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atom-in-the-universe/bild-3 | 2023-11-01T19:18:41.000Z | [
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atom-in-the-universe/bild-6 | 2023-10-27T18:07:09.000Z | [
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RF0000/pubmed-kinase-abstract | 2023-10-27T20:11:43.000Z | [
"language:en",
"license:mit",
"region:us"
] | RF0000 | null | null | 0 | 0 | 2023-10-27T18:23:14 | ---
dataset_info:
features:
- name: 'Unnamed: 0'
dtype: int64
- name: Title
dtype: string
- name: Abstract
dtype: string
- name: Journal
dtype: string
- name: Language
dtype: string
- name: Year
dtype: string
- name: Month
dtype: string
splits:
- name: train
num_bytes: 11898592
num_examples: 9999
download_size: 6290504
dataset_size: 11898592
license: mit
language:
- en
---
# Dataset Card for "pubmed-kinase-abstract"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 612 | [
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citiusLTL/Twitter-COVID-19 | 2023-10-27T18:35:38.000Z | [
"task_categories:text-classification",
"language:es",
"language:en",
"license:gpl-3.0",
"region:us"
] | citiusLTL | null | null | 0 | 0 | 2023-10-27T18:30:15 | ---
license: gpl-3.0
task_categories:
- text-classification
language:
- es
- en
---
**General description**:
This dataset comprisses a set of tweets crawled during the COVID-19 pandemic (from March 2020 to June 2021). Tweets are located in two different regions: Spain and USA. This adds value to the collection, as it contains data in two languages.
This data was used as part of a broader study that aimed to determine the evolution of different personality traits and disorders during the pandemic. Thus, weak labels for different dimensions, such as sentiment, personality prevalence, and others, are also available.
Further details about this experimentation can be found in the [paper](https://link.springer.com/article/10.1007/s10844-023-00810-3) or [Github](https://github.com/MarcosFP97/COVID-19-Personality).
**Data**:
A sample of the data can be visualised and downloaded from this card. More specifically, it corresponds to the month of January 2021 and tweets are located on USA. Tweets were anonymized for privacy reasons.
The whole dataset is available upon request to fullfil Twitter's restrictions. You can contact either with marcosfernandez.pichel@usc.es or ezra.aragon@usc.es to obtain it.
**Citation**:
For all the future studies using our data, we kindly ask to quote our paper:
@article{fernandez2023personality, \
title={Personality trait analysis during the COVID-19 pandemic: a comparative study on social media}, \
author={Fern{\'a}ndez-Pichel, Marcos and Arag{\'o}n, Mario Ezra and Saborido-Pati{\~n}o, Juli{\'a}n and Losada, David E}, \
journal={Journal of Intelligent Information Systems}, \
pages={1--26}, \
year={2023}, \
publisher={Springer} \
}
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bongoServer2112/splitEighteen-brela-CM-attachements-archived | 2023-10-27T19:23:56.000Z | [
"region:us"
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] |
api-misuse/java_repo_star | 2023-10-27T19:27:54.000Z | [
"region:us"
] | api-misuse | null | null | 0 | 0 | 2023-10-27T19:20:41 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: repo_name
dtype: string
- name: stars_count
dtype: int64
- name: repo_head_hexsha
dtype: string
splits:
- name: train
num_bytes: 767389.0
num_examples: 9641
download_size: 652097
dataset_size: 767389.0
---
# Dataset Card for "java_repo_star"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 532 | [
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atom-in-the-universe/bild-4 | 2023-11-02T23:10:24.000Z | [
"region:us"
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bongoServer2112/splitNineteen-brela-CM-attachements-archived | 2023-10-27T21:06:27.000Z | [
"region:us"
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acozma/imagenet-1k-rand_colorjitter | 2023-11-02T05:45:57.000Z | [
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Seif-Sallam/FoodOrderingTrainingDataset | 2023-10-27T20:41:32.000Z | [
"region:us"
] | Seif-Sallam | null | null | 0 | 0 | 2023-10-27T20:35:21 | Entry not found | 15 | [
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ltg/lambada-context | 2023-10-30T10:53:07.000Z | [
"task_categories:text-generation",
"size_categories:1K<n<10K",
"source_datasets:https://huggingface.co/datasets/EleutherAI/lambada_openai",
"language:en",
"license:mit",
"region:us"
] | ltg | null | null | 0 | 0 | 2023-10-27T21:20:16 | ---
license: mit
task_categories:
- text-generation
language:
- en
pretty_name: LAMBADA
size_categories:
- 1K<n<10K
source_datasets:
- https://huggingface.co/datasets/EleutherAI/lambada_openai
---
## Dataset Description
- **Repository:** [openai/gpt2](https://github.com/openai/gpt-2)
- **Paper:** Radford et al. [Language Models are Unsupervised Multitask Learners](https://d4mucfpksywv.cloudfront.net/better-language-models/language-models.pdf)
### Dataset Summary
This is the LAMBADA test split modified for bidirectional language models (for example BERT). The original is appended by punctuation symbols (for example `."`), as predicted by GPT-2 (small). The original is the LAMBADA test split [as pre-processed by OpenAI](https://huggingface.co/datasets/EleutherAI/lambada_openai),
LAMBADA is used to evaluate the capabilities of computational models for text understanding by means of a word prediction task. LAMBADA is a collection of narrative texts sharing the characteristic that human subjects are able to guess their last word if they are exposed to the whole text, but not if they only see the last sentence preceding the target word. To succeed on LAMBADA, computational models cannot simply rely on local context, but must be able to keep track of information in the broader discourse.
### Languages
English
### Source Data
[EleutherAI/lambada_openai](https://huggingface.co/datasets/EleutherAI/lambada_openai)
### Licensing
License: [Modified MIT](https://github.com/openai/gpt-2/blob/master/LICENSE)
### Citation
```bibtex
@article{radford2019language,
title={Language Models are Unsupervised Multitask Learners},
author={Radford, Alec and Wu, Jeff and Child, Rewon and Luan, David and Amodei, Dario and Sutskever, Ilya},
year={2019}
}
```
```bibtex
@misc{
author={Paperno, Denis and Kruszewski, Germán and Lazaridou, Angeliki and Pham, Quan Ngoc and Bernardi, Raffaella and Pezzelle, Sandro and Baroni, Marco and Boleda, Gemma and Fernández, Raquel},
title={The LAMBADA dataset},
DOI={10.5281/zenodo.2630551},
publisher={Zenodo},
year={2016},
month={Aug}
}
``` | 2,124 | [
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fzliu/gist1m | 2023-10-30T21:23:57.000Z | [
"region:us"
] | fzliu | null | null | 0 | 0 | 2023-10-27T21:29:34 | Entry not found | 15 | [
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] |
ayoub999/test_1 | 2023-10-28T17:07:24.000Z | [
"region:us"
] | ayoub999 | null | null | 0 | 0 | 2023-10-27T21:38:49 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: id
dtype: string
- name: image
dtype: image
- name: bboxes
sequence:
sequence: int64
- name: ner_tags
sequence:
class_label:
names:
'0': O
'1': Ref
'2': NumFa
'3': Fourniss
'4': DateFa
'5': DateLim
'6': TotalHT
'7': TVA
'8': TotalTTc
'9': unitP
'10': Qt
'11': TVAP
'12': descp
- name: tokens
sequence: string
splits:
- name: train
num_bytes: 470848.6666666667
num_examples: 2
- name: test
num_bytes: 184985.0
num_examples: 1
download_size: 678107
dataset_size: 655833.6666666667
---
# Dataset Card for "test_1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 1,011 | [
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] |
Lelon/caissabase_chess | 2023-10-27T22:39:47.000Z | [
"region:us"
] | Lelon | null | null | 0 | 0 | 2023-10-27T22:17:03 | ---
dataset_info:
features:
- name: pgn
dtype: string
- name: event
dtype: string
- name: site
dtype: string
- name: date
dtype: string
- name: round
dtype: string
- name: white
dtype: string
- name: black
dtype: string
- name: result
dtype: string
- name: whiteelo
dtype: string
- name: blackelo
dtype: string
- name: eco
dtype: string
- name: eventdate
dtype: string
- name: whitetitle
dtype: string
- name: blacktitle
dtype: string
- name: opening
dtype: string
- name: variation
dtype: string
- name: whitefideid
dtype: string
- name: blackfideid
dtype: string
splits:
- name: train
num_bytes: 3242443485
num_examples: 4874268
download_size: 1719313925
dataset_size: 3242443485
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "caissabase_chess"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 1,068 | [
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sirpps/nyobaaja | 2023-10-27T22:41:45.000Z | [
"region:us"
] | sirpps | null | null | 0 | 0 | 2023-10-27T22:40:45 | Entry not found | 15 | [
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dsatya6/cstraindata | 2023-10-27T23:01:00.000Z | [
"region:us"
] | dsatya6 | null | null | 0 | 0 | 2023-10-27T23:01:00 | Entry not found | 15 | [
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Ka4on/CT | 2023-10-27T23:56:01.000Z | [
"region:us"
] | Ka4on | null | null | 0 | 0 | 2023-10-27T23:50:54 | Entry not found | 15 | [
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bongoServer2112/splitTwentyandtwentyone-brela-CM-attachements-archived | 2023-10-28T00:21:00.000Z | [
"region:us"
] | bongoServer2112 | null | null | 0 | 0 | 2023-10-28T00:08:20 | Entry not found | 15 | [
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aisyahhrazak/crawl-diagnosa | 2023-10-28T00:39:37.000Z | [
"region:us"
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felipeoes/qa_blue_amazon_legislation_v2_68k | 2023-10-28T00:50:16.000Z | [
"region:us"
] | felipeoes | null | null | 0 | 0 | 2023-10-28T00:50:13 | ---
dataset_info:
features:
- name: file_name
dtype: string
- name: prompt
dtype: string
- name: question
dtype: string
- name: answer
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 523816873
num_examples: 67781
download_size: 30293785
dataset_size: 523816873
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "qa_blue_amazon_legislation_v2_68k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 612 | [
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] |
chirunder/GRE_all_text | 2023-10-28T01:47:03.000Z | [
"region:us"
] | chirunder | null | null | 0 | 0 | 2023-10-28T01:47:01 | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 5668464
num_examples: 1
download_size: 2779298
dataset_size: 5668464
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "GRE_all_text"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 437 | [
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DanielCerda/pid-object-detection | 2023-10-28T01:50:16.000Z | [
"task_categories:object-detection",
"roboflow",
"roboflow2huggingface",
"region:us"
] | DanielCerda | null | @misc{ pid_dataset_dataset,
title = { pid_dataset Dataset },
type = { Open Source Dataset },
author = { PID Smart Reader },
howpublished = { \\url{ https://universe.roboflow.com/pid-smart-reader/pid_dataset } },
url = { https://universe.roboflow.com/pid-smart-reader/pid_dataset },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2023 },
month = { feb },
note = { visited on 2023-10-28 },
} | 0 | 0 | 2023-10-28T01:48:38 | ---
task_categories:
- object-detection
tags:
- roboflow
- roboflow2huggingface
---
<div align="center">
<img width="640" alt="DanielCerda/pid-object-detection" src="https://huggingface.co/datasets/DanielCerda/pid-object-detection/resolve/main/thumbnail.jpg">
</div>
### Dataset Labels
```
['ball-valve', 'butterfly-valve', 'centrifugal-pump', 'check-valve', 'gate-valve']
```
### Number of Images
```json
{'valid': 12, 'test': 12, 'train': 128}
```
### How to Use
- Install [datasets](https://pypi.org/project/datasets/):
```bash
pip install datasets
```
- Load the dataset:
```python
from datasets import load_dataset
ds = load_dataset("DanielCerda/pid-object-detection", name="full")
example = ds['train'][0]
```
### Roboflow Dataset Page
[https://universe.roboflow.com/pid-smart-reader/pid_dataset/dataset/2](https://universe.roboflow.com/pid-smart-reader/pid_dataset/dataset/2?ref=roboflow2huggingface)
### Citation
```
@misc{ pid_dataset_dataset,
title = { pid_dataset Dataset },
type = { Open Source Dataset },
author = { PID Smart Reader },
howpublished = { \\url{ https://universe.roboflow.com/pid-smart-reader/pid_dataset } },
url = { https://universe.roboflow.com/pid-smart-reader/pid_dataset },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2023 },
month = { feb },
note = { visited on 2023-10-28 },
}
```
### License
CC BY 4.0
### Dataset Summary
This dataset was exported via roboflow.com on February 10, 2023 at 3:14 AM GMT
Roboflow is an end-to-end computer vision platform that helps you
* collaborate with your team on computer vision projects
* collect & organize images
* understand and search unstructured image data
* annotate, and create datasets
* export, train, and deploy computer vision models
* use active learning to improve your dataset over time
For state of the art Computer Vision training notebooks you can use with this dataset,
visit https://github.com/roboflow/notebooks
To find over 100k other datasets and pre-trained models, visit https://universe.roboflow.com
The dataset includes 152 images.
