datasetId stringlengths 2 117 | card stringlengths 19 1.01M |
|---|---|
Lenylvt/FourSouls | ---
license: mit
language:
- en
pretty_name: Four Souls
size_categories:
- 1K<n<10K
---
# The Binding of Isaac: Four Souls Card
Here you can find all card from the The Binding of Isaac: Four Souls card game in different format :
- Normal Card
- Printable-Ready card, for more information : https://printfoursouls.com/
- Cropped Illustration Image
- Illustration without Background
All image are downloaded from [Four Souls site](https://foursouls.com/) |
results-sd-v1-5-sd-v2-1-if-v1-0-karlo/e7874b25 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 182
num_examples: 10
download_size: 1341
dataset_size: 182
---
# Dataset Card for "e7874b25"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
AndrewMetaBlock/andrew_test | ---
license: apache-2.0
---
|
Darkosta/omen | ---
license: openrail
---
|
facebook/PUG_ImageNet | ---
license: cc-by-nc-4.0
dataset_info:
features:
- name: image
dtype: image
- name: world_name
dtype: string
- name: character_name
dtype: string
- name: character_label
dtype: string
- name: character_rotation_yaw
dtype: int64
- name: character_rotation_roll
dtype: int64
- name: character_rotation_pitch
dtype: int64
- name: character_scale
dtype: float64
- name: camera_roll
dtype: int64
- name: camera_pitch
dtype: int64
- name: camera_yaw
dtype: int64
- name: character_texture
dtype: string
- name: scene_light
dtype: string
splits:
- name: train
num_bytes: 29382707151.112
num_examples: 88328
download_size: 29358745565
dataset_size: 29382707151.112
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
## PUG: ImageNet
The PUG: ImageNet dataset contains 88,328 pre-rendered images based on Unreal Engine using 724 assets representing 151 ImageNet classes with 64 environments, 7 sizes, 9 textures, 18 different camera orientations, 18 different character orientations and 7 light intensities. In contrast to PUG: Animals, PUG: ImageNet was created by varying only a single factor at a time (which explains the lower number of images than PUG: Animals despite using more factors). The main purpose of this dataset is to provide a novel, useful benchmark, paralleling ImageNet, but for fine-grained evaluation of the robustness of image classifiers, along several factors of variation.
## LICENSE
The datasets are distributed under the CC-BY-NC, with the addenda that they should not be used to train Generative AI models.
## Citing PUG
If you use one of the PUG datasets, please cite:
```
@misc{bordes2023pug,
title={PUG: Photorealistic and Semantically Controllable Synthetic Data for Representation Learning},
author={Florian Bordes and Shashank Shekhar and Mark Ibrahim and Diane Bouchacourt and Pascal Vincent and Ari S. Morcos},
year={2023},
eprint={2308.03977},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
## To learn more about the PUG datasets:
Please visit the [website](https://pug.metademolab.com/) and the [github](https://github.com/facebookresearch/PUG) |
AppleHarem/kokona_bluearchive | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of kokona (Blue Archive)
This is the dataset of kokona (Blue Archive), containing 150 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).([LittleAppleWebUI](https://github.com/LittleApple-fp16/LittleAppleWebUI))
| Name | Images | Download | Description |
|:----------------|---------:|:----------------------------------------|:-----------------------------------------------------------------------------------------|
| raw | 150 | [Download](dataset-raw.zip) | Raw data with meta information. |
| raw-stage3 | 416 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. |
| raw-stage3-eyes | 505 | [Download](dataset-raw-stage3-eyes.zip) | 3-stage cropped (with eye-focus) raw data with meta information. |
| 384x512 | 150 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. |
| 512x704 | 150 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. |
| 640x880 | 150 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. |
| stage3-640 | 416 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. |
| stage3-800 | 416 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. |
| stage3-p512-640 | 390 | [Download](dataset-stage3-p512-640.zip) | 3-stage cropped dataset with the area not less than 512x512 pixels. |
| stage3-eyes-640 | 505 | [Download](dataset-stage3-eyes-640.zip) | 3-stage cropped (with eye-focus) dataset with the shorter side not exceeding 640 pixels. |
| stage3-eyes-800 | 505 | [Download](dataset-stage3-eyes-800.zip) | 3-stage cropped (with eye-focus) dataset with the shorter side not exceeding 800 pixels. |
|
waynehwang/customkopocode | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 5826
num_examples: 39
download_size: 2572
dataset_size: 5826
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
bilgedogan/facebook_mms-tts-eng_GPU-CPU | ---
license: apache-2.0
dataset_info:
- config_name: facebook_mms-tts-eng_CPU
features:
- name: audio
dtype: audio
- name: id
dtype: string
- name: text
dtype: string
- name: time
dtype: float64
splits:
- name: train
num_bytes: 4001284.0
num_examples: 20
download_size: 3813822
dataset_size: 4001284.0
- config_name: facebook_mms-tts-eng_GPU
features:
- name: audio
dtype: audio
- name: id
dtype: string
- name: text
dtype: string
- name: time
dtype: float64
splits:
- name: train
num_bytes: 3943428.0
num_examples: 20
download_size: 3758962
dataset_size: 3943428.0
configs:
- config_name: facebook_mms-tts-eng_CPU
data_files:
- split: train
path: facebook_mms-tts-eng_CPU/train-*
- config_name: facebook_mms-tts-eng_GPU
data_files:
- split: train
path: facebook_mms-tts-eng_GPU/train-*
---
|
results-sd-v1-5-sd-v2-1-if-v1-0-karlo/1a05946e | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 182
num_examples: 10
download_size: 1337
dataset_size: 182
---
# Dataset Card for "1a05946e"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
YutoNishimura-v2/text-to-kanji-v2 | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 27051189.53
num_examples: 6410
download_size: 32115722
dataset_size: 27051189.53
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
coastalcph/fm-updates-llama-7b | ---
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
- split: validation
path: data/validation-*
dataset_info:
features:
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struct:
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- name: objects
list:
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sequence: string
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- name: prediction
struct:
- name: predictions
list:
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dtype: string
- name: first_token_probability
dtype: float64
- name: per_token_probability
sequence: float64
- name: perplexity
dtype: float64
- name: query
dtype: string
- name: f1
dtype: float64
- name: relation
dtype: string
- name: type
dtype: string
- name: original_answer
dtype: string
- name: updates
sequence: string
splits:
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num_examples: 480
- name: validation
num_bytes: 46827.315551366635
num_examples: 51
download_size: 380771
dataset_size: 487554.99132893496
---
# Dataset Card for "fm-updates-llama-7b"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
gguichard/wsd_myriade_synth_data_gpt4turbo_3 | ---
dataset_info:
features:
- name: tokens
sequence: string
- name: wn_sens
sequence: int64
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 26145840
num_examples: 39519
download_size: 5496417
dataset_size: 26145840
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
aciborowska/test_dataset | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: Consumer_complaint_narrative
dtype: string
splits:
- name: train
num_bytes: 981959
num_examples: 1000
download_size: 493502
dataset_size: 981959
---
# Dataset Card for "test_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Mitsuki-Sakamoto/alpaca_farm-deberta-re-pref-64-fil_self_160m_bo16_2_mix_50_kl_0.1_prm_70m_thr_0.0_seed_2_tp_0.3 | ---
dataset_info:
config_name: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1
features:
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dtype: string
- name: input
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- name: gen_kwargs
struct:
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- name: max_new_tokens
dtype: int64
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- name: top_k
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- name: top_p
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- name: reward_1
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- name: reward_2
dtype: float64
- name: n_samples
dtype: int64
- name: reject_select
dtype: string
- name: index
dtype: int64
- name: prompt
dtype: string
- name: chosen
dtype: string
- name: rejected
dtype: string
- name: filtered_epoch
dtype: int64
- name: gen_reward
dtype: float64
- name: gen_response
dtype: string
splits:
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num_examples: 18928
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num_bytes: 44199746
num_examples: 18928
- name: epoch_29
num_bytes: 44199365
num_examples: 18928
download_size: 680389342
dataset_size: 1325642636
configs:
- config_name: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1
data_files:
- split: epoch_0
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_0-*
- split: epoch_1
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_1-*
- split: epoch_2
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_2-*
- split: epoch_3
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_3-*
- split: epoch_4
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_4-*
- split: epoch_5
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_5-*
- split: epoch_6
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_6-*
- split: epoch_7
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_7-*
- split: epoch_8
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_8-*
- split: epoch_9
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_9-*
- split: epoch_10
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_10-*
- split: epoch_11
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_11-*
- split: epoch_12
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_12-*
- split: epoch_13
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_13-*
- split: epoch_14
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_14-*
- split: epoch_15
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_15-*
- split: epoch_16
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_16-*
- split: epoch_17
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_17-*
- split: epoch_18
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_18-*
- split: epoch_19
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_19-*
- split: epoch_20
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_20-*
- split: epoch_21
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_21-*
- split: epoch_22
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_22-*
- split: epoch_23
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_23-*
- split: epoch_24
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_24-*
- split: epoch_25
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_25-*
- split: epoch_26
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_26-*
- split: epoch_27
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_27-*
- split: epoch_28
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_28-*
- split: epoch_29
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_29-*
---
|
Ateeqq/AI-and-Human-Generated-Text | ---
license: mit
language:
- en
size_categories:
- 10K<n<100K
task_categories:
