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autoevaluate/autoeval-eval-adversarial_qa-adversarialQA-abccc5-96424146663 | 2023-10-20T18:21:58.000Z | [
"region:us"
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khang119966/news_qa_2 | 2023-10-20T18:49:48.000Z | [
"region:us"
] | khang119966 | null | null | 0 | 0 | 2023-10-20T18:49:10 | Entry not found | 15 | [
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khang119966/trivia_qa | 2023-10-20T19:09:43.000Z | [
"region:us"
] | khang119966 | null | null | 0 | 0 | 2023-10-20T19:09:12 | Entry not found | 15 | [
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khang119966/search_qa | 2023-10-20T19:12:20.000Z | [
"region:us"
] | khang119966 | null | null | 0 | 0 | 2023-10-20T19:11:19 | Entry not found | 15 | [
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khang119966/hotpot_qa | 2023-10-20T19:13:07.000Z | [
"region:us"
] | khang119966 | null | null | 0 | 0 | 2023-10-20T19:12:44 | Entry not found | 15 | [
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] |
oscorrea/autotrain-data-tt-scorer-class-balanced | 2023-10-20T20:09:38.000Z | [
"task_categories:text-classification",
"language:es",
"region:us"
] | oscorrea | null | null | 0 | 0 | 2023-10-20T20:08:11 | ---
language:
- es
task_categories:
- text-classification
---
# AutoTrain Dataset for project: tt-scorer-class-balanced
## Dataset Description
This dataset has been automatically processed by AutoTrain for project tt-scorer-class-balanced.
### Languages
The BCP-47 code for the dataset's language is es.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"feat_Palabras clave": "faldas largas juveniles",
"feat_Producto y sus caracter\u00edsticas": "{productName: Falda plisada con cintur\u00f3n para mujer, brand: Esprit, categories: ['/Ropa/Faldas/midi/', '/Ropa/Faldas/', '/Ropa/'], description: Una falda midi para que tus piernas ocupen el lugar que se merecen. Dise\u00f1amos esta falda plisada para mayor capacidad de movimiento en el d\u00eda a d\u00eda. Su el\u00e1stico en cintura la hace muy pr\u00e1ctica, mientras que su peque\u00f1o cintur\u00f3n es un toque sofisticado que favorece tu figura. Ll\u00e9vala con una prenda superior ajustada y prep\u00e1rate para brillar., properties: [{name: Falda plisada con cintur\u00f3n para mujer Caf\u00e9 L, characteristics: Caf\u00e9 L}, {name: Falda plisada con cintur\u00f3n para mujer Caf\u00e9 M, characteristics: Caf\u00e9 M}, {name: Falda plisada con cintur\u00f3n para mujer Caf\u00e9 S, characteristics: Caf\u00e9 S}, {name: Falda plisada con cintur\u00f3n para mujer Caf\u00e9 XL, characteristics: Caf\u00e9 XL}]}",
"target": 1,
"text": "faldas largas juveniles [SEP] {productName: Falda plisada con cintur\u00f3n para mujer, brand: Esprit, categories: ['/Ropa/Faldas/midi/', '/Ropa/Faldas/', '/Ropa/'], description: Una falda midi para que tus piernas ocupen el lugar que se merecen. Dise\u00f1amos esta falda plisada para mayor capacidad de movimiento en el d\u00eda a d\u00eda. Su el\u00e1stico en cintura la hace muy pr\u00e1ctica, mientras que su peque\u00f1o cintur\u00f3n es un toque sofisticado que favorece tu figura. Ll\u00e9vala con una prenda superior ajustada y prep\u00e1rate para brillar., properties: [{name: Falda plisada con cintur\u00f3n para mujer Caf\u00e9 L, characteristics: Caf\u00e9 L}, {name: Falda plisada con cintur\u00f3n para mujer Caf\u00e9 M, characteristics: Caf\u00e9 M}, {name: Falda plisada con cintur\u00f3n para mujer Caf\u00e9 S, characteristics: Caf\u00e9 S}, {name: Falda plisada con cintur\u00f3n para mujer Caf\u00e9 XL, characteristics: Caf\u00e9 XL}]}",
"feat_text2": "A continuaci\u00f3n hay una instrucci\u00f3n que describe una tarea. Escriba una respuesta que complete adecuadamente la solicitud.\n### Instrucci\u00f3n:\nDadas las palabras clave y el producto clasifica al producto con 1 para relacionado y 0 para no relacionado con las palabras clave.\nPalabras clave: faldas largas juveniles\nProducto: {productName: Falda plisada con cintur\u00f3n para mujer, brand: Esprit, categories: ['/Ropa/Faldas/midi/', '/Ropa/Faldas/', '/Ropa/'], description: Una falda midi para que tus piernas ocupen el lugar que se merecen. Dise\u00f1amos esta falda plisada para mayor capacidad de movimiento en el d\u00eda a d\u00eda. Su el\u00e1stico en cintura la hace muy pr\u00e1ctica, mientras que su peque\u00f1o cintur\u00f3n es un toque sofisticado que favorece tu figura. Ll\u00e9vala con una prenda superior ajustada y prep\u00e1rate para brillar., properties: [{name: Falda plisada con cintur\u00f3n para mujer Caf\u00e9 L, characteristics: Caf\u00e9 L}, {name: Falda plisada con cintur\u00f3n para mujer Caf\u00e9 M, characteristics: Caf\u00e9 M}, {name: Falda plisada con cintur\u00f3n para mujer Caf\u00e9 S, characteristics: Caf\u00e9 S}, {name: Falda plisada con cintur\u00f3n para mujer Caf\u00e9 XL, characteristics: Caf\u00e9 XL}]}\n### Respuesta:\n1"
},
{
"feat_Palabras clave": "Beb\u00e9s Juguetes para Ni\u00f1as",
"feat_Producto y sus caracter\u00edsticas": "{productName: Juguete para Beb\u00e9s Linkimals B\u00faho Ojos Luminosos - Fisher Price, brand: Fisher Price, categories: [/Jugueteria/Juguetes de Bebe/Didacticos/, /Jugueteria/Juguetes de Bebe/, /Jugueteria/], description: La hora de jugar es un verdadero ulular con el Linkimals B\u00faho Ojos Luminosos de Fisher Price. Mientras los peque\u00f1os presionan los botones de la barriga del b\u00faho y lo rotan de lado a lado, este expresivo amigo se ilumina con colores brillantes y m\u00e1s de 90 canciones, sonidos y frases para ense\u00f1ar el alfabeto, contar del 1 hasta el 20, el clima, las emociones y mucho m\u00e1s. Encuentra en Pepe Ganga una gran variedad de juguetes para beb\u00e9s y sorprende a los m\u00e1s peque\u00f1os de casa con este lindo B\u00faho Ojos Luminosos de Fisher Price. Recomendado para beb\u00e9s de 18 meses en adelante., properties: [{name: Juguete para Beb\u00e9s Linkimals B\u00faho Ojos Luminosos - Fisher Price, characteristics: Juguete para Beb\u00e9s Linkimals B\u00faho Ojos Luminosos - Fisher Price}]}",
"target": 1,
"text": "Beb\u00e9s Juguetes para Ni\u00f1as [SEP] {productName: Juguete para Beb\u00e9s Linkimals B\u00faho Ojos Luminosos - Fisher Price, brand: Fisher Price, categories: [/Jugueteria/Juguetes de Bebe/Didacticos/, /Jugueteria/Juguetes de Bebe/, /Jugueteria/], description: La hora de jugar es un verdadero ulular con el Linkimals B\u00faho Ojos Luminosos de Fisher Price. Mientras los peque\u00f1os presionan los botones de la barriga del b\u00faho y lo rotan de lado a lado, este expresivo amigo se ilumina con colores brillantes y m\u00e1s de 90 canciones, sonidos y frases para ense\u00f1ar el alfabeto, contar del 1 hasta el 20, el clima, las emociones y mucho m\u00e1s. Encuentra en Pepe Ganga una gran variedad de juguetes para beb\u00e9s y sorprende a los m\u00e1s peque\u00f1os de casa con este lindo B\u00faho Ojos Luminosos de Fisher Price. Recomendado para beb\u00e9s de 18 meses en adelante., properties: [{name: Juguete para Beb\u00e9s Linkimals B\u00faho Ojos Luminosos - Fisher Price, characteristics: Juguete para Beb\u00e9s Linkimals B\u00faho Ojos Luminosos - Fisher Price}]}",
"feat_text2": "A continuaci\u00f3n hay una instrucci\u00f3n que describe una tarea. Escriba una respuesta que complete adecuadamente la solicitud.\n### Instrucci\u00f3n:\nDadas las palabras clave y el producto clasifica al producto con 1 para relacionado y 0 para no relacionado con las palabras clave.\nPalabras clave: Beb\u00e9s Juguetes para Ni\u00f1as\nProducto: {productName: Juguete para Beb\u00e9s Linkimals B\u00faho Ojos Luminosos - Fisher Price, brand: Fisher Price, categories: [/Jugueteria/Juguetes de Bebe/Didacticos/, /Jugueteria/Juguetes de Bebe/, /Jugueteria/], description: La hora de jugar es un verdadero ulular con el Linkimals B\u00faho Ojos Luminosos de Fisher Price. Mientras los peque\u00f1os presionan los botones de la barriga del b\u00faho y lo rotan de lado a lado, este expresivo amigo se ilumina con colores brillantes y m\u00e1s de 90 canciones, sonidos y frases para ense\u00f1ar el alfabeto, contar del 1 hasta el 20, el clima, las emociones y mucho m\u00e1s. Encuentra en Pepe Ganga una gran variedad de juguetes para beb\u00e9s y sorprende a los m\u00e1s peque\u00f1os de casa con este lindo B\u00faho Ojos Luminosos de Fisher Price. Recomendado para beb\u00e9s de 18 meses en adelante., properties: [{name: Juguete para Beb\u00e9s Linkimals B\u00faho Ojos Luminosos - Fisher Price, characteristics: Juguete para Beb\u00e9s Linkimals B\u00faho Ojos Luminosos - Fisher Price}]}\n### Respuesta:\n1"
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"feat_Palabras clave": "Value(dtype='string', id=None)",
"feat_Producto y sus caracter\u00edsticas": "Value(dtype='string', id=None)",
"target": "ClassLabel(names=['0', '1'], id=None)",
"text": "Value(dtype='string', id=None)",
"feat_text2": "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 | 6055 |
| valid | 1515 |
| 8,103 | [
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sandeep12345/Llama_text_class | 2023-10-20T21:23:47.000Z | [
"region:us"
] | sandeep12345 | null | null | 0 | 0 | 2023-10-20T20:43:32 | Entry not found | 15 | [
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euclaise/tex-stackexchange | 2023-10-20T21:20:52.000Z | [
"license:cc-by-sa-4.0",
"region:us"
] | euclaise | null | null | 0 | 0 | 2023-10-20T21:01:31 | ---
dataset_info:
features:
- name: parent_url
dtype: string
- name: parent_score
dtype: string
- name: parent_body
dtype: string
- name: parent_user
dtype: string
- name: parent_title
dtype: string
- name: accepted
dtype: bool
- name: body
dtype: string
- name: score
dtype: string
- name: user
dtype: string
- name: answer_id
dtype: string
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 505025688
num_examples: 190807
download_size: 221660047
dataset_size: 505025688
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
license: cc-by-sa-4.0
---
# Dataset Card for "tex-stackexchange"
This is a dump of [the TeX StackExchange community](https://tex.stackexchange.com/), converted to markdown.
Data from [The StackExchange data dump](https://archive.org/details/stackexchange), 2023-09-12 release.
Posts where the *questions* included images are excluded. Images in the answers are stripped out. | 1,044 | [
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mayhemfa/Pear_DiaMOS | 2023-10-20T21:18:10.000Z | [
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Sm4sh1ngp1x3l/8l1nx | 2023-10-20T21:37:37.000Z | [
"region:us"
] | Sm4sh1ngp1x3l | null | null | 0 | 0 | 2023-10-20T21:37:02 | Entry not found | 15 | [
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Augusto777/mascotas | 2023-10-20T23:27:45.000Z | [
"region:us"
] | Augusto777 | null | null | 0 | 0 | 2023-10-20T23:02:35 | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': gato
'1': perro
splits:
- name: train
num_bytes: 8003519.0
num_examples: 837
- name: validation
num_bytes: 8464732.0
num_examples: 19
- name: test
num_bytes: 9318416.0
num_examples: 17
download_size: 25151561
dataset_size: 25786667.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
# Dataset Card for "mascotas"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 759 | [
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manu/IFTEval | 2023-10-24T08:02:49.000Z | [
"arxiv:2310.14103",
"region:us"
] | manu | null | null | 0 | 0 | 2023-10-21T00:53:13 | # Revisiting Instruction Fine-tuned Model Evaluation to Guide Industrial Applications
This folder contains the data used in the EMNLP 2023 paper [Revisiting Instruction Fine-tuned Model Evaluation to Guide Industrial Applications](https://arxiv.org/abs/2310.14103).
Code is available on Github at https://github.com/ManuelFay/IFTEval.
## Abstract
Instruction Fine-Tuning (IFT) is a powerful paradigm that strengthens the zero-shot capabilities of Large Language Models (LLMs), but in doing so induces new evaluation metric requirements. We show LLM-based metrics to be well adapted to these requirements, and leverage them to conduct an investigation of task-specialization strategies, quantifying the trade-offs that emerge in practical industrial settings. Our findings offer practitioners actionable insights for real-world IFT model deployment.
## Citation
If you use this code for your research, please cite our paper:
```
@misc{faysse2023revisiting,
title={Revisiting Instruction Fine-tuned Model Evaluation to Guide Industrial Applications},
author={Manuel Faysse and Gautier Viaud and Céline Hudelot and Pierre Colombo},
year={2023},
eprint={2310.14103},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
## Data
To facilitate data hosting and distribution, datasets are hosted on the HuggingFace hub.
These data include the instruction training sets used for both parts of the paper, the raw results of all experiments, and the final aggregated results.