Piping-elements are annotated in COCO format.
The following pre-processing was applied to each image:
No image augmentation techniques were applied.
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Slient/LionFace | 2023-10-28T02:53:59.000Z | [
"region:us"
] | Slient | null | null | 0 | 0 | 2023-10-28T02:53:59 | Entry not found | 15 | [
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] |
chirunder/Vince_GRE_frequency | 2023-10-28T02:55:54.000Z | [
"region:us"
] | chirunder | null | null | 0 | 0 | 2023-10-28T02:55:53 | ---
dataset_info:
features:
- name: word
dtype: string
- name: frequency
dtype: int64
splits:
- name: train
num_bytes: 58131
num_examples: 2882
download_size: 31861
dataset_size: 58131
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "Vince_GRE_frequency"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 478 | [
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daishen/legal-cfm | 2023-10-28T03:16:45.000Z | [
"region:us"
] | daishen | null | null | 0 | 0 | 2023-10-28T03:14:20 | Entry not found | 15 | [
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HomoLiang/ADL_HW2 | 2023-10-31T03:29:03.000Z | [
"region:us"
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daishen/legal-scm | 2023-10-28T03:23:33.000Z | [
"region:us"
] | daishen | null | null | 0 | 0 | 2023-10-28T03:22:58 | Entry not found | 15 | [
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Malubululu3/Somesh | 2023-10-28T03:51:02.000Z | [
"region:us"
] | Malubululu3 | null | null | 0 | 0 | 2023-10-28T03:51:02 | Entry not found | 15 | [
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arroz098/lucas | 2023-10-28T04:23:04.000Z | [
"region:us"
] | arroz098 | null | null | 0 | 0 | 2023-10-28T04:22:00 | Entry not found | 15 | [
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] |
zion84006/tencentdata_speech_tokenizer | 2023-10-28T06:46:16.000Z | [
"region:us"
] | zion84006 | null | null | 0 | 0 | 2023-10-28T05:06:44 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: valid
path: data/valid-*
- split: test
path: data/test-*
dataset_info:
features:
- name: file_id
dtype: int64
- name: wav_id
dtype: int64
- name: instruction
dtype: string
- name: transcription
dtype: string
- name: src_speech_tokenizer_0
sequence: int64
- name: src_speech_tokenizer_1
sequence: int64
- name: src_speech_tokenizer_2
sequence: int64
- name: src_speech_tokenizer_3
sequence: int64
- name: src_speech_tokenizer_4
sequence: int64
- name: src_speech_tokenizer_5
sequence: int64
- name: src_speech_tokenizer_6
sequence: int64
- name: src_speech_tokenizer_7
sequence: int64
- name: tgt_speech_tokenizer_0
sequence: int64
- name: tgt_speech_tokenizer_1
sequence: int64
- name: tgt_speech_tokenizer_2
sequence: int64
- name: tgt_speech_tokenizer_3
sequence: int64
- name: tgt_speech_tokenizer_4
sequence: int64
- name: tgt_speech_tokenizer_5
sequence: int64
- name: tgt_speech_tokenizer_6
sequence: int64
- name: tgt_speech_tokenizer_7
sequence: int64
splits:
- name: train
num_bytes: 12411789302
num_examples: 266780
- name: valid
num_bytes: 352527534
num_examples: 7620
- name: test
num_bytes: 339549288
num_examples: 7620
download_size: 711826377
dataset_size: 13103866124
---
# Dataset Card for "tencentdata_speech_tokenizer"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 1,639 | [
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EarthnDusk/SD-Convert-1-5 | 2023-10-28T05:26:48.000Z | [
"size_categories:n<1K",
"language:en",
"license:creativeml-openrail-m",
"code",
"region:us"
] | EarthnDusk | null | null | 0 | 0 | 2023-10-28T05:25:40 | ---
license: creativeml-openrail-m
language:
- en
tags:
- code
pretty_name: Convert SD 1.5 to Diffusers
size_categories:
- n<1K
---
# **SD 1.5 Model Converter**
<a target="_blank" href="https://colab.research.google.com/github/kieranxsomer/convert-scripts/blob/main/Converter_SD1_5_V2_Duct_TapeVersion.ipynb">
<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a>
**A Colab Notebook To Convert SD 1.5 Checkpoint to Diffusers format**
But a horribly duct taped edition. THIS IS IN ALPHA STAGES, WILL BE PATCHING THE CODE AS I GO ALONG.
♻ - USE ONLY FOR NOW: Converter_SD1_5_V2_Duct_TapeVersion.ipynb
♻ - THIS IN THEORY SHOULD WORK ON VAST/RUNPOD - BUT IT IS UNTESTED, JUST CHANGE YOUR DIRECTORIES AS NEEDED!
RIGHT NOW THE INSTRUCTIONS ARE AS FOLLOWS:
♻ - Install/Clone etc
♻ - Download model - Direct port from Linaqruf.
♻ - Open code panel, replace model details. - don't move after you hit play, it does it really quickly.
♻ - Check file browser, if the model/yourmodelhere looks like a diffusers format you're good to go!
♻ - Write Token + Set up your Repo!
♻ - Upload Diffusers!
---
***Patched from*** : https://colab.research.google.com/github/Linaqruf/sdxl-model-converter/blob/main/sdxl_model_converter.ipynb
***Linaqruf @ Github***: https://github.com/Linaqruf
 [](https://lookup.guru/850007095775723532) [](https://ko-fi.com/linaqruf) <a href="https://saweria.co/linaqruf"><img alt="Saweria" src="https://img.shields.io/badge/Saweria-7B3F00?style=flat&logo=ko-fi&logoColor=white"/></a>
**Please use their main scripts for SDXL HERE:**
| Notebook Name | Description | Link |
| --- | --- | --- |
| [Kohya LoRA Trainer XL](https://github.com/Linaqruf/kohya-trainer/blob/main/kohya-LoRA-trainer-XL.ipynb) | LoRA Training | [](https://colab.research.google.com/github/Linaqruf/kohya-trainer/blob/main/kohya-LoRA-trainer-XL.ipynb) |
| [Kohya Trainer XL](https://github.com/Linaqruf/kohya-trainer/blob/main/kohya-trainer-XL.ipynb) | Native Training | [](https://colab.research.google.com/github/Linaqruf/kohya-trainer/blob/main/kohya-trainer-XL.ipynb) |
SD 1.5 Scripts:
| Notebook Name | Description | Link | V14 |
| --- | --- | --- | --- |
| [Kohya LoRA Dreambooth](https://github.com/Linaqruf/kohya-trainer/blob/main/kohya-LoRA-dreambooth.ipynb) | LoRA Training (Dreambooth method) | [](https://colab.research.google.com/github/Linaqruf/kohya-trainer/blob/main/kohya-LoRA-dreambooth.ipynb) | [](https://colab.research.google.com/github/Linaqruf/kohya-trainer/blob/ff701379c65380c967cd956e4e9e8f6349563878/kohya-LoRA-dreambooth.ipynb) |
| [Kohya LoRA Fine-Tuning](https://github.com/Linaqruf/kohya-trainer/blob/main/kohya-LoRA-finetuner.ipynb) | LoRA Training (Fine-tune method) | [](https://colab.research.google.com/github/Linaqruf/kohya-trainer/blob/main/kohya-LoRA-finetuner.ipynb) | [](https://colab.research.google.com/github/Linaqruf/kohya-trainer/blob/ff701379c65380c967cd956e4e9e8f6349563878/kohya-LoRA-finetuner.ipynb) |
| [Kohya Trainer](https://github.com/Linaqruf/kohya-trainer/blob/main/kohya-trainer.ipynb) | Native Training | [](https://colab.research.google.com/github/Linaqruf/kohya-trainer/blob/main/kohya-trainer.ipynb) | [](https://colab.research.google.com/github/Linaqruf/kohya-trainer/blob/ff701379c65380c967cd956e4e9e8f6349563878/kohya-trainer.ipynb) |
| [Kohya Dreambooth](https://github.com/Linaqruf/kohya-trainer/blob/main/kohya-dreambooth.ipynb) | Dreambooth Training | [](https://colab.research.google.com/github/Linaqruf/kohya-trainer/blob/main/kohya-dreambooth.ipynb) | [](https://colab.research.google.com/github/Linaqruf/kohya-trainer/blob/ff701379c65380c967cd956e4e9e8f6349563878/kohya-dreambooth.ipynb) |
Ahoy! you're looking for our Huggingface backup that is again patched from Linaqruf and others?
| Notebook Name | Description | Link |
| --- | --- | --- |
| [Huggingface Backup](https://colab.research.google.com/github/kieranxsomer/HuggingFace_Backup/blob/main/HuggingFace_Backup.ipynb) | backup checkpoints! | [](https://colab.research.google.com/github/kieranxsomer/HuggingFace_Backup/blob/main/HuggingFace_Backup.ipynb)
| [1.5 Conversions](https://github.com/kieranxsomer/convert-scripts/blob/main/Converter_SD1_5_V2_Duct_TapeVersion.ipynb) | Convert to Diffusers! | [](https://github.com/kieranxsomer/convert-scripts/blob/main/Converter_SD1_5_V2_Duct_TapeVersion.ipynb)
## Duskfall/ Earth & Dusk Socials
| Social Network | Link |
| --- | --- |
|Discord|[Invite](https://discord.gg/5t2kYxt7An)
|CivitAi|[Duskfallcrew](https://civitai.com/user/duskfallcrew/)
|Huggingface|[Earth & Dusk](https://huggingface.co/EarthnDusk)
|Ko-Fi| [Dusk's Kofi](https://ko-fi.com/duskfallcrew/) | 6,640 | [
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artivus/toolformer-sharegpt | 2023-10-28T05:58:28.000Z | [
"region:us"
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dread1900/Wrench-WD2 | 2023-10-28T06:20:53.000Z | [
"region:us"
] | dread1900 | null | null | 0 | 0 | 2023-10-28T06:20:00 | Entry not found | 15 | [
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] |
winglian/slimorca-i18n | 2023-11-03T01:20:56.000Z | [
"region:us"
] | winglian | null | null | 0 | 0 | 2023-10-28T06:26:33 | ---
dataset_info:
features:
- name: old_conversation
list:
- name: from
dtype: string
- name: value
dtype: string
- name: weight
dtype: float64
- name: conversation
list:
- name: from
dtype: string
- name: value
dtype: string
- name: target_language
dtype: string
splits:
- name: train
num_bytes: 221027935
num_examples: 82828
download_size: 120745182
dataset_size: 221027935
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "slimorca-i18n"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 719 | [
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] |
acozma/imagenet-1k-rand_hog | 2023-11-01T07:18:59.000Z | [
"region:us"
] | acozma | null | null | 0 | 0 | 2023-10-28T06:30:09 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: image
dtype: image
- name: conditioning_image
dtype: image
- name: text
dtype: string
- name: params
struct:
- name: orientations
dtype: int64
- name: pixels_per_cell
dtype: int64
splits:
- name: train
num_bytes: 235174567045.0
num_examples: 500000
download_size: 89659059126
dataset_size: 235174567045.0
---
# Dataset Card for "imagenet-1k-rand_hog"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 667 | [
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kingcobramaleenhancementgummies/kingcobramaleenhancementgummies | 2023-10-28T06:40:12.000Z | [
"region:us"
] | kingcobramaleenhancementgummies | null | null | 0 | 0 | 2023-10-28T06:36:04 | [King cobra gummies](https://www.facebook.com/people/King-Cobra-Male-Enhancement/61552991065581/) are a revolutionary testosterone booster with peak performance in the aphrodisiac department. This product will supercharge your energy level, enhance your mood, and will aid in boosting stamina and vigor.
