id stringlengths 2 115 | author stringlengths 2 42 ⌀ | last_modified timestamp[us, tz=UTC] | downloads int64 0 8.87M | likes int64 0 3.84k | paperswithcode_id stringlengths 2 45 ⌀ | tags list | lastModified timestamp[us, tz=UTC] | createdAt stringlengths 24 24 | key stringclasses 1 value | created timestamp[us] | card stringlengths 1 1.01M | embedding list | library_name stringclasses 21 values | pipeline_tag stringclasses 27 values | mask_token null | card_data null | widget_data null | model_index null | config null | transformers_info null | spaces null | safetensors null | transformersInfo null | modelId stringlengths 5 111 ⌀ | embeddings list |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
megabyte-gaming/echte-pokemon-kaarten | megabyte-gaming | 2023-11-16T17:06:04Z | 0 | 0 | null | [
"license:unknown",
"region:us"
] | 2023-11-16T17:06:04Z | 2023-11-16T17:06:04.000Z | 2023-11-16T17:06:04 | ---
license: unknown
---
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LuckyLioness/test | LuckyLioness | 2023-11-19T18:49:59Z | 0 | 0 | null | [
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MoonIcee/gabrieljunio | MoonIcee | 2023-11-16T17:31:40Z | 0 | 0 | null | [
"license:openrail",
"region:us"
] | 2023-11-16T17:31:40Z | 2023-11-16T17:27:32.000Z | 2023-11-16T17:27:32 | ---
license: openrail
---
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Eathan/Orthopedic_QA | Eathan | 2023-11-16T17:31:58Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | 2023-11-16T17:31:58Z | 2023-11-16T17:31:00.000Z | 2023-11-16T17:31:00 | ---
license: apache-2.0
---
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wop/h | wop | 2023-11-16T17:35:35Z | 0 | 0 | null | [
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] | 2023-11-16T17:35:35Z | 2023-11-16T17:34:49.000Z | 2023-11-16T17:34:49 | ---
task_categories:
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--- | [
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MoonIcee/gabrieljunior | MoonIcee | 2023-11-16T17:35:23Z | 0 | 0 | null | [
"license:openrail",
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] | 2023-11-16T17:35:23Z | 2023-11-16T17:35:06.000Z | 2023-11-16T17:35:06 | ---
license: openrail
---
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license: openrail
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Tumbal123/tumbal2 | Tumbal123 | 2023-11-16T17:45:57Z | 0 | 0 | null | [
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cnmoro/Instruct-PTBR-ENUS-11M | cnmoro | 2023-11-16T18:36:06Z | 0 | 1 | null | [
"task_categories:question-answering",
"task_categories:summarization",
"task_categories:text-generation",
"task_categories:text2text-generation",
"size_categories:10M<n<100M",
"language:en",
"language:pt",
"license:llama2",
"region:us"
] | 2023-11-16T18:36:06Z | 2023-11-16T17:54:56.000Z | 2023-11-16T17:54:56 | ---
license: llama2
task_categories:
- question-answering
- summarization
- text-generation
- text2text-generation
language:
- en
- pt
size_categories:
- 10M<n<100M
---
This dataset is a mix of multiple instruct datasets found on huggingface, while also including a bunch of other datasets (self-made) for tasks such as question-answering focused on RAG, summarization, keyword generation and others.
Most of the original dataset was in the English language. I have translated most of it to Brazillian Portuguese. There is a “LANGUAGE” column, which indicates if its PT or EN. It is possible that the translation contains errors.
For RAG, summarization and keyword generation tasks, the instruct layout looks like this:
`Context:\n{YourRetrievedContext}\nBased on the context, answer: “{YourQuestion}”.`
`Context:\n{YourRetrievedContext}\nBased on the context, write a summary.`
`Context:\n{YourRetrievedContext}\nBased on the context, what are the keywords?.`
or, in Portuguese:
`Contexto:\n{SeuContextoBuscado}\nBaseado no contexto, responda: “{SuaPergunta}”.`
`Contexto:\n{SeuContextoBuscado}\nBaseado no contexto, escreva um resumo.`
`Contexto:\n{SeuContextoBuscado}\nBaseado no contexto, quais são as palavras-chave?.`
The total row count for the dataset is 11165249.