The datasets are available at the following link:
https://huggingface.co/datasets/manu/IFTEval
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hotamago/kalapa-2023 | 2023-10-21T01:30:31.000Z | [
"region:us"
] | hotamago | null | null | 0 | 0 | 2023-10-21T01:28:40 | Entry not found | 15 | [
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Jorachi/Daiki | 2023-10-21T21:56:51.000Z | [
"region:us"
] | Jorachi | null | null | 0 | 0 | 2023-10-21T02:43:17 | Entry not found | 15 | [
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tuandunghcmut/Text_based_Person_Search | 2023-10-21T03:46:52.000Z | [
"region:us"
] | tuandunghcmut | null | null | 0 | 0 | 2023-10-21T03:46:52 | Entry not found | 15 | [
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katielink/race-based_medicine_questions | 2023-10-21T04:05:03.000Z | [
"license:cc-by-4.0",
"medical",
"region:us"
] | katielink | null | null | 0 | 0 | 2023-10-21T04:03:21 | ---
license: cc-by-4.0
tags:
- medical
---
Questions used in the paper, Omiye et al (2023) ["Large language models propagate race-based medicine"](https://www.nature.com/articles/s41746-023-00939-z) | 199 | [
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deepghs/waifuc_unittest | 2023-10-31T08:24:02.000Z | [
"license:mit",
"region:us"
] | deepghs | null | null | 0 | 0 | 2023-10-21T04:19:01 | ---
license: mit
---
Unittest files for [deepghs/waifuc](https://github.com/deepghs/waifuc). | 93 | [
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] |
difraud/difraud | 2023-10-21T05:46:50.000Z | [
"task_categories:text-classification",
"task_categories:zero-shot-classification",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"language:en",
"license:mit",
"fraud-detection",
"deception-detection",
"phishing",
"fake-news",
"benchmark",
"opinion-spam",
"multi-domain",
"region:us"
] | difraud | DIFrauD -- (Domain Independent Fraud Detection) is a corpus of deceptive and truthful texts from 7 domains:
"fake_news",
"job_scams",
"phishing",
"political_statements",
"product_reviews",
"sms",
"twitter_rumours"
To load a specific domain, pass it as the "name" parameter to load_dataset() | TODO: Add citation here | 0 | 0 | 2023-10-21T05:16:53 | ---
language:
- en
license:
- mit
size_categories:
- 10K<n<100K
multilinguality:
- monolingual
task_categories:
- text-classification
- zero-shot-classification
pretty_name: DIFrauD - Domain-Independent Fraud Detection benchmark
tags:
- fraud-detection
- deception-detection
- phishing
- fake-news
- benchmark
- opinion-spam
- multi-domain
configs:
- config_name: fake_news
data_files:
- split: train
path: fake_news/train.jsonl
- split: test
path: fake_news/test.jsonl
- split: validation
path: fake_news/validation.jsonl
- config_name: job_scams
data_files:
- split: train
path: job_scams/train.jsonl
- split: test
path: job_scams/test.jsonl
- split: validation
path: job_scams/validation.jsonl
- config_name: phishing
data_files:
- split: train
path: phishing/train.jsonl
- split: test
path: phishing/test.jsonl
- split: validation
path: phishing/validation.jsonl
- config_name: political_statements
data_files:
- split: train
path: political_statements/train.jsonl
- split: test
path: political_statements/test.jsonl
- split: validation
path: political_statements/validation.jsonl
- config_name: product_reviews
data_files:
- split: train
path: product_reviews/train.jsonl
- split: test
path: product_reviews/test.jsonl
- split: validation
path: product_reviews/validation.jsonl
- config_name: sms
data_files:
- split: train
path: sms/train.jsonl
- split: test
path: sms/test.jsonl
- split: validation
path: sms/validation.jsonl
- config_name: twitter_rumours
data_files:
- split: train
path: twitter_rumours/train.jsonl
- split: test
path: twitter_rumours/test.jsonl
- split: validation
path: twitter_rumours/validation.jsonl
---
# DIFrauD - Domain Independent Fraud Detection Benchmark
Domain Independent Fraud Detection Benchmark is a labeled corpus containing over 95,854 samples of deceitful
and truthful texts from a number of independent domains and tasks. Deception, however, can be different --
in this corpus we made sure to gather strictly real examples of deception that are intentionally malicious
and cause real harm, despite them often having very little in common. Covering seven domains, this benchmark
is designed to serve as a representative slice of the various security challenges that remain open problems
today.
## DATASET
The entire dataset contains 95854 samples, 37282 are deceptive and 58572 non-deceptive.
There are 7 independent domains in the dataset.
Each task is (or has been converted to) a binary classification problem where `y` is an indicator of deception.
1) **Phishing** (2020 Email phishing benchmark with manually labeled emails)
*- total: 15272 deceptive: 6074 non-deceptive: 9198*
2) **Fake News** (News Articles)
*- total: 20456 deceptive: 8832 non-deceptive: 11624*
3) **Political Statements** (Claims and statements by politicians and other entities, made from Politifact by relabeling LIAR)
*- total: 12497 deceptive: 8042 non-deceptive: 4455*
4) **Product Reviews** (Amazon product reviews)
*- total: 20971 deceptive: 10492 non-deceptive: 10479*
5) **Job Scams** (Job postings on an online board)
*- total: 14295 deceptive: 599 non-deceptive: 13696*
6) **SMS** (combination of SMS Spam from UCI repository and SMS Phishing datasets)
*- total: 6574 deceptive: 1274 non-deceptive: 5300*
7) **Twitter Rumours** (Collection of rumours from PHEME dataset, covers multiple topics)
*- total: 5789 deceptive: 1969 non-deceptive: 3820*
Each one was constructed from one or more datasets. Some tasks were not initially binary and had to be relabeled.
The inputs vary wildly both stylistically and syntactically, as well as in terms of the goal of deception
(or absence of thereof) being performed in the context of each dataset. Nonetheless, all seven datasets contain a significant
fraction of texts that are meant to deceive the person reading them one way or another.
Each subdirectory/config contains the domain/individual dataset split into three files:
`train.jsonl`, `test.jsonl`, and `validation.jsonl`
that contain train, test, and validation sets, respectively.
The splits are:
-- train=80%
-- test=10%
-- valid=10%
The sampling process was random with seed=42. It was stratified with respect to `y` (label) for each domain.
### Fields
Each `jsonl` file has two fields (columns): `text` (string) and `label` (integer)
`text` contains a statement or a claim that is either deceptive or thruthful.
It is guaranteed to be valid unicode, less than 1 million characters, and contains no empty entries or non-values.
`label` answers the question whether text is deceptive: `1` means yes, it is deceptive, `0` means no,
the text is not deceptive (it is truthful).
### Processing and Cleaning
Each dataset has been cleaned using Cleanlab. Non-english entries, erroneous (parser error) entries, empty entries, duplicate entries,
entries of length less than 2 characters or exceeding 1000000 characters were all removed.
Labels were manually curated and corrected in cases of clear error.
Whitespace, quotes, bulletpoints, unicode is normalized.
### Layout
The directory layout of `difraud` is like so:
``
difraud
fake_news/
train.jsonl
test.jsonl
validation.jsonl
README.md
...
...
...
sms/
train.jsonl
test.jsonl
validation.jsonl
README.md
README.md
LICENSE.txt
``
### Documentation
Primary documentation is this README file. Each dataset's directory contains a `README.md` file with additional details.
The contents of these files are also included at the end of this document in the Appendix.
LICENSE.txt contains the MIT license this dataset is distributed under.
## CHANGES
This dataset is a successor of [the GDD dataset](https://zenodo.org/record/6512468).
Noteable changes from GDD are:
1) Addition of SMS and Twitter Rumours datasets, making it 7 deception datasets from different domains in total
2) Re-labeling of Political Statements dataset using a scheme that better fits with prior published work that used it and is stricter in terms of non-deceptive statement criteria of acceptance (see the README file specific to the dataset within its directory)
3) Job Scams datasets' labeles were previously inverted, with ~13500 labeled as deceptive (is_deceptive=True) and ~600 as non-deceptive. This could lead to potential issues with using metrics such as f1-score, which for binary classification is computed for the class considered to be positive. This issue has been addressed and the deceptive texts are labeled as 1 (e.g. positive or True) while non-deceptive as 0 (e.g. negative or False)
4) All datasets have been processed using Cleanlab, with problematic samples maually examined and issues addressed if needed. See the details in each of the individual datasets README files.
5) All datasets now come in 2 formats: the entirety of the data in a single jsonl file located in the `data/` subdirectory of each dataset, and a standard train-test-valid stratified split of 80-10-10, in 3 separate jsonl files.
6) All datasets have two fields: "text" (string) and "label" (integer, 0 or 1 - 0 indicates that the text is non-deceptive, 1 means it is deceptive)
7) '\n' has been normalized to ' ' for all datasets as it causes issues with BERT's tokenizer in some cases (and to be in line with general whitespace normalization). Broken unicode has been fixed. Whitespace, quotations, and bullet points were normalized. Text is limited to 1,000,000 characters in length and guaranteed to be non-empty. Duplicates within the the same dataset (even in text only) were dropped, so were empty and None values.
## LICENSE
This dataset is published under the MIT license and can be used and modified by anyone free of charge.
See LICENSE.txt file for details.
## CITING
If you found this dataset useful in your research, please consider citing it as:
TODO: ADD our paper reference
## REFERENCES
Original GDD paper:
@inproceedings{10.1145/3508398.3519358,
author = {Zeng, Victor and Liu, Xuting and Verma, Rakesh M.},
title = {Does Deception Leave a Content Independent Stylistic Trace?},
year = {2022},
isbn = {9781450392204},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3508398.3519358},
doi = {10.1145/3508398.3519358},
abstract = {A recent survey claims that there are em no general linguistic cues for deception. Since Internet societies are plagued with deceptive attacks such as phishing and fake news, this claim means that we must build individual datasets and detectors for each kind of attack. It also implies that when a new scam (e.g., Covid) arrives, we must start the whole process of data collection, annotation, and model building from scratch. In this paper, we put this claim to the test by building a quality domain-independent deception dataset and investigating whether a model can perform well on more than one form of deception.},
booktitle = {Proceedings of the Twelfth ACM Conference on Data and Application Security and Privacy},
pages = {349–351},
numpages = {3},
keywords = {domain-independent deception detection, dataset quality/cleaning},
location = {Baltimore, MD, USA},
series = {CODASPY '22}
}
## APPENDIX: Dataset and Domain Details
This section describes each domain/dataset in greater detail.
### FAKE NEWS
Fake News used WELFake as a basis. The WELFake dataset combines 72,134 news articles from four pre-existing datasets
(Kaggle, McIntire, Reuters, and BuzzFeed Political). The dataset was cleaned of data leaks in the form of citations of
often reputable sources, such as "[claim] (Reuters)". It contains 35,028 real news articles and 37,106 fake news articles.
We found a number of out-of-domain statements that are clearly not relevant to news, such as "Cool", which is a potential
problem for transfer learning as well as classification.
The training set contains 16364 samples, the validation and the test sets have 2064 and 2064 samles, respectively.
### JOB SCAMS
The Employment Scam Aegean Dataset, henceforth referred to as the Job Scams dataset, consisted of 17,880 human-annotated job listings of
job descriptions labeled as fraudulent or not.
#### Relabeling
The original Job Labels dataset had the labels inverted when released. The problem is now fixed, the labels are correct.
#### Cleaning
It was cleaned by removing all HTML tags, empty descriptions, and duplicates.
The final dataset is heavily imbalanced, with 599 deceptive and 13696 non-deceptive samples out of the 14295 total.
### PHISHING
This dataset consists of various phishing attacks as well as benign emails collected from real users.
The training set contains 12217 samples, the validation and the test sets have 1527 and 1528 samples, respectively.
### POLITICAL STATEMENTS
This corpus was created from the Liar dataset which consists of political statements made by US speakers assigned
a fine-grain truthfulness label by PolitiFact.
#### Labeling
The primary difference is the change in the re-labeling scheme when converting the task from multiclass to binary.
#### Old scheme
We use the claim field as the text and map labels “pants-fire,” “false,”
“barely-true,” to deceptive and “half-true,” “mostly-true,” and “true”
to non-deceptive, resulting in 5,669 deceptive and 7,167 truthful
statements.
#### New scheme
Following
*Upadhayay, B., Behzadan, V.: "Sentimental liar: Extended corpus and deep learning models for fake claim classification" (2020)*
and
*Shahriar, Sadat, Arjun Mukherjee, and Omprakash Gnawali. "Deception Detection with Feature-Augmentation by Soft Domain Transfer."
International Conference on Social Informatics. Cham: Springer International Publishing, 2022.*
we map the labels map labels “pants-fire,” “false,”
“barely-true,” **and “half-true,”** to deceptive; the labels "mostly-true" and "true" are mapped to non-deceptive.
The statements that are only half-true are now considered to be deceptive, making the criterion for statement being non-deceptive stricter:
now 2 out of 6 labels map to non-deceptive and 4 map to deceptive.
#### Cleaning
The dataset has been cleaned using cleanlab with visual inspection of problems found. Partial sentences, such as "On Iran nuclear deal",
"On inflation", were removed. Text with large number of errors induced by a parser were also removed.
Statements in language other than English (namely, Spanish) were also removed.
The training set contains 9997 samples, the validation and the test sets have 1250 samples each in them.
### PRODUCT REVIEWS
The dataset is produced from English Amazon Reviews labeled as either real or fake, relabeled as deceptive and non-deceptive respectively.
The reviews cover a variety of products with no particular product dominating the dataset. Although the dataset authors filtered out
non-English reviews, through outlier detection we found that the dataset still contains reviews in Spanish and other languages.
Problematic label detection shows that over 6713 samples are potentially mislabeled; since this technique is error-prone,
we visually examine 67 reviews that are found to be the largest potential sources of error (the top percentile) and confirm that
most of them appear to be mislabeled. The final dataset of 20,971 reviews is evenly balanced with 10,492 deceptive and 10,479
non-deceptive samples.
The training set contains 16776 samples, the validation and the test sets have 2097 and 2098 samples, respectively.
### SMS
This dataset was created from the SMS Spam Collection and SMS Phishing Dataset for Machine Learning and Pattern Recognition,
which contained 5,574 and 5,971 real English SMS messages, respectively. As these two datasets overlap, after de-duplication,
the final dataset is made up of 6574 texts released by a private UK-based wireless operator; 1274 of them are deceptive,
and the remaining 5300 are not.
The training set contains 5259 samples, the validation and the test sets have 657 and 658 samples,
respectively.
### TWITTER RUMOURS
This deception dataset was created using PHEME dataset from
https://figshare.com/articles/dataset/PHEME_dataset_of_rumours_and_non-rumours/4010619/1
was used in creation of this dataset. We took source tweets only, and ignored replies to them.
We used source tweet's label as being a rumour or non-rumour to label it as deceptive or non-deceptive.
The training set contains 4631 samples, the validation and the test sets have 579 samples each | 14,620 | [
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LeonardoEsteves/teste | 2023-10-21T05:19:40.000Z | [
"region:us"
] | LeonardoEsteves | null | null | 0 | 0 | 2023-10-21T05:17:11 | Entry not found | 15 | [
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bobotran/spoiler-matching | 2023-10-22T06:51:14.000Z | [
"task_categories:sentence-similarity",
"size_categories:100K<n<1M",
"language:en",
"license:cc-by-sa-3.0",
"region:us"
] | bobotran | null | null | 0 | 0 | 2023-10-21T05:26:50 | ---
license: cc-by-sa-3.0
task_categories:
- sentence-similarity
language:
- en
size_categories:
- 100K<n<1M
---
# Episode-Specific Spoilers
This is the spoiler matching dataset as presented in Spoiler Detection as Semantic Text Matching. It consists of comments discussing episodes from various TV shows. Unlike other spoiler datasets, this dataset assigns an episode number (and show name) to each comment, enabling matching to specific episodes and very fine-grain spoiler detection. This dataset also includes an episode summary for each (show, episode) pair. For a given show, the task is to rank the summaries for each comment.
# Usage
See the [Spoiler Matching repository](https://github.com/bobotran/spoiler-matching) for examples on how to train a spoiler matching model on this dataset.
# Annotation
522,991 comments from 13 TV shows were scraped from episode discussion threads on Reddit. Of these comments, some are actually *relevant* to their respective episode discussion and others are *irrelevant*. A subset of these comments (11,032) were hand-labeled as *irrelevant* or *relevant*. This hand-labeled dataset was used to train an irrelevant/relevant classifier which auto-labeled the remaining comments. All *relevant* comments were formatted into the `matching` dataset.