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### **[King Cobra Male Enhancement Gummies](https://snoppymart.com/us-kingcobra-gummies)**
[](https://snoppymart.com/us-kingcobra-gummies)
### [King Cobra Gummies Reviews](https://www.facebook.com/people/King-Cobra-Male-Enhancement/61552991065581/), Where to buy? DO they really work?
At some point in our life, we all experience diminished performance. For some, it is temporary while others struggle to find the cure. Rarely do we have a dietary supplement with natural herbal ingredients that can solve our problems. Today we are reviewing one such product that can supercharge your abilities. This King cobra gummies reviews will help you make an informed decision.
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### What are King cobra gummies?
King cobra gummies are a revolutionary testosterone booster with peak performance in the aphrodisiac department. This product will supercharge your energy level, enhance your mood, and will aid in boosting stamina and vigor. This supplement has the power to boost your performance in the gym.
The benefits of King cobra gummies are not limited to one area. This is a complete package capable of improving the overall quality of life.
### Why do men suffer from poor performance?
* **Performance Anxiety**
* **Low Testosterone**
* **Stress**
* **Weight Gain**
* **Aging
**
[**King cobra gummies**](https://www.facebook.com/people/King-Cobra-Male-Enhancement/61552991065581/) are a revolutionary testosterone booster with peak performance in the aphrodisiac department. This product will supercharge your energy level, enhance your mood, and will aid in boosting stamina and vigor.
Benefits of King cobra gummies:
Improved Power and Strength:
Supercharge your performance
Increased self-confidence
Improved muscle mass
Maximum pleasure, bigger and longer lasting sessions
Ramps up stamina and staying power
Experience peak performance
Improved intimacy
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### Where can I buy King cobra gummies?
[**Click on the link here to visit the official website.**](https://snoppymart.com/us-kingcobra-gummies)
---------------------------------------------------------------------------------------------------------
You can easily buy King cobra gummies from the official website. The packaging is discrete and is delivered quickly within 3 to 5 working days.
### Can I buy King cobra gummies over the counter?
Yes, King cobra gummies is an OTC product. It doesn’t contain any ingredient that requires a prescription. All ingredients are natural and tested on strict quality parameters. This product has nothing that is habit-forming and you will suffer any hazardous side effects.
### Why is King cobra gummies for men your best bet?
**• Proven results**
**• Natural Ingredients**
**• Added benefits**
**• No side effects**
**• Improved metabolism, enhanced energy level**
Because of the carefully crafted formula. This product is natural with very little chance of any side effects. Most men using King cobra gummies are happy with the results and have experienced an improvement in their quality of life to a great extent.
Improved intimacy with your partner. With boosted performance, you can experience the love and vigor that will strengthen the bond between you and your partner.
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-----------------------------------------------------------------------------------------------------------
### Why is testosterone a vital hormone for men?
Testosterone is an essential hormone for men's health, responsible for maintaining muscle mass, bone density, and masculinity. Some men may experience a decrease in testosterone levels due to aging, stress, or other factors, which can result in various symptoms, including fatigue, decreased libido, and decreased muscle mass.
Testosterone boosters are supplements that claim to increase testosterone levels in the body, and some studies suggest that they may be beneficial for men with low testosterone levels. However, it's essential to note that not all testosterone boosters are created equal, and some may have potential side effects.
Working
#### The working of [King cobra gummies](https://www.facebook.com/people/King-Cobra-Male-Enhancement/61552991065581/) is simple to understand. It is a 4-in-one formula. It is going:
**• Boost testosterone production**
**• Improved metabolism**
**• Increased serotonin level**
**• Fighting Free radicals**
### Boost Testosterone Production:
The herbal ingredients will provide the raw material to our body to help it produce more testosterone naturally. Most men experience a reduction in testosterone production after the age of 30. But this age bar is lowering day by day due to various reasons, mostly stress and anxiety.
Increased production of testosterone will aid in boosting the energy level and will improve libido and stamina. Testosterone is the hormone that can improve metabolism, increase energy levels so you don’t feel lethargic, and can keep you healthy and happy for a long time.
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#### Improved Metabolism
High energy level, weight reduction, and improved cognitive abilities. These are a few benefits of boosted metabolism. King cobra gummies for men burn more calories than you consume, keeping fit and slim. This extra energy is used to help you live day-to-day life with ease.
It is necessary to maintain a healthy weight to live a quality life. But, it is almost impossible to not gain weight.
So, what do we do, if workouts and dieting are not working? This is where King cobra gummies are going to work like a magic charm and going to aid in boosting the quality of life and enhancing performance.
#### Enhanced Serotonin Level
There are a few ingredients in King cobra gummies that can help our body boost serotonin production. During stress and anxiety, our body produces too much cortisol (a hormone that increases stress). We need to lower the production of cortisol and increase the production of serotonin.
A slight increase in serotonin will keep you in a good mood all day. It can aid in a better relationship with your partner and will boost your performance.
#### Fighting Free radicals
The primary cause of aging cells is free radicals. Only antioxidants can neutralize them. This is why the manufacturers of King cobra gummies are using antioxidant-rich herbs that can fight free radicals and give us a healthy carefree life.
Aging is natural, and there is no way you can stop aging. What you can do is delay the process. And if you can do it with natural ingredients without any side effects and sacrifice, then that is the best solution to your problem.
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-----------------------------------------------------------------------------------------------------------
[](https://snoppymart.com/us-kingcobra-gummies)
### Ingredients
#### **Tribulus Terrestris Fruit Extract:**
Proven to boost natural testosterone production. This ingredient will activate the Leydig cells with the help of the luteinizing hormone. The natural increase in testosterone is what is going to make a big difference in our performance.
#### Saw Palmetto Fruit Extract
A very popular and potent testosterone booster with proven aphrodisiac power. Not only has it aided in boosting drive and energy levels. It aids in serotonin production. In the eastern medicine system, it plays a vital role in the rejuvenation of men's vigor and vitality.
#### L-Arginine
It is a precursor to nitric oxide production. Increased NO2 production helps in improved blood flow. And improved blood flow to muscles and other parts of our body increases the size and strength.
#### Eurycoma Longifolia Extract
The bark and root extract are known to boost athletic powers. Where you want to use your athletic power is up to you. Helps in muscle building and can aid in improving results.
### What benefits of natural ingredients?
The primary reason I care so much about ingredients is to avoid any potential side effects. With nature, you can be assured that you will get the desired results without any suffering.
Some products contain ingredients that can provide instant results. But the harm they do to our bodies is irreparable. Almost in every case, these products contain the habit-forming steroids of free testosterone. Avoid them at all costs. Only go for products with natural ingredients.
### Benefits of King cobra gummies:
#### Peak Performance:
As you already know, with boosted metabolism, increased blood flow, and enhanced testosterone production you will be able to live the life that you desire. In every aspect of life, you will be able to give your 100% without fail.
#### Increased staying power and stamina:
King cobra gummies will increase staying power and aid in boosting stamina. And this will directly affect the performance. The longer lasting session, 100% satisfaction everywhere.
[Click Here To Visit Official Website – Get Special Discount](https://snoppymart.com/us-kingcobra-gummies)
-----------------------------------------------------------------------------------------------------------
### King cobra gummies near me?
You can buy King cobra gummies at a discounted rate from the official website. The prices on the official website are lower and geared towards equity for men.
Brick-and-mortar stores only increase the cost of the product and may even cause anxiety when men are trying to buy products. This is why the manufacturers are selling it on the website with reasonable rates and discounts to make sure it is within the affordable range.
### Any side effects?
Very few men have experienced the mild side effects of the product. As the ingredients are natural and are deemed safe by FDA for oral consumption, there is not much that you and I need to worry about. Some of the side effects include.
**• Bloating**
**• Dizziness**
**• Initial low energy level (goes away in the second week)**
**• Mild headache**
If you experience any side effects, then you should stop consuming them immediately. Consult a physician and then resume the dosage.
### Why is it better than other products?
As per my research, there are hundreds of products that claim to offer results with herbal ingredients. But in reality, only a few of those supplements can live up to the hype. Most are simple filler materials trying to take advantage of customers.
To avoid falling into the trap of glossy advertisements I tried King cobra gummies, as I already have experience with other similar products, I knew what to look into for any performance-enhancing supplement.
The positive results and added benefits of the products made me recommend King cobra gummies. I can assure you, any penny spent on King cobra gummies for men is money well spent.
### Dosage?
You have to take two King cobra gummies in one day. Take one in the morning after breakfast and another at night. Take them with water.
I will repeat, never take more than two gummies thinking it helps in improving results. Overdose will never help in anything. It only increases stress and the chances of side effects. So, be careful.
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-----------------------------------------------------------------------------------------------------------
[](https://snoppymart.com/us-kingcobra-gummies)
### Do king cobra gummies work?
Yes, they do work. A lot of men are using and improving their performance with the help of these gummies. We did a thorough research about this product.
Not only did I try this product myself, but also talked to many men who have benefitted from its regular use. Some of the King cobra gummy reviews from the men I talked to are here.
#### How to boost your results?
All you have to do is be regular with your dose. And if you want to step up your game and try to live a healthy life. Making sure to eat the right food and do some workouts/ a little bit of strength training can do wonders for you. And never combine the King cobra gummies with any type of alcohol or prescription medication.
Although, as a standalone product King cobra gummies are very effective and do ask much from the consumer. However, you can always help reduce the burden of stress and anxiety on yourself. Try not to worry about things too much. And live a simple healthy life.
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-----------------------------------------------------------------------------------------------------------
### Conclusion
All in all, King cobra gummies is a potent dietary supplement that will supercharge your energy level and will enable you to experience peak performance in the gym.
Made with natural herbal ingredients that passed the strict quality control parameters, King cobra gummies are a sure-shot way to boost your performance.
Along with the aid benefits, it alleviates stress and anxiety. A bonus that you are certainly one of the biggest for me to try and test the product.
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finleyhu/vogueman | 2023-10-28T06:44:14.000Z | [
"region:us"
] | finleyhu | null | null | 0 | 0 | 2023-10-28T06:42:48 | Entry not found | 15 | [
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] |
parksimon0808/prm800k-llama-v2 | 2023-10-29T03:57:45.000Z | [
"region:us"
] | parksimon0808 | null | null | 0 | 0 | 2023-10-28T07:07:03 | ---
dataset_info:
features:
- name: texts
dtype: string
- name: input_ids
sequence: int32
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 3512663173
num_examples: 1052488
- name: test
num_bytes: 114367280
num_examples: 32408
download_size: 336233397
dataset_size: 3627030453
---
# Dataset Card for "prm800k-llama-v2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 511 | [
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] |
quocanh34/private_model_tts3_no_denoise | 2023-10-28T08:37:46.000Z | [
"region:us"
] | quocanh34 | null | null | 0 | 0 | 2023-10-28T08:36:14 | ---
dataset_info:
features:
- name: id
dtype: string
- name: audio
struct:
- name: array
sequence: float32
- name: path
dtype: string
- name: sampling_rate
dtype: int64
- name: pred_str
dtype: string
- name: pred_str_norm
dtype: string
- name: intent
dtype: string
- name: entities
list:
- name: filler
dtype: string
- name: type
dtype: string
- name: file
dtype: string
splits:
- name: train
num_bytes: 568313120
num_examples: 2139
download_size: 462242144
dataset_size: 568313120
---
# Dataset Card for "private_model_tts3_no_denoise"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 779 | [
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nhantruongcse/data_summary_news | 2023-10-28T08:56:59.000Z | [
"region:us"
] | nhantruongcse | null | null | 0 | 0 | 2023-10-28T08:56:59 | Entry not found | 15 | [
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] |
BossBossNJb/cifar10_dataset_th_en | 2023-10-28T09:13:12.000Z | [
"region:us"
] | BossBossNJb | null | null | 0 | 0 | 2023-10-28T09:13:04 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: img
dtype: image
- name: label
dtype:
class_label:
names:
'0': airplane
'1': automobile
'2': bird
'3': cat
'4': deer
'5': dog
'6': frog
'7': horse
'8': ship
'9': truck
- name: en
dtype: string
- name: th
dtype: string
splits:
- name: train
num_bytes: 115003310.0
num_examples: 50000
- name: test
num_bytes: 23002580.0
num_examples: 10000
download_size: 144125889
dataset_size: 138005890.0
---
# Dataset Card for "cifar10_dataset_th_en"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 894 | [
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s-nlp/kgqa_mintaka_subgraphs_t5large | 2023-10-28T10:23:55.000Z | [
"region:us"
] | s-nlp | null | null | 0 | 0 | 2023-10-28T10:21:42 | ---
dataset_info:
features:
- name: id
dtype: string
- name: question
dtype: string
- name: answerEntity
dtype: string
- name: questionEntity
dtype: string
- name: groundTruthAnswerEntity
dtype: string
- name: complexityType
dtype: string
- name: graph
dtype: string
- name: correct
dtype: bool
splits:
- name: train
num_bytes: 68304334
num_examples: 91831
- name: test
num_bytes: 17265835
num_examples: 23168
download_size: 14913149
dataset_size: 85570169
---
# Dataset Card for "kgqa_mintaka_subgraphs_t5large"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 721 | [
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s-nlp/kgqa_mintaka_subgraphs_t5xl | 2023-10-28T10:24:36.000Z | [
"region:us"
] | s-nlp | null | null | 0 | 0 | 2023-10-28T10:24:32 | ---
dataset_info:
features:
- name: id
dtype: string
- name: question
dtype: string
- name: answerEntity
dtype: string
- name: questionEntity
dtype: string
- name: groundTruthAnswerEntity
dtype: string
- name: complexityType
dtype: string
- name: graph
dtype: string
- name: correct
dtype: bool
splits:
- name: train
num_bytes: 64961813
num_examples: 87924
- name: test
num_bytes: 16272721
num_examples: 21967
download_size: 15067714
dataset_size: 81234534
---
# Dataset Card for "kgqa_mintaka_subgraphs_t5xl"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 718 | [
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acozma/imagenet-1k-rand_entropy | 2023-11-02T18:51:03.000Z | [
"region:us"
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] |
j23349/autotrain-data-test | 2023-10-28T11:09:51.000Z | [
"task_categories:summarization",
"language:en",
"region:us"
] | j23349 | null | null | 0 | 0 | 2023-10-28T11:07:09 | ---
language:
- en
task_categories:
- summarization
---
# AutoTrain Dataset for project: test
## Dataset Description
This dataset has been automatically processed by AutoTrain for project test.