Row count for Portuguese instructions: 5926086
Row count for English instructions: 5239163 | [
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Freela/zeteste | Freela | 2023-11-16T17:58:15Z | 0 | 0 | null | [
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"region:us"
] | 2023-11-16T17:58:15Z | 2023-11-16T17:56:03.000Z | 2023-11-16T17:56:03 | ---
license: openrail
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Freela/zeahhhhh | Freela | 2023-11-16T18:05:25Z | 0 | 0 | null | [
"license:openrail",
"region:us"
] | 2023-11-16T18:05:25Z | 2023-11-16T18:05:03.000Z | 2023-11-16T18:05:03 | ---
license: openrail
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bhaskars113/Diageo-Segment-24 | bhaskars113 | 2023-11-16T21:32:35Z | 0 | 0 | null | [
"region:us"
] | 2023-11-16T21:32:35Z | 2023-11-16T18:15:20.000Z | 2023-11-16T18:15:20 | Entry not found | [
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Gharaee/BIOSCAN_1M_Insect_Dataset | Gharaee | 2023-11-17T04:24:48Z | 0 | 0 | null | [
"license:other",
"arxiv:2307.10455",
"region:us"
] | 2023-11-17T04:24:48Z | 2023-11-16T18:16:17.000Z | 2023-11-16T18:16:17 | ---
license: other
license_name: cc-by-nc-sa-4.0
license_link: https://creativecommons.org/licenses/by-nc-sa/4.0/deed.en
---
# BIOSCAN_1M Insect Dataset
<div align="center">
<img src="images/Fig1.png" alt="Alt Text" width="1000" style="display: block; margin: 0 auto;">
</div>
Website: https://biodiversitygenomics.net/1M_insects/
GitHub: https://github.com/zahrag/BIOSCAN-1M
Zenodo: https://zenodo.org/records/8030065
Kaggle: https://www.kaggle.com/datasets/zahragharaee/bioscan-1m-insect-dataset
Paper: https://arxiv.org/abs/2307.10455
```
cite as:
@inproceedings{gharaee2023step,
title={A Step Towards Worldwide Biodiversity Assessment: The {BIOSCAN-1M} Insect Dataset},
booktitle = {Advances in Neural Information Processing Systems ({NeurIPS}) Datasets \& Benchmarks Track},
author={Gharaee, Z. and Gong, Z. and Pellegrino, N. and Zarubiieva, I. and Haurum, J. B. and Lowe, S. C. and McKeown, J. T. A. and Ho, C. Y. and McLeod, J. and Wei, Y. C. and Agda, J. and Ratnasingham, S. and Steinke, D. and Chang, A. X. and Taylor, G. W. and Fieguth, P.},
year={2023},
}
```
## A Dataset Record
BIOSCAN dataset provides researchers with information about insects.
Each record of the BIOSCAN-1M Insect dataset contains four primary attributes:
* DNA barcode sequence
* Barcode Index Number (BIN)
* Biological taxonomy ranking annotations
* RGB image
###### <h4> I. DNA barcode sequence
The provided DNA barcode sequence showcases the arrangement of nucleotides:
* Adenine (A): Red
* Thymine (T): Blue
* Cytosine (C): Green
* Guanine (G): Yellow
```
TTTATATTTTATTTTTGGAGCATGATCAGGAATAGTTGGAACTTCAATAAGTTTATTAATTCGAACAGAATTAAGCCAACCAGGAATTTTTA ...
```
<div align="center">
<img src="images/DNA_sequence.png" alt="Alt Text" width="1000" style="display: block; margin: 0 auto;">
</div>
###### <h4> II. Barcode Index Number (BIN)
BINs, acting as an alternative to Linnean names, provide a genetic-centric classification for organisms,
emphasizing the significance of genetic code in taxonomy.
```
BOLD:AER5166
```
<div align="center">
<img src="images/BIN.png" alt="Alt Text" width="1000" style="display: block; margin: 0 auto;">
</div>
###### <h4> III. Biological taxonomy ranking annotations
Taxonomic group ranking annotations categorize organisms hierarchically based on evolutionary relationships.
It organizes species into groups based on shared characteristics and genetic relatedness.
<div align="center">
<img src="images/Taxonomy_horiz_upd1.png" alt="Alt Text" width="1000" style="display: block; margin: 0 auto;">
</div>
###### <h4> IV. RGB image
Original insect images from 16 most densly populated orders of the BIOSCAN-1M Insect dataset.
The numbers below each image identify the number of images in each class, and clearly illustrate the degree of class imbalance in the BIOSCAN-1M Insect dataset.