- text-classification
---
# AI & Human Generated Text
## I am Using this dataset for AI Text Detection for https://exnrt.com.
Check Original DataSet GitHub Repository Here: https://github.com/panagiotisanagnostou/AI-GA
## Description
The AI-GA dataset, short for Artificial Intelligence Generated Abstracts, comprises abstracts and titles. Half of these abstracts are generated by AI, while the remaining half are original. Primarily intended for research and experimentation in natural language processing, especially concerning language generation and machine learning, this dataset offers ample opportunities for exploration and analysis.
The AI-GA dataset comprises 28,662 samples, each containing an abstract, a title, and a label. It is evenly divided into two categories: "AI-generated abstracts" and "original abstracts." The label distinguishes between an original abstract (labeled 0) and an AI-generated one (labeled 1). Notably, the AI-generated abstracts are crafted using cutting-edge language generation techniques, notably leveraging the GPT-3 model.
### Large Alternative:
This compilation encompasses https://github.com/sakibsh/LLM both human-authored and LLM-generated (utilizing GPT-4 and BARD) texts spanning various genres such as essays, stories, poetry, and Python code. It serves as a valuable asset for investigating LLM text detection methodologies. |
nicholasbien/lakh-dataset-full-opt | ---
dataset_info:
features:
- name: text
dtype: string
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
splits:
- name: train
num_bytes: 1478210437
num_examples: 13560
- name: test
num_bytes: 372102436
num_examples: 3390
download_size: 656124125
dataset_size: 1850312873
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
LambdaTests/VQAv2Validation_ViT_H_14_A_T_C_Q_benchmarks_partition_global_14_1000 | ---
dataset_info:
features:
- name: id
dtype: int64
- name: response
dtype: string
splits:
- name: train
num_bytes: 926
num_examples: 32
download_size: 2016
dataset_size: 926
---
# Dataset Card for "VQAv2Validation_ViT_H_14_A_T_C_Q_benchmarks_partition_global_14_1000"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
rharit/stackoverflow_hw_dataset | ---
license: llama2
---
|
shidowake/glaive-code-assistant-v1-sharegpt-format_split_1 | ---
dataset_info:
features:
- name: conversations
list:
- name: from
dtype: string
- name: value
dtype: string
splits:
- name: train
num_bytes: 10505381.150871728
num_examples: 6806
download_size: 5143965
dataset_size: 10505381.150871728
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
aryamannningombam/indian-english-lady-embeddings-v3 | ---
dataset_info:
features:
- name: text
dtype: string
- name: file
dtype: string
- name: y
sequence: float32
splits:
- name: train
num_bytes: 11408261281
num_examples: 46993
download_size: 11437777844
dataset_size: 11408261281
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
CyberHarem/florence_neuralcloud | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of florence/フローレンス/芙洛伦 (Neural Cloud)
This is the dataset of florence/フローレンス/芙洛伦 (Neural Cloud), containing 265 images and their tags.
The core tags of this character are `blue_eyes, breasts, twintails, symbol-shaped_pupils, long_hair, blue_hair, heart-shaped_pupils, bangs, hair_between_eyes, small_breasts, hair_ornament, medium_breasts`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:----------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 265 | 436.70 MiB | [Download](https://huggingface.co/datasets/CyberHarem/florence_neuralcloud/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 265 | 204.71 MiB | [Download](https://huggingface.co/datasets/CyberHarem/florence_neuralcloud/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 707 | 491.11 MiB | [Download](https://huggingface.co/datasets/CyberHarem/florence_neuralcloud/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 265 | 364.44 MiB | [Download](https://huggingface.co/datasets/CyberHarem/florence_neuralcloud/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 707 | 759.36 MiB | [Download](https://huggingface.co/datasets/CyberHarem/florence_neuralcloud/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/florence_neuralcloud',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 5 |  |  |  |  |  | 1girl, black_gloves, blush, china_dress, half_gloves, heart, looking_at_viewer, no_panties, official_alternate_costume, pelvic_curtain, solo, twin_braids, white_background, blue_thighhighs, simple_background, smile, thighs, armpits, closed_mouth, open_mouth |
| 1 | 7 |  |  |  |  |  | 1girl, bare_shoulders, black_gloves, blue_thighhighs, china_dress, covered_navel, looking_at_viewer, official_alternate_costume, pelvic_curtain, solo, thighs, twin_braids, blue_dress, half_gloves, blush, heart, no_panties, smile, choker, sitting, white_background, collarbone, light_blue_hair, open_mouth, simple_background |
| 2 | 5 |  |  |  |  |  | 1girl, bare_shoulders, blue_thighhighs, looking_at_viewer, single_thighhigh, solo, white_background, black_leotard, heart, simple_background, covered_navel, grey_hair, sitting, smile, black_gloves, highleg_leotard, light_blue_hair |
| 3 | 36 |  |  |  |  |  | 1girl, looking_at_viewer, smile, solo, heart, nurse_cap, black_gloves, dress, open_mouth, white_thighhighs, holding_syringe, blush, id_card, white_background, grey_hair, intravenous_drip, simple_background, panties, pill |
| 4 | 19 |  |  |  |  |  | 1girl, official_alternate_costume, bare_shoulders, solo, hair_ribbon, looking_at_viewer, off_shoulder, blush, collarbone, long_sleeves, smile, black_choker, thigh_strap, white_background, blue_ribbon, barefoot, glasses, heart_print, two_side_up, white_shirt, blue-framed_eyewear, bottomless, simple_background, blue_nails, holding, naked_shirt, no_panties |
| 5 | 14 |  |  |  |  |  | 1girl, official_alternate_costume, school_uniform, solo, white_shirt, black_skirt, fox_ears, blush, fox_tail, heart, long_sleeves, looking_at_viewer, simple_background, white_thighhighs, collared_shirt, pleated_skirt, animal_ear_fluff, fox_girl, white_background, hairclip, plaid_skirt, smile, black_choker, open_mouth, sweater_vest, thighs, blue_bowtie, fang, miniskirt, cowboy_shot, holding, medium_hair, sitting |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | black_gloves | blush | china_dress | half_gloves | heart | looking_at_viewer | no_panties | official_alternate_costume | pelvic_curtain | solo | twin_braids | white_background | blue_thighhighs | simple_background | smile | thighs | armpits | closed_mouth | open_mouth | bare_shoulders | covered_navel | blue_dress | choker | sitting | collarbone | light_blue_hair | single_thighhigh | black_leotard | grey_hair | highleg_leotard | nurse_cap | dress | white_thighhighs | holding_syringe | id_card | intravenous_drip | panties | pill | hair_ribbon | off_shoulder | long_sleeves | black_choker | thigh_strap | blue_ribbon | barefoot | glasses | heart_print | two_side_up | white_shirt | blue-framed_eyewear | bottomless | blue_nails | holding | naked_shirt | school_uniform | black_skirt | fox_ears | fox_tail | collared_shirt | pleated_skirt | animal_ear_fluff | fox_girl | hairclip | plaid_skirt | sweater_vest | blue_bowtie | fang | miniskirt | cowboy_shot | medium_hair |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:---------------|:--------|:--------------|:--------------|:--------|:--------------------|:-------------|:-----------------------------|:-----------------|:-------|:--------------|:-------------------|:------------------|:--------------------|:--------|:---------|:----------|:---------------|:-------------|:-----------------|:----------------|:-------------|:---------|:----------|:-------------|:------------------|:-------------------|:----------------|:------------|:------------------|:------------|:--------|:-------------------|:------------------|:----------|:-------------------|:----------|:-------|:--------------|:---------------|:---------------|:---------------|:--------------|:--------------|:-----------|:----------|:--------------|:--------------|:--------------|:----------------------|:-------------|:-------------|:----------|:--------------|:-----------------|:--------------|:-----------|:-----------|:-----------------|:----------------|:-------------------|:-----------|:-----------|:--------------|:---------------|:--------------|:-------|:------------|:--------------|:--------------|