### Languages
The BCP-47 code for the dataset's language is en.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"feat_id": "13829542",
"text": "Kasia: When are u coming back?\r\nMatt: Back where?\r\nKasia: Oh come on\r\nKasia: you know what i mean\r\nMatt: I really don't \r\nKasia: When are you coming back to Warsaw\r\nMatt: I have no idea\r\nMatt: maybe around easter\r\nKasia: will you let me know\r\nMatt: sure if I know something then I will let you know asap\r\nKasia: ok \r\nMatt: are you mad?\r\nKasia: a bit\r\nMatt: oh come on\r\nMatt: this is not my fault \r\nMatt: there is no way that I can answer that question\r\nMatt: not now\r\nKasia: Fine",
"target": "Matt doesn't know when he's coming back to Warsaw. He might come around Easter. When he knows more, he will let Kasia know. Kasia is a bit upset."
},
{
"feat_id": "13862523",
"text": "Oliver: Have you beaten the game yet?\nTom: Not yet\nOliver: Ok... what mission are you playing?\nTom: The one before the final one, it's pretty hard\nOliver: I didn't find it particularly hard\nTom: I mean, combat is easy at this point in the game but the puzzles are difficult\nOliver: Ok, I got it\nTom: It's fun how most horror action games let you get really powerful by the end of the story\nOliver: Well, you know, being a pussy from start to finish ain't my idea of fun even in a horror game\nTom: I know... but do you remember Bioshock? You turned into some sort of superhero and the final boss was pretty much a joke\nOliver: Don't you dare talk like that about my favorite game\nTom: I know, I love it too, but it had its flaws\nOliver: No it didn't XD\nTom: Lol\nOliver: Well, keep playing\nTom: I'll let you know when I finish this one and my overall impressions\nOliver: Ok",
"target": "Tom will contact Oliver after finishing new horror action game."
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"feat_id": "Value(dtype='string', id=None)",
"text": "Value(dtype='string', id=None)",
"target": "Value(dtype='string', id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 11785 |
| valid | 2947 |
| 2,587 | [
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] |
22Plaruno/100_image | 2023-10-28T11:40:25.000Z | [
"region:us"
] | 22Plaruno | null | null | 0 | 0 | 2023-10-28T11:36:08 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: image
dtype: image
splits:
- name: train
num_bytes: 10359951.0
num_examples: 100
download_size: 0
dataset_size: 10359951.0
---
# Dataset Card for "100_image"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 436 | [
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mabzak/kamus-daerah-indo | 2023-10-30T13:04:12.000Z | [
"language:id",
"region:us"
] | mabzak | null | null | 0 | 0 | 2023-10-28T11:36:09 | ---
language:
- id
---
# Data Kamus Daerah Indonesia
## Deskripsi
Database ini berisi kumpulan kata-kata dari berbagai bahasa di Indonesia, termasuk kamus untuk menerjemahkan ke bahasa-bahasa regional seperti Lampung dan Jawa. Berikut adalah sumber data dan detail lebih lanjut tentang setiap kamus:
1. **Kumpulan Kata Indonesia**
- [Sumber Data](https://github.com/Wikidepia/indonesian_datasets/tree/master/dictionary/wordlist/data)
- Berisi kumpulan kata bahasa indonesia
cat indonesia.txt
2. **Lampung-Indonesia**
- [Sumber Data](https://github.com/meizano/lampung/blob/master/db/parser/Indonesia-Lampung.csv)
- Berisi kumpulan kata bahasa lampung dan terjemahan indonesianya
3. **Jawa-Indonesia**
- [Sumber Data](https://github.com/milstrike/Kamus-Jawa-Indonesia/blob/master/MyApplication/app/src/main/assets/databases/kamusjawaindonesia.sqlite)
- Berisi kumpulan kata bahasa jawa dan terjemahan indonesianya
## Catatan Penting
- Bahasa saya kumpulkan seiring waktu, jika berminat silahkan tambahkan bahasa daerah lain ke sini, kata pada bahasa daerah yang di inputkan mungkin belum lengkap.
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22Plaruno/image | 2023-10-28T11:48:28.000Z | [
"region:us"
] | 22Plaruno | null | null | 0 | 0 | 2023-10-28T11:41:08 | ---
dataset_info:
features:
- name: image
dtype: image
splits:
- name: train
num_bytes: 10359951.0
num_examples: 100
download_size: 0
dataset_size: 10359951.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "image"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 432 | [
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paowick/Banknote_thai | 2023-10-28T12:20:50.000Z | [
"region:us"
] | paowick | null | null | 0 | 0 | 2023-10-28T12:15:32 | Entry not found | 15 | [
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] |
datamol-io/safe-drugs | 2023-10-28T12:23:11.000Z | [
"license:cc-by-4.0",
"arxiv:2310.10773",
"region:us"
] | datamol-io | null | null | 0 | 0 | 2023-10-28T12:18:50 | ---
license: cc-by-4.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: dosed_ingredient
dtype: bool
- name: indication_class
dtype: string
- name: molecule_chembl_id
dtype: string
- name: molecule_type
dtype: string
- name: oral
dtype: bool
- name: pref_name
dtype: string
- name: therapeutic_flag
dtype: bool
- name: usan_stem
dtype: string
- name: usan_stem_definition
dtype: string
- name: usan_year
dtype: float64
- name: withdrawn_flag
dtype: bool
- name: smiles
dtype: string
- name: inchikey
dtype: string
- name: slices
dtype: string
- name: morphing
dtype: string
- name: motif
dtype: string
- name: scaffold
dtype: string
- name: superstructure
dtype: string
splits:
- name: train
num_bytes: 12691
num_examples: 26
download_size: 18556
dataset_size: 12691
---
# SAFE
Sequential Attachment-based Fragment Embedding (SAFE) is a novel molecular line notation that represents molecules as an unordered sequence of fragment blocks to improve molecule design using generative models.
This is the drugs dataset used for benchmarking.
Find the details and how to use at SAFE in the repo https://github.com/datamol-io/safe or the paper https://arxiv.org/pdf/2310.10773.pdf. | 1,374 | [
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abhieshek/lol | 2023-10-28T12:33:17.000Z | [
"region:us"
] | abhieshek | null | null | 0 | 0 | 2023-10-28T12:31:50 | Entry not found | 15 | [
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eniokilder/Banco-Imagem | 2023-10-28T12:48:31.000Z | [
"region:us"
] | eniokilder | null | null | 0 | 0 | 2023-10-28T12:31:58 | # Projeto
Banco-Imagem
### Nome do aluno
Enio Kilder Oliveira da Silva
|**Tipo de Projeto**|**Modelo Selecionado**|**Linguagem**|
|--|--|--|
Classificação de Objetos |YOLOv5|PyTorch|
## Performance
O modelo treinado possui performance de **98.6%**.
### Output do bloco de treinamento
<details>
<summary>Expandir Conteúdo!</summary>
```text
%%time
%cd ../yolov5
!python classify/train.py --model yolov5n-cls.pt --data $DATASET_NAME --epochs 128 --batch 16 --img 320 --pretrained weights/yolov5n-cls.pt
/content/yolov5
2023-10-28 01:49:35.242300: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:9342] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
2023-10-28 01:49:35.242363: E tensorflow/compiler/xla/stream_executor/cuda/cuda_fft.cc:609] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
2023-10-28 01:49:35.242406: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:1518] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
classify/train: model=yolov5n-cls.pt, data=Banco-Imagem-1, epochs=128, batch_size=16, imgsz=320, nosave=False, cache=None, device=, workers=8, project=runs/train-cls, name=exp, exist_ok=False, pretrained=weights/yolov5n-cls.pt, optimizer=Adam, lr0=0.001, decay=5e-05, label_smoothing=0.1, cutoff=None, dropout=None, verbose=False, seed=0, local_rank=-1
github: up to date with https://github.com/ultralytics/yolov5 ✅
YOLOv5 🚀 v7.0-230-g53efd07 Python-3.10.12 torch-2.1.0+cu118 CUDA:0 (Tesla T4, 15102MiB)
TensorBoard: Start with 'tensorboard --logdir runs/train-cls', view at http://localhost:6006/
albumentations: RandomResizedCrop(p=1.0, height=320, width=320, scale=(0.08, 1.0), ratio=(0.75, 1.3333333333333333), interpolation=1), HorizontalFlip(p=0.5), ColorJitter(p=0.5, brightness=[0.6, 1.4], contrast=[0.6, 1.4], saturation=[0.6, 1.4], hue=[0, 0]), Normalize(p=1.0, mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225), max_pixel_value=255.0), ToTensorV2(always_apply=True, p=1.0, transpose_mask=False)
Downloading https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5n-cls.pt to yolov5n-cls.pt...
100% 4.87M/4.87M [00:00<00:00, 48.4MB/s]
Model summary: 149 layers, 1218405 parameters, 1218405 gradients, 3.0 GFLOPs
optimizer: Adam(lr=0.001) with parameter groups 32 weight(decay=0.0), 33 weight(decay=5e-05), 33 bias
Image sizes 320 train, 320 test
Using 1 dataloader workers
Logging results to runs/train-cls/exp
Starting yolov5n-cls.pt training on Banco-Imagem-1 dataset with 5 classes for 128 epochs...