<div align="center">
<table>
<!-- First Row -->
<tr>
<td align="center" ><img src="images/Diptera.jpg" width="400px" height="400px" class="image"></td>
<td align="center" ><img src="images/Hymenoptera.jpg" width="400px" height="400px" class="image"></td>
<td align="center" ><img src="images/Coleoptera.jpg" width="400px" height="400px" class="image"></td>
<td align="center" ><img src="images/Hemiptera.jpg" width="400px" height="400px" class="image"></td>
</tr>
<tr>
<td align="center"><strong>Diptera: 896,234</strong></td>
<td align="center"><strong>Hymenoptera: 89,311</strong></td>
<td align="center"><strong>Coleoptera: 47,328</strong></td>
<td align="center"><strong>Hemiptera: 46,970</strong></td>
</tr>
<!-- Second Row -->
<tr>
<td align="center" ><img src="images/Lepidoptera.jpg" width="400px" height="400px" class="image"></td>
<td align="center" ><img src="images/Psocodea.jpg" width="400px" height="400px" class="image"></td>
<td align="center" ><img src="images/Thysanoptera.jpg" width="400px" height="400px" class="image"></td>
<td align="center" ><img src="images/Trichoptera.jpg" width="400px" height="400px" class="image"></td>
</tr>
<tr>
<td align="center"><strong>Lepidoptera: 32,538</strong></td>
<td align="center"><strong>Psocodea: 9,635</strong></td>
<td align="center"><strong>Thysanoptera: 2,088</strong></td>
<td align="center"><strong>Trichoptera: 1,296</strong></td>
</tr>
<!-- Third Row -->
<tr>
<td align="center" ><img src="images/Orthoptera.jpg" width="400px" height="400px" class="image"></td>
<td align="center" ><img src="images/Blattodea.jpg" width="400px" height="400px" class="image"></td>
<td align="center" ><img src="images/Neuroptera.jpg" width="400px" height="400px" class="image"></td>
<td align="center" ><img src="images/Ephemeroptera.jpg" width="400px" height="400px" class="image"></td>
</tr>
<tr>
<td align="center"><strong>Orthoptera: 1,057</strong></td>
<td align="center"><strong>Blattodea: 824</strong></td>
<td align="center"><strong>Neuroptera: 676</strong></td>
<td align="center"><strong>Ephemeroptera: 96</strong></td>
</tr>
<!-- Fourth Row -->
<tr>
<td align="center" ><img src="images/Dermaptera.jpg" width="400px" height="400px" class="image"></td>
<td align="center" ><img src="images/Archaeognatha.jpg" width="400px" height="400px" class="image"></td>
<td align="center" ><img src="images/Plecoptera.jpg" width="400px" height="400px" class="image"></td>
<td align="center" ><img src="images/Embioptera.jpg" width="400px" height="400px" class="image"></td>
</tr>
<tr>
<td align="center"><strong>Dermaptera: 66</strong></td>
<td align="center"><strong>Archaeognatha: 63</strong></td>
<td align="center"><strong>Plecoptera: 30</strong></td>
<td align="center"><strong>Embioptera: 6</strong></td>
</tr>
</table>
</div>
## Class Distribution
Class distribution and class imbalance in the BIOSCAN-1M Insect dataset. Orders (top) and diptera families (bottom).
The image demonstrates that class imbalance is an inherent characteristic within the insect community.
<div align="center">
<img src="images/BIOSCAN_Fig2_upd3.png" alt="Alt Text" width="1000" style="display: block; margin: 0 auto;">
</div>
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PaulLoisel/Enhanced_classifier_baseline_model | PaulLoisel | 2023-11-16T20:18:02Z | 0 | 0 | null | [
"region:us"
] | 2023-11-16T20:18:02Z | 2023-11-16T18:47:49.000Z | 2023-11-16T18:47:49 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_bytes: 563
num_examples: 5
download_size: 0
dataset_size: 563
---
# Dataset Card for "Enhanced_classifier_baseline_model"
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... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
JWBickel/KJVPericopeTopics_bertopic | JWBickel | 2023-11-26T14:48:31Z | 0 | 0 | null | [
"task_categories:text-classification",
"size_categories:1K<n<10K",
"language:en",
"bible",
"region:us"
] | 2023-11-26T14:48:31Z | 2023-11-16T18:58:58.000Z | 2023-11-16T18:58:58 | ---
task_categories:
- text-classification
language:
- en
tags:
- bible
pretty_name: KJV Topical Verse Groups
size_categories:
- 1K<n<10K
---
With bertopic (https://maartengr.github.io/BERTopic/), I ran a dataset of pericopes which covers the entire Bible.
The pericope became the topic, and under each heading, 3 verses were selected as representative.
A useful feature of this is that the representative verses are guaranteed to come from the section of Scripture
that's connected with the pericope, which gives much better quality than if they were, for instance, semantically
chosen from a vector database of the entire Bible.
| [
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KnutJaegersberg/webglm_dataset | KnutJaegersberg | 2023-11-16T19:03:57Z | 0 | 0 | null | [
"license:cc-by-nc-4.0",
"region:us"
] | 2023-11-16T19:03:57Z | 2023-11-16T19:01:54.000Z | 2023-11-16T19:01:54 | ---
license: cc-by-nc-4.0
---
| [
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uatafaque/galamba | uatafaque | 2023-11-16T19:10:02Z | 0 | 0 | null | [
"license:openrail",
"region:us"
] | 2023-11-16T19:10:02Z | 2023-11-16T19:09:29.000Z | 2023-11-16T19:09:29 | ---
license: openrail
---
| [
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PaulLoisel/Enhanced_classifier_everything_to_text | PaulLoisel | 2023-11-16T20:24:56Z | 0 | 0 | null | [
"region:us"
] | 2023-11-16T20:24:56Z | 2023-11-16T19:17:15.000Z | 2023-11-16T19:17:15 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_bytes: 1458
num_examples: 5
download_size: 0
dataset_size: 1458
---
# Dataset Card for "Enhanced_classifier_everything_to_text"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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ihaflix1/mariliamandonca | ihaflix1 | 2023-11-16T19:23:33Z | 0 | 0 | null | [
"license:openrail",
"region:us"
] | 2023-11-16T19:23:33Z | 2023-11-16T19:20:19.000Z | 2023-11-16T19:20:19 | ---
license: openrail
---
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tsunemoto/MJ_dataset | tsunemoto | 2023-11-19T02:22:04Z | 0 | 0 | null | [
"region:us"
] | 2023-11-19T02:22:04Z | 2023-11-16T20:01:21.000Z | 2023-11-16T20:01:21 | # Midjourney Dataset
This is a backup of https://huggingface.co/datasets/vivym/midjourney-messages
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eminorhan/gutenberg_en | eminorhan | 2023-11-17T20:55:28Z | 0 | 0 | null | [
"task_categories:text-generation",
"size_categories:10M<n<100M",
"language:en",
"license:mit",
"region:us"
] | 2023-11-17T20:55:28Z | 2023-11-16T20:31:30.000Z | 2023-11-16T20:31:30 | ---
license: mit
task_categories:
- text-generation
language:
- en
size_categories:
- 10M<n<100M
configs:
- config_name: chunk_size_1024
data_files: "gutenberg_en_paragraph_1024.jsonl"
- config_name: chunk_size_2048
data_files: "gutenberg_en_paragraph_2048.jsonl"
---
**Description of the dataset**
This is the November 16, 2023 snapshot of the English subset of the Project Gutenberg corpus (containing 56712 documents in total), downloaded and preprocessed with code from [this repository](https://github.com/eminorhan/gutenberg).