| 0 | 5 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 7 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 5 |  |  |  |  |  | X | X | | | | X | X | | | | X | | X | X | X | X | | | | | X | X | | | X | | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 36 |  |  |  |  |  | X | X | X | | | X | X | | | | X | | X | | X | X | | | | X | | | | | | | | | | X | | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 19 |  |  |  |  |  | X | | X | | | | X | X | X | | X | | X | | X | X | | | | | X | | | | | X | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | |
| 5 | 14 |  |  |  |  |  | X | | X | | | X | X | | X | | X | | X | | X | X | X | | | X | | | | | X | | | | | | | | | X | | | | | | | | X | X | | | | | | | X | | | | X | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
|
CristianaLazar/librispeech_augm_validation-tiny | ---
dataset_info:
features:
- name: file
dtype: string
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: text
dtype: string
- name: speaker_id
dtype: int64
- name: chapter_id
dtype: int64
- name: id
dtype: string
- name: input_features
sequence:
sequence: float32
- name: labels
sequence: int64
splits:
- name: validation
num_bytes: 3218271771.125
num_examples: 2703
download_size: 1320733851
dataset_size: 3218271771.125
---
# Dataset Card for "librispeech_augm_validation-tiny"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
RogerB/unsupervised_kin_tweets | ---
dataset_info:
features:
- name: cased_tweet
dtype: string
- name: uncased_tweet
dtype: string
splits:
- name: train
num_bytes: 10083279
num_examples: 40998
download_size: 7360726
dataset_size: 10083279
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "unsupervised_kin_tweets"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
readerbench/ro-fb-offense | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- ro
license:
- apache-2.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- hate-speech-detection
pretty_name: RO-FB-Offense
extra_gated_prompt: 'Warning: this repository contains harmful content (abusive language,
hate speech).'
tags:
- hate-speech-detection
---
# Dataset Card for "RO-FB-Offense"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://github.com/readerbench/ro-fb-offense](https://github.com/readerbench/ro-fb-offense)
- **Repository:** [https://github.com/readerbench/ro-fb-offense](https://github.com/readerbench/ro-fb-offense)
- **Paper:** FB-RO-Offense – A Romanian Dataset and Baseline Models for detecting Offensive Language in Facebook Comments
- **Point of Contact:** [Andrei Paraschiv](https://github.com/AndyTheFactory)
### Dataset Summary
FB-RO-Offense corpus, an offensive speech dataset containing 4,455 user-generated comments from Facebook live broadcasts available in Romanian
The annotation follows the hierarchical tagset proposed in the Germeval 2018 Dataset.
The following Classes are available:
* OTHER: Non-Offensive Language
* OFFENSIVE:
- PROFANITY
- INSULT
- ABUSE
### Languages
Romanian
## Dataset Structure
### Data Instances
An example of 'train' looks as follows.
```
{
'sender': '$USER1208',
'no_reacts': 1,
'text': 'PLACEHOLDER TEXT',
'label': OTHER,
}
```
### Data Fields
- `sender`: a `string` feature.
- 'no_reacts': a `integer`
- `text`: a `string`.
- `label`: categorical `OTHER`, `PROFANITY`, `INSULT`, `ABUSE`
### Data Splits
| name |train|test|
|---------|----:|---:|
|ro|x|x|
## Dataset Creation
### Curation Rationale
Collecting data for abusive language classification for Romanian Language.
### Source Data
Facebook comments
#### Initial Data Collection and Normalization
#### Who are the source language producers?
Social media users
### Annotations
#### Annotation process
#### Who are the annotators?
Native speakers
### Personal and Sensitive Information
The data was public at the time of collection. No PII removal has been performed.
## Considerations for Using the Data
### Social Impact of Dataset
The data definitely contains abusive language. The data could be used to develop and propagate offensive language against every target group involved, i.e. ableism, racism, sexism, ageism, and so on.
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
This data is available and distributed under Apache-2.0 license
### Citation Information
```
@inproceedings{busuioc2022fb-ro-offense,
title={FB-RO-Offense – A Romanian Dataset and Baseline Models for detecting Offensive Language in Facebook Comments},
author={ Busuioc, Gabriel-Razvan and Paraschiv, Andrei and Dascalu, Mihai},
booktitle={International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC) 2022},
year={2022}
}
```
### Contributions
|
huggingartists/system-of-a-down | ---
language:
- en
tags:
- huggingartists
- lyrics
---
# Dataset Card for "huggingartists/system-of-a-down"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [How to use](#how-to-use)
- [Dataset Structure](#dataset-structure)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [About](#about)
## Dataset Description
- **Homepage:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
- **Repository:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of the generated dataset:** 0.178799 MB
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/5688d59e74bfc07b0531636114f56c1e.1000x1000x1.jpg')">
</div>
</div>
<a href="https://huggingface.co/huggingartists/system-of-a-down">
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
</a>
<div style="text-align: center; font-size: 16px; font-weight: 800">System of a Down</div>
<a href="https://genius.com/artists/system-of-a-down">
<div style="text-align: center; font-size: 14px;">@system-of-a-down</div>
</a>
</div>
### Dataset Summary
The Lyrics dataset parsed from Genius. This dataset is designed to generate lyrics with HuggingArtists.
Model is available [here](https://huggingface.co/huggingartists/system-of-a-down).
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
en
## How to use
How to load this dataset directly with the datasets library:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/system-of-a-down")
```
## Dataset Structure
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"text": "Look, I was gonna go easy on you\nNot to hurt your feelings\nBut I'm only going to get this one chance\nSomething's wrong, I can feel it..."