Epoch GPU_mem train_loss test_loss top1_acc top5_acc
1/128 0.508G 1.55 1.51 0.194 1: 100% 16/16 [00:06<00:00, 2.59it/s]
2/128 0.508G 1.39 1.86 0.222 1: 100% 16/16 [00:02<00:00, 6.81it/s]
3/128 0.508G 1.4 2.07 0.194 1: 100% 16/16 [00:02<00:00, 7.04it/s]
4/128 0.508G 1.35 1.75 0.222 1: 100% 16/16 [00:02<00:00, 6.38it/s]
5/128 0.508G 1.34 2.17 0.222 1: 100% 16/16 [00:02<00:00, 5.51it/s]
6/128 0.508G 1.26 1.76 0.25 1: 100% 16/16 [00:04<00:00, 3.51it/s]
7/128 0.508G 1.32 1.3 0.306 1: 100% 16/16 [00:02<00:00, 6.76it/s]
8/128 0.508G 1.27 1.57 0.333 1: 100% 16/16 [00:02<00:00, 6.99it/s]
9/128 0.508G 1.38 1.5 0.306 1: 100% 16/16 [00:02<00:00, 6.51it/s]
10/128 0.508G 1.3 1.39 0.278 1: 100% 16/16 [00:02<00:00, 5.73it/s]
11/128 0.508G 1.3 1.55 0.361 1: 100% 16/16 [00:03<00:00, 4.95it/s]
12/128 0.508G 1.28 1.45 0.306 1: 100% 16/16 [00:02<00:00, 6.98it/s]
13/128 0.508G 1.28 1.33 0.528 1: 100% 16/16 [00:02<00:00, 6.34it/s]
14/128 0.508G 1.24 1.19 0.417 1: 100% 16/16 [00:02<00:00, 6.90it/s]
15/128 0.508G 1.27 1.81 0.222 1: 100% 16/16 [00:03<00:00, 4.79it/s]
16/128 0.508G 1.25 1.52 0.361 1: 100% 16/16 [00:02<00:00, 6.45it/s]
17/128 0.508G 1.28 1.2 0.361 1: 100% 16/16 [00:02<00:00, 6.15it/s]
18/128 0.508G 1.25 1.33 0.528 1: 100% 16/16 [00:02<00:00, 6.79it/s]
19/128 0.508G 1.18 1.17 0.5 1: 100% 16/16 [00:02<00:00, 6.67it/s]
20/128 0.508G 1.23 1.33 0.306 1: 100% 16/16 [00:04<00:00, 3.52it/s]
21/128 0.508G 1.21 1.39 0.417 1: 100% 16/16 [00:02<00:00, 6.89it/s]
22/128 0.508G 1.18 1.36 0.528 1: 100% 16/16 [00:02<00:00, 6.43it/s]
23/128 0.508G 1.14 1.38 0.5 1: 100% 16/16 [00:02<00:00, 6.70it/s]
24/128 0.508G 1.17 1.3 0.556 1: 100% 16/16 [00:03<00:00, 4.59it/s]
25/128 0.508G 1.2 1.13 0.583 1: 100% 16/16 [00:02<00:00, 6.26it/s]
26/128 0.508G 1.11 1.12 0.528 1: 100% 16/16 [00:02<00:00, 6.69it/s]
27/128 0.508G 1.12 1.06 0.583 1: 100% 16/16 [00:02<00:00, 6.37it/s]
28/128 0.508G 1.12 1.45 0.417 1: 100% 16/16 [00:02<00:00, 6.95it/s]
29/128 0.508G 1.19 1.11 0.5 1: 100% 16/16 [00:03<00:00, 4.33it/s]
30/128 0.508G 1.14 1.2 0.583 1: 100% 16/16 [00:02<00:00, 6.86it/s]
31/128 0.508G 1.1 1.34 0.5 1: 100% 16/16 [00:02<00:00, 5.83it/s]
32/128 0.508G 1.17 2.32 0.278 1: 100% 16/16 [00:02<00:00, 6.40it/s]
33/128 0.508G 1.11 1.02 0.667 1: 100% 16/16 [00:02<00:00, 5.47it/s]
34/128 0.508G 1.16 1.37 0.5 1: 100% 16/16 [00:03<00:00, 5.17it/s]
35/128 0.508G 1.1 1.12 0.472 1: 100% 16/16 [00:02<00:00, 6.79it/s]
36/128 0.508G 1.08 1.2 0.556 1: 100% 16/16 [00:03<00:00, 4.22it/s]
37/128 0.508G 1.11 1.08 0.556 1: 100% 16/16 [00:02<00:00, 6.21it/s]
38/128 0.508G 1.13 1.26 0.528 1: 100% 16/16 [00:03<00:00, 4.65it/s]
39/128 0.508G 1.12 1.11 0.667 1: 100% 16/16 [00:02<00:00, 6.73it/s]
40/128 0.508G 1.11 1.19 0.639 1: 100% 16/16 [00:02<00:00, 6.53it/s]
41/128 0.508G 1.07 0.947 0.556 1: 100% 16/16 [00:02<00:00, 6.87it/s]
42/128 0.508G 1.07 1.18 0.611 1: 100% 16/16 [00:03<00:00, 5.17it/s]
43/128 0.508G 1.14 1.44 0.528 1: 100% 16/16 [00:02<00:00, 5.41it/s]
44/128 0.508G 1.05 1.01 0.667 1: 100% 16/16 [00:02<00:00, 6.64it/s]
45/128 0.508G 1.08 1.14 0.639 1: 100% 16/16 [00:02<00:00, 6.77it/s]
46/128 0.508G 1.07 1.33 0.528 1: 100% 16/16 [00:02<00:00, 6.31it/s]
47/128 0.508G 1.03 1 0.639 1: 100% 16/16 [00:03<00:00, 4.78it/s]
48/128 0.508G 1.04 1.71 0.611 1: 100% 16/16 [00:02<00:00, 5.78it/s]
49/128 0.508G 1.04 1.64 0.528 1: 100% 16/16 [00:02<00:00, 6.66it/s]
50/128 0.508G 1.02 1 0.75 1: 100% 16/16 [00:02<00:00, 6.63it/s]
51/128 0.508G 1.02 1.11 0.667 1: 100% 16/16 [00:02<00:00, 6.63it/s]
52/128 0.508G 1.06 1.59 0.611 1: 100% 16/16 [00:03<00:00, 4.26it/s]
53/128 0.508G 0.973 1.07 0.667 1: 100% 16/16 [00:02<00:00, 6.46it/s]
54/128 0.508G 0.925 1.34 0.556 1: 100% 16/16 [00:02<00:00, 6.46it/s]
55/128 0.508G 1.1 0.927 0.667 1: 100% 16/16 [00:03<00:00, 4.46it/s]
56/128 0.508G 1 1.97 0.583 1: 100% 16/16 [00:05<00:00, 3.06it/s]
57/128 0.508G 0.993 1.34 0.611 1: 100% 16/16 [00:02<00:00, 6.75it/s]
58/128 0.508G 0.954 1.17 0.639 1: 100% 16/16 [00:02<00:00, 6.50it/s]
59/128 0.508G 1.03 1.54 0.5 1: 100% 16/16 [00:02<00:00, 6.59it/s]
60/128 0.508G 1.01 1.12 0.611 1: 100% 16/16 [00:03<00:00, 5.32it/s]
61/128 0.508G 1 1.13 0.583 1: 100% 16/16 [00:03<00:00, 5.28it/s]
62/128 0.508G 0.943 0.986 0.639 1: 100% 16/16 [00:02<00:00, 6.75it/s]
63/128 0.508G 0.909 1.12 0.639 1: 100% 16/16 [00:02<00:00, 6.97it/s]
64/128 0.508G 0.888 0.867 0.75 1: 100% 16/16 [00:02<00:00, 6.32it/s]
65/128 0.508G 0.958 0.975 0.667 1: 100% 16/16 [00:03<00:00, 4.41it/s]
66/128 0.508G 0.939 0.947 0.639 1: 100% 16/16 [00:02<00:00, 6.54it/s]
67/128 0.508G 1.02 1.11 0.694 1: 100% 16/16 [00:03<00:00, 5.04it/s]
68/128 0.508G 0.998 0.971 0.667 1: 100% 16/16 [00:02<00:00, 5.55it/s]
69/128 0.508G 0.968 0.98 0.694 1: 100% 16/16 [00:03<00:00, 4.52it/s]
70/128 0.508G 0.965 1.11 0.722 1: 100% 16/16 [00:02<00:00, 6.55it/s]
71/128 0.508G 0.965 1.47 0.583 1: 100% 16/16 [00:02<00:00, 6.84it/s]
72/128 0.508G 0.953 1.2 0.611 1: 100% 16/16 [00:02<00:00, 6.54it/s]
73/128 0.508G 0.863 0.772 0.722 1: 100% 16/16 [00:02<00:00, 6.90it/s]
74/128 0.508G 0.946 0.884 0.667 1: 100% 16/16 [00:03<00:00, 4.25it/s]
75/128 0.508G 0.911 0.942 0.694 1: 100% 16/16 [00:02<00:00, 6.78it/s]
76/128 0.508G 0.964 1.16 0.694 1: 100% 16/16 [00:02<00:00, 6.80it/s]
77/128 0.508G 0.917 1.2 0.694 1: 100% 16/16 [00:02<00:00, 6.44it/s]
78/128 0.508G 0.941 0.955 0.639 1: 100% 16/16 [00:02<00:00, 6.22it/s]
79/128 0.508G 0.885 1.02 0.722 1: 100% 16/16 [00:03<00:00, 4.58it/s]
80/128 0.508G 0.864 0.802 0.694 1: 100% 16/16 [00:02<00:00, 6.33it/s]
81/128 0.508G 0.908 1.11 0.833 1: 100% 16/16 [00:02<00:00, 6.52it/s]
82/128 0.508G 0.915 0.843 0.778 1: 100% 16/16 [00:02<00:00, 6.82it/s]
83/128 0.508G 0.899 1.14 0.722 1: 100% 16/16 [00:03<00:00, 4.96it/s]
84/128 0.508G 0.826 0.81 0.75 1: 100% 16/16 [00:02<00:00, 5.77it/s]
85/128 0.508G 0.831 0.883 0.694 1: 100% 16/16 [00:02<00:00, 6.61it/s]
86/128 0.508G 0.804 0.95 0.694 1: 100% 16/16 [00:02<00:00, 6.42it/s]
87/128 0.508G 0.805 0.916 0.694 1: 100% 16/16 [00:02<00:00, 6.60it/s]
88/128 0.508G 0.824 0.936 0.667 1: 100% 16/16 [00:03<00:00, 4.40it/s]
89/128 0.508G 0.854 0.854 0.639 1: 100% 16/16 [00:02<00:00, 6.48it/s]
90/128 0.508G 0.79 1.14 0.694 1: 100% 16/16 [00:02<00:00, 6.72it/s]
91/128 0.508G 0.83 0.848 0.75 1: 100% 16/16 [00:02<00:00, 6.59it/s]
92/128 0.508G 0.805 1.32 0.639 1: 100% 16/16 [00:02<00:00, 6.47it/s]
93/128 0.508G 0.813 1.22 0.75 1: 100% 16/16 [00:03<00:00, 4.23it/s]
94/128 0.508G 0.796 0.91 0.722 1: 100% 16/16 [00:02<00:00, 6.68it/s]
95/128 0.508G 0.823 0.778 0.75 1: 100% 16/16 [00:02<00:00, 6.70it/s]
96/128 0.508G 0.827 0.898 0.806 1: 100% 16/16 [00:02<00:00, 6.50it/s]
97/128 0.508G 0.777 0.833 0.778 1: 100% 16/16 [00:02<00:00, 5.78it/s]
98/128 0.508G 0.79 0.735 0.806 1: 100% 16/16 [00:03<00:00, 4.78it/s]
99/128 0.508G 0.824 0.797 0.778 1: 100% 16/16 [00:02<00:00, 6.19it/s]
100/128 0.508G 0.802 0.893 0.806 1: 100% 16/16 [00:02<00:00, 5.94it/s]
101/128 0.508G 0.778 1.11 0.778 1: 100% 16/16 [00:02<00:00, 6.61it/s]
102/128 0.508G 0.795 1.15 0.722 1: 100% 16/16 [00:03<00:00, 4.30it/s]
103/128 0.508G 0.777 1.54 0.667 1: 100% 16/16 [00:02<00:00, 6.39it/s]
104/128 0.508G 0.764 0.916 0.722 1: 100% 16/16 [00:02<00:00, 6.66it/s]
105/128 0.508G 0.737 1.04 0.778 1: 100% 16/16 [00:02<00:00, 6.57it/s]
106/128 0.508G 0.689 0.792 0.75 1: 100% 16/16 [00:02<00:00, 6.55it/s]
107/128 0.508G 0.769 0.945 0.75 1: 100% 16/16 [00:03<00:00, 4.40it/s]
108/128 0.508G 0.78 1.21 0.75 1: 100% 16/16 [00:02<00:00, 6.61it/s]
109/128 0.508G 0.768 0.958 0.75 1: 100% 16/16 [00:02<00:00, 6.37it/s]
110/128 0.508G 0.802 0.953 0.75 1: 100% 16/16 [00:02<00:00, 6.41it/s]
111/128 0.508G 0.765 0.71 0.75 1: 100% 16/16 [00:02<00:00, 5.42it/s]
112/128 0.508G 0.709 1.07 0.722 1: 100% 16/16 [00:03<00:00, 5.15it/s]
113/128 0.508G 0.683 1.1 0.694 1: 100% 16/16 [00:02<00:00, 6.57it/s]
114/128 0.508G 0.685 0.892 0.778 1: 100% 16/16 [00:02<00:00, 6.41it/s]
115/128 0.508G 0.678 0.78 0.722 1: 100% 16/16 [00:02<00:00, 6.25it/s]
116/128 0.508G 0.714 1.19 0.722 1: 100% 16/16 [00:03<00:00, 4.29it/s]
117/128 0.508G 0.718 0.777 0.694 1: 100% 16/16 [00:02<00:00, 6.04it/s]
118/128 0.508G 0.744 0.855 0.778 1: 100% 16/16 [00:02<00:00, 6.72it/s]
119/128 0.508G 0.732 0.708 0.75 1: 100% 16/16 [00:02<00:00, 6.66it/s]
120/128 0.508G 0.7 0.88 0.778 1: 100% 16/16 [00:02<00:00, 5.85it/s]
121/128 0.508G 0.687 0.852 0.778 1: 100% 16/16 [00:03<00:00, 4.65it/s]
122/128 0.508G 0.671 1.01 0.778 1: 100% 16/16 [00:02<00:00, 6.46it/s]
123/128 0.508G 0.695 0.708 0.75 1: 100% 16/16 [00:02<00:00, 6.40it/s]
124/128 0.508G 0.685 0.725 0.778 1: 100% 16/16 [00:02<00:00, 6.69it/s]
125/128 0.508G 0.681 0.991 0.75 1: 100% 16/16 [00:03<00:00, 4.79it/s]
126/128 0.508G 0.674 0.72 0.75 1: 100% 16/16 [00:03<00:00, 4.96it/s]
127/128 0.508G 0.674 0.733 0.75 1: 100% 16/16 [00:02<00:00, 6.52it/s]
128/128 0.508G 0.687 0.682 0.75 1: 100% 16/16 [00:02<00:00, 6.48it/s]
Training complete (0.105 hours)
Results saved to runs/train-cls/exp
Predict: python classify/predict.py --weights runs/train-cls/exp/weights/best.pt --source im.jpg
Validate: python classify/val.py --weights runs/train-cls/exp/weights/best.pt --data Banco-Imagem-1
Export: python export.py --weights runs/train-cls/exp/weights/best.pt --include onnx
PyTorch Hub: model = torch.hub.load('ultralytics/yolov5', 'custom', 'runs/train-cls/exp/weights/best.pt')
Visualize: https://netron.app
CPU times: user 4.67 s, sys: 452 ms, total: 5.12 s
Wall time: 6min 43s
!python classify/val.py --weights runs/train-cls/exp/weights/best.pt --data $DATASET_NAME
classify/val: data=Banco-Imagem-1, weights=['runs/train-cls/exp/weights/best.pt'], batch_size=128, imgsz=224, device=, workers=8, verbose=True, project=runs/val-cls, name=exp, exist_ok=False, half=False, dnn=False
YOLOv5 🚀 v7.0-230-g53efd07 Python-3.10.12 torch-2.1.0+cu118 CUDA:0 (Tesla T4, 15102MiB)
Fusing layers...