Two different versions of the data are provided:
* The `chunk_size_1024` version divides the data into ~14.2M records consisting of a few paragraph long chunks of text, where each chunk is at least 1024 chars long, and the corresponding metadata.
* The `chunk_size_2048` version divides the data into ~8.2M records consisting of a few paragraph long chunks of text, where each chunk is at least 2048 chars long, and the corresponding metadata.
This dataset is ideal for generating fine-grained embeddings of the documents. | [
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lglsanchez/zico | lglsanchez | 2023-11-21T22:56:31Z | 0 | 0 | null | [
"region:us"
] | 2023-11-21T22:56:31Z | 2023-11-16T21:00:02.000Z | 2023-11-16T21:00:02 | Entry not found | [
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MWilinski/cats_dogs_cv_labs | MWilinski | 2023-11-16T21:46:40Z | 0 | 0 | null | [
"region:us"
] | 2023-11-16T21:46:40Z | 2023-11-16T21:20:51.000Z | 2023-11-16T21:20:51 | # meow & woof woof | [
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... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
hayden-donnelly/flattened-cifar-10 | hayden-donnelly | 2023-11-16T21:40:37Z | 0 | 0 | null | [
"task_categories:unconditional-image-generation",
"size_categories:10K<n<100K",
"region:us"
] | 2023-11-16T21:40:37Z | 2023-11-16T21:29:12.000Z | 2023-11-16T21:29:12 | ---
pretty_name: Flattened CIFAR-10
task_categories:
- unconditional-image-generation
size_categories:
- 10K<n<100K
---
# Flattened CIFAR-10
CIFAR-10 in jpg format with a flattened directory structure for easy unconditional image generation. | [
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hippocrates/PubMed_Summ_train | hippocrates | 2023-11-16T21:47:36Z | 0 | 0 | null | [
"region:us"
] | 2023-11-16T21:47:36Z | 2023-11-16T21:34:16.000Z | 2023-11-16T21:34:16 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: id
dtype: string
- name: conversations
list:
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dtype: string
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dtype: string
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dtype: string
splits:
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num_bytes: 54379474
num_examples: 26570
download_size: 29277288
dataset_size: 54379474
---
# Dataset Card for "PubMed_Summ_train"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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Aspik101/common_voice_13_0_hi_pseudo_labelled | Aspik101 | 2023-11-16T22:00:02Z | 0 | 0 | null | [
"region:us"
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DamarJati/face-hands-YOLOv5 | DamarJati | 2023-11-17T12:46:24Z | 0 | 1 | null | [
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BarBar288/TorAlerts | BarBar288 | 2023-11-16T22:38:27Z | 0 | 0 | null | [
"license:unknown",
"region:us"
] | 2023-11-16T22:38:27Z | 2023-11-16T22:38:27.000Z | 2023-11-16T22:38:27 | ---
license: unknown
---
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YazzRey/Caso_3_Transformadores | YazzRey | 2023-11-16T22:48:51Z | 0 | 0 | null | [
"region:us"
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Decompile/ygo_monsters | Decompile | 2023-11-16T23:30:17Z | 0 | 0 | null | [
"license:unlicense",
"region:us"
] | 2023-11-16T23:30:17Z | 2023-11-16T22:56:57.000Z | 2023-11-16T22:56:57 | ---
license: unlicense
dataset_info:
features:
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dtype: image
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dtype: string
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dtype: string
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dtype: int64
splits:
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num_bytes: 734500516.2
num_examples: 8352
download_size: 738954078
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configs:
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data_files:
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path: data/train-*
---
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Tverous/misinfo-meta | Tverous | 2023-11-19T05:58:10Z | 0 | 0 | null | [
"region:us"
] | 2023-11-19T05:58:10Z | 2023-11-16T23:00:46.000Z | 2023-11-16T23:00:46 | ---
dataset_info:
features:
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dtype: 'null'
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dtype: 'null'
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dtype: 'null'
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dtype: 'null'
splits:
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num_bytes: 0
num_examples: 0
download_size: 0
dataset_size: 0
configs:
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data_files:
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path: data/train-*
---
# Dataset Card for "misinfo-meta"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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miragepa/Evelyn | miragepa | 2023-11-17T00:24:16Z | 0 | 0 | null | [
"license:openrail",
"region:us"