# Details
The `matching` folder contains the spoiler matching dataset, and the `filtering` folder contains intermediate data from the auto-labeling step.
## matching/
This folder contains the datasets for training spoiler matching models. All comments in these files were determined to be *relevant*, whether that was done by a human annotator or the auto-labeler. `summaries.json` contains the summary for each episode. `summaries.json` and `test.json` are the same across `with_autolabels` and `handlabeled_only`.
### matching/with_autolabels/
The `with_autolabels` folder contains the main dataset. `test.json` and `val.json` consist of hand-labeled *relevants* while `train.json` contains auto-labeled *relevants*. To measure the performance of spoiler matching models on unseen shows, `test.json` was constructed such that it consists of comments from 4 TV shows which are neither present in `val.json` nor `train.json`.
### matching/handlabeled_only/
The `handlabeled_only` folder shares the same `test.json` with `matching/with_autolabels/`, but `train.json` and `val.json` are split 80-20 from `matching/with_autolabels/val.json` respectively.
## filtering/
This folder contains data from the auto-labeling step.
### filtering/handlabeled
This folder contains the dataset used to train the autolabeler. Comments with a `1` in the first column were hand-labeled as `irrelevant`. Comments with a `0` in the first column were hand-labeled as `relevant`. The last two columns are the show name and episode number respectively, which are not used during this step.
### filtering/unlabeled
The unlabeled comments were split into two chunks to make them more manageable to load into memory during inference. All comments have a `-1` in the first column to represent that they are unlabeled.
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labDaVinci/labDaVinci_erlenmeyerFlask_imageSet_v1.0 | 2023-10-21T05:51:06.000Z | [
"region:us"
] | labDaVinci | null | null | 0 | 0 | 2023-10-21T05:50:28 | Entry not found | 15 | [
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ma2za/career_urls | 2023-10-21T05:54:20.000Z | [
"region:us"
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flexavicospain/Flexavico | 2023-10-21T06:11:36.000Z | [
"region:us"
] | flexavicospain | null | null | 0 | 0 | 2023-10-21T06:10:43 | In the pursuit of better health and well-being, finding the right supplements is paramount. One name that's been making waves in the world of wellness is **Flexavico Pills**. If you're wondering how Flexavico Pills can boost your vitality, relieve discomfort, or enhance your overall quality of life, you've come to the right place.
_We've prepared a detailed guide to help you navigate the world of Flexavico Pills. From its benefits and uses to personal experiences and expert insights, we've got it all covered. So, let's dive into the world of Flexavico Pills and unlock its secrets for a healthier you._
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**Flexavico Pills** is a game changer when it comes to wellness. Its unique blend of ingredients is carefully crafted to target various aspects of your health, making it a versatile addition to your daily routine.
[.png)](https://www.glitco.com/es-flexavico)
### **Unlocking the Benefits**
➣ Boosts Energy Levels: Flexavico Pills are known to provide a natural energy boost, helping you stay active and focused throughout the day.
➣ Relieves Discomfort: It's a great companion for those dealing with discomfort, as it offers relief without any harsh side effects.
➣ Supports Joint Health: If you're looking to improve joint mobility and reduce joint-related issues, Flexavico Pills are worth considering.
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➣ Promotes Relaxation: Stress and anxiety can take a toll on your well-being. Flexavico Pills have ingredients that help promote relaxation and reduce stress.
Incorporating Flexavico Pills into Your Routine
--------------------------------------------------
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What the Experts Say?
---------------------
To provide you with the most accurate and trustworthy information, we've consulted with experts in the field. Here are some insights from healthcare professionals:
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Real Stories, Real Results
--------------------------
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➣ **John's Journey** : John, a 45-year-old office worker, shares his experience with Flexavico Pills. "I used to feel tired all the time, and my joints were constantly bothering me. Since I started taking Flexavico Pills, I have more energy, and my joint discomfort has significantly reduced."
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_[.png)](https://www.glitco.com/es-flexavico)_
Flexavico Pills: Frequently Asked Questions
-------------------------------------------
**Q: Are Flexavico Pills safe to use?** A: Yes, Flexavico Pills are generally safe to use. However, it's always a good idea to consult with a healthcare professional before adding any new supplement to your routine, especially if you have underlying health conditions or are taking **medications.**
**Q: How long does it take to see results with Flexavico Pills?** A: The time it takes to experience results can vary from person to person. Some individuals may notice changes in a few weeks, while others might take a bit longer. Consistency in taking the supplement is key.
**Q: Can Flexavico Pills be taken with other medications?** A: It's best to consult with your healthcare provider if you are taking other medications. They can provide guidance on potential interactions and the best way to incorporate Flexavico Pills into your routine.
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**Q: Can I take Flexavico Pills on an empty stomach?** A: Flexavico Pills are generally well-tolerated on an empty stomach, but it's advisable to follow the dosage instructions provided on the packaging.
**Q: Where can I purchase Flexavico Pills?** A: Flexavico Pills are available at reputable health stores and online retailers. Ensure you purchase from a trusted source to guarantee product authenticity.
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Conclusion
----------
Flexavico Pills offer a promising path to enhanced wellness. With the power to boost energy, relieve discomfort, support joint health, enhance the immune system, and promote relaxation, they have become a valuable addition to many people's daily lives. It's important to consult with a healthcare professional before starting any new supplement, but the potential benefits of **Flexavico Pills** are certainly worth considering. | 6,989 | [
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labDaVinci/labDaVinci_retortStand_imageSet_v1.0 | 2023-10-21T06:48:33.000Z | [
"region:us"
] | labDaVinci | null | null | 0 | 0 | 2023-10-21T06:39:27 | Entry not found | 15 | [
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] |
yuyijiong/Book_Summary_Chinese | 2023-10-21T08:35:15.000Z | [
"task_categories:text-generation",
"size_categories:1K<n<10K",
"language:zh",
"license:cc-by-nc-4.0",
"region:us"
] | yuyijiong | null | null | 0 | 0 | 2023-10-21T08:30:30 | ---
license: cc-by-nc-4.0
task_categories:
- text-generation
language:
- zh
size_categories:
- 1K<n<10K
---
# 中文图书总结数据集
每个样本包含：
<font color=red> 图书的一个章节、此章节的总结、图书名字</font>，可以训练模型总结长文本的能力。\
数据主要来自较为著名的中文版小说。
| 207 | [
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Potino/nyt | 2023-10-21T08:34:17.000Z | [
"region:us"
] | Potino | null | null | 0 | 0 | 2023-10-21T08:34:17 | Entry not found | 15 | [
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] |
acozma/imagenet-1k-rand_canny_colorgrid | 2023-10-31T08:53:51.000Z | [
"region:us"
] | acozma | null | null | 0 | 0 | 2023-10-21T08:39:14 | ---
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: downsample
dtype: int64
- name: grid_size
dtype: int64
- name: high_threshold
dtype: int64
- name: low_threshold
dtype: int64
- name: sigma
dtype: float64
splits:
- name: train
num_bytes: 189495511555.0
num_examples: 500000
download_size: 14160878993
dataset_size: 189495511555.0
---
# Dataset Card for "imagenet-1k-rand_canny_colorgrid"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 801 | [
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wanadzhar913/crawl-vikatan-my | 2023-10-21T11:41:57.000Z | [
"language:ta",
"license:apache-2.0",
"region:us"
] | wanadzhar913 | null | null | 0 | 0 | 2023-10-21T08:55:43 | ---
license: apache-2.0
language:
- ta
---
#### **TLDR**
- website: [Vikatan-MY](https://www.vikatan.com/topics/malaysia)
- num. of webpages scraped: 65 (7 locked behind paywall)
- link to dataset: https://huggingface.co/datasets/wanadzhar913/crawl-vikatan-my/resolve/main/vikatan-my-scraped-data.jsonl
- date of scraping: 21st October 2023
- pull request: mesolitica/malaysian-dataset#353
- contributed to: https://github.com/mesolitica/malaysian-dataset | 455 | [
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TExtPhish/TExtPhish | 2023-10-21T09:09:59.000Z | [
"task_categories:text-classification",
"task_categories:sentence-similarity",
"language:en",
"license:cc-by-nc-nd-4.0",
"security",
"ML",
"NLP",
"sentiment",
"region:us"
] | TExtPhish | null | null | 0 | 0 | 2023-10-21T08:57:29 | ---
license: cc-by-nc-nd-4.0
task_categories:
- text-classification
- sentence-similarity
language:
- en
tags:
- security
- ML
- NLP
- sentiment
pretty_name: TExtPhish
extra_gated_heading: "You need to agree to share your contact information to access TExtPhish"
extra_gated_prompt: "The emails in the **TExtPhish** Email Collection Corpus are under the license of ***cc-by-nc-nd-4.0***, and their use is governed by the following agreements: \n - You agree to not distribute or reproduce any derivatives, in whole or in part, any document from the Collection. \n- You agree to not attempt to identify, or speculate on the identity of, any individual in **TExtPhish** Collection, even if that information is available from public sources.\n - Re-use of this data is also subject to Reddit API terms which includes: \n * not encouraging or promoting illegal activity. \n * not using this dataset with the intent of introducing any viruses, worms, defects, Trojan horses, malware, or any other items of a destructive nature. \n * no selling, leasing, or sublicensing this data whether for direct commercial or monetary gain. \n\nIn the event that End User violates the terms of this agreement, then upon notice from the dataset maintainers, end users shall cease use of the collection and destroy all copies of the collection and other documents that contain excerpts from the Collection.\n\nWe would like to keep track of this dataset users for statistics purposes (how many users and affiliations) and agreement only."
I agree to use TExtPhish dataset for non-commercial intended use ONLY: checkbox
extra_gated_button_content: "Acknowledge License"
viewer: False
---
# Dataset Card for TExtPhish
## Dataset Description
### Dataset Summary
This dataset card aims to describe the **TExtPhish** collection and its intended use.
### Languages
The current version only includes data samples in English, as spoken partially by Reddit users on the [r/Scams](https://www.reddit.com/r/Scams/comments/n00kg3/the_blackmail_email_scam_part_7/###) blackmail subreddits.
In the Future, we would like to explore more in different languages. Collaborators are encouraged to contact the authors to extend the current version with more diverse extortion emails in different languages.
## Dataset Structure
### Initial Data Collection and Sanitization
First, we select benign samples from the publicly available dataset, such as Enron and SpamAssassin.
We extract each email from email threads and tokenize personally sensitive information using name entity recognition, regular expression and synthetically replaced information.
Second, we collect extortion attacks from reddit posts |[r/Scams](https://www.reddit.com/r/Scams/comments/n00kg3/the_blackmail_email_scam_part_7/###) and botnet ransomware emails from |[Malware Traffic Analysis repository](https://www.malware-traffic-analysis.net).
We remove unecessary comment from the reddit thread and we only keep extortion emails.
To make the dataset challenging, we keep only the most semantically similar benign emails to the extortion attacks.
For semantic textual similarity, we first applied sentence transformers (SBERT) to get contextual sentence embeddings of benign and extortion samples.
Then, we apply the Facebook AI Similarity Search (FAISS) measure to search for similar benign instances to extortion attacks.
### Data Instances
|Extortion Class| Examples from Sentence-level subset|
|---|---|
|Blackmail| - I will delete the corresponding recording and I will not blackmail you ever again.|
|Ransomware| - Tap to Download Attachment Xinalink_servicescom (10.3 KB).|
|Sextortion| - In case you ignore me, within 96 h, ur sex tape will be posted on the net.|
### Data Sources
The following tables describe the data sources used to generate this dataset.
* **Extortion Data**
|Source|Total number of Emails| Total number of Sentences|
|---|---|---|
|[r/Scams](https://www.reddit.com/r/Scams/comments/n00kg3/the_blackmail_email_scam_part_7/###) Extortion Emails | 1,113 | 17,393 |
|Botnet Ransomware Emails | 150 | 1,510 |
* **Benign Data**
|Source|Total number of Emails| Total number of Sentences|
|---|---|---|
|[Enron](https://www.cs.cmu.edu/~enron/)| 1,360 | 26,835 |
|[SpamAssasin](https://spamassassin.apache.org/old/publiccorpus/)| 1,010 | 12,348 |
### Data Fields
The dataset is structered as follow:
list[{
"src": str, # Data source (e.g, SpamAssassin, Enron, Reddit)
"content": str, # Content (sentence-level or email-level)
"label": str, # Extortion label (blackmail, ransomware, sextortion) or benign label
}]
### Loading TExtPhish Dataset
To load the email-level subset, use the following instructions:
email_subset = load_dataset("TExtPhish/TExtPhish", data_dir="email-level", split="train", sep=";")
To load the sentence-level subset, use the following instructions:
sentence_subset = load_dataset("TExtPhish/TExtPhish", data_dir="sentence-level", split="train", sep=";")
To load the Homograph-Perturbed subset on sentences, use the following instructions:
homograph_subset = load_dataset("TExtPhish/TExtPhish", data_dir="homograph-perturbed-sentences", split="train", sep=";")
### Splitting TExtPhish Dataset
If you would like to load the dataset under cross validation setting,
you can load (train or test) which will be divided into k folds (example below k=10).
test_folds = load_dataset('TExtPhish/TExtPhish', split=[f"train[{k}%:{k+10}%]" for k in range(0, 100, 10)], data_dir="sentence-level", sep=';')
train_folds = load_dataset('TExtPhish/TExtPhish',split=[f"train[:{k}%]+train[{k+10}%:]" for k in range(0, 100, 10)], data_dir="sentence-level", sep=';')
This easy and ready-to-use divided folds consist of dividing randomly TExtPhish into k=10 parts.
Nine of these parts are used for training while one tenth is reserved for testing.
This procedure will be repeated k=10 times each time reserving a different tenth for testing. In other words, each testing set is a 10% chunk, and the training set makes up the remaining complementary 90% chunk.
### Binarize Labels
from sklearn.preprocessing import LabelEncoder
# Transforming text labels to encoded labels using the MultiLabelBinarizer
multibin = LabelEncoder()
Y_train = multibin.fit_transform(Y_train)
Y_test = multibin.fit_transform(Y_test)
### Personal and Sensitive Information
We ensure to remove any personal and sensitive information before uploading our dataset.
The emails provided in this corpus are stripped from sensitive information that are replaced with tokens (e.g., url_token), synthetically replaced, or originally obfuscated (***) in order to anonymize the data.
## Considerations for Using the Data
### Intended Uses
Our collection may only be used for linguistic non-profit research including but not limited to Information Retrieval, Text Classification, Natural Language Processing, Machine Learning, Phishing Detection, Data Privacy and Security, and like fields.
### Social Impact of Dataset
Users are totally responsible for any misuse of the dataset that goes against the original intended use of this dataset.
The extortion dataset should not be used for any harmful means to institute and propagate attacks.
*Positive Social Impact*
* Researchers can use **TExtPhish** to study the tactics and techniques used by attackers, identify vulnerabilities, and develop effective countermeasures against extortion.
* Educators can use **TExtPhish** to teach students about online safety, how to recognize phishing extortion attempts, and best practices for protecting personal information and financial loss.
* Cybersecurity professionals can use **TExtPhish** to train machine learning models to detect and block phishing emails with money extortion attempts, improving incident response strategies, and minimizing financial loss exposure.