Model summary: 117 layers, 1214869 parameters, 0 gradients, 2.9 GFLOPs
testing: 100% 1/1 [00:00<00:00, 1.05it/s]
Class Images top1_acc top5_acc
all 36 0.639 1
avioes 7 0.571 1
barcos 6 0.667 1
carros 11 0.545 1
helicopteros 8 0.875 1
motos 4 0.5 1
Speed: 0.1ms pre-process, 14.8ms inference, 0.6ms post-process per image at shape (1, 3, 224, 224)
Results saved to runs/val-cls/exp
```
</details>
### Evidências do treinamento
#### Gráficos de precisão e perdas
#### Matriz de Confusão
#### Inferindo com o modelo personalizado
```
#Pega a localização de uma imagem do conjunto de testes ou validações
if os.path.exists(os.path.join(dataset.location, "test")):
split_path = os.path.join(dataset.location, "test")
else:
os.path.join(dataset.location, "valid")
example_class = os.listdir(split_path)[4]
example_image_name = os.listdir(os.path.join(split_path, example_class))[4]
example_image_path = os.path.join(split_path, example_class, example_image_name)
os.environ["TEST_IMAGE_PATH"] = example_image_path
print(f"Inferindo sobre um exemplo da classe '{example_class}'")
#Infer
!python classify/predict.py --weights runs/train-cls/exp/weights/best.pt --source $TEST_IMAGE_PATH
Inferindo sobre um exemplo da classe 'carros'
classify/predict: weights=['runs/train-cls/exp/weights/best.pt'], source=/content/yolov5/Banco-Imagem-1/test/carros/00012_jpg.rf.9f0d32646e83139878c5788b040038f7.jpg, data=data/coco128.yaml, imgsz=[224, 224], device=, view_img=False, save_txt=False, nosave=False, augment=False, visualize=False, update=False, project=runs/predict-cls, name=exp, exist_ok=False, half=False, dnn=False, vid_stride=1
YOLOv5 🚀 v7.0-230-g53efd07 Python-3.10.12 torch-2.1.0+cu118 CUDA:0 (Tesla T4, 15102MiB)
Fusing layers...
Model summary: 117 layers, 1214869 parameters, 0 gradients, 2.9 GFLOPs
image 1/1 /content/yolov5/Banco-Imagem-1/test/carros/00012_jpg.rf.9f0d32646e83139878c5788b040038f7.jpg: 224x224 carros 0.91, avioes 0.08, motos 0.01, helicopteros 0.00, barcos 0.00, 2.7ms
Speed: 0.3ms pre-process, 2.7ms inference, 5.1ms NMS per image at shape (1, 3, 224, 224)
Results saved to runs/predict-cls/exp14
```
```
#### Modelo treinado com 80% ou mais de acurácia/precisão
=========================================================
```
```
#carro
import requests
image_url = "https://i.imgur.com/GB9Tihf.jpg"
response = requests.get(image_url)
response.raise_for_status()
with open('carro.jpg', 'wb') as handler:
handler.write(response.content)
!python classify/predict.py --weights ./weights/yolov5x-cls.pt --source carro.jpg
classify/predict: weights=['./weigths/yolov5x-cls.pt'], source=carro.jpg, data=data/coco128.yaml, imgsz=[224, 224], device=, view_img=False, save_txt=False, nosave=False, augment=False, visualize=False, update=False, project=runs/predict-cls, name=exp, exist_ok=False, half=False, dnn=False, vid_stride=1
YOLOv5 🚀 v7.0-230-g53efd07 Python-3.10.12 torch-2.1.0+cu118 CUDA:0 (Tesla T4, 15102MiB)
Fusing layers...
Model summary: 264 layers, 48072600 parameters, 0 gradients, 129.9 GFLOPs
image 1/1 /content/yolov5/carro.jpg: 224x224 sports car 0.95, race car 0.02, convertible 0.01, car wheel 0.00, grille 0.00, 12.9ms
Speed: 0.4ms pre-process, 12.9ms inference, 6.9ms NMS per image at shape (1, 3, 224, 224)
Results saved to runs/predict-cls/exp13
### Modelo treinado com ao menos 50% de acurácia/precisão
=========================================================
```
```
#Moto
import requests
image_url = "https://i.imgur.com/ASwjAT5.jpg"
response = requests.get(image_url)
response.raise_for_status()
with open('moto.jpg', 'wb') as handler:
handler.write(response.content)
!python classify/predict.py --weights ./weights/yolov5m-cls.pt --source moto.jpg
classify/predict: weights=['./weigths/yolov5m-cls.pt'], source=moto.jpg, data=data/coco128.yaml, imgsz=[224, 224], device=, view_img=False, save_txt=False, nosave=False, augment=False, visualize=False, update=False, project=runs/predict-cls, name=exp, exist_ok=False, half=False, dnn=False, vid_stride=1
YOLOv5 🚀 v7.0-230-g53efd07 Python-3.10.12 torch-2.1.0+cu118 CUDA:0 (Tesla T4, 15102MiB)
Fusing layers...
Model summary: 166 layers, 12947192 parameters, 0 gradients, 31.7 GFLOPs
image 1/1 /content/yolov5/moto.jpg: 224x224 moped 0.64, scooter 0.17, disc brake 0.06, crash helmet 0.05, snowmobile 0.01, 5.4ms
Speed: 0.4ms pre-process, 5.4ms inference, 6.9ms NMS per image at shape (1, 3, 224, 224)
Results saved to runs/predict-cls/exp16
```
## Roboflow
Banco-Imagem > 2023-10-24 9:29pm
https://universe.roboflow.com/eniokilder/banco-imagem
Provided by a Roboflow user
License: CC BY 4.0
## HuggingFace
Link para o HuggingFace:
https://huggingface.co/datasets/eniokilder/Banco-Imagem
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ali-vakil/PMP_QA_dataset_not_clean | 2023-10-28T12:41:12.000Z | [
"task_categories:question-answering",
"size_categories:n<1K",
"language:en",
"license:apache-2.0",
"region:us"
] | ali-vakil | null | null | 0 | 0 | 2023-10-28T12:32:43 | ---
license: apache-2.0
task_categories:
- question-answering
language:
- en
pretty_name: PMP test QA
size_categories:
- n<1K
---
Print ("This dataset includes 580 Q/A records, not separated, not cleaned yet.-I am working to clean it up-therefore I'm not sharing it publicly.")