] | 2023-11-17T00:24:16Z | 2023-11-16T23:16:21.000Z | 2023-11-16T23:16:21 | ---
license: openrail
---
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Goldyhghoul/uichan | Goldyhghoul | 2023-11-16T23:29:04Z | 0 | 0 | null | [
"region:us"
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mush42/piper-rt | mush42 | 2023-11-17T12:33:13Z | 0 | 0 | null | [
"license:mit",
"region:us"
] | 2023-11-17T12:33:13Z | 2023-11-16T23:26:23.000Z | 2023-11-16T23:26:23 | ---
license: mit
---
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Illuminatus-27/processed_bert_dataset | Illuminatus-27 | 2023-11-17T01:25:47Z | 0 | 0 | null | [
"region:us"
] | 2023-11-17T01:25:47Z | 2023-11-17T00:09:09.000Z | 2023-11-17T00:09:09 | ---
dataset_info:
features:
- name: input_ids
sequence: int32
- name: token_type_ids
sequence: int8
- name: attention_mask
sequence: int8
- name: special_tokens_mask
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splits:
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num_examples: 4358
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- name: validation
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num_examples: 3760
download_size: 6557867
dataset_size: 161409600
configs:
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data_files:
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path: data/test-*
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path: data/validation-*
---
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Mariaaaaa/Google_with_COT | Mariaaaaa | 2023-11-17T00:56:32Z | 0 | 0 | null | [
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ikhandel/temps | ikhandel | 2023-11-17T00:58:17Z | 0 | 0 | null | [
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nekofura/rpd2 | nekofura | 2023-11-17T01:02:34Z | 0 | 0 | null | [
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berkouille/Ultrachat_ChatML_Golf | berkouille | 2023-11-17T01:28:43Z | 0 | 0 | null | [
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cosminn82/mosanto_trees0_gold_test | cosminn82 | 2023-11-17T01:46:09Z | 0 | 0 | null | [
"region:us"
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Kaue123456/VegetaAlfredoRollo | Kaue123456 | 2023-11-17T02:02:21Z | 0 | 0 | null | [
"license:openrail",
"region:us"
] | 2023-11-17T02:02:21Z | 2023-11-17T02:01:31.000Z | 2023-11-17T02:01:31 | ---
license: openrail
---
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bri25yu-temp/prompt_playbook | bri25yu-temp | 2023-11-17T04:23:17Z | 0 | 0 | null | [
"region:us"
] | 2023-11-17T04:23:17Z | 2023-11-17T02:15:04.000Z | 2023-11-17T02:15:04 | ---
configs:
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dataset_size: 1282888
---
# Dataset Card for "prompt_playbook"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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cheonboy/vessel_QA | cheonboy | 2023-11-17T02:33:21Z | 0 | 0 | null | [
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coolboy0821/Test | coolboy0821 | 2023-11-17T02:44:34Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | 2023-11-17T02:44:34Z | 2023-11-17T02:44:34.000Z | 2023-11-17T02:44:34 | ---
license: apache-2.0
---
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icaro23/jeas | icaro23 | 2023-11-17T02:52:19Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | 2023-11-17T02:52:19Z | 2023-11-17T02:51:06.000Z | 2023-11-17T02:51:06 | ---
license: apache-2.0
---
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aisyahhrazak/public-transport-station-malaysia | aisyahhrazak | 2023-11-17T03:14:59Z | 0 | 0 | null | [
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alpayariyak/glaive_questions | alpayariyak | 2023-11-17T03:07:50Z | 0 | 0 | null | [
"region:us"
] | 2023-11-17T03:07:50Z | 2023-11-17T03:07:50.000Z | 2023-11-17T03:07:50 | Entry not found | [
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Amareshwari/langchain-docs | Amareshwari | 2023-11-17T03:32:35Z | 0 | 0 | null | [
"region:us"
] | 2023-11-17T03:32:35Z | 2023-11-17T03:30:48.000Z | 2023-11-17T03:30:48 | Entry not found | [
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MessiasGabriel/VozIA | MessiasGabriel | 2023-11-17T03:34:40Z | 0 | 0 | null | [
"region:us"
] | 2023-11-17T03:34:40Z | 2023-11-17T03:33:29.000Z | 2023-11-17T03:33:29 | Entry not found | [
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ianel/Models | ianel | 2023-11-17T08:27:03Z | 0 | 0 | null | [
"region:us"
] | 2023-11-17T08:27:03Z | 2023-11-17T03:46:14.000Z | 2023-11-17T03:46:14 | Entry not found | [
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Norquinal/discord_rp | Norquinal | 2023-11-27T10:24:45Z | 0 | 1 | null | [
"license:cc-by-nc-4.0",
"region:us"
] | 2023-11-27T10:24:45Z | 2023-11-17T04:54:18.000Z | 2023-11-17T04:54:18 | ---
license: cc-by-nc-4.0
---
This dataset is comprised of roleplay chat conversations scraped from several Discord RP fandom servers. The conversations have been split in terms of days, the assumption being that a majority of long-form roleplays are started/continued and completed within a day.