*Negative Social Impact*
* Attackers might use **TExtPhish** to create automatic botnets that generate better extortion attacks.
* Attackers might use **TExtPhish** to propagate deception and propaganda online.
* Attackers might attempt to use **TExtPhish** as an initializing phase to perform malware, ransomware, or embed trojans within a targeted system to gain remote access.
## Additional Information
### Licensing Information
As the maintainers of this dataset, we choose to follow licensing Attribution- NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) to ensure that the dataset is non-commercial and it cannot be distributed or reproduced, in whole or in part, any document from the Collection.
A portion of our dataset was downloaded using Reddit's API Wrapper through the PRAW package for the python programming language. Re-use of this data is subject to Reddit API terms, which include:
* Users shall not encourage or promote illegal activity throughout the use of this dataset.
* Users shall not use this dataset with the intent of introducing any viruses, worms, defects, Trojan horses, malware, or any other items of a destructive nature.
* Users shall not sell, lease, or sublicense this data whether for direct commercial or monetary gain.
### Citation Information
Information about citation will soon be updated.
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cntommy/PKChat-MultiTurns-Question | 2023-10-21T09:16:48.000Z | [
"region:us"
] | cntommy | null | null | 0 | 0 | 2023-10-21T09:15:09 | Entry not found | 15 | [
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MajdTannous/Dataset3 | 2023-10-21T09:23:13.000Z | [
"region:us"
] | MajdTannous | null | null | 0 | 0 | 2023-10-21T09:23:13 | Entry not found | 15 | [
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yuyijiong/Long-Instruction-Chinese | 2023-11-01T11:54:37.000Z | [
"task_categories:text-generation",
"size_categories:10K<n<100K",
"language:zh",
"region:us"
] | yuyijiong | null | null | 3 | 0 | 2023-10-21T09:34:43 | ---
task_categories:
- text-generation
language:
- zh
size_categories:
- 10K<n<100K
---
# 中文超长文本指令微调数据集合
此页面是各个数据集的链接集合
# 单轮QA
## 全文信息总结
1. [图书总结](https://huggingface.co/datasets/yuyijiong/Book_Summary_Chinese)
2. [论文总结 ](https://huggingface.co/datasets/yuyijiong/Paper_QA_and_Abstract_Chinese)
涉及到知网数据，受限访问。
## 局部信息问答
1. [多文档问答（检索）](https://huggingface.co/datasets/yuyijiong/Multi-Doc-QA-Chinese)
2. [论文问答](https://huggingface.co/datasets/yuyijiong/Paper_QA_and_Abstract_Chinese)
涉及到知网数据，受限访问。
# 多轮QA
1. [多文档问答](https://huggingface.co/datasets/yuyijiong/Multi-Doc-Multi-QA-Chinese/tree/main)
2. [论文问答](https://huggingface.co/datasets/yuyijiong/Paper_mutli_QA_Chinese) | 677 | [
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faizalnf1800/karambit-knife-object | 2023-10-21T10:42:37.000Z | [
"license:apache-2.0",
"region:us"
] | faizalnf1800 | null | null | 0 | 0 | 2023-10-21T10:09:44 | ---
license: apache-2.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: image
dtype: image
splits:
- name: train
num_bytes: 791480.0
num_examples: 27
download_size: 767049
dataset_size: 791480.0
---
Still Collecting Dataset. Karambit Knife Object Raw Picture Google Drive [Link](https://drive.google.com/file/d/1fFRSxeTt9Tvj6d7PFdJWJ56LXR5shoH4/view?usp=share_link) | 459 | [
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Bradleyyyb/LLaMA | 2023-10-21T10:38:43.000Z | [
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Kailash3113/LLM_DATASET | 2023-10-21T11:48:10.000Z | [
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NaturesRemedyFungiRemover/Natures-Remedy-Fungi-Remover | 2023-10-21T12:21:02.000Z | [
"region:us"
] | NaturesRemedyFungiRemover | null | null | 0 | 0 | 2023-10-21T12:19:05 | <p><strong>Supplement Name : <a href="https://sites.google.com/view/naturesremedyfungiremover/home" target="_blank"><span style="color: red;">Nature's Remedy Fungi Remover</span></a> (</strong><strong><a href="https://sites.google.com/view/fungi-remover/home" target="_blank"><span style="color: red;">Fungi Remover</span></a></strong><strong>)</strong></p>
<p><strong>Category : </strong>Nail Health Formula<strong><br /></strong></p>
<p><strong>Ingredients :</strong> Tea Tree Oil , Clove Bud Oil, Almond Oil, Flaxseed Oil,</p>
<p><strong>Customer Rating : </strong>4.5/5</p>
<p><strong>Base Price : <span style="color: red;">$39.95 Per Bottle </span></strong> (<a href="https://www.healthsupplement24x7.com/get-natures-remedy-fungi-remover" target="_blank"><span style="color: red;"><strong>Discounts Are Available on Bulk Order</strong></span></a>)</p>
<p><strong>Formulation: Pills</strong></p>
<p><strong>Benefits: </strong>The #1 Solution To Supporting the Healthy <strong>Toenail!<br /></strong></p>
<p><strong>Dosage: </strong>Read On Lable</p>
<p><strong>Side Effects: </strong>There are no significant side effects</p>
<p><strong>Official Website:- <a href="https://www.healthsupplement24x7.com/get-natures-remedy-fungi-remover" target="_blank"><span style="color: red;">Click Here to Visit Official Website</span></a></strong></p>
<p><strong>Nature's Remedy Fungi Remover (Medical Experts Reviews):</strong> Toenail fungus is a common problem that millions of people worldwide face. It causes discoloration, brittle and smelly nails, and sometimes loss of nails. Forget about homemade remedies and use <a href="https://natures-remedy-fungi-remover.clubeo.com/page/natures-remedy-fungi-remover-medical-experts-reviews-1-nail-health-spray.html">Nature's Remedy Fungi Remover</a>, an effective nail spray that eliminates the most aggressive toenail fungus.</p>
<p>The spray protects your skin and nails, strengthens the nails, improves growth, and promotes overall health.The following <a href="https://natures-remedy-fungi-remover.clubeo.com/page/natures-remedy-fungi-remover-1-unique-fungal-eliminator-spray-promotes-nails-and-foot-health.html">Nature's Remedy Fungi Remover</a> review will reveal every aspect of the product.</p>
<p style="text-align: center;"><a style="margin-left: 1em; margin-right: 1em;" href="https://www.healthsupplement24x7.com/get-natures-remedy-fungi-remover" target="_blank"><img src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEg-sZ8C5m9z2U1hUWitwQDmRypubbo-0-IoePS0a3c7xTN-KWz4lnOyV6OBwt3MKoypxHPKngdtcZFOwgMq5h6-yPnRsMdvyc9HpMxCVJQGzxqBt1duFRkPb7qb6XJQNVbFqbz1UArtMOmVTgUTcLA0qngWMrmfxuaQgnWbRmsmj8rzf01ZH8qOGoHv4Co/w640-h360/Nature's%20Remedy%20Fungi%20Remover%209.png" alt="" width="640" height="360" border="0" data-original-height="617" data-original-width="1095" /></a></p>
<h2>What is Nature's Remedy Fungi Remover?</h2>
<p><a href="https://groups.google.com/g/naturesremedyfungiremover/c/jFax3KkarYY">Nature's Remedy Fungi Remover</a> is a unique fungal eliminator spray that promotes nails and foot health. It improves nail growth and nourishes your nails and skin. The spray contains 20 natural ingredients that fight fungal infection and prevent future recurrence.</p>
<p>The nail spray is rich in essential vitamins, minerals, and nutrients, giving your nails and feet a clean and beautiful look. It improves the appearance of your nails by eliminating discoloration and dullness. <a href="https://natures-remedy-fungi-remover.clubeo.com/calendar/2023/10/20/natures-remedy-fungi-remover-au-nz-reviews-1-spray-for-permanent-solution?_ga=2.135930616.810308565.1697864054-1222946029.1697864051">Nature's Remedy Fungi Remover</a> protects the nails from damage, reduces brittleness, and gives long-lasting results.</p>
<p>The 20-in-1 formula eliminates the embarrassment of smelly and ugly feet. It helps nourish your nails and skin while enhancing nail growth. <a href="https://groups.google.com/g/natures-remedy-fungi-remover/c/Xkfr6Rq76v4">Nature's Remedy Fungi Remover</a> works on all nail types without causing sensitivity or allergies. It contains 100% natural and clinically proven ingredients that support overall nail and skin health.</p>
<p>The advanced nail spray is pure and free from GMOs, gluten, chemicals, additives, fillers, and toxins. It is the best healthy choice for your nails and skin, giving you a noticeable difference within weeks. <a href="https://grabcad.com/library/nature-s-remedy-fungi-remover-things-you-need-to-know-about-shocking-price-where-to-buy-1">Nature's Remedy Fungi Remover</a> is a premium formula produced in an FDA-compliant and GMP-certified facility that ensures quality and safety. The manufacturer ensures 100% secure encrypted transactions and provides a 60-day satisfaction guarantee on each package.</p>
<h2 style="text-align: center;"><a href="https://www.healthsupplement24x7.com/get-natures-remedy-fungi-remover"><span style="color: #ff0000;">BULK SAVINGS - BUY NATURE"S REMEDY FUNGI REMOVER BEFORE STOCK RUNS OUT</span></a></h2>
<h2>Nature's Remedy Fungi Remover Working Mechanism</h2>
<p><a href="https://nature-remedy-fungi-remover.unicornplatform.page/">Nature's Remedy Fungi Remover</a> works by addressing the root cause of toenail fungus. According to the website, the type of fungus that causes toenail fungus is T.Rubrum. The fungus grows in moist environments that penetrate your toes. If left untreated, it can affect your lungs, eyes, brain, and mouth.</p>
<p>The advanced spray has hydrating properties that help moisturize your nails and skin, preventing brittleness, breakage, and dryness. It contains Vitamin C, which is crucial in collagen production. Collagen protein promotes healthy hair, skin, and nail growth and improves bone structure.</p>
<p>The nail spray has antioxidants, including green tea, aloe vera, and Vitamin C, strengthening the skin barrier function and preventing future fungal attacks. Aloe vera is rich in anti-inflammatory properties that reduce inflammation and soothe skin.</p>
<h2>Nature's Remedy Fungi Remover Ingredients</h2>
<p><a href="https://doogeemall.com/community/xenforum/topic/111811/nature-s-remedy-fungi-remover-aunz-reviews-1-spray-for-permanent-solution">Nature's Remedy Fungi Remover</a> contains pure, science-backed ingredients that fight toenail fungus and improve skin barrier function. The compounds in the spray are 100% natural and do not cause any risk of side effects.</p>
<div class="bullseye_container-0-2-308"><strong>Tea Tree Oil</strong></div>
<ul>
<li>Fights existing fungal infection</li>
<li>Prevents re-infection</li>
<li>Reduces yellow discoloration</li>
</ul>
<p><strong>Clove Bud Oil</strong></p>
<ul>
<li>Fights nail fungus</li>
<li>Stimulates nail regrowth</li>
<li>Reduces burning & itching</li>
</ul>
<p><strong>Almond Oil</strong></p>
<ul>
<li>Nail & skin superfood</li>
<li>Anti-microbial properties</li>
<li>Nail-soothing benefits</li>
</ul>
<p><strong>Flaxseed Oil</strong></p>
<ul>
<li>Effective anti-fungal oil</li>
<li>Reduces dermal inflammation</li>
<li>Supports cuticle strengthening</li>
</ul>
<p><strong>Lemongrass Oil</strong></p>
<ul>
<li>Treats dermatophyte</li>
<li>Reduces nail discoloration</li>
<li>Prevents nail cracking</li>
</ul>
<h2 style="text-align: center;"><a href="https://www.healthsupplement24x7.com/get-natures-remedy-fungi-remover"><span style="color: #ff0000;">BULK SAVINGS - BUY NATURE"S REMEDY FUNGI REMOVER BEFORE STOCK RUNS OUT</span></a></h2>
<p style="text-align: center;"> <a style="margin-left: 1em; margin-right: 1em;" href="https://www.healthsupplement24x7.com/get-natures-remedy-fungi-remover" target="_blank"><img src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjAVLtii8k1JEbJzb77QwKKt9CpckCYXtoiBBU68Js-h1Qln9g0oM3sD7V91t4XLwxMQAT2p_8ibfXBce6Q8CCPRo-IqE-QjQdtQuEOKz_BcfsQy71qRxwap5EjeGYxwfZdtNv4TqAhx-c8ZU-iDjizPpDN469she9v0QRwDyc2zxtgdLe-COiCpsuICDI/w640-h288/Nature's%20Remedy%20Fungi%20Remover%206.jpg" alt="" width="640" height="288" border="0" data-original-height="802" data-original-width="1778" /></a></p>
<h2>The Benefits of Nature's Remedy Fungi Remover</h2>
<ul>
<li><strong>Strengthens your nails-</strong> The spray contains keratin and biotin, which support the formation of strong nails. The ingredients work by increasing the nail plate’s thickness and reducing the risk of breakage and brittleness.</li>
<li><strong>Improve skin and nail appearance-</strong> fungus attacks cause your nails to look unpleasant. Nature's Remedy Fungi Remover gives your nails a new and healthy appearance, reducing discoloration and dullness.</li>
<li><strong>Enhance nail growth-</strong> Nature's Remedy Fungi Remover contains all the essential compounds that support nail growth. It improves the production of new nail cells and uses nutrients to nourish and repair damaged cells.</li>
<li><strong>Nourish and moisturize nails and cuticles-</strong> Nature's Remedy Fungi Remover contains powerful glycerin and hyaluronic acid that help hydrate your cuticles, making them softer. It is also rich in nutrients that nourish your nails and skin.</li>
<li><strong>Antioxidant support-</strong> The nail spray contains antioxidant-rich ingredients that help protect your nails from damage caused by environmental stressors. The compounds prevent premature aging and promote overall skin health. The antioxidants strengthen the skin barrier function, preventing fungal attacks.</li>
<li><strong>Improve nail and foot health-</strong> Nature's Remedy Fungi Remover contains 20 natural elements that protect your nails, reduce dryness, support nail growth, and nourish your nails and skin, thus contributing to overall nail and foot health.</li>
</ul>
<h2>How to use Nature's Remedy Fungi Remover</h2>
<p><a href="http://nature-remedy-fungi-remover.tilda.ws/">Nature's Remedy Fungi Remover</a> comes in a gel-like spray, allowing for easy application. Using the spray is straightforward. Apply Sray on the affected area. Ensure your skin or nail is clean before using the product. You can use the product twice daily to improve its effectiveness.</p>
<p>The nail spray suits individuals suffering from toenails, skin fungus, and those with unhealthy or brittle nails. It also benefits people with discolored, fragile, dry nails and damaged cuticles.</p>
<p>Nature's Remedy Fungi Remover is a natural formula containing all-natural and science-backed ingredients free from gluten, stimulants, toxins, and chemicals.Use Nature's Remedy Fungi Remover externally. Discontinue the fungus eliminator if you notice any redness or itching.</p>
<h2 style="text-align: center;"><a href="https://www.healthsupplement24x7.com/get-natures-remedy-fungi-remover"><span style="color: #ff0000;">BULK SAVINGS - BUY NATURE"S REMEDY FUNGI REMOVER BEFORE STOCK RUNS OUT</span></a></h2>
<p style="text-align: center;"> <a style="margin-left: 1em; margin-right: 1em;" href="https://www.healthsupplement24x7.com/get-natures-remedy-fungi-remover" target="_blank"><img src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhnIVa_7plWtetl7lm2mLkFIG_Or1C3Vt3SnDAxngCSbzXQAmN9CsthjQ9IyqvdVLBe00jAiDIsSpUAJPc0bQHuxYcc8A6BSLzwOGs10uTmbiD89cJfElFgtYRf5qHSHz3IUiF0OeXju7HJlO0GxX7JaD0ACyTUJHlTi-K-8RDD_LMVLU-Rk0gJjRv4VO4/w640-h422/Screenshot%20(1295).png" alt="" width="640" height="422" border="0" data-original-height="696" data-original-width="1056" /></a></p>
<h2>Pricing and Money-Back Guarantee</h2>
<p><a href="https://pdfhost.io/v/HM0pM~QMU_Natures_Remedy_Fungi_Remover_AUNZ_Reviews_1_Spray_For_Permanent_Solution_">Nature's Remedy Fungi Remover</a> is exclusively available online on the official website at discounted prices. The special pricing is only for a limited period. The product’s price options are as follows:</p>
<ul>
<li>One Nature's Remedy Fungi Remover at $69.95 + small shipping fee;</li>
<li>Two Bottles + One Free Bottle at $49.95 free US shipping;</li>