}
```
### Data Fields
The data fields are the same among all splits.
- `text`: a `string` feature.
### Data Splits
| train |validation|test|
|------:|---------:|---:|
|129| -| -|
'Train' can be easily divided into 'train' & 'validation' & 'test' with few lines of code:
```python
from datasets import load_dataset, Dataset, DatasetDict
import numpy as np
datasets = load_dataset("huggingartists/system-of-a-down")
train_percentage = 0.9
validation_percentage = 0.07
test_percentage = 0.03
train, validation, test = np.split(datasets['train']['text'], [int(len(datasets['train']['text'])*train_percentage), int(len(datasets['train']['text'])*(train_percentage + validation_percentage))])
datasets = DatasetDict(
{
'train': Dataset.from_dict({'text': list(train)}),
'validation': Dataset.from_dict({'text': list(validation)}),
'test': Dataset.from_dict({'text': list(test)})
}
)
```
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@InProceedings{huggingartists,
author={Aleksey Korshuk}
year=2021
}
```
## About
*Built by Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
|
lilyhg/test | ---
license: apache-2.0
---
|
lorinma/Slim-COIG-Kun | ---
task_categories:
- text-generation
- conversational
language:
- zh
size_categories:
- 1K<n<10K
---
This is a Slim version of [COIG-Kun](https://huggingface.co/datasets/m-a-p/COIG-Kun)
因为原始的数据集有53万条之多，所以进行了subsample。
采样方法大致为，使用[bert-base-chinese](https://huggingface.co/bert-base-chinese)将Instruction转换为embedding，使用[类knn的方法](https://arxiv.org/pdf/1708.00489.pdf)抽取了1万条。并转换成了sharegpt格式。
为了更直观的查看效果，文件中还有一个仅采样了1千条的版本。采样前后的Embedding使用tsne进行可视化。
original Kun(蓝色)和Moss003（红色）的区别，是否可解读为虽然Kun的数量很高，但是首个instruction的语义多样化不如Moss。
|
AdapterOcean/med_alpaca_standardized_cluster_21_std | ---
dataset_info:
features:
- name: message
dtype: string
- name: message_type
dtype: string
- name: message_id
dtype: int64
- name: conversation_id
dtype: int64
- name: cluster
dtype: float64
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 13641035
num_examples: 31134
download_size: 6689762
dataset_size: 13641035
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "med_alpaca_standardized_cluster_21_std"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
autoevaluate/autoeval-staging-eval-project-896d78da-9e5e-4706-b736-32d4a31ff571-5549 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- autoevaluate/mnist-sample
eval_info:
task: image_multi_class_classification
model: autoevaluate/image-multi-class-classification
metrics: ['matthews_correlation']
dataset_name: autoevaluate/mnist-sample
dataset_config: autoevaluate--mnist-sample
dataset_split: test
col_mapping:
image: image
target: label
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Multi-class Image Classification
* Model: autoevaluate/image-multi-class-classification
* Dataset: autoevaluate/mnist-sample
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
goodemagod/sommy-2.3 | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 956074
num_examples: 1000
download_size: 553417
dataset_size: 956074
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
abgf9145/mymodels | ---
license: openrail
---
|
Xenova/ai-tube-my-chess-bot | ---
license: cc-by-nc-sa-4.0
pretty_name: My Chess Bot
tags:
- "ai-tube:Chess Bot"
---
## Description
I am a chess expert.
## Prompt
A video channel managed by a renowed grandmaster, Mongoose Carlsun.
The videos are informative, but playful and fun.
|
BangumiBase/studentcouncilsdiscretion | ---
license: mit
tags:
- art
size_categories:
- 1K<n<10K
---
# Bangumi Image Base of Student Council's Discretion
This is the image base of bangumi Student Council's Discretion, we detected 18 characters, 3613 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|
| 0 | 491 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 887 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 26 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 473 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 64 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 75 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 45 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 31 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 14 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 83 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 162 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 444 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 18 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 10 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 708 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 12 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 9 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 61 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
Mitsuki-Sakamoto/alpaca_farm-deberta-re-pref-64-fil_self_160m_bo16_2_mix_50_kl_0.1_prm_70m_thr_0.0_seed_2_tp_0.5 | ---
dataset_info:
config_name: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
- name: preference
dtype: int64
- name: output_1
dtype: string
- name: output_2
dtype: string
- name: reward_model_prompt_format
dtype: string
- name: gen_prompt_format
dtype: string
- name: gen_kwargs
struct:
- name: do_sample
dtype: bool
- name: max_new_tokens
dtype: int64
- name: pad_token_id
dtype: int64
- name: top_k
dtype: int64
- name: top_p
dtype: float64
- name: reward_1
dtype: float64
- name: reward_2
dtype: float64
- name: n_samples
dtype: int64
- name: reject_select
dtype: string
- name: index
dtype: int64
- name: prompt
dtype: string
- name: chosen
dtype: string
- name: rejected
dtype: string
- name: filtered_epoch
dtype: int64
- name: gen_reward
dtype: float64
- name: gen_response
dtype: string
splits:
- name: epoch_0
num_bytes: 43649296
num_examples: 18928
- name: epoch_1
num_bytes: 44179241
num_examples: 18928
- name: epoch_2
num_bytes: 44233071
num_examples: 18928
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- name: epoch_4
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- name: epoch_5
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num_bytes: 44216649
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- name: epoch_28
num_bytes: 44216470
num_examples: 18928
- name: epoch_29
num_bytes: 44216767
num_examples: 18928
download_size: 684866038
dataset_size: 1326100134
configs:
- config_name: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1
data_files:
- split: epoch_0
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_0-*
- split: epoch_1
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_1-*
- split: epoch_2
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_2-*
- split: epoch_3
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_3-*
- split: epoch_4
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_4-*
- split: epoch_5
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_5-*
- split: epoch_6
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_6-*
- split: epoch_7
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_7-*
- split: epoch_8
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_8-*
- split: epoch_9
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_9-*
- split: epoch_10
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_10-*
- split: epoch_11
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_11-*
- split: epoch_12
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_12-*
- split: epoch_13
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_13-*
- split: epoch_14
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_14-*
- split: epoch_15
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_15-*
- split: epoch_16
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_16-*
- split: epoch_17
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_17-*
- split: epoch_18
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_18-*
- split: epoch_19
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_19-*
- split: epoch_20
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_20-*
- split: epoch_21
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_21-*
- split: epoch_22
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_22-*
- split: epoch_23
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_23-*
- split: epoch_24
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_24-*
- split: epoch_25
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_25-*
- split: epoch_26
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_26-*
- split: epoch_27
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_27-*
- split: epoch_28
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_28-*
- split: epoch_29
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_29-*
---
|
lol-cod/captchadataset | ---
license: unknown
language:
- en
tags:
- keras captcha solving
pretty_name: database
--- |
jage/dataset_from_synthea_for_NER_with_train_val_test_splits | ---
dataset_info:
features:
- name: id
dtype: string
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
0: O
1: B-DATE
2: I-DATE
3: B-NAME
4: I-NAME
5: B-AGE
6: I-AGE
- name: input_ids
sequence: int32
- name: token_type_ids
sequence: int8
- name: attention_mask
sequence: int8
- name: labels
sequence: int64
splits:
- name: test
num_bytes: 6614328
num_examples: 19176
- name: train
num_bytes: 32139432.0
num_examples: 92300
- name: val
num_bytes: 13463574.0
num_examples: 38138
download_size: 4703482
dataset_size: 52217334.0
---
# Dataset Card for "dataset_from_synthea_for_NER_with_train_val_test_splits"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
ak-hugging-face/fine_tune_llama_v2 | ---
license: mit
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 12544460
num_examples: 8000
download_size: 7412466
dataset_size: 12544460
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
CyberHarem/peter_strasser_azurlane | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of peter_strasser/ペーター・シュトラッサー/彼得·史特拉塞 (Azur Lane)
This is the dataset of peter_strasser/ペーター・シュトラッサー/彼得·史特拉塞 (Azur Lane), containing 98 images and their tags.