The original dataset consisted of ~30K samples. Light filtering striped that down to ~6K samples. Stricter filtering striped it down to ~900 samples.
Some effort was made to remove OOC, links, and other miscellanous fluff, but more work still needs to be done. This isn't a "completed" dataset so much as a test to see if the data gathered is conducive to training LLMs for roleplay purposes. If determined to be useful, I will continue to scrape more data.
| [
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Pablao0948/Xaropinho | Pablao0948 | 2023-11-17T05:10:55Z | 0 | 0 | null | [
"license:openrail",
"region:us"
] | 2023-11-17T05:10:55Z | 2023-11-17T05:10:31.000Z | 2023-11-17T05:10:31 | ---
license: openrail
---
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mesolitica/kesalahan-tatabahasa-choice | mesolitica | 2023-11-17T05:33:55Z | 0 | 0 | null | [
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Simonk97/songyen | Simonk97 | 2023-11-17T05:41:40Z | 0 | 0 | null | [
"license:openrail",
"region:us"
] | 2023-11-17T05:41:40Z | 2023-11-17T05:41:06.000Z | 2023-11-17T05:41:06 | ---
license: openrail
---
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mesolitica/dewanbahasa-jdbp-choice | mesolitica | 2023-11-17T05:41:23Z | 0 | 0 | null | [
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Hessa/tqa_val_instruction_sol | Hessa | 2023-11-17T05:43:50Z | 0 | 0 | null | [
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Jenray/audio | Jenray | 2023-11-17T06:14:46Z | 0 | 0 | null | [
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AarushSah/caribou-0.1 | AarushSah | 2023-11-17T06:46:29Z | 0 | 0 | null | [
"region:us"
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kwaikeg/KAgentBench | kwaikeg | 2023-11-17T06:47:21Z | 0 | 0 | null | [
"license:cc-by-nc-nd-4.0",
"region:us"
] | 2023-11-17T06:47:21Z | 2023-11-17T06:47:21.000Z | 2023-11-17T06:47:21 | ---
license: cc-by-nc-nd-4.0
---
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Zangs3011/no_robots_llama2 | Zangs3011 | 2023-11-17T06:59:16Z | 0 | 0 | null | [
"region:us"
] | 2023-11-17T06:59:16Z | 2023-11-17T06:57:11.000Z | 2023-11-17T06:57:11 | ---
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---
# Dataset Card for "no_robots_llama2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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xDAN-datasets/ultrachat_500k | xDAN-datasets | 2023-11-20T07:45:20Z | 0 | 0 | null | [
"region:us"
] | 2023-11-20T07:45:20Z | 2023-11-17T07:09:24.000Z | 2023-11-17T07:09:24 | ---
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---
# Dataset Card for "ultrachat_500k"
**数据集名称:**
*HuggingFaceH4/ultrachat_200k*
**数据集原型来源:**
*https://huggingface.co/datasets/HuggingFaceH4/ultrachat_200k*
**数据规模:**
*200k*
**数据生成:**
*chatGPT生成*
**数据领域:**
*包含广泛主题的对话*
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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marcus2000/hse_spam_dataset | marcus2000 | 2023-11-17T07:14:30Z | 0 | 0 | null | [
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lalazzz/123 | lalazzz | 2023-11-17T07:43:46Z | 0 | 0 | null | [
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icaro23/jeanv2 | icaro23 | 2023-11-17T07:34:12Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | 2023-11-17T07:34:12Z | 2023-11-17T07:32:32.000Z | 2023-11-17T07:32:32 | ---
license: apache-2.0
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kwaikeg/KAgentInstruct | kwaikeg | 2023-11-17T07:35:20Z | 0 | 0 | null | [
"license:cc-by-nc-nd-4.0",
"region:us"
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license: cc-by-nc-nd-4.0
---
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ksanjeev284/octane | ksanjeev284 | 2023-11-17T08:37:50Z | 0 | 0 | null | [
"license:mit",
"region:us"
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license: mit
---
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TrainingDataPro/dermatology-dataset-acne-redness-and-bags-under-the-eyes | TrainingDataPro | 2023-11-17T12:07:00Z | 0 | 1 | null | [
"task_categories:image-classification",
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"language:en",
"license:cc-by-nc-nd-4.0",
"medical",
"biology",
"code",
"region:us"
] | 2023-11-17T12:07:00Z | 2023-11-17T08:40:20.000Z | 2023-11-17T08:40:20 | ---
language:
- en
license: cc-by-nc-nd-4.0
task_categories:
- image-classification
- image-to-image
- object-detection
tags:
- medical
- biology
- code
dataset_info:
features:
- name: id
dtype: uint16
- name: front
dtype: image
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dtype: image
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dtype: image
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---
# Skin Defects Dataset
The dataset contains images of individuals with various skin conditions: **acne, skin redness, and bags under the eyes**. Each person is represented by **3 images** showcasing their specific skin issue. The dataset encompasses diverse *demographics, age, ethnicities, and genders.*
**Types of defects in the dataset**: acne, skin redness & bags under the eyes
- **Acne photos**: display different severities and types of acne such as whiteheads, blackheads, and cystic acne.