<li>Three Bottles + Two Free Bottles at $39.95 free US shipping.</li>
</ul>
<p>A 60-day money-back guarantee covers each <a href="https://www.ivoox.com/nature-s-remedy-fungi-remover-audios-mp3_rf_118141375_1.html">Nature's Remedy Fungi Remover</a> package option to ensure customer satisfaction. If the spray doesn’t improve your nail’s health, the manufacturer will be more than willing to give a 100% refund.</p>
<h2>Conclusion</h2>
<p><a href="https://gamma.app/public/Natures-Remedy-Fungi-Remover-AUNZ-Reviews-1-Spray-For-Permanent-S-5w7jphrj9ew5aaf?mode=doc">Nature's Remedy Fungi Remover</a> is a natural solution that helps eliminate toenail fungus. It uses plant-based ingredients that are clinically proven to promote healthy skin and nails. The spray helps deal with brittle nails, dry cuticles, weak nails, and damaged nails.</p>
<p>The revolutionary nail spray improves the skin barrier function, thus preventing fungus from penetrating your skin and nails. It promotes the growth of stronger and healthier nails and improves collagen production for healthy skin and hair.</p>
<p><a href="https://sketchfab.com/3d-models/natures-remedy-fungi-remover-aunz-reviews-2d45d22c02ab426ea1d0c945f99e5fd7">Nature's Remedy Fungi Remover</a> is a comprehensive nail spray that promises long-term results without the risk of side effects. The fungus remover is safe and free from toxins, chemicals, or additives. Additionally, the manufacturer offers a 60-day satisfaction guarantee and free bonuses.</p>
<h2 style="text-align: center;"><a href="https://www.healthsupplement24x7.com/get-natures-remedy-fungi-remover"><span style="color: #ff0000;">BULK SAVINGS - BUY NATURE"S REMEDY FUNGI REMOVER BEFORE STOCK RUNS OUT</span></a></h2>
<h3>READ MORE ON OFFICIAL WEBSITE:</h3>
<p><a href="https://natures-remedy-fungi-remover.clubeo.com/calendar/2023/10/20/natures-remedy-fungi-remover-au-nz-reviews-1-spray-for-permanent-solution?_ga=2.135930616.810308565.1697864054-1222946029.1697864051">https://natures-remedy-fungi-remover.clubeo.com/calendar/2023/10/20/natures-remedy-fungi-remover-au-nz-reviews-1-spray-for-permanent-solution</a></p>
<p><a href="https://sites.google.com/view/naturesremedyfungiremover/home">https://sites.google.com/view/naturesremedyfungiremover/home</a></p>
<p><a href="https://groups.google.com/g/natures-remedy-fungi-remover/c/Xkfr6Rq76v4">https://groups.google.com/g/natures-remedy-fungi-remover/c/Xkfr6Rq76v4</a></p>
<p><a href="https://natures-remedy-fungi-remover.clubeo.com/page/natures-remedy-fungi-remover-medical-experts-reviews-1-nail-health-spray.html">https://natures-remedy-fungi-remover.clubeo.com/page/natures-remedy-fungi-remover-medical-experts-reviews-1-nail-health-spray.html</a></p>
<p><a href="https://sites.google.com/view/fungi-remover/home">https://sites.google.com/view/fungi-remover/home</a></p>
<p><a href="https://natures-remedy-fungi-remover.clubeo.com/page/natures-remedy-fungi-remover-1-unique-fungal-eliminator-spray-promotes-nails-and-foot-health.html">https://natures-remedy-fungi-remover.clubeo.com/page/natures-remedy-fungi-remover-1-unique-fungal-eliminator-spray-promotes-nails-and-foot-health.html</a></p>
<p><a href="https://groups.google.com/g/naturesremedyfungiremover/c/jFax3KkarYY">https://groups.google.com/g/naturesremedyfungiremover/c/jFax3KkarYY</a></p>
<p><a href="https://grabcad.com/library/nature-s-remedy-fungi-remover-things-you-need-to-know-about-shocking-price-where-to-buy-1">https://grabcad.com/library/nature-s-remedy-fungi-remover-things-you-need-to-know-about-shocking-price-where-to-buy-1</a></p>
<p><a href="https://nature-remedy-fungi-remover.unicornplatform.page/">https://nature-remedy-fungi-remover.unicornplatform.page/</a></p>
<p><a href="https://doogeemall.com/community/xenforum/topic/111811/nature-s-remedy-fungi-remover-aunz-reviews-1-spray-for-permanent-solution">https://doogeemall.com/community/xenforum/topic/111811/nature-s-remedy-fungi-remover-aunz-reviews-1-spray-for-permanent-solution</a></p>
<p><a href="https://www.ivoox.com/nature-s-remedy-fungi-remover-audios-mp3_rf_118141375_1.html">https://www.ivoox.com/nature-s-remedy-fungi-remover-audios-mp3_rf_118141375_1.html</a></p>
<p><a href="https://gamma.app/public/Natures-Remedy-Fungi-Remover-AUNZ-Reviews-1-Spray-For-Permanent-S-5w7jphrj9ew5aaf?mode=doc">https://gamma.app/public/Natures-Remedy-Fungi-Remover-AUNZ-Reviews-1-Spray-For-Permanent-S-5w7jphrj9ew5aaf</a></p>
<p><a href="https://pdfhost.io/v/HM0pM~QMU_Natures_Remedy_Fungi_Remover_AUNZ_Reviews_1_Spray_For_Permanent_Solution_">https://pdfhost.io/v/HM0pM~QMU_Natures_Remedy_Fungi_Remover_AUNZ_Reviews_1_Spray_For_Permanent_Solution_</a></p>
<p><a href="https://sketchfab.com/3d-models/natures-remedy-fungi-remover-aunz-reviews-2d45d22c02ab426ea1d0c945f99e5fd7">https://sketchfab.com/3d-models/natures-remedy-fungi-remover-aunz-reviews-2d45d22c02ab426ea1d0c945f99e5fd7</a></p>
<p><a href="http://nature-remedy-fungi-remover.tilda.ws/">http://nature-remedy-fungi-remover.tilda.ws/</a></p> | 16,977 | [
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lipi17/Building-Cracks-Merged | 2023-10-21T12:21:54.000Z | [
"task_categories:object-detection",
"roboflow",
"roboflow2huggingface",
"region:us"
] | lipi17 | null | null | 0 | 0 | 2023-10-21T12:20:50 | ---
task_categories:
- object-detection
tags:
- roboflow
- roboflow2huggingface
---
<div align="center">
<img width="640" alt="lipi17/building-cracks-merged" src="https://huggingface.co/datasets/lipi17/building-cracks-merged/resolve/main/thumbnail.jpg">
</div>
### Dataset Labels
```
['crack', 'stairstep_crack']
```
### Number of Images
```json
{'test': 11, 'valid': 433, 'train': 947}
```
### 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("lipi17/building-cracks-merged", name="full")
example = ds['train'][0]
```
### Roboflow Dataset Page
[https://universe.roboflow.com/lipi-deepaakshi-patnaik-ktyz8/merged-building-cracks/dataset/1](https://universe.roboflow.com/lipi-deepaakshi-patnaik-ktyz8/merged-building-cracks/dataset/1?ref=roboflow2huggingface)
### Citation
```
@misc{ merged-building-cracks_dataset,
title = { Merged-Building-Cracks Dataset },
type = { Open Source Dataset },
author = { Lipi Deepaakshi Patnaik },
howpublished = { \\url{ https://universe.roboflow.com/lipi-deepaakshi-patnaik-ktyz8/merged-building-cracks } },
url = { https://universe.roboflow.com/lipi-deepaakshi-patnaik-ktyz8/merged-building-cracks },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2023 },
month = { oct },
note = { visited on 2023-10-21 },
}
```
### License
MIT
### Dataset Summary
This dataset was exported via roboflow.com on October 21, 2023 at 12:21 PM 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 1391 images.
Cracks are annotated in COCO format.
The following pre-processing was applied to each image:
* Auto-orientation of pixel data (with EXIF-orientation stripping)
* Resize to 640x640 (Stretch)
No image augmentation techniques were applied.
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akachdaeee/nva-konosora | 2023-10-21T12:33:18.000Z | [
"region:us"
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openpecha/tibetan-voice-550 | 2023-10-24T10:17:45.000Z | [
"region:us"
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ani2857/xquad_xtreme1 | 2023-10-21T12:57:19.000Z | [
"region:us"
] | ani2857 | null | null | 0 | 0 | 2023-10-21T12:57:19 | Entry not found | 15 | [
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akachdaeee/konosora | 2023-10-21T13:11:07.000Z | [
"region:us"
] | akachdaeee | null | null | 0 | 0 | 2023-10-21T13:11:07 | Entry not found | 15 | [
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void0721/PCA-EVAL | 2023-10-21T13:35:51.000Z | [
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open-llm-leaderboard/details_WizardLM__WizardLM-30B-V1.0 | 2023-10-21T17:46:29.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | 0 | 0 | 2023-10-21T14:56:59 | ---
pretty_name: Evaluation run of WizardLM/WizardLM-30B-V1.0
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [WizardLM/WizardLM-30B-V1.0](https://huggingface.co/WizardLM/WizardLM-30B-V1.0)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_WizardLM__WizardLM-30B-V1.0\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-21T17:46:17.809151](https://huggingface.co/datasets/open-llm-leaderboard/details_WizardLM__WizardLM-30B-V1.0/blob/main/results_2023-10-21T17-46-17.809151.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\": 0.0,\n \"\
em_stderr\": 0.0,\n \"f1\": 0.0,\n \"f1_stderr\": 0.0,\n \"\
acc\": 0.24348855564325178,\n \"acc_stderr\": 0.007023859196998832\n },\n\
\ \"harness|drop|3\": {\n \"em\": 0.0,\n \"em_stderr\": 0.0,\n\
\ \"f1\": 0.0,\n \"f1_stderr\": 0.0\n },\n \"harness|gsm8k|5\"\
: {\n \"acc\": 0.0,\n \"acc_stderr\": 0.0\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.48697711128650356,\n \"acc_stderr\": 0.014047718393997663\n\
\ }\n}\n```"
repo_url: https://huggingface.co/WizardLM/WizardLM-30B-V1.0
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_21T14_56_55.024771
path:
- '**/details_harness|drop|3_2023-10-21T14-56-55.024771.parquet'
- split: 2023_10_21T17_46_17.809151
path:
- '**/details_harness|drop|3_2023-10-21T17-46-17.809151.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-21T17-46-17.809151.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_21T14_56_55.024771
path:
- '**/details_harness|gsm8k|5_2023-10-21T14-56-55.024771.parquet'
- split: 2023_10_21T17_46_17.809151
path:
- '**/details_harness|gsm8k|5_2023-10-21T17-46-17.809151.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-21T17-46-17.809151.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_21T14_56_55.024771
path:
- '**/details_harness|winogrande|5_2023-10-21T14-56-55.024771.parquet'
- split: 2023_10_21T17_46_17.809151
path:
- '**/details_harness|winogrande|5_2023-10-21T17-46-17.809151.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-21T17-46-17.809151.parquet'
- config_name: results
data_files:
- split: 2023_10_21T14_56_55.024771
path:
- results_2023-10-21T14-56-55.024771.parquet
- split: 2023_10_21T17_46_17.809151
path:
- results_2023-10-21T17-46-17.809151.parquet
- split: latest
path:
- results_2023-10-21T17-46-17.809151.parquet
---
# Dataset Card for Evaluation run of WizardLM/WizardLM-30B-V1.0
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/WizardLM/WizardLM-30B-V1.0
- **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 [WizardLM/WizardLM-30B-V1.0](https://huggingface.co/WizardLM/WizardLM-30B-V1.0) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_WizardLM__WizardLM-30B-V1.0",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-21T17:46:17.809151](https://huggingface.co/datasets/open-llm-leaderboard/details_WizardLM__WizardLM-30B-V1.0/blob/main/results_2023-10-21T17-46-17.809151.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": 0.0,
"em_stderr": 0.0,
"f1": 0.0,
"f1_stderr": 0.0,
"acc": 0.24348855564325178,
"acc_stderr": 0.007023859196998832
},
"harness|drop|3": {
"em": 0.0,
"em_stderr": 0.0,
"f1": 0.0,
"f1_stderr": 0.0
},
"harness|gsm8k|5": {
"acc": 0.0,
"acc_stderr": 0.0
},
"harness|winogrande|5": {
"acc": 0.48697711128650356,
"acc_stderr": 0.014047718393997663
}
}
```
### 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] | 7,331 | [
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GhostWhite/Lucas | 2023-10-21T15:42:36.000Z | [
"region:us"
] | GhostWhite | null | null | 0 | 0 | 2023-10-21T14:57:47 | Entry not found | 15 | [
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leosilveiralss/leo_silveira | 2023-10-21T14:59:50.000Z | [
"region:us"
] | leosilveiralss | null | null | 0 | 0 | 2023-10-21T14:57:48 | Entry not found | 15 | [
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ugshanyu/1million | 2023-10-21T15:04:14.000Z | [
"region:us"
] | ugshanyu | null | null | 0 | 0 | 2023-10-21T15:04:14 | Entry not found | 15 | [
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open-llm-leaderboard/details_TheTravellingEngineer__llama2-7b-chat-hf-dpo | 2023-10-21T15:24:36.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | 0 | 0 | 2023-10-21T15:24:28 | ---
pretty_name: Evaluation run of TheTravellingEngineer/llama2-7b-chat-hf-dpo
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [TheTravellingEngineer/llama2-7b-chat-hf-dpo](https://huggingface.co/TheTravellingEngineer/llama2-7b-chat-hf-dpo)\
\ 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_TheTravellingEngineer__llama2-7b-chat-hf-dpo\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-21T15:24:24.824403](https://huggingface.co/datasets/open-llm-leaderboard/details_TheTravellingEngineer__llama2-7b-chat-hf-dpo/blob/main/results_2023-10-21T15-24-24.824403.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\": 0.06763842281879194,\n\
\ \"em_stderr\": 0.0025717489509556085,\n \"f1\": 0.13085570469798627,\n\
\ \"f1_stderr\": 0.0028825856446422905,\n \"acc\": 0.39549166962367155,\n\
\ \"acc_stderr\": 0.009921949302668327\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.06763842281879194,\n \"em_stderr\": 0.0025717489509556085,\n\
\ \"f1\": 0.13085570469798627,\n \"f1_stderr\": 0.0028825856446422905\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.07354056103108415,\n \
\ \"acc_stderr\": 0.0071898357543652685\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7174427782162589,\n \"acc_stderr\": 0.012654062850971384\n\
\ }\n}\n```"
repo_url: https://huggingface.co/TheTravellingEngineer/llama2-7b-chat-hf-dpo
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_21T15_24_24.824403
path:
- '**/details_harness|drop|3_2023-10-21T15-24-24.824403.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-21T15-24-24.824403.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_21T15_24_24.824403
path:
- '**/details_harness|gsm8k|5_2023-10-21T15-24-24.824403.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-21T15-24-24.824403.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_21T15_24_24.824403
path:
- '**/details_harness|winogrande|5_2023-10-21T15-24-24.824403.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-21T15-24-24.824403.parquet'
- config_name: results
data_files:
- split: 2023_10_21T15_24_24.824403
path:
- results_2023-10-21T15-24-24.824403.parquet
- split: latest
path:
- results_2023-10-21T15-24-24.824403.parquet
---
# Dataset Card for Evaluation run of TheTravellingEngineer/llama2-7b-chat-hf-dpo
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/TheTravellingEngineer/llama2-7b-chat-hf-dpo
- **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 [TheTravellingEngineer/llama2-7b-chat-hf-dpo](https://huggingface.co/TheTravellingEngineer/llama2-7b-chat-hf-dpo) 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_TheTravellingEngineer__llama2-7b-chat-hf-dpo",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-21T15:24:24.824403](https://huggingface.co/datasets/open-llm-leaderboard/details_TheTravellingEngineer__llama2-7b-chat-hf-dpo/blob/main/results_2023-10-21T15-24-24.824403.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": 0.06763842281879194,
"em_stderr": 0.0025717489509556085,
"f1": 0.13085570469798627,
"f1_stderr": 0.0028825856446422905,
"acc": 0.39549166962367155,
"acc_stderr": 0.009921949302668327
},
"harness|drop|3": {
"em": 0.06763842281879194,
"em_stderr": 0.0025717489509556085,
"f1": 0.13085570469798627,
"f1_stderr": 0.0028825856446422905
},
"harness|gsm8k|5": {
"acc": 0.07354056103108415,
"acc_stderr": 0.0071898357543652685
},
"harness|winogrande|5": {
"acc": 0.7174427782162589,
"acc_stderr": 0.012654062850971384
}
}
```
