | 0 | 0 | 2023-10-20T16:20:12 | ---
dataset_info:
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download_size: 265371700
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---
# Dataset Card for "paxqa_train"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 1,372 | [
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autoevaluate/autoeval-eval-acronym_identification-default-ecf72e-96411146660 | 2023-10-20T17:03:30.000Z | [
"region:us"
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] |
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