- **Skin redness photos**: display individuals with this condition, which may be caused by rosacea or eczema.
- **Bags under the eyes photos**: depicts individuals with noticeable bags under their eyes, often associated with lack of sleep, aging, or genetics.
## Full version of the dataset includes much more photos of people, leave a request on **[TrainingData](https://trainingdata.pro/data-market/skin-problems?utm_source=huggingface&utm_medium=cpc&utm_campaign=skin-defects)** to buy the dataset
The dataset is a valuable resource for researchers, developers, and organizations working at the **dermatology, cosmetics and medical sphere** to train, evaluate, and fine-tune **AI models** for real-world applications. It can be applied in various domains like *skincare, scientific research and advertising*.
# Get the Dataset
## This is just an example of the data
Leave a request on **[https://trainingdata.pro/data-market](https://trainingdata.pro/data-market/skin-problems?utm_source=huggingface&utm_medium=cpc&utm_campaign=skin-defects) to learn about the price and buy the dataset**
# Content
The folder **files** includes:
- **3 folders** with images of people with the conditions mentioned in the name of the folder (**acne, skin redness or bags under the eyes**)
- each folder includes sub-folders with **3 images** of each person from different angles: **front, left side and right side**
### File with the extension .csv
- **id**: id of the person,
- **front**: link to access the front photo,
- **left_side**: link to access the left side's photo,
- **right_side**: link to access the right side's photo,
- **type**: type of the defect (**acne, skin redness or bags under the eyes**)
## **[TrainingData](https://trainingdata.pro/data-market/skin-problems?utm_source=huggingface&utm_medium=cpc&utm_campaign=skin-defects)** provides high-quality data annotation tailored to your needs
More datasets in TrainingData's Kaggle account: **https://www.kaggle.com/trainingdatapro/datasets**
TrainingData's GitHub: **https://github.com/Trainingdata-datamarket/TrainingData_All_datasets**
*keywords: biometric dataset, face recognition database, face recognition dataset, face detection dataset, facial analysis, dermatology dataset, skin on the face, IGA scale, medical data, whiteheads, blackheads, cystic acne, rosacea, eczema disease dataset, cosmetology, multi-task learning approach, facial acne image dataset, bumps on face, facial skin lesions, skin conditions, skin images, skin characteristics, automatic facial skin defect detection system, human face images, acne marks, stains, skincare, skin problems, skin disease dataset, human images, deep learning, computer vision* | [
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wbxlala/Dreamer_Valence | wbxlala | 2023-11-17T09:09:52Z | 0 | 0 | null | [
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wbxlala/Dreamer_Dominance | wbxlala | 2023-11-17T09:10:11Z | 0 | 0 | null | [
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mesolitica/majalahsains-choice | mesolitica | 2023-11-17T09:22:59Z | 0 | 0 | null | [
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malucoelhaofc/MelanieMartinezVoiceV2 | malucoelhaofc | 2023-11-17T09:39:35Z | 0 | 0 | null | [
"license:openrail",
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license: openrail
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eugene1985/test | eugene1985 | 2023-11-17T09:41:42Z | 0 | 0 | null | [
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license: other
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Aspik101/common_voice_13_0_pl_pseudo_labelled | Aspik101 | 2023-11-17T21:49:13Z | 0 | 0 | null | [
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jzjiao/fever-sft | jzjiao | 2023-11-17T09:49:28Z | 0 | 0 | null | [
"region:us"
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dataset_size: 239382472
---
# Dataset Card for "fever-sft"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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yangjinlong/dzqge | yangjinlong | 2023-11-17T09:55:54Z | 0 | 0 | null | [
"license:mit",
"region:us"
] | 2023-11-17T09:55:54Z | 2023-11-17T09:51:33.000Z | 2023-11-17T09:51:33 | ---
license: mit
---
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xDAN-datasets/no_robots_10k | xDAN-datasets | 2023-11-20T07:41:55Z | 0 | 0 | null | [
"region:us"
] | 2023-11-20T07:41:55Z | 2023-11-17T09:57:25.000Z | 2023-11-17T09:57:25 | ---
configs:
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data_files:
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path: data/train-*
dataset_info:
features:
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list:
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dtype: string
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dtype: string
- name: input
sequence: string
- name: output
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splits:
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num_bytes: 24839837
num_examples: 10000
download_size: 15750304
dataset_size: 24839837
---
# Dataset Card for "no_robots_10k"
**数据集名称:**
*HuggingFaceH4/no_robots*
**数据集原型来源:**
*https://huggingface.co/datasets/HuggingFaceH4/no_robots*
**数据规模:**
*10k*
**数据生成:**
*人工编辑 *
**数据领域:**
*单轮，多轮对话，聊天，编程，总结，提取信息，重写*
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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Newato/Sugar | Newato | 2023-11-17T10:21:18Z | 0 | 0 | null | [