The core tags of this character are `long_hair, breasts, black_hair, large_breasts, purple_eyes, very_long_hair, bangs, twintails, hat, hair_ornament, hair_flower`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:-------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 98 | 188.79 MiB | [Download](https://huggingface.co/datasets/CyberHarem/peter_strasser_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 98 | 90.99 MiB | [Download](https://huggingface.co/datasets/CyberHarem/peter_strasser_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 248 | 195.14 MiB | [Download](https://huggingface.co/datasets/CyberHarem/peter_strasser_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 98 | 159.14 MiB | [Download](https://huggingface.co/datasets/CyberHarem/peter_strasser_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 248 | 302.60 MiB | [Download](https://huggingface.co/datasets/CyberHarem/peter_strasser_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/peter_strasser_azurlane',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 30 |  |  |  |  |  | white_headwear, 1girl, looking_at_viewer, peaked_cap, double-breasted, solo, dress, white_gloves, cross, military_uniform, fur-trimmed_cape, simple_background, white_cape |
| 1 | 10 |  |  |  |  |  | 1girl, cleavage, elbow_gloves, looking_at_viewer, official_alternate_costume, solo, white_dress, white_gloves, bare_shoulders, white_rose, detached_collar, simple_background, cross, red_eyes, smile, white_background, white_pantyhose |
| 2 | 20 |  |  |  |  |  | bare_shoulders, looking_at_viewer, 1girl, solo, black_kimono, cleavage, official_alternate_costume, wide_sleeves, black_headwear, holding_fan, off_shoulder, iron_cross, folded_fan, cross_necklace, feather_boa, obi, sun_hat, fur_trim, hat_flower |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | white_headwear | 1girl | looking_at_viewer | peaked_cap | double-breasted | solo | dress | white_gloves | cross | military_uniform | fur-trimmed_cape | simple_background | white_cape | cleavage | elbow_gloves | official_alternate_costume | white_dress | bare_shoulders | white_rose | detached_collar | red_eyes | smile | white_background | white_pantyhose | black_kimono | wide_sleeves | black_headwear | holding_fan | off_shoulder | iron_cross | folded_fan | cross_necklace | feather_boa | obi | sun_hat | fur_trim | hat_flower |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-----------------|:--------|:--------------------|:-------------|:------------------|:-------|:--------|:---------------|:--------|:-------------------|:-------------------|:--------------------|:-------------|:-----------|:---------------|:-----------------------------|:--------------|:-----------------|:-------------|:------------------|:-----------|:--------|:-------------------|:------------------|:---------------|:---------------|:-----------------|:--------------|:---------------|:-------------|:-------------|:-----------------|:--------------|:------|:----------|:-----------|:-------------|
| 0 | 30 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 10 |  |  |  |  |  | | X | X | | | X | | X | X | | | X | | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | |
| 2 | 20 |  |  |  |  |  | | X | X | | | X | | | | | | | | X | | X | | X | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X |
|
presencesw/dataset1_translated_cleaned | ---
dataset_info:
features:
- name: question
dtype: string
- name: answer
dtype: string
- name: references
sequence: string
- name: question_vi
dtype: string
- name: answer_vi
dtype: string
- name: references_vi
sequence: string
splits:
- name: train
num_bytes: 75856324.71303704
num_examples: 12481
download_size: 39144513
dataset_size: 75856324.71303704
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
mask-distilled-onesec-cv12-each-chunk-uniq/chunk_114 | ---
dataset_info:
features:
- name: logits
sequence: float32
- name: mfcc
sequence:
sequence: float64
splits:
- name: train
num_bytes: 1443103352.0
num_examples: 283406
download_size: 1472220901
dataset_size: 1443103352.0
---
# Dataset Card for "chunk_114"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
tyzhu/find_second_sent_train_100_eval_40 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: inputs
dtype: string
- name: targets
dtype: string
- name: title
dtype: string
- name: context
dtype: string
splits:
- name: train
num_bytes: 310428
num_examples: 240
- name: validation
num_bytes: 38893
num_examples: 40
download_size: 0
dataset_size: 349321
---
# Dataset Card for "find_second_sent_train_100_eval_40"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
vargr/yt_full_image_dataset | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: channelId
dtype: string
- name: videoId
dtype: string
- name: title
dtype: string
- name: description
dtype: string
- name: views
dtype: int64
- name: url
dtype: string
- name: publishDate
dtype: timestamp[ns]
- name: lengthSeconds
dtype: int64
- name: subscriberCount
dtype: int64
- name: videoCount
dtype: int64
- name: isVerified
dtype: bool
- name: keywords
dtype: string
- name: country
dtype: string
- name: imagePath
dtype: string
- name: image
dtype: image
splits:
- name: train
num_bytes: 16107504583.48
num_examples: 114680
download_size: 950988308
dataset_size: 16107504583.48
---
# Dataset Card for "yt_full_image_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
charlieoneill/ioi_resid_streams_heads_last_pos | ---
dataset_info:
features:
- name: resid_streams
sequence:
sequence: float32
splits:
- name: train
num_bytes: 88589600
num_examples: 200
download_size: 89118621
dataset_size: 88589600
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
autoevaluate/autoeval-staging-eval-launch__gov_report-plain_text-1abd3a-16146234 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- launch/gov_report
eval_info:
task: summarization
model: pszemraj/bigbird-pegasus-large-K-booksum
metrics: ['bertscore']
dataset_name: launch/gov_report
dataset_config: plain_text
dataset_split: test
col_mapping:
text: document
target: summary
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: pszemraj/bigbird-pegasus-large-K-booksum
* Dataset: launch/gov_report
* Config: plain_text
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@nonchalant-nagavalli](https://huggingface.co/nonchalant-nagavalli) for evaluating this model. |
deetsadi/GTZAN_audio | ---
license: mit
---
|
obrito/celeb-identities | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': Armadillo
'1': Cat
'2': Corgi
'3': Emma_Stone
'4': Platypus
'5': Ryan_Gosling
splits:
- name: train
num_bytes: 1589786.0
num_examples: 18
download_size: 1591720
dataset_size: 1589786.0
---
# Dataset Card for "celeb-identities"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
A-Bar/de-nl_top_cs_dev | ---
dataset_info:
features:
- name: query
dtype: string
- name: passage
dtype: string
- name: label
dtype: float64
splits:
- name: train
num_bytes: 43655125
num_examples: 100000
download_size: 17413869
dataset_size: 43655125
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
gaeunseo/all_data_for_first_finetuning_shuffled | ---
dataset_info:
features:
- name: title
dtype: string
- name: context
dtype: string
- name: question
dtype: string
- name: id