### 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] | 7,431 | [
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CCRs/33tsv_100krows_kz | 2023-10-21T16:33:15.000Z | [
"region:us"
] | CCRs | null | null | 0 | 0 | 2023-10-21T16:08:42 | Entry not found | 15 | [
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open-llm-leaderboard/details_KoboldAI__OPT-350M-Nerys-v2 | 2023-10-21T16:22:34.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | 0 | 0 | 2023-10-21T16:22:26 | ---
pretty_name: Evaluation run of KoboldAI/OPT-350M-Nerys-v2
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [KoboldAI/OPT-350M-Nerys-v2](https://huggingface.co/KoboldAI/OPT-350M-Nerys-v2)\
\ 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_KoboldAI__OPT-350M-Nerys-v2\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-21T16:22:23.406290](https://huggingface.co/datasets/open-llm-leaderboard/details_KoboldAI__OPT-350M-Nerys-v2/blob/main/results_2023-10-21T16-22-23.406290.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\": 0.0014681208053691276,\n\
\ \"em_stderr\": 0.0003921042190298392,\n \"f1\": 0.041601300335570565,\n\
\ \"f1_stderr\": 0.001164099674986064,\n \"acc\": 0.26150165183377183,\n\
\ \"acc_stderr\": 0.008156331616616547\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.0014681208053691276,\n \"em_stderr\": 0.0003921042190298392,\n\
\ \"f1\": 0.041601300335570565,\n \"f1_stderr\": 0.001164099674986064\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.006823351023502654,\n \
\ \"acc_stderr\": 0.0022675371022544935\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.516179952644041,\n \"acc_stderr\": 0.0140451261309786\n\
\ }\n}\n```"
repo_url: https://huggingface.co/KoboldAI/OPT-350M-Nerys-v2
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_21T16_22_23.406290
path:
- '**/details_harness|drop|3_2023-10-21T16-22-23.406290.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-21T16-22-23.406290.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_21T16_22_23.406290
path:
- '**/details_harness|gsm8k|5_2023-10-21T16-22-23.406290.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-21T16-22-23.406290.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_21T16_22_23.406290
path:
- '**/details_harness|winogrande|5_2023-10-21T16-22-23.406290.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-21T16-22-23.406290.parquet'
- config_name: results
data_files:
- split: 2023_10_21T16_22_23.406290
path:
- results_2023-10-21T16-22-23.406290.parquet
- split: latest
path:
- results_2023-10-21T16-22-23.406290.parquet
---
# Dataset Card for Evaluation run of KoboldAI/OPT-350M-Nerys-v2
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/KoboldAI/OPT-350M-Nerys-v2
- **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 [KoboldAI/OPT-350M-Nerys-v2](https://huggingface.co/KoboldAI/OPT-350M-Nerys-v2) 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_KoboldAI__OPT-350M-Nerys-v2",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-21T16:22:23.406290](https://huggingface.co/datasets/open-llm-leaderboard/details_KoboldAI__OPT-350M-Nerys-v2/blob/main/results_2023-10-21T16-22-23.406290.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": 0.0014681208053691276,
"em_stderr": 0.0003921042190298392,
"f1": 0.041601300335570565,
"f1_stderr": 0.001164099674986064,
"acc": 0.26150165183377183,
"acc_stderr": 0.008156331616616547
},
"harness|drop|3": {
"em": 0.0014681208053691276,
"em_stderr": 0.0003921042190298392,
"f1": 0.041601300335570565,
"f1_stderr": 0.001164099674986064
},
"harness|gsm8k|5": {
"acc": 0.006823351023502654,
"acc_stderr": 0.0022675371022544935
},
"harness|winogrande|5": {
"acc": 0.516179952644041,
"acc_stderr": 0.0140451261309786
}
}
```
### 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] | 7,231 | [
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kdawoud91/Archive_New | 2023-10-21T16:32:04.000Z | [
"region:us"
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] |
chashaotm/impressionist_paintings | 2023-10-21T16:36:42.000Z | [
"region:us"
] | chashaotm | null | null | 0 | 0 | 2023-10-21T16:30:22 | ---
dataset_info:
features:
- name: image
dtype: image
splits:
- name: train
num_bytes: 963894728.236
num_examples: 2018
download_size: 972521426
dataset_size: 963894728.236
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "impressionist_paintings"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 465 | [
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pedrohavay/portuguese-male-voice-A-dataset | 2023-10-21T20:51:10.000Z | [
"region:us"
] | pedrohavay | null | null | 0 | 0 | 2023-10-21T17:31:45 | Entry not found | 15 | [
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kjappelbaum/chemnlp-core-mof | 2023-10-22T13:20:11.000Z | [
"region:us"
] | kjappelbaum | null | null | 0 | 0 | 2023-10-21T17:36:20 | Entry not found | 15 | [
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yangjinlong/zyf42 | 2023-10-21T18:16:51.000Z | [
"region:us"
] | yangjinlong | null | null | 0 | 0 | 2023-10-21T17:39:03 | Entry not found | 15 | [
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PranshuA/PumaTshirtsDataset | 2023-10-21T17:47:28.000Z | [
"region:us"
] | PranshuA | null | null | 0 | 0 | 2023-10-21T17:47:28 | Entry not found | 15 | [
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] |
sayakpaul/drawbench-kandinsky-v22 | 2023-10-21T17:59:14.000Z | [
"region:us"
] | sayakpaul | null | null | 0 | 0 | 2023-10-21T17:59:05 | ---
dataset_info:
features:
- name: Prompt
dtype: string
- name: Image
dtype: image
- name: Upsampled_Prompt
dtype: string
- name: Image_With_Upsampled_Prompt
dtype: image
- name: model_name
dtype: string
- name: seed
dtype: int64
splits:
- name: train
num_bytes: 157049386.0
num_examples: 200
download_size: 157025231
dataset_size: 157049386.0
---
# Dataset Card for "drawbench-kandinsky-v22"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 580 | [
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sayakpaul/drawbench-sdxl-refiner | 2023-10-21T18:39:43.000Z | [
"region:us"
] | sayakpaul | null | null | 0 | 0 | 2023-10-21T18:39:20 | ---
dataset_info:
features:
- name: Prompt
dtype: string
- name: Image
dtype: image
- name: Upsampled_Prompt
dtype: string
- name: Image_With_Upsampled_Prompt
dtype: image
- name: model_name
dtype: string
- name: seed
dtype: int64
splits:
- name: train
num_bytes: 619027012.0
num_examples: 200
download_size: 619026117
dataset_size: 619027012.0
---
# Dataset Card for "drawbench-sdxl-refiner"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 579 | [
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] |
abfhad/guanaco-llama2-1k | 2023-10-21T18:53:38.000Z | [
"region:us"
] | abfhad | null | null | 0 | 0 | 2023-10-21T18:53:36 | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 1654448
num_examples: 1000
download_size: 966693
dataset_size: 1654448
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "guanaco-llama2-1k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 444 | [
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open-llm-leaderboard/details_dvruette__oasst-pythia-12b-reference | 2023-10-21T19:14:17.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | 0 | 0 | 2023-10-21T19:14:10 | ---
pretty_name: Evaluation run of dvruette/oasst-pythia-12b-reference
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [dvruette/oasst-pythia-12b-reference](https://huggingface.co/dvruette/oasst-pythia-12b-reference)\
\ 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_dvruette__oasst-pythia-12b-reference\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-21T19:14:07.226959](https://huggingface.co/datasets/open-llm-leaderboard/details_dvruette__oasst-pythia-12b-reference/blob/main/results_2023-10-21T19-14-07.226959.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\": 0.001363255033557047,\n\
\ \"em_stderr\": 0.00037786091964608703,\n \"f1\": 0.05910759228187943,\n\
\ \"f1_stderr\": 0.0013983745600314773,\n \"acc\": 0.3308481527645552,\n\
\ \"acc_stderr\": 0.008212170959780564\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.001363255033557047,\n \"em_stderr\": 0.00037786091964608703,\n\
\ \"f1\": 0.05910759228187943,\n \"f1_stderr\": 0.0013983745600314773\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.012130401819560273,\n \
\ \"acc_stderr\": 0.0030152942428909465\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.6495659037095501,\n \"acc_stderr\": 0.013409047676670182\n\
\ }\n}\n```"
repo_url: https://huggingface.co/dvruette/oasst-pythia-12b-reference
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_21T19_14_07.226959
path:
- '**/details_harness|drop|3_2023-10-21T19-14-07.226959.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-21T19-14-07.226959.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_21T19_14_07.226959
path:
- '**/details_harness|gsm8k|5_2023-10-21T19-14-07.226959.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-21T19-14-07.226959.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_21T19_14_07.226959
path:
- '**/details_harness|winogrande|5_2023-10-21T19-14-07.226959.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-21T19-14-07.226959.parquet'
- config_name: results
data_files:
- split: 2023_10_21T19_14_07.226959
path:
- results_2023-10-21T19-14-07.226959.parquet
- split: latest
path:
- results_2023-10-21T19-14-07.226959.parquet
---
# Dataset Card for Evaluation run of dvruette/oasst-pythia-12b-reference
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/dvruette/oasst-pythia-12b-reference
- **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 [dvruette/oasst-pythia-12b-reference](https://huggingface.co/dvruette/oasst-pythia-12b-reference) 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_dvruette__oasst-pythia-12b-reference",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-21T19:14:07.226959](https://huggingface.co/datasets/open-llm-leaderboard/details_dvruette__oasst-pythia-12b-reference/blob/main/results_2023-10-21T19-14-07.226959.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": 0.001363255033557047,
"em_stderr": 0.00037786091964608703,
"f1": 0.05910759228187943,
"f1_stderr": 0.0013983745600314773,
"acc": 0.3308481527645552,
"acc_stderr": 0.008212170959780564
},
"harness|drop|3": {
"em": 0.001363255033557047,
"em_stderr": 0.00037786091964608703,
"f1": 0.05910759228187943,
"f1_stderr": 0.0013983745600314773
},
"harness|gsm8k|5": {
"acc": 0.012130401819560273,
"acc_stderr": 0.0030152942428909465
},
"harness|winogrande|5": {
"acc": 0.6495659037095501,
"acc_stderr": 0.013409047676670182
}
}
```
### 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] | 7,343 | [
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pytony17/SportsOrders.csv | 2023-10-21T20:08:35.000Z | [
"region:us"
] | pytony17 | null | null | 0 | 0 | 2023-10-21T20:05:29 | Entry not found | 15 | [
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farshadafx/leda-data | 2023-10-21T20:17:32.000Z | [
"region:us"
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wf8888884/backup | 2023-10-21T21:03:56.000Z | [
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open-llm-leaderboard/details_TheBloke__WizardLM-7B-uncensored-GPTQ | 2023-10-21T21:04:37.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | 0 | 0 | 2023-10-21T21:04:30 | ---
pretty_name: Evaluation run of TheBloke/WizardLM-7B-uncensored-GPTQ
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [TheBloke/WizardLM-7B-uncensored-GPTQ](https://huggingface.co/TheBloke/WizardLM-7B-uncensored-GPTQ)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the 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_TheBloke__WizardLM-7B-uncensored-GPTQ\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-21T21:04:26.590858](https://huggingface.co/datasets/open-llm-leaderboard/details_TheBloke__WizardLM-7B-uncensored-GPTQ/blob/main/results_2023-10-21T21-04-26.590858.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\": 0.0,\n \"\
em_stderr\": 0.0,\n \"f1\": 0.0,\n \"f1_stderr\": 0.0,\n \"\
acc\": 0.2478295185477506,\n \"acc_stderr\": 0.007025978032038456\n },\n\
\ \"harness|drop|3\": {\n \"em\": 0.0,\n \"em_stderr\": 0.0,\n\
\ \"f1\": 0.0,\n \"f1_stderr\": 0.0\n },\n \"harness|gsm8k|5\"\
: {\n \"acc\": 0.0,\n \"acc_stderr\": 0.0\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.4956590370955012,\n \"acc_stderr\": 0.014051956064076911\n\
\ }\n}\n```"
repo_url: https://huggingface.co/TheBloke/WizardLM-7B-uncensored-GPTQ
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_10_21T21_04_26.590858
path:
- '**/details_harness|drop|3_2023-10-21T21-04-26.590858.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-21T21-04-26.590858.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_21T21_04_26.590858
path:
- '**/details_harness|gsm8k|5_2023-10-21T21-04-26.590858.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-21T21-04-26.590858.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_21T21_04_26.590858
path:
- '**/details_harness|winogrande|5_2023-10-21T21-04-26.590858.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-21T21-04-26.590858.parquet'
- config_name: results
data_files:
- split: 2023_10_21T21_04_26.590858
path:
- results_2023-10-21T21-04-26.590858.parquet
- split: latest
path:
- results_2023-10-21T21-04-26.590858.parquet
---
# Dataset Card for Evaluation run of TheBloke/WizardLM-7B-uncensored-GPTQ
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/TheBloke/WizardLM-7B-uncensored-GPTQ
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** clementine@hf.co
### Dataset Summary
Dataset automatically created during the evaluation run of model [TheBloke/WizardLM-7B-uncensored-GPTQ](https://huggingface.co/TheBloke/WizardLM-7B-uncensored-GPTQ) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the 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_TheBloke__WizardLM-7B-uncensored-GPTQ",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-21T21:04:26.590858](https://huggingface.co/datasets/open-llm-leaderboard/details_TheBloke__WizardLM-7B-uncensored-GPTQ/blob/main/results_2023-10-21T21-04-26.590858.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": 0.0,
"em_stderr": 0.0,
"f1": 0.0,
"f1_stderr": 0.0,
"acc": 0.2478295185477506,
"acc_stderr": 0.007025978032038456
},
"harness|drop|3": {
"em": 0.0,
"em_stderr": 0.0,
"f1": 0.0,
"f1_stderr": 0.0
},
"harness|gsm8k|5": {
"acc": 0.0,
"acc_stderr": 0.0
},
"harness|winogrande|5": {
"acc": 0.4956590370955012,
"acc_stderr": 0.014051956064076911
}
}
```
### 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] | 6,992 | [
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open-llm-leaderboard/details_lmsys__vicuna-13b-v1.3 | 2023-10-21T21:13:54.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | 0 | 0 | 2023-10-21T21:13:46 | ---
pretty_name: Evaluation run of lmsys/vicuna-13b-v1.3
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [lmsys/vicuna-13b-v1.3](https://huggingface.co/lmsys/vicuna-13b-v1.3) 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_lmsys__vicuna-13b-v1.3\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-21T21:13:42.887863](https://huggingface.co/datasets/open-llm-leaderboard/details_lmsys__vicuna-13b-v1.3/blob/main/results_2023-10-21T21-13-42.887863.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\": 0.016673657718120804,\n\
\ \"em_stderr\": 0.0013113056398144408,\n \"f1\": 0.08239932885906037,\n\
\ \"f1_stderr\": 0.0019047507167675335,\n \"acc\": 0.427940733843833,\n\
\ \"acc_stderr\": 0.010367986551641579\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.016673657718120804,\n \"em_stderr\": 0.0013113056398144408,\n\