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mponty/reordered_python_starcoderdata | mponty | 2023-11-17T19:53:20Z | 0 | 0 | null | [
"region:us"
] | 2023-11-17T19:53:20Z | 2023-11-17T11:05:24.000Z | 2023-11-17T11:05:24 | ---
dataset_info:
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configs:
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---
# Python repositories from StarCoderData | [
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bene-ges/wikipedia_en | bene-ges | 2023-11-17T11:40:25Z | 0 | 0 | null | [
"license:cc-by-sa-4.0",
"region:us"
] | 2023-11-17T11:40:25Z | 2023-11-17T11:09:04.000Z | 2023-11-17T11:09:04 | ---
license: cc-by-sa-4.0
---
| [
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indiejoseph/c4-cantonese-filtered | indiejoseph | 2023-11-17T11:18:29Z | 0 | 0 | null | [
"region:us"
] | 2023-11-17T11:18:29Z | 2023-11-17T11:18:26.000Z | 2023-11-17T11:18:26 | ---
dataset_info:
features:
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splits:
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download_size: 1820474
dataset_size: 2304453
configs:
- config_name: default
data_files:
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path: data/train-*
---
# Dataset Card for "c4-cantonese-filterd"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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SteffRhes/APIS_OEBL__abbreviations | SteffRhes | 2023-11-17T12:13:55Z | 0 | 0 | null | [
"region:us"
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BoltonB07/Boltons-Terrible-Singing | BoltonB07 | 2023-11-17T11:32:43Z | 0 | 0 | null | [
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] | 2023-11-17T11:32:43Z | 2023-11-17T11:30:41.000Z | 2023-11-17T11:30:41 | Entry not found | [
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DewiBrynJones/common_voice_13_0_cy_pseudo_labelled | DewiBrynJones | 2023-11-17T13:36:03Z | 0 | 0 | null | [
"region:us"
] | 2023-11-17T13:36:03Z | 2023-11-17T11:44:55.000Z | 2023-11-17T11:44:55 | ---
dataset_info:
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---
| [
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varox34/med-par | varox34 | 2023-11-17T11:54:24Z | 0 | 1 | null | [
"region:us"
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kosta-naumenko/medflex-test | kosta-naumenko | 2023-11-17T12:38:43Z | 0 | 0 | null | [
"region:us"
] | 2023-11-17T12:38:43Z | 2023-11-17T12:38:41.000Z | 2023-11-17T12:38:41 | ---
dataset_info:
features:
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splits:
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num_examples: 498
download_size: 103974
dataset_size: 610623
configs:
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data_files:
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path: data/train-*
---
# Dataset Card for "medflex-test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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NobodyExistsOnTheInternet/testgiftedv2zip | NobodyExistsOnTheInternet | 2023-11-17T12:44:40Z | 0 | 0 | null | [
"license:mit",
"region:us"
] | 2023-11-17T12:44:40Z | 2023-11-17T12:41:38.000Z | 2023-11-17T12:41:38 | ---
license: mit
---
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Pablao0948/Freya | Pablao0948 | 2023-11-17T12:50:29Z | 0 | 0 | null | [
"license:openrail",
"region:us"
] | 2023-11-17T12:50:29Z | 2023-11-17T12:43:01.000Z | 2023-11-17T12:43:01 | ---
license: openrail
---
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espidermon/Babar_Azam | espidermon | 2023-11-17T12:45:18Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | 2023-11-17T12:45:18Z | 2023-11-17T12:44:36.000Z | 2023-11-17T12:44:36 | ---
license: apache-2.0
---
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sparkyfina/test | sparkyfina | 2023-11-17T12:52:14Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | 2023-11-17T12:52:14Z | 2023-11-17T12:52:14.000Z | 2023-11-17T12:52:14 | ---
license: apache-2.0
---
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csinva/tabular-benchmark-797-classification | csinva | 2023-11-17T13:53:53Z | 0 | 0 | null | [
"region:us"
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fillipean/shotokan | fillipean | 2023-11-17T15:34:24Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | 2023-11-17T15:34:24Z | 2023-11-17T13:40:24.000Z | 2023-11-17T13:40:24 | ---
license: apache-2.0
---
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GOAT-AI/generated-novels | GOAT-AI | 2023-11-17T13:56:58Z | 0 | 1 | null | [
"license:mit",
"region:us"
] | 2023-11-17T13:56:58Z | 2023-11-17T13:55:34.000Z | 2023-11-17T13:55:34 | ---
license: mit
---
| [
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kiennguyen1703/Toxic-Vulgar | kiennguyen1703 | 2023-11-17T14:25:05Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | 2023-11-17T14:25:05Z | 2023-11-17T14:25:05.000Z | 2023-11-17T14:25:05 | ---
license: apache-2.0
---
| [
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KnutJaegersberg/Auton | KnutJaegersberg | 2023-11-17T14:37:39Z | 0 | 0 | null | [
"license:mit",
"region:us"
] | 2023-11-17T14:37:39Z | 2023-11-17T14:36:58.000Z | 2023-11-17T14:36:58 | ---
license: mit
---
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0.1953392624855041... | null | null | null | null | null | null | null | null | null | null | null | null | null |
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