dtype: string
- name: answers
struct:
- name: answer_start
sequence: int64
- name: text
sequence: string
- name: document_id
dtype: int64
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 4277822359
num_examples: 725256
download_size: 2054078335
dataset_size: 4277822359
---
# Dataset Card for "all_data_for_first_finetuning_shuffled"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
MHCreaive/youtubeTranscript | ---
license: afl-3.0
---
|
datht/FINDSum | ---
task_categories:
- summarization
language:
- en
tags:
- finance
pretty_name: findsum
size_categories:
- 10K<n<100K
--- |
oriolgds/LSE | ---
license: apache-2.0
---
|
wenbopan/Chinese-dpo-pairs | ---
license: mit
dataset_info:
config_name: train
features:
- name: prompt
dtype: string
- name: system
dtype: string
- name: chosen
dtype: string
- name: rejected
dtype: string
- name: source
dtype: string
- name: id
dtype: string
splits:
- name: train
num_bytes: 28322152
num_examples: 10735
download_size: 17430997
dataset_size: 28322152
configs:
- config_name: train
data_files:
- split: train
path: train/train-*
default: true
language:
- zh
---
# Dataset Card for Chinese-dpo-pairs
Well-curated 10K reference pairs in Chinese. Data are created by GPT-3.5 translation from multiple sources, including:
- flan_v2, sharegpt, ultrachat, evol_instruct and false_qa. Sampled from [argilla/ultrafeedback-binarized-preferences-cleaned](https://huggingface.co/datasets/argilla/ultrafeedback-binarized-preferences-cleaned)
- open_orca. From [Intel/orca_dpo_pairs](https://huggingface.co/datasets/Intel/orca_dpo_pairs)
- truthy_dpo. From [jondurbin/truthy-dpo-v0.1](https://huggingface.co/datasets/jondurbin/truthy-dpo-v0.1)
To ensure quality, I originally translated over 30K samples, then dropped all tranlations with unmatched line number or topic. The dataset is best used together with above English dataset. |
DNW/newbury_opening_times_qa | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 31347
num_examples: 233
download_size: 8252
dataset_size: 31347
---
# Dataset Card for "newbury_opening_times_qa"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
nitinbhayana/hp_global | ---
dataset_info:
features:
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 431766
num_examples: 2770
- name: test
num_bytes: 201909
num_examples: 1283
download_size: 287803
dataset_size: 633675
---
# Dataset Card for "hp_global"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
ChanceFocus/flare-ner | ---
license: mit
dataset_info:
features:
- name: query
dtype: string
- name: answer
dtype: string
- name: label
sequence: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 470523
num_examples: 408
- name: valid
num_bytes: 101644
num_examples: 103
- name: test
num_bytes: 156592
num_examples: 98
download_size: 224350
dataset_size: 728759
---
|
iamkaikai/PEOPLE-ILLUSTRATION-ART | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 17963824.0
num_examples: 333
download_size: 17935443
dataset_size: 17963824.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
El-chapoo/Complex_data-v1 | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 913595049
num_examples: 351239
download_size: 368663480
dataset_size: 913595049
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
TurtleLiu/counselbot_for_mistral | ---
license: apache-2.0
---
|
DanielDimas/teste | ---
license: openrail
---
|
elsheikhams/Shakkelha | ---
dataset_info:
features:
- name: text
dtype: string
- name: undiacrtizied
dtype: string
splits:
- name: train
num_bytes: 579339698
num_examples: 533384
download_size: 276101045
dataset_size: 579339698
---
# Dataset Card for "Shakkelha"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
deepghs/tagger_vocabs | ---
license: openrail
---
|
hlhdatscience/guanaco-spanish-dataset | ---
language:
- es
license: apache-2.0
pretty_name: d
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: text
dtype: string
- name: partition
dtype: string
splits:
- name: train
num_bytes: 4071580
num_examples: 2173
- name: test
num_bytes: 333135
num_examples: 196
download_size: 2267485
dataset_size: 4404715
---
# Dataset Card for "guanaco-spanish-dataset"
**CLEANING AND CURATION OF THE DATASET HAS BEEN PERFORMED. NOW IT IS FULLY IN SPANISH (Date:12/01/2024)**
This dataset is a subset of original timdettmers/openassistant-guanaco,which is also a subset o/f the Open Assistant dataset .You can find here: https://huggingface.co/datasets/OpenAssistant/oasst1/tree/main/
This subset of the data only contains the highest-rated paths in the conversation tree, with a total of 2,369 samples, translated with the help of GPT 3.5. turbo.
It represents the 40% and 41% of train and test from timdettmers/openassistant-guanaco respectively.
You can find the github repository for the code used here: https://github.com/Hector1993prog/guanaco_translation
For further information, please see the original dataset.
**CLEANING AND CURATION OF THE DATASET HAS BEEN PERFORMED. NOW IT IS FULLY IN SPANISH**
License: Apache 2.0
Dataset Details
Dataset Sources [Open Assistant](https://huggingface.co/datasets/OpenAssistant/oasst1)
Repository: [Link to Repository](https://huggingface.co/datasets/OpenAssistant/oasst1/tree/main)
# Uses
## Direct Use
The dataset is suitable for training and evaluating models in the context of Open Assistant applications, focusing on the highest-rated paths in conversation trees.
## Out-of-Scope Use
Usage outside the scope of Open Assistant applications may not yield optimal results.
# Dataset Structure
The dataset is organized into conversation paths, each containing the highest-rated samples. Samples are translated versions generated with the assistance of GPT 3.5 turbo.
# Dataset Creation
Curation Rationale
This subset was created to provide a focused collection of the highest-rated conversation paths from the original Open Assistant dataset, with translations performed using GPT 3.5 turbo.
# Dataset Creation
Curation Rationale
This subset was created to provide a focused collection of the highest-rated conversation paths from the original Open Assistant dataset, with translations performed using GPT 3.5 turbo.
# Source Data
## Data Collection and Processing
The source data is a subset of the timdettmers/openassistant-guanaco dataset, itself a subset of the Open Assistant dataset. The translation process involved GPT 3.5 turbo.
# Who are the source data producers?
The original data producers include contributors to the Open Assistant dataset, and the translation process involved the use of GPT 3.5 turbo.
# Annotations [optional]
## Annotation process
The dataset includes translated samples, and annotations were generated through the translation process.
## Who are the annotators?
Annotations were generated through the translation process using GPT 3.5 turbo. Dataset needs to be curated yet.
# Personal and Sensitive Information
The dataset does not contain personal or sensitive information.
# Bias, Risks, and Limitations
Users should be aware of potential biases introduced during the translation process. Limitations include the focus on the highest-rated conversation paths.
# Recommendations
Users are encouraged to consider potential biases and limitations when utilizing the dataset for model training and applications.