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open-llm-leaderboard/details_PulsarAI__2x-LoRA-Assemble-13B | 2023-10-28T12:51:44.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | 0 | 0 | 2023-10-28T12:51:37 | ---
pretty_name: Evaluation run of PulsarAI/2x-LoRA-Assemble-13B
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [PulsarAI/2x-LoRA-Assemble-13B](https://huggingface.co/PulsarAI/2x-LoRA-Assemble-13B)\
\ 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_PulsarAI__2x-LoRA-Assemble-13B\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-28T12:51:33.520951](https://huggingface.co/datasets/open-llm-leaderboard/details_PulsarAI__2x-LoRA-Assemble-13B/blob/main/results_2023-10-28T12-51-33.520951.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\": 0.01740771812080537,\n\
\ \"em_stderr\": 0.0013393597649753585,\n \"f1\": 0.12005662751677883,\n\
\ \"f1_stderr\": 0.002244767452564408,\n \"acc\": 0.42864652552428745,\n\
\ \"acc_stderr\": 0.009950202519105519\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.01740771812080537,\n \"em_stderr\": 0.0013393597649753585,\n\
\ \"f1\": 0.12005662751677883,\n \"f1_stderr\": 0.002244767452564408\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.09249431387414708,\n \
\ \"acc_stderr\": 0.007980396874560173\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7647987371744278,\n \"acc_stderr\": 0.011920008163650865\n\
\ }\n}\n```"
repo_url: https://huggingface.co/PulsarAI/2x-LoRA-Assemble-13B
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_28T12_51_33.520951
path:
- '**/details_harness|drop|3_2023-10-28T12-51-33.520951.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-28T12-51-33.520951.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_28T12_51_33.520951
path:
- '**/details_harness|gsm8k|5_2023-10-28T12-51-33.520951.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-28T12-51-33.520951.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_28T12_51_33.520951
path:
- '**/details_harness|winogrande|5_2023-10-28T12-51-33.520951.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-28T12-51-33.520951.parquet'
- config_name: results
data_files:
- split: 2023_10_28T12_51_33.520951
path:
- results_2023-10-28T12-51-33.520951.parquet
- split: latest
path:
- results_2023-10-28T12-51-33.520951.parquet
---
# Dataset Card for Evaluation run of PulsarAI/2x-LoRA-Assemble-13B
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/PulsarAI/2x-LoRA-Assemble-13B
- **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 [PulsarAI/2x-LoRA-Assemble-13B](https://huggingface.co/PulsarAI/2x-LoRA-Assemble-13B) 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_PulsarAI__2x-LoRA-Assemble-13B",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-28T12:51:33.520951](https://huggingface.co/datasets/open-llm-leaderboard/details_PulsarAI__2x-LoRA-Assemble-13B/blob/main/results_2023-10-28T12-51-33.520951.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": 0.01740771812080537,
"em_stderr": 0.0013393597649753585,
"f1": 0.12005662751677883,
"f1_stderr": 0.002244767452564408,
"acc": 0.42864652552428745,
"acc_stderr": 0.009950202519105519
},
"harness|drop|3": {
"em": 0.01740771812080537,
"em_stderr": 0.0013393597649753585,
"f1": 0.12005662751677883,
"f1_stderr": 0.002244767452564408
},
"harness|gsm8k|5": {
"acc": 0.09249431387414708,
"acc_stderr": 0.007980396874560173
},
"harness|winogrande|5": {
"acc": 0.7647987371744278,
"acc_stderr": 0.011920008163650865
}
}
```
### 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] | 7,257 | [
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PlayVest/txt_invx_research | 2023-10-28T12:58:36.000Z | [
"region:us"
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kanemogi/your-dataset-name | 2023-10-28T13:26:11.000Z | [
"region:us"
] | kanemogi | null | null | 0 | 0 | 2023-10-28T13:26:11 | Entry not found | 15 | [
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22Plaruno/faces64x64 | 2023-10-28T13:32:05.000Z | [
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] |
316usman/my_dataset | 2023-10-28T13:59:53.000Z | [
"region:us"
] | 316usman | null | null | 0 | 0 | 2023-10-28T13:59:51 | ---
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_bytes: 31
num_examples: 1
download_size: 1349
dataset_size: 31
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "my_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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] |
haryoaw/COPAL | 2023-11-02T19:55:05.000Z | [
"task_categories:multiple-choice",
"size_categories:n<1K",
"language:id",
"license:cc-by-sa-4.0",
"region:us"
] | haryoaw | null | null | 0 | 0 | 2023-10-28T14:35:55 | ---
license: cc-by-sa-4.0
task_categories:
- multiple-choice
language:
- id
size_categories:
- n<1K
configs:
- config_name: id
data_files:
- split: test
path: test_copal.csv
- split: test_colloquial
path: test_copal_colloquial.csv
---
(IN PROGRESS WRITING README.md)
## How to Use
```py
from datasets import load_dataset
copal_id_dataset = load_dataset('haryoaw/COPAL', 'id', subset='test')
copal_id_colloquial_dataset = load_dataset('haryoaw/COPAL', 'id', subset='test_colloquial')
``` | 503 | [
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] |
langvision/badges | 2023-10-28T15:26:27.000Z | [
"license:mit",
"badges",
"langvision",
"region:us"
] | langvision | null | null | 1 | 0 | 2023-10-28T15:22:27 | ---
tags: [badges, langvision]
license: mit
thumbnail: "https://huggingface.co/datasets/huggingface/badges/resolve/main/badges-thumbnail.png"
---
<style>
.prose img {
display: inline;
margin: 0 6px !important;
}
.prose table {
max-width: 320px;
margin: 0;
}
</style>
# Badges
A set of badges you can use anywhere. Just update the anchor URL to point to the correct action for your Space. Light or dark background with 4 sizes available: small, medium, large, and extra large.
## How to use?
- With markdown, just copy the badge from: https://huggingface.co/datasets/huggingface/badges/blob/main/README.md?code=true
- With HTML, inspect this page with your web browser and copy the outer html.
## Available sizes
| Small | Medium | Large | Extra large |
| ------------- | :-----------: | ------------- | ------------- |
| 20px (height) | 24px (height) | 36px (height) | 48px (height) |
## Paper page
[](https://huggingface.co/papers)
[](https://huggingface.co/papers)
[](https://huggingface.co/papers)
[](https://huggingface.co/papers)
[](https://huggingface.co/papers)
[](https://huggingface.co/papers)
[](https://huggingface.co/papers)
[](https://huggingface.co/papers)
## Deploy on Spaces
[](https://huggingface.co/new-space)
[](https://huggingface.co/new-space)
[](https://huggingface.co/new-space)
[](https://huggingface.co/new-space)
[](https://huggingface.co/new-space)
[](https://huggingface.co/new-space)
[](https://huggingface.co/new-space)
[](https://huggingface.co/new-space)
## Duplicate this Space
[](https://huggingface.co/spaces/huggingface-projects/diffusers-gallery?duplicate=true)
[](https://huggingface.co/spaces/huggingface-projects/diffusers-gallery?duplicate=true)
[](https://huggingface.co/spaces/huggingface-projects/diffusers-gallery?duplicate=true)
[](https://huggingface.co/spaces/huggingface-projects/diffusers-gallery?duplicate=true)
[](https://huggingface.co/spaces/huggingface-projects/diffusers-gallery?duplicate=true)
[](https://huggingface.co/spaces/huggingface-projects/diffusers-gallery?duplicate=true)
[](https://huggingface.co/spaces/huggingface-projects/diffusers-gallery?duplicate=true)
[](https://huggingface.co/spaces/huggingface-projects/diffusers-gallery?duplicate=true)
## Open in HF Spaces
[](https://huggingface.co/spaces)
[](https://huggingface.co/spaces)
[](https://huggingface.co/spaces)
[](https://huggingface.co/spaces)
[](https://huggingface.co/spaces)
[](https://huggingface.co/spaces)
[](https://huggingface.co/spaces)
[](https://huggingface.co/spaces)
## Open a Discussion
[](https://huggingface.co/spaces)
[](https://huggingface.co/spaces)
[](https://huggingface.co/spaces)
[](https://huggingface.co/spaces)
[](https://huggingface.co/spaces)
[](https://huggingface.co/spaces)
[](https://huggingface.co/spaces)
[](https://huggingface.co/spaces)
## Share to Community
[](https://huggingface.co/spaces)
[](https://huggingface.co/spaces)
[](https://huggingface.co/spaces)
[](https://huggingface.co/spaces)
[](https://huggingface.co/spaces)
[](https://huggingface.co/spaces)
[](https://huggingface.co/spaces)
[](https://huggingface.co/spaces)
## Sign in with Hugging Face
[](https://huggingface.co/)
[](https://huggingface.co/)
[](https://huggingface.co/)
[](https://huggingface.co/)
[](https://huggingface.co/)
[](https://huggingface.co/)
[](https://huggingface.co/)
[](https://huggingface.co/)
## Open a Pull Request
[](https://huggingface.co/spaces/victor/ChatUI/discussions)
[](https://huggingface.co/spaces/victor/ChatUI/discussions)
[](https://huggingface.co/spaces/victor/ChatUI/discussions)
[](https://huggingface.co/spaces/victor/ChatUI/discussions)
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seonglae/chroma-psgs_w100 | 2023-11-01T04:29:31.000Z | [
"region:us"
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xwar/ninox_docs | 2023-11-01T22:20:56.000Z | [
"region:us"
] | xwar | null | null | 0 | 0 | 2023-10-28T15:47:26 | Test Dataset to finetune a LLM for Ninox Scrpit | 47 | [
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Hi-ToM/Hi-ToM_Dataset | 2023-10-29T04:32:30.000Z | [
"region:us"
] | Hi-ToM | null | null | 0 | 0 | 2023-10-28T15:48:30 | # Hi-ToM Dataset
This is the dataset for the paper "Hi-ToM: A Benchmark for Evaluating Higher-Order Theory of Mind Reasoning in Large Language Models".
<img src=media/Picture1.png height=430>
### The `Hi-ToM_data` folder
Contains ToMh data consisting of story-question pairs and the corresponding answers.
The names of subfolder branches have the following meanings:
- `Tell` / `No_Tell`: whether or not the stories contain communications among agents.
- `MC` / `CoT`: the prompting style. `MC` corresponds to Vanilla Prompting (VP) in the paper, while `CoT` stands for Chain-of-Thought Prompting (CoTP).
- `length_n`: the story length, i.e. the number of chapters in a story. From 1 to 3.
- `sample_n`: the numbering of different sample stories.
- `order_n`: the ToM order of the question. From 0 to 4.
### The `Hi-ToM_prompt` folder
Contains prompt files that can be directly input to API.
The data in it are almost the same as `Hi-ToM_data`, except that answers are eliminated.
### Generate new data and prompts
Run the script `generate_tomh.sh`.
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] |
akkasi/go_emotions | 2023-10-28T16:02:47.000Z | [
"region:us"
] | akkasi | null | null | 0 | 0 | 2023-10-28T16:02:44 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: text
dtype: string
- name: labels
sequence: float64
- name: label2idx
dtype: string
- name: idx2label
dtype: string
splits:
- name: train
num_bytes: 210169067
num_examples: 168980
- name: test
num_bytes: 52552436
num_examples: 42245
download_size: 13348134
dataset_size: 262721503
---
# Dataset Card for "go_emotions"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 662 | [
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williamliu/ChiMed-VL | 2023-10-28T17:09:23.000Z | [
"region:us"
] | williamliu | null | null | 1 | 0 | 2023-10-28T16:37:05 | # ChiMed-VL Dataset
## ChiMed-VL-Alignment dataset ##
ChiMed-VL-Alignment consists of 580,014 image-text couplings, each pair falling into one of two categories: context information of an image or descriptions of an image. The context category contains 167M tokens, presenting a median text length of 435 (Q1: 211, Q3: 757). Conversely, descriptions, more concise and image-specific, contain inline descriptions and captions. They comprise 63M tokens, with median lengths settling at 59 (Q1: 45, Q3: 83).
## ChiMed-VL-Instruction dataset ##
ChiMed-VL-Instruction comprises 469,441 question-answer pairs. Within this subset, the questions section contains 10M tokens with a median length of 20 (Q1: 16, Q3: 25), posing a concise inquiry reflective of medical queries. The answers consist of 13M tokens with a median length slightly longer at 22 (Q1: 12, Q3: 34), providing clear, direct, and informative responses.
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Starkate/original | 2023-10-28T17:15:38.000Z | [
"region:us"
] | Starkate | null | null | 0 | 0 | 2023-10-28T16:54:47 | Entry not found | 15 | [
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derejee/text2sql | 2023-10-28T16:59:01.000Z | [
"region:us"
] | derejee | null | null | 0 | 0 | 2023-10-28T16:59:01 | Entry not found | 15 | [
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skarskar/learn_llm_ds | 2023-10-28T16:59:49.000Z | [
"region:us"
] | skarskar | null | null | 0 | 0 | 2023-10-28T16:59:49 | Entry not found | 15 | [
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williamliu/ChiMed | 2023-10-30T02:21:20.000Z | [
"region:us"
] | williamliu | null | null | 0 | 0 | 2023-10-28T17:22:17 | Entry not found | 15 | [
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] |
AurumnPegasus/AurumnPegasus | 2023-10-28T17:43:57.000Z | [
"region:us"
] | AurumnPegasus | null | null | 0 | 0 | 2023-10-28T17:29:23 | ---
dataset_info:
features:
- name: context
sequence: string
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
splits:
- name: train
num_bytes: 132102296
num_examples: 2649
download_size: 26192269
dataset_size: 132102296
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "AurumnPegasus"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 536 | [
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Maldoju/Roddier | 2023-10-28T17:40:19.000Z | [
"region:us"
] | Maldoju | null | null | 0 | 0 | 2023-10-28T17:40:19 | Entry not found | 15 | [
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] |
akkasi/dutch_social | 2023-10-28T18:21:48.000Z | [
"region:us"
] | akkasi | null | null | 0 | 0 | 2023-10-28T18:21:45 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: text
dtype: string
- name: labels
sequence: float64
- name: label2idx
dtype: string
- name: idx2label
dtype: string
splits:
- name: train
num_bytes: 196538058
num_examples: 162805
- name: test
num_bytes: 65499632
num_examples: 54268
download_size: 24975837
dataset_size: 262037690
---
# Dataset Card for "dutch_social"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 663 | [
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] |
akkasi/EnglishNLPDataset | 2023-10-28T18:27:28.000Z | [
"region:us"
] | akkasi | null | null | 0 | 0 | 2023-10-28T18:27:25 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
dataset_info:
features:
- name: text
dtype: string
- name: labels
sequence: float64
- name: label2idx
dtype: string
- name: idx2label
dtype: string
splits:
- name: train
num_bytes: 16432106
num_examples: 80616
- name: validation
num_bytes: 2421791
num_examples: 10000
- name: test
num_bytes: 2456653
num_examples: 10000
download_size: 5458653
dataset_size: 21310550
---
# Dataset Card for "EnglishNLPDataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 781 | [
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biu-nlp/NERetrieve_IR | 2023-10-28T19:15:50.000Z | [
"region:us"
] | biu-nlp | null | null | 0 | 0 | 2023-10-28T18:36:36 | Entry not found | 15 | [
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] |
imessam/Python_code_assistant_with_prompt | 2023-10-28T20:07:49.000Z | [
"license:apache-2.0",
"region:us"
] | imessam | null | null | 1 | 0 | 2023-10-28T18:51:38 | ---
license: apache-2.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: prompt
dtype: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 650929658
num_examples: 429059
download_size: 111027031
dataset_size: 650929658
---
Formatted with a prompt template.