\ \"f1\": 0.08239932885906037,\n \"f1_stderr\": 0.0019047507167675335\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.10765731614859743,\n \
\ \"acc_stderr\": 0.008537484003023366\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7482241515390686,\n \"acc_stderr\": 0.012198489100259792\n\
\ }\n}\n```"
repo_url: https://huggingface.co/lmsys/vicuna-13b-v1.3
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_21T21_13_42.887863
path:
- '**/details_harness|drop|3_2023-10-21T21-13-42.887863.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-21T21-13-42.887863.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_21T21_13_42.887863
path:
- '**/details_harness|gsm8k|5_2023-10-21T21-13-42.887863.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-21T21-13-42.887863.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_21T21_13_42.887863
path:
- '**/details_harness|winogrande|5_2023-10-21T21-13-42.887863.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-21T21-13-42.887863.parquet'
- config_name: results
data_files:
- split: 2023_10_21T21_13_42.887863
path:
- results_2023-10-21T21-13-42.887863.parquet
- split: latest
path:
- results_2023-10-21T21-13-42.887863.parquet
---
# Dataset Card for Evaluation run of lmsys/vicuna-13b-v1.3
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/lmsys/vicuna-13b-v1.3
- **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 [lmsys/vicuna-13b-v1.3](https://huggingface.co/lmsys/vicuna-13b-v1.3) 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_lmsys__vicuna-13b-v1.3",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-21T21:13:42.887863](https://huggingface.co/datasets/open-llm-leaderboard/details_lmsys__vicuna-13b-v1.3/blob/main/results_2023-10-21T21-13-42.887863.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": 0.016673657718120804,
"em_stderr": 0.0013113056398144408,
"f1": 0.08239932885906037,
"f1_stderr": 0.0019047507167675335,
"acc": 0.427940733843833,
"acc_stderr": 0.010367986551641579
},
"harness|drop|3": {
"em": 0.016673657718120804,
"em_stderr": 0.0013113056398144408,
"f1": 0.08239932885906037,
"f1_stderr": 0.0019047507167675335
},
"harness|gsm8k|5": {
"acc": 0.10765731614859743,
"acc_stderr": 0.008537484003023366
},
"harness|winogrande|5": {
"acc": 0.7482241515390686,
"acc_stderr": 0.012198489100259792
}
}
```
### 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] | 7,165 | [
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mugithi/ubuntu_question_answer_llama2 | 2023-10-21T22:09:19.000Z | [
"region:us"
] | mugithi | null | null | 0 | 0 | 2023-10-21T22:09:17 | ---
dataset_info:
features:
- name: '###question'
dtype: string
- name: '###answer'
dtype: string
splits:
- name: train
num_bytes: 2047241
num_examples: 12024
- name: test
num_bytes: 887478
num_examples: 5154
download_size: 1926803
dataset_size: 2934719
---
# Dataset Card for "ubuntu_question_answer_llama2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 481 | [
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savaskaplan/sk-review-dataset-sample | 2023-10-21T22:30:46.000Z | [
"region:us"
] | savaskaplan | null | null | 0 | 0 | 2023-10-21T22:30:44 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: review
dtype: string
- name: review_length
dtype: int64
splits:
- name: train
num_bytes: 1336616.5478017514
num_examples: 3600
- name: validation
num_bytes: 148512.94975575016
num_examples: 400
download_size: 951377
dataset_size: 1485129.4975575015
---
# Dataset Card for "sk-review-dataset-sample"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 643 | [
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TobiasKG/werehogsonic | 2023-10-21T23:01:29.000Z | [
"region:us"
] | TobiasKG | null | null | 0 | 0 | 2023-10-21T22:58:19 | Entry not found | 15 | [
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SatwikKambham/test_samples | 2023-10-21T23:51:45.000Z | [
"region:us"
] | SatwikKambham | null | null | 0 | 0 | 2023-10-21T23:50:10 | Entry not found | 15 | [
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open-llm-leaderboard/details_dvruette__oasst-pythia-12b-flash-attn-5000-steps | 2023-10-21T23:53:43.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | 0 | 0 | 2023-10-21T23:53:36 | ---
pretty_name: Evaluation run of dvruette/oasst-pythia-12b-flash-attn-5000-steps
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [dvruette/oasst-pythia-12b-flash-attn-5000-steps](https://huggingface.co/dvruette/oasst-pythia-12b-flash-attn-5000-steps)\
\ 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_dvruette__oasst-pythia-12b-flash-attn-5000-steps\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-21T23:53:32.630430](https://huggingface.co/datasets/open-llm-leaderboard/details_dvruette__oasst-pythia-12b-flash-attn-5000-steps/blob/main/results_2023-10-21T23-53-32.630430.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\": 0.001572986577181208,\n\
\ \"em_stderr\": 0.0004058451132417735,\n \"f1\": 0.054836409395973375,\n\
\ \"f1_stderr\": 0.001356882457395664,\n \"acc\": 0.3206343687936557,\n\
\ \"acc_stderr\": 0.00813976207357049\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.001572986577181208,\n \"em_stderr\": 0.0004058451132417735,\n\
\ \"f1\": 0.054836409395973375,\n \"f1_stderr\": 0.001356882457395664\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.009855951478392721,\n \
\ \"acc_stderr\": 0.002721076577041663\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.6314127861089187,\n \"acc_stderr\": 0.013558447570099316\n\
\ }\n}\n```"
repo_url: https://huggingface.co/dvruette/oasst-pythia-12b-flash-attn-5000-steps
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_21T23_53_32.630430
path:
- '**/details_harness|drop|3_2023-10-21T23-53-32.630430.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-21T23-53-32.630430.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_21T23_53_32.630430
path:
- '**/details_harness|gsm8k|5_2023-10-21T23-53-32.630430.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-21T23-53-32.630430.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_21T23_53_32.630430
path:
- '**/details_harness|winogrande|5_2023-10-21T23-53-32.630430.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-21T23-53-32.630430.parquet'
- config_name: results
data_files:
- split: 2023_10_21T23_53_32.630430
path:
- results_2023-10-21T23-53-32.630430.parquet
- split: latest
path:
- results_2023-10-21T23-53-32.630430.parquet
---
# Dataset Card for Evaluation run of dvruette/oasst-pythia-12b-flash-attn-5000-steps
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/dvruette/oasst-pythia-12b-flash-attn-5000-steps
- **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 [dvruette/oasst-pythia-12b-flash-attn-5000-steps](https://huggingface.co/dvruette/oasst-pythia-12b-flash-attn-5000-steps) 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_dvruette__oasst-pythia-12b-flash-attn-5000-steps",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-21T23:53:32.630430](https://huggingface.co/datasets/open-llm-leaderboard/details_dvruette__oasst-pythia-12b-flash-attn-5000-steps/blob/main/results_2023-10-21T23-53-32.630430.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": 0.001572986577181208,
"em_stderr": 0.0004058451132417735,
"f1": 0.054836409395973375,
"f1_stderr": 0.001356882457395664,
"acc": 0.3206343687936557,
"acc_stderr": 0.00813976207357049
},
"harness|drop|3": {
"em": 0.001572986577181208,
"em_stderr": 0.0004058451132417735,
"f1": 0.054836409395973375,
"f1_stderr": 0.001356882457395664
},
"harness|gsm8k|5": {
"acc": 0.009855951478392721,
"acc_stderr": 0.002721076577041663
},
"harness|winogrande|5": {
"acc": 0.6314127861089187,
"acc_stderr": 0.013558447570099316
}
}
```
### 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] | 7,479 | [
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open-llm-leaderboard/details_OpenAssistant__llama2-13b-megacode2-oasst | 2023-10-22T00:14:58.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | 0 | 0 | 2023-10-22T00:14:50 | ---
pretty_name: Evaluation run of OpenAssistant/llama2-13b-megacode2-oasst
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [OpenAssistant/llama2-13b-megacode2-oasst](https://huggingface.co/OpenAssistant/llama2-13b-megacode2-oasst)\
\ 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_OpenAssistant__llama2-13b-megacode2-oasst\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-22T00:14:46.537259](https://huggingface.co/datasets/open-llm-leaderboard/details_OpenAssistant__llama2-13b-megacode2-oasst/blob/main/results_2023-10-22T00-14-46.537259.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\": 0.002936241610738255,\n\
\ \"em_stderr\": 0.0005541113054709714,\n \"f1\": 0.07735004194630882,\n\
\ \"f1_stderr\": 0.0015929098030113627,\n \"acc\": 0.4585312232784996,\n\
\ \"acc_stderr\": 0.010977319038600733\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.002936241610738255,\n \"em_stderr\": 0.0005541113054709714,\n\
\ \"f1\": 0.07735004194630882,\n \"f1_stderr\": 0.0015929098030113627\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.155420773313116,\n \
\ \"acc_stderr\": 0.009979689409499152\n },\n \"harness|winogrande|5\":\
\ {\n \"acc\": 0.7616416732438832,\n \"acc_stderr\": 0.011974948667702314\n\
\ }\n}\n```"
repo_url: https://huggingface.co/OpenAssistant/llama2-13b-megacode2-oasst
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_22T00_14_46.537259
path:
- '**/details_harness|drop|3_2023-10-22T00-14-46.537259.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-22T00-14-46.537259.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_22T00_14_46.537259
path:
- '**/details_harness|gsm8k|5_2023-10-22T00-14-46.537259.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-22T00-14-46.537259.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_22T00_14_46.537259
path:
- '**/details_harness|winogrande|5_2023-10-22T00-14-46.537259.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-22T00-14-46.537259.parquet'
- config_name: results
data_files:
- split: 2023_10_22T00_14_46.537259
path:
- results_2023-10-22T00-14-46.537259.parquet
- split: latest
path:
- results_2023-10-22T00-14-46.537259.parquet
---
# Dataset Card for Evaluation run of OpenAssistant/llama2-13b-megacode2-oasst
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/OpenAssistant/llama2-13b-megacode2-oasst
- **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 [OpenAssistant/llama2-13b-megacode2-oasst](https://huggingface.co/OpenAssistant/llama2-13b-megacode2-oasst) 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_OpenAssistant__llama2-13b-megacode2-oasst",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-22T00:14:46.537259](https://huggingface.co/datasets/open-llm-leaderboard/details_OpenAssistant__llama2-13b-megacode2-oasst/blob/main/results_2023-10-22T00-14-46.537259.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": 0.002936241610738255,
"em_stderr": 0.0005541113054709714,
"f1": 0.07735004194630882,
"f1_stderr": 0.0015929098030113627,
"acc": 0.4585312232784996,
"acc_stderr": 0.010977319038600733
},
"harness|drop|3": {
"em": 0.002936241610738255,
"em_stderr": 0.0005541113054709714,
"f1": 0.07735004194630882,
"f1_stderr": 0.0015929098030113627
},
"harness|gsm8k|5": {
"acc": 0.155420773313116,
"acc_stderr": 0.009979689409499152
},
"harness|winogrande|5": {
"acc": 0.7616416732438832,
"acc_stderr": 0.011974948667702314
}
}
```
### 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] | 7,392 | [
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jibopabo/Choso | 2023-10-22T00:54:20.000Z | [
"region:us"
] | jibopabo | null | null | 0 | 0 | 2023-10-22T00:54:20 | Entry not found | 15 | [
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fun1021183/test_cvtGS3 | 2023-10-22T01:42:06.000Z | [
"region:us"
] | fun1021183 | null | null | 0 | 0 | 2023-10-22T01:42:01 | ---
dataset_info:
features:
- name: image
dtype: image
- name: caption
dtype: string
splits:
- name: train
num_bytes: 15127712.0
num_examples: 100
download_size: 15105334
dataset_size: 15127712.0
---
# Dataset Card for "test_cvtGS3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 395 | [
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aisyahhrazak/crawl-kakimuvee | 2023-10-22T01:47:56.000Z | [
"region:us"
] | aisyahhrazak | null | null | 0 | 0 | 2023-10-22T01:47:35 | Entry not found | 15 | [
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fishytorts/new_dataset_test | 2023-10-22T03:21:45.000Z | [
"region:us"
] | fishytorts | null | null | 0 | 0 | 2023-10-22T02:12:10 | ---
dataset_info:
features:
- name: audio
dtype: audio
- name: audio_names
dtype: string
- name: labels
dtype: int64
splits:
- name: train
num_bytes: 12388426.0
num_examples: 6
download_size: 12391206
dataset_size: 12388426.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "new_dataset_test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 522 | [
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fun1021183/cvt1_GS3_test | 2023-10-22T02:31:06.000Z | [
"region:us"
] | fun1021183 | null | null | 0 | 0 | 2023-10-22T02:30:03 | ---
dataset_info:
features:
- name: image
dtype: image
- name: caption
dtype: string
splits:
- name: train
num_bytes: 15191009.0
num_examples: 100
- name: test
num_bytes: 1715702.0
num_examples: 10
download_size: 174403
dataset_size: 16906711.0
---
# Dataset Card for "cv1_tGS3_test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 456 | [
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senthil3226w/autotrain-data-dfun-lk90-yhtx | 2023-10-22T02:32:54.000Z | [
"region:us"
] | senthil3226w | null | null | 0 | 0 | 2023-10-22T02:32:52 | ---
dataset_info:
features:
- name: Context
dtype: string
- name: Answers
dtype: string
- name: Length
dtype: int64
- name: Language
dtype: string
- name: autotrain_text
dtype: string
splits:
- name: train
num_bytes: 6008875
num_examples: 200
- name: validation
num_bytes: 6008875
num_examples: 200
download_size: 5303902
dataset_size: 12017750
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
---
# Dataset Card for "autotrain-data-dfun-lk90-yhtx"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 726 | [
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open-llm-leaderboard/details_jondurbin__airoboros-33b-gpt4 | 2023-10-22T02:33:30.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | 0 | 0 | 2023-10-22T02:33:22 | ---
pretty_name: Evaluation run of jondurbin/airoboros-33b-gpt4
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [jondurbin/airoboros-33b-gpt4](https://huggingface.co/jondurbin/airoboros-33b-gpt4)\
\ 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_jondurbin__airoboros-33b-gpt4\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-22T02:33:18.318001](https://huggingface.co/datasets/open-llm-leaderboard/details_jondurbin__airoboros-33b-gpt4/blob/main/results_2023-10-22T02-33-18.318001.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\": 0.16977768456375839,\n\
\ \"em_stderr\": 0.003844830120605158,\n \"f1\": 0.23901740771812038,\n\
\ \"f1_stderr\": 0.003895286466470977,\n \"acc\": 0.44846733402227057,\n\
\ \"acc_stderr\": 0.010490680442459668\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.16977768456375839,\n \"em_stderr\": 0.003844830120605158,\n\
\ \"f1\": 0.23901740771812038,\n \"f1_stderr\": 0.003895286466470977\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.12661106899166036,\n \
\ \"acc_stderr\": 0.009159715283081096\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7703235990528808,\n \"acc_stderr\": 0.01182164560183824\n\
\ }\n}\n```"
repo_url: https://huggingface.co/jondurbin/airoboros-33b-gpt4
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_22T02_33_18.318001