[Contact information for dataset inquiries](https://www.linkedin.com/in/hlh-generative-ai/)
|
Cubpaw/voxelgym_5c_new_critic_42x42_10 | ---
dataset_info:
features:
- name: image
dtype: image
- name: astar_path
dtype: image
- name: pred_path
sequence:
sequence: float32
splits:
- name: train
num_bytes: 60356.0
num_examples: 8
- name: validation
num_bytes: 15100.0
num_examples: 2
download_size: 17839
dataset_size: 75456.0
---
# Dataset Card for "voxelgym_5c_new_critic_42x42_10"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
capleaf/viVoice | ---
license: cc-by-nc-sa-4.0
---
viVoice is currently undergoing processing and quality checks. It will be made available later. Thank you for your patience and understanding! 🤗 |
tilos/ASR-CCANTCSC | ---
license: cc-by-nc-nd-4.0
pretty_name: ASR-CCANTCSC
language:
- zh
dataset_info:
features:
- name: audio
dtype: Audio
- name: sentence
dtype: string
---
|
san5167/new-user-data | ---
license: bigcode-openrail-m
language:
- aa
--- |
tyzhu/random_letter_same_length_find_passage_train10_eval40_rare | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: inputs
dtype: string
- name: targets
dtype: string
splits:
- name: train
num_bytes: 21549
num_examples: 60
- name: validation
num_bytes: 15551
num_examples: 40
download_size: 31545
dataset_size: 37100
---
# Dataset Card for "random_letter_same_length_find_passage_train10_eval40_rare"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Lambent/shakespeare_sonnets_backtranslated | ---
license: apache-2.0
---
|
results-sd-v1-5-sd-v2-1-if-v1-0-karlo/b5c4c9cc | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 182
num_examples: 10
download_size: 1338
dataset_size: 182
---
# Dataset Card for "b5c4c9cc"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
studyinglover/RobomasterDateset-GKD | ---
license: mit
---
|
umarigan/turkish_clip_dataset_200k_300k | ---
dataset_info:
features:
- name: SAMPLE_ID
dtype: int64
- name: URL
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- name: LICENSE
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- name: LANGUAGE
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- name: NSFW
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- name: similarity
dtype: float64
- name: image
dtype: image
splits:
- name: train
num_bytes: 2915027197.0
num_examples: 100000
download_size: 2881727105
dataset_size: 2915027197.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
presencesw/dataset4_translated_cleaned | ---
dataset_info:
features:
- name: question
dtype: string
- name: answer
dtype: string
- name: references
sequence: string
- name: question_vi
dtype: string
- name: answer_vi
dtype: string
- name: references_vi
sequence: string
splits:
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num_examples: 7023
- name: validation
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num_examples: 917
- name: test
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num_examples: 362
download_size: 26061969
dataset_size: 50028864.173976906
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
|
im-nayeem/llama_dataset | ---
task_categories:
- text-generation
size_categories:
- 1K<n<10K
--- |
AdapterOcean/gptindex-standardized_unified | ---
dataset_info:
features:
- name: text
dtype: string
- name: conversation_id
dtype: int64
splits:
- name: train
num_bytes: 807609
num_examples: 1234
download_size: 395344
dataset_size: 807609
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "gptindex-standardized_unified"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
ctu-aic/qacg-cs | ---
dataset_info:
- config_name: balanced
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- name: evidence
sequence: string
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- name: validation
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- name: test
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- config_name: balanced_shuf
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download_size: 14960384
dataset_size: 21278057
- config_name: default
features:
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splits:
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download_size: 47350094
dataset_size: 66765318
- config_name: fever_size
features:
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dtype: string
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sequence: string
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- name: validation
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num_examples: 9999
- name: test
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num_examples: 9999
download_size: 8485306
dataset_size: 12037006
configs:
- config_name: balanced
data_files:
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path: balanced/train-*
- split: validation
path: balanced/validation-*
- split: test
path: balanced/test-*
- config_name: balanced_shuf
data_files:
- split: train
path: balanced_shuf/train-*
- split: validation
path: balanced_shuf/validation-*
- split: test
path: balanced_shuf/test-*
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
- config_name: fever_size
data_files:
- split: train
path: fever_size/train-*
- split: validation
path: fever_size/validation-*
- split: test
path: fever_size/test-*
---
|
BedfordD/casesummary | ---
task_categories:
- summarization
language:
- en
tags:
- legal
size_categories:
- 1K<n<10K
--- |
atmallen/qm_alice_easy_2_grader_last_1.0e | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
dataset_info:
features:
- name: alice_label
dtype: bool
- name: bob_label
dtype: bool
- name: difficulty
dtype: int64
- name: statement
dtype: string
- name: choices
sequence: string
- name: character
dtype: string
- name: label
dtype:
class_label:
names:
'0': 'False'
'1': 'True'
splits:
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- name: validation
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num_examples: 11279
- name: test
num_bytes: 825258.0
num_examples: 11186
download_size: 2481199
dataset_size: 10259738.0
---
# Dataset Card for "qm_alice_easy_2_grader_last_1.0e"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
DarqueDante/TinyDataSet | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 5906108510
num_examples: 1000000
- name: validation
num_bytes: 2779386
num_examples: 500
- name: test
num_bytes: 58558191
num_examples: 10000
download_size: 3176294664
dataset_size: 5967446087
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
|
BiMediX/medqa-test_arabic | ---
dataset_info:
features:
- name: question
dtype: string
- name: options
struct:
- name: A
dtype: string
- name: B
dtype: string
- name: C
dtype: string
- name: D
dtype: string
- name: E
dtype: string
- name: answer_idx
dtype: string
- name: meta_info
dtype: string
splits:
- name: train
num_bytes: 1762994
num_examples: 1273
download_size: 889370
dataset_size: 1762994
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
arubenruben/primeiro_harem_selective_ours | ---
dataset_info:
features:
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': O
'1': B-PESSOA
'2': I-PESSOA
'3': B-ORGANIZACAO
'4': I-ORGANIZACAO
'5': B-LOCAL
'6': I-LOCAL
'7': B-TEMPO
'8': I-TEMPO
'9': B-VALOR
'10': I-VALOR
splits:
- name: train
num_bytes: 1515061
num_examples: 182
download_size: 294948
dataset_size: 1515061
---
# Dataset Card for "primeiro_harem_selective_ours"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
open-llm-leaderboard/details_ehartford__Samantha-1.11-70b | ---
pretty_name: Evaluation run of ehartford/Samantha-1.11-70b
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [ehartford/Samantha-1.11-70b](https://huggingface.co/ehartford/Samantha-1.11-70b)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 64 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_ehartford__Samantha-1.11-70b\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-19T17:02:54.174662](https://huggingface.co/datasets/open-llm-leaderboard/details_ehartford__Samantha-1.11-70b/blob/main/results_2023-10-19T17-02-54.174662.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\": 0.5320889261744967,\n\
\ \"em_stderr\": 0.0051099120270992685,\n \"f1\": 0.5767973993288609,\n\
\ \"f1_stderr\": 0.004860619911447506,\n \"acc\": 0.5660724533007654,\n\
\ \"acc_stderr\": 0.011553454771173869\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.5320889261744967,\n \"em_stderr\": 0.0051099120270992685,\n\
\ \"f1\": 0.5767973993288609,\n \"f1_stderr\": 0.004860619911447506\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.29946929492039426,\n \
\ \"acc_stderr\": 0.012616300735519658\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.8326756116811366,\n \"acc_stderr\": 0.010490608806828079\n\
\ }\n}\n```"
repo_url: https://huggingface.co/ehartford/Samantha-1.11-70b
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_08_23T18_30_58.468070
path:
- '**/details_harness|arc:challenge|25_2023-08-23T18:30:58.468070.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-08-23T18:30:58.468070.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_10_19T17_02_54.174662
path:
- '**/details_harness|drop|3_2023-10-19T17-02-54.174662.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-19T17-02-54.174662.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_19T17_02_54.174662
path:
- '**/details_harness|gsm8k|5_2023-10-19T17-02-54.174662.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-19T17-02-54.174662.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_08_23T18_30_58.468070
path:
- '**/details_harness|hellaswag|10_2023-08-23T18:30:58.468070.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-08-23T18:30:58.468070.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_08_23T18_30_58.468070
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-23T18:30:58.468070.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-23T18:30:58.468070.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-23T18:30:58.468070.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-23T18:30:58.468070.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-23T18:30:58.468070.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-23T18:30:58.468070.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-23T18:30:58.468070.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-23T18:30:58.468070.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-23T18:30:58.468070.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-23T18:30:58.468070.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-23T18:30:58.468070.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-23T18:30:58.468070.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-23T18:30:58.468070.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-23T18:30:58.468070.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-23T18:30:58.468070.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-23T18:30:58.468070.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-23T18:30:58.468070.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-23T18:30:58.468070.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-23T18:30:58.468070.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-23T18:30:58.468070.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-23T18:30:58.468070.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-23T18:30:58.468070.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-23T18:30:58.468070.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-23T18:30:58.468070.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-23T18:30:58.468070.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-23T18:30:58.468070.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-23T18:30:58.468070.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-23T18:30:58.468070.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-23T18:30:58.468070.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-23T18:30:58.468070.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-23T18:30:58.468070.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-23T18:30:58.468070.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-23T18:30:58.468070.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-23T18:30:58.468070.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-23T18:30:58.468070.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-23T18:30:58.468070.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-23T18:30:58.468070.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-23T18:30:58.468070.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-23T18:30:58.468070.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-23T18:30:58.468070.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-23T18:30:58.468070.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-23T18:30:58.468070.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-23T18:30:58.468070.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-23T18:30:58.468070.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-23T18:30:58.468070.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-23T18:30:58.468070.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-23T18:30:58.468070.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-23T18:30:58.468070.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-23T18:30:58.468070.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-23T18:30:58.468070.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-23T18:30:58.468070.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-23T18:30:58.468070.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-23T18:30:58.468070.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-23T18:30:58.468070.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-23T18:30:58.468070.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-23T18:30:58.468070.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-23T18:30:58.468070.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-23T18:30:58.468070.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-23T18:30:58.468070.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-23T18:30:58.468070.