Modified from this dataset https://huggingface.co/datasets/Nan-Do/reason_code-search-net-python | 469 | [
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PsiPi/CodeAlpaca_20k_NoBlanks | 2023-10-29T06:06:30.000Z | [
"task_categories:text-generation",
"size_categories:10K<n<100K",
"license:cc-by-4.0",
"code",
"region:us"
] | PsiPi | null | null | 0 | 0 | 2023-10-28T19:33:39 | ---
license: cc-by-4.0
task_categories:
- text-generation
tags:
- code
size_categories:
- 10K<n<100K
---
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BRVC/SakiHatsuneRVC | 2023-10-28T19:52:10.000Z | [
"region:us"
] | BRVC | null | null | 0 | 0 | 2023-10-28T19:51:30 | Entry not found | 15 | [
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] |
quocanh34/new_nlu_tts3_with_correction | 2023-10-28T20:26:11.000Z | [
"region:us"
] | quocanh34 | null | null | 0 | 0 | 2023-10-28T20:24:35 | ---
dataset_info:
features:
- name: id
dtype: string
- name: audio
struct:
- name: array
sequence: float32
- name: path
dtype: string
- name: sampling_rate
dtype: int64
- name: pred_str
dtype: string
- name: pred_str_norm
dtype: string
- name: intent
dtype: string
- name: entities
list:
- name: filler
dtype: string
- name: type
dtype: string
- name: file
dtype: string
splits:
- name: train
num_bytes: 568308476
num_examples: 2139
download_size: 462240620
dataset_size: 568308476
---
# Dataset Card for "new_nlu_tts3_with_correction"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 778 | [
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open-llm-leaderboard/details_HuggingFaceH4__starchat-alpha | 2023-10-28T20:45:38.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | 0 | 0 | 2023-10-28T20:45:31 | ---
pretty_name: Evaluation run of HuggingFaceH4/starchat-alpha
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [HuggingFaceH4/starchat-alpha](https://huggingface.co/HuggingFaceH4/starchat-alpha)\
\ 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_HuggingFaceH4__starchat-alpha\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-28T20:45:27.557635](https://huggingface.co/datasets/open-llm-leaderboard/details_HuggingFaceH4__starchat-alpha/blob/main/results_2023-10-28T20-45-27.557635.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\": 0.003460570469798658,\n\
\ \"em_stderr\": 0.0006013962884271187,\n \"f1\": 0.07069001677852364,\n\
\ \"f1_stderr\": 0.0015946422775582956,\n \"acc\": 0.28758422975957604,\n\
\ \"acc_stderr\": 0.009108733644571125\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.003460570469798658,\n \"em_stderr\": 0.0006013962884271187,\n\
\ \"f1\": 0.07069001677852364,\n \"f1_stderr\": 0.0015946422775582956\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.024260803639120546,\n \
\ \"acc_stderr\": 0.004238007900001399\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.5509076558800315,\n \"acc_stderr\": 0.01397945938914085\n\
\ }\n}\n```"
repo_url: https://huggingface.co/HuggingFaceH4/starchat-alpha
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_28T20_45_27.557635
path:
- '**/details_harness|drop|3_2023-10-28T20-45-27.557635.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-28T20-45-27.557635.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_28T20_45_27.557635
path:
- '**/details_harness|gsm8k|5_2023-10-28T20-45-27.557635.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-28T20-45-27.557635.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_28T20_45_27.557635
path:
- '**/details_harness|winogrande|5_2023-10-28T20-45-27.557635.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-28T20-45-27.557635.parquet'
- config_name: results
data_files:
- split: 2023_10_28T20_45_27.557635
path:
- results_2023-10-28T20-45-27.557635.parquet
- split: latest
path:
- results_2023-10-28T20-45-27.557635.parquet
---
# Dataset Card for Evaluation run of HuggingFaceH4/starchat-alpha
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/HuggingFaceH4/starchat-alpha
- **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 [HuggingFaceH4/starchat-alpha](https://huggingface.co/HuggingFaceH4/starchat-alpha) 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_HuggingFaceH4__starchat-alpha",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-28T20:45:27.557635](https://huggingface.co/datasets/open-llm-leaderboard/details_HuggingFaceH4__starchat-alpha/blob/main/results_2023-10-28T20-45-27.557635.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": 0.003460570469798658,
"em_stderr": 0.0006013962884271187,
"f1": 0.07069001677852364,
"f1_stderr": 0.0015946422775582956,
"acc": 0.28758422975957604,
"acc_stderr": 0.009108733644571125
},
"harness|drop|3": {
"em": 0.003460570469798658,
"em_stderr": 0.0006013962884271187,
"f1": 0.07069001677852364,
"f1_stderr": 0.0015946422775582956
},
"harness|gsm8k|5": {
"acc": 0.024260803639120546,
"acc_stderr": 0.004238007900001399
},
"harness|winogrande|5": {
"acc": 0.5509076558800315,
"acc_stderr": 0.01397945938914085
}
}
```
### 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] | 7,253 | [
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Bsbell21/MFA_tweet_topics | 2023-10-28T20:52:50.000Z | [
"region:us"
] | Bsbell21 | null | null | 0 | 0 | 2023-10-28T20:52:48 | ---
dataset_info:
features:
- name: tweet
dtype: string
- name: topics
dtype: string
splits:
- name: train
num_bytes: 21732
num_examples: 121
download_size: 18513
dataset_size: 21732
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "MFA_tweet_topics"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 473 | [
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sanak/test | 2023-10-28T21:53:02.000Z | [
"region:us"
] | sanak | null | null | 0 | 0 | 2023-10-28T21:53:02 | Entry not found | 15 | [
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] |
kinianlo/wiki_20220301_en_nltk_uncased_phrases_clean | 2023-10-28T22:42:23.000Z | [
"region:us"
] | kinianlo | null | null | 0 | 0 | 2023-10-28T22:42:16 | ---
dataset_info:
features:
- name: phrase_id
dtype: uint32
- name: adj_id
dtype: uint32
- name: noun_id
dtype: uint32
- name: count
dtype: uint64
splits:
- name: train
num_bytes: 67986800
num_examples: 3399340
download_size: 41983842
dataset_size: 67986800
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "wiki_20220301_en_nltk_uncased_phrases_clean"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 587 | [
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satwant/ExpertMedQA | 2023-10-28T22:44:57.000Z | [
"license:cc-by-nc-4.0",
"region:us"
] | satwant | null | null | 1 | 0 | 2023-10-28T22:43:33 | ---
license: cc-by-nc-4.0
---
This dataset provides the complete ExpertMedQA dataset along with responses generated by BooksMed, highlighting the dataset's diversity and complexity, and providing a comprehensive overview of dataset questions. ExpertMedQA is a novel benchmark characterized by open-ended, expert-level clinical questions, which bridge this gap by requiring not only an understanding of the most recent clinical literature but also an analysis of the strength of the evidence presented. From current treatment guidelines to open-ended discussions requiring knowledge and analysis based on current clinical research studies, this dataset covers a wide range of topics. | 683 | [
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creativelybrainstorm/maqsa | 2023-10-28T22:50:59.000Z | [
"region:us"
] | creativelybrainstorm | null | null | 0 | 0 | 2023-10-28T22:43:52 | Entry not found | 15 | [
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Cyclcrclicly/tldne_testing | 2023-10-29T00:22:09.000Z | [
"region:us"
] | Cyclcrclicly | null | null | 0 | 0 | 2023-10-28T23:37:10 | Entry not found | 15 | [
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NandinhoVinicius/matheusaleixo | 2023-10-30T20:56:37.000Z | [
"region:us"
] | NandinhoVinicius | null | null | 0 | 0 | 2023-10-28T23:38:34 | Entry not found | 15 | [
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quocanh34/old_nlu_new_asr_v1 | 2023-10-28T23:39:11.000Z | [
"region:us"
] | quocanh34 | null | null | 0 | 0 | 2023-10-28T23:38:40 | ---
dataset_info:
features:
- name: id
dtype: string
- name: audio
struct:
- name: array
sequence: float32
- name: path
dtype: string
- name: sampling_rate
dtype: int64
- name: pred_str
dtype: string
- name: pred_str_norm
dtype: string
- name: intent
dtype: string
- name: entities
list:
- name: filler
dtype: string
- name: type
dtype: string
- name: file
dtype: string
splits:
- name: train
num_bytes: 568314186
num_examples: 2139
download_size: 462244745
dataset_size: 568314186
---
# Dataset Card for "old_nlu_new_asr_v1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 768 | [
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creativelybrainstorm/kjdhkjdhf | 2023-10-28T23:58:25.000Z | [
"region:us"
] | creativelybrainstorm | null | null | 0 | 0 | 2023-10-28T23:57:52 | Entry not found | 15 | [
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Kabatubare/medical-alpaca | 2023-10-29T00:17:30.000Z | [
"region:us"
] | Kabatubare | null | null | 0 | 0 | 2023-10-28T23:58:55 | Entry not found | 15 | [
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furry-br/AI-reference | 2023-10-29T01:44:09.000Z | [
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nolove9527/nolove | 2023-10-30T12:06:44.000Z | [
"region:us"
] | nolove9527 | null | null | 0 | 0 | 2023-10-29T02:21:24 | Entry not found | 15 | [
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venkat-srinivasan-nexusflow/cve_train_prompt_change_only | 2023-10-29T04:22:49.000Z | [
"region:us"
] | venkat-srinivasan-nexusflow | null | null | 0 | 0 | 2023-10-29T02:43:34 | ---
dataset_info:
features:
- name: Input
dtype: string
- name: Output
dtype: string
- name: Cot
dtype: string
splits:
- name: train
num_bytes: 396691
num_examples: 302
download_size: 119758
dataset_size: 396691
---
# Dataset Card for "cve_train_main"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 420 | [
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] |
yubo0306/fed_ja | 2023-10-29T04:26:57.000Z | [
"task_categories:conversational",
"language:ja",
"license:unknown",
"region:us"
] | yubo0306 | null | null | 0 | 0 | 2023-10-29T03:55:00 | ---
configs:
- config_name: default
data_files:
- split: train
path: fed_data.json
language:
- ja
pretty_name: fed_ja
task_categories:
- conversational
license: unknown
---
[FEDデータセット](http://shikib.com/fed_data.json)をGoogle Cloud Translate API v2で日本語化したデータセットです．
機械翻訳のため，一部dimensionはアノテーションとの整合性が適切ではない可能性があります．
使用するdimensionには注意してください．
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] |
PsiPi/PascalQnA100 | 2023-10-29T05:52:25.000Z | [
"task_categories:text-generation",
"size_categories:n<1K",
"language:en",
"license:cc-by-4.0",
"code",
"region:us"
] | PsiPi | null | null | 0 | 0 | 2023-10-29T04:04:23 | ---
license: cc-by-4.0
task_categories:
- text-generation
language:
- en
tags:
- code
pretty_name: pascal100
size_categories:
- n<1K
---
100 Pascal Q and A
60% with an input string of some kind | 195 | [
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Mutugi/Sparta_dataset | 2023-10-29T04:25:16.000Z | [
"region:us"
] | Mutugi | null | null | 0 | 0 | 2023-10-29T04:25:16 | Entry not found | 15 | [
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0.0233917236328125,
0.02923583984375,
0.040771484375,
0.00934600830078125,
0.0552978515625,
0.020721435546875,
-0.01129150390625,
0.017913818359375,
-0.030242919921875,
-0.0019140243530273438,
-0.0038604736328125,
0.02056884765625,
-0.068115234375
]
] |
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