path:
- '**/details_harness|drop|3_2023-10-22T02-33-18.318001.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-22T02-33-18.318001.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_22T02_33_18.318001
path:
- '**/details_harness|gsm8k|5_2023-10-22T02-33-18.318001.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-22T02-33-18.318001.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_22T02_33_18.318001
path:
- '**/details_harness|winogrande|5_2023-10-22T02-33-18.318001.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-22T02-33-18.318001.parquet'
- config_name: results
data_files:
- split: 2023_10_22T02_33_18.318001
path:
- results_2023-10-22T02-33-18.318001.parquet
- split: latest
path:
- results_2023-10-22T02-33-18.318001.parquet
---
# Dataset Card for Evaluation run of jondurbin/airoboros-33b-gpt4
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/jondurbin/airoboros-33b-gpt4
- **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 [jondurbin/airoboros-33b-gpt4](https://huggingface.co/jondurbin/airoboros-33b-gpt4) 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_jondurbin__airoboros-33b-gpt4",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-22T02:33:18.318001](https://huggingface.co/datasets/open-llm-leaderboard/details_jondurbin__airoboros-33b-gpt4/blob/main/results_2023-10-22T02-33-18.318001.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": 0.16977768456375839,
"em_stderr": 0.003844830120605158,
"f1": 0.23901740771812038,
"f1_stderr": 0.003895286466470977,
"acc": 0.44846733402227057,
"acc_stderr": 0.010490680442459668
},
"harness|drop|3": {
"em": 0.16977768456375839,
"em_stderr": 0.003844830120605158,
"f1": 0.23901740771812038,
"f1_stderr": 0.003895286466470977
},
"harness|gsm8k|5": {
"acc": 0.12661106899166036,
"acc_stderr": 0.009159715283081096
},
"harness|winogrande|5": {
"acc": 0.7703235990528808,
"acc_stderr": 0.01182164560183824
}
}
```
### 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] | 7,239 | [
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ziqin/for-test | 2023-10-22T07:44:31.000Z | [
"task_categories:text-classification",
"language:zh",
"license:apache-2.0",
"code",
"region:us"
] | ziqin | null | null | 0 | 0 | 2023-10-22T03:07:18 | ---
license: apache-2.0
task_categories:
- text-classification
language:
- zh
tags:
- code
---
## For TEST
this is a dataset for test
just for test... | 151 | [
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lu-vae/Miracle-Conversation | 2023-10-30T12:12:09.000Z | [
"task_categories:conversational",
"language:en",
"region:us"
] | lu-vae | null | null | 0 | 0 | 2023-10-22T03:08:45 | ---
task_categories:
- conversational
language:
- en
---
This is the dataset curated from ChatGPT with personalized prompt from [Our EMNLP2023-findings Miracle](https://github.com/LZY-the-boys/MIRACLE)
We offer three personality aspects:
- 'a' = attitude (positive/negative)
- 'l' = language style (lyrical/plain)
- 'm' = mental characteristics (critical/emotional)
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satellite-image-deep-learning/SODA-A | 2023-10-22T05:19:07.000Z | [
"license:mit",
"remote-sensing",
"oriented-bounding-boxes",
"object-detection",
"region:us"
] | satellite-image-deep-learning | null | null | 0 | 0 | 2023-10-22T03:38:59 | ---
license: mit
tags:
- remote-sensing
- oriented-bounding-boxes
- object-detection
---
SODA-A comprises 2513 high-resolution images of aerial scenes, which has 872069 instances annotated with oriented rectangle box annotations over 9 classes.
- [Website](https://shaunyuan22.github.io/SODA/)
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Amoua/AmouData | 2023-10-22T04:28:06.000Z | [
"region:us"
] | Amoua | null | null | 0 | 0 | 2023-10-22T03:51:39 | Entry not found | 15 | [
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] |
nbalepur/CAQA | 2023-10-22T05:30:37.000Z | [
"region:us"
] | nbalepur | null | null | 0 | 0 | 2023-10-22T05:30:34 | ---
dataset_info:
features:
- name: concept
dtype: string
- name: concept_category
dtype: string
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struct:
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struct:
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sequence: string
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sequence: float64
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struct:
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sequence: string
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sequence: float64
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struct:
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struct:
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struct:
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struct:
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struct:
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struct:
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struct:
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struct:
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struct:
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struct:
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sequence: string
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struct:
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sequence: float64
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struct:
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struct:
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- name: Sub-Saharan Africa
struct:
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sequence: string
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struct:
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struct:
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sequence: string
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sequence: float64
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struct:
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sequence: string
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sequence: float64
- name: continents_data
struct:
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struct:
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sequence: string
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sequence: float64
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struct:
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struct:
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struct:
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struct:
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sequence: string
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sequence: float64
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struct:
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sequence: string
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sequence: float64
- name: countries_data
struct:
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struct:
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sequence: string
- name: scores
sequence: float64
- name: Albania
struct:
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- name: num_regions_data
dtype: int64
- name: num_continents_data
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- name: num_countries_data
dtype: int64
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dtype: int64
- name: total_num
dtype: int64
splits:
- name: train
num_bytes: 40802603
num_examples: 17118
download_size: 8234348
dataset_size: 40802603
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "CAQA"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 33,442 | [
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RecDol/PLAYLIST_airflow | 2023-10-23T10:10:52.000Z | [
"region:us"
] | RecDol | null | null | 0 | 0 | 2023-10-22T05:51:30 | Entry not found | 15 | [
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maxolotl/must-c-en-es-01 | 2023-10-22T06:27:42.000Z | [
"region:us"
] | maxolotl | null | null | 0 | 0 | 2023-10-22T06:27:36 | ---
dataset_info:
features:
- name: en
dtype: string
- name: es
dtype: string
splits:
- name: train
num_bytes: 59876087
num_examples: 259892
- name: test
num_bytes: 658233
num_examples: 3035
- name: validation
num_bytes: 310169
num_examples: 1309
download_size: 37505201
dataset_size: 60844489
---
# Dataset Card for "must-c-en-es-01"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 517 | [
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hosnasn/dreambooth-hackathon-images | 2023-10-22T06:52:46.000Z | [
"region:us"
] | hosnasn | null | null | 0 | 0 | 2023-10-22T06:52:44 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: image
dtype: image
splits:
- name: train
num_bytes: 1078145.0
num_examples: 24
download_size: 839878
dataset_size: 1078145.0
---
# Dataset Card for "dreambooth-hackathon-images"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 456 | [
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ouasdg/leerik-verses | 2023-10-22T07:12:31.000Z | [
"region:us"
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RecDol/faiss_index_airflow | 2023-10-23T10:12:14.000Z | [
"region:us"
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open-llm-leaderboard/details_TFLai__bloomz-1b7-4bit-alpaca | 2023-10-22T07:20:42.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | 0 | 0 | 2023-10-22T07:20:35 | ---
pretty_name: Evaluation run of TFLai/bloomz-1b7-4bit-alpaca
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [TFLai/bloomz-1b7-4bit-alpaca](https://huggingface.co/TFLai/bloomz-1b7-4bit-alpaca)\
\ 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_TFLai__bloomz-1b7-4bit-alpaca\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-22T07:20:32.245905](https://huggingface.co/datasets/open-llm-leaderboard/details_TFLai__bloomz-1b7-4bit-alpaca/blob/main/results_2023-10-22T07-20-32.245905.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\": 0.15845218120805368,\n\
\ \"em_stderr\": 0.0037396259228482218,\n \"f1\": 0.19149119127516798,\n\
\ \"f1_stderr\": 0.0038329285857522646,\n \"acc\": 0.2691397000789266,\n\
\ \"acc_stderr\": 0.007005621297482063\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.15845218120805368,\n \"em_stderr\": 0.0037396259228482218,\n\
\ \"f1\": 0.19149119127516798,\n \"f1_stderr\": 0.0038329285857522646\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \"acc_stderr\"\
: 0.0\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.5382794001578532,\n\
\ \"acc_stderr\": 0.014011242594964127\n }\n}\n```"
repo_url: https://huggingface.co/TFLai/bloomz-1b7-4bit-alpaca
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_22T07_20_32.245905
path:
- '**/details_harness|drop|3_2023-10-22T07-20-32.245905.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-22T07-20-32.245905.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_22T07_20_32.245905
path:
- '**/details_harness|gsm8k|5_2023-10-22T07-20-32.245905.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-22T07-20-32.245905.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_22T07_20_32.245905
path:
- '**/details_harness|winogrande|5_2023-10-22T07-20-32.245905.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-22T07-20-32.245905.parquet'
- config_name: results
data_files:
- split: 2023_10_22T07_20_32.245905
path:
- results_2023-10-22T07-20-32.245905.parquet
- split: latest
path:
- results_2023-10-22T07-20-32.245905.parquet
---
# Dataset Card for Evaluation run of TFLai/bloomz-1b7-4bit-alpaca
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/TFLai/bloomz-1b7-4bit-alpaca
- **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 [TFLai/bloomz-1b7-4bit-alpaca](https://huggingface.co/TFLai/bloomz-1b7-4bit-alpaca) 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_TFLai__bloomz-1b7-4bit-alpaca",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-22T07:20:32.245905](https://huggingface.co/datasets/open-llm-leaderboard/details_TFLai__bloomz-1b7-4bit-alpaca/blob/main/results_2023-10-22T07-20-32.245905.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": 0.15845218120805368,
"em_stderr": 0.0037396259228482218,
"f1": 0.19149119127516798,
"f1_stderr": 0.0038329285857522646,
"acc": 0.2691397000789266,
"acc_stderr": 0.007005621297482063
},
"harness|drop|3": {
"em": 0.15845218120805368,
"em_stderr": 0.0037396259228482218,
"f1": 0.19149119127516798,
"f1_stderr": 0.0038329285857522646
},
"harness|gsm8k|5": {
"acc": 0.0,
"acc_stderr": 0.0
},
"harness|winogrande|5": {
"acc": 0.5382794001578532,
"acc_stderr": 0.014011242594964127
}
}
```
### 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
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### Citation Information
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### Contributions
[More Information Needed] | 7,176 | [
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] |
maxolotl/must-c-en-de-01 | 2023-10-22T07:42:57.000Z | [
"region:us"
] | maxolotl | null | null | 0 | 0 | 2023-10-22T07:42:50 | ---
dataset_info:
features:
- name: en
dtype: string
- name: de
dtype: string
splits:
- name: train
num_bytes: 55588148
num_examples: 249032
- name: test
num_bytes: 683511
num_examples: 3159
- name: validation
num_bytes: 320578
num_examples: 1410
download_size: 35050288
dataset_size: 56592237
---
# Dataset Card for "must-c-en-de-01"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 517 | [
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R7x/007 | 2023-10-22T08:32:10.000Z | [
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mesolitica/translated-MetaMathQA | 2023-10-22T07:59:48.000Z | [
"region:us"
] | mesolitica | null | null | 0 | 0 | 2023-10-22T07:56:19 | Entry not found | 15 | [
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xjj12/xjjkaggle | 2023-10-27T10:40:31.000Z | [
"region:us"
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fun1021183/cvt1_GS3_test_f | 2023-10-22T09:22:19.000Z | [
"region:us"
] | fun1021183 | null | null | 0 | 0 | 2023-10-22T09:17:52 | ---
dataset_info:
features:
- name: image
dtype: image
- name: caption
dtype: string
splits:
- name: train
num_bytes: 2139623378.875
num_examples: 13257
- name: test
num_bytes: 745774671.875
num_examples: 4721
download_size: 2721265703
dataset_size: 2885398050.75
---
# Dataset Card for "cvt1_GS3_test_f"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 477 | [
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mb23/cvt1_GS3_1 | 2023-10-25T05:57:53.000Z | [
"region:us"
] | mb23 | null | null | 0 | 0 | 2023-10-22T09:25:44 | ---
dataset_info:
features:
- name: image
dtype: image
- name: caption
dtype: string
splits:
- name: train
num_bytes: 2139623378.875
num_examples: 13257
- name: test
num_bytes: 745774671.875
num_examples: 4721
download_size: 2721265703
dataset_size: 2885398050.75
---
# Dataset Card for "cvt1_GS3_1"
* ヒストグラム平坦化をGraySpectrogram3に適用
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 504 | [
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akki99/testtraining | 2023-10-22T10:22:28.000Z | [
"region:us"
] | akki99 | null | null | 0 | 0 | 2023-10-22T10:22:28 | Entry not found | 15 | [
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PokeABear00/ASA | 2023-10-22T10:37:25.000Z | [
"region:us"
] | PokeABear00 | null | null | 0 | 0 | 2023-10-22T10:37:25 | Entry not found | 15 | [
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salaxieb/phrases | 2023-10-22T13:29:40.000Z | [
"region:us"
] | salaxieb | null | null | 0 | 0 | 2023-10-22T10:57:06 | Entry not found | 15 | [
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] |
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