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-23T18:30:58.468070.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-23T18:30:58.468070.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-23T18:30:58.468070.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-23T18:30:58.468070.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-23T18:30:58.468070.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-23T18:30:58.468070.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-23T18:30:58.468070.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-23T18:30:58.468070.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-23T18:30:58.468070.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-23T18:30:58.468070.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-23T18:30:58.468070.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-23T18:30:58.468070.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-23T18:30:58.468070.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-23T18:30:58.468070.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-23T18:30:58.468070.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-23T18:30:58.468070.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-23T18:30:58.468070.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-23T18:30:58.468070.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-23T18:30:58.468070.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-23T18:30:58.468070.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-23T18:30:58.468070.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-23T18:30:58.468070.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-23T18:30:58.468070.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-23T18:30:58.468070.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-23T18:30:58.468070.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-23T18:30:58.468070.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-23T18:30:58.468070.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-23T18:30:58.468070.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-23T18:30:58.468070.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-23T18:30:58.468070.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-23T18:30:58.468070.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-23T18:30:58.468070.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-23T18:30:58.468070.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-23T18:30:58.468070.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-23T18:30:58.468070.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-23T18:30:58.468070.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-23T18:30:58.468070.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-23T18:30:58.468070.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-23T18:30:58.468070.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-23T18:30:58.468070.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-23T18:30:58.468070.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-23T18:30:58.468070.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-23T18:30:58.468070.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-23T18:30:58.468070.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-23T18:30:58.468070.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-23T18:30:58.468070.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-23T18:30:58.468070.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-23T18:30:58.468070.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-23T18:30:58.468070.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-23T18:30:58.468070.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-23T18:30:58.468070.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-23T18:30:58.468070.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-23T18:30:58.468070.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-23T18:30:58.468070.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_08_23T18_30_58.468070
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-23T18:30:58.468070.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-23T18:30:58.468070.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_08_23T18_30_58.468070
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-23T18:30:58.468070.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-23T18:30:58.468070.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_08_23T18_30_58.468070
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-23T18:30:58.468070.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-23T18:30:58.468070.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_08_23T18_30_58.468070
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-23T18:30:58.468070.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-23T18:30:58.468070.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_08_23T18_30_58.468070
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-23T18:30:58.468070.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-23T18:30:58.468070.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_08_23T18_30_58.468070
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-23T18:30:58.468070.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-23T18:30:58.468070.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_08_23T18_30_58.468070
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-23T18:30:58.468070.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-23T18:30:58.468070.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_08_23T18_30_58.468070
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-23T18:30:58.468070.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-23T18:30:58.468070.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_08_23T18_30_58.468070
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-23T18:30:58.468070.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-23T18:30:58.468070.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_08_23T18_30_58.468070
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-23T18:30:58.468070.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-23T18:30:58.468070.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_08_23T18_30_58.468070
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-23T18:30:58.468070.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-23T18:30:58.468070.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_08_23T18_30_58.468070
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-23T18:30:58.468070.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-23T18:30:58.468070.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_08_23T18_30_58.468070
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-23T18:30:58.468070.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-23T18:30:58.468070.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_08_23T18_30_58.468070
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-23T18:30:58.468070.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-23T18:30:58.468070.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_08_23T18_30_58.468070
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-23T18:30:58.468070.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-23T18:30:58.468070.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_08_23T18_30_58.468070
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-23T18:30:58.468070.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-23T18:30:58.468070.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_08_23T18_30_58.468070
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-23T18:30:58.468070.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-23T18:30:58.468070.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_08_23T18_30_58.468070
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-23T18:30:58.468070.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-23T18:30:58.468070.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_08_23T18_30_58.468070
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-23T18:30:58.468070.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-23T18:30:58.468070.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_08_23T18_30_58.468070
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-23T18:30:58.468070.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-23T18:30:58.468070.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_08_23T18_30_58.468070
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-23T18:30:58.468070.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-23T18:30:58.468070.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_08_23T18_30_58.468070
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-23T18:30:58.468070.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-23T18:30:58.468070.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_08_23T18_30_58.468070
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-23T18:30:58.468070.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-23T18:30:58.468070.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_08_23T18_30_58.468070
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-23T18:30:58.468070.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-23T18:30:58.468070.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_08_23T18_30_58.468070
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-23T18:30:58.468070.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-23T18:30:58.468070.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_08_23T18_30_58.468070
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-23T18:30:58.468070.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-23T18:30:58.468070.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_08_23T18_30_58.468070
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-23T18:30:58.468070.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-23T18:30:58.468070.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_08_23T18_30_58.468070
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-23T18:30:58.468070.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-23T18:30:58.468070.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_08_23T18_30_58.468070
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-23T18:30:58.468070.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-23T18:30:58.468070.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_08_23T18_30_58.468070
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-23T18:30:58.468070.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-23T18:30:58.468070.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_08_23T18_30_58.468070
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-23T18:30:58.468070.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-23T18:30:58.468070.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_08_23T18_30_58.468070
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-23T18:30:58.468070.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-23T18:30:58.468070.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_08_23T18_30_58.468070
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-23T18:30:58.468070.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-23T18:30:58.468070.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_08_23T18_30_58.468070
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-23T18:30:58.468070.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-23T18:30:58.468070.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_08_23T18_30_58.468070
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-23T18:30:58.468070.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-23T18:30:58.468070.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_08_23T18_30_58.468070
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-23T18:30:58.468070.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-23T18:30:58.468070.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_08_23T18_30_58.468070
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-23T18:30:58.468070.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-23T18:30:58.468070.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_08_23T18_30_58.468070
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-23T18:30:58.468070.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-23T18:30:58.468070.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_08_23T18_30_58.468070
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-23T18:30:58.468070.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-23T18:30:58.468070.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_08_23T18_30_58.468070
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-23T18:30:58.468070.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-23T18:30:58.468070.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_08_23T18_30_58.468070
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-23T18:30:58.468070.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-23T18:30:58.468070.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_08_23T18_30_58.468070
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-23T18:30:58.468070.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-23T18:30:58.468070.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_08_23T18_30_58.468070
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-23T18:30:58.468070.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-23T18:30:58.468070.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_08_23T18_30_58.468070
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-23T18:30:58.468070.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-23T18:30:58.468070.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_08_23T18_30_58.468070
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-23T18:30:58.468070.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-23T18:30:58.468070.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_08_23T18_30_58.468070
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-23T18:30:58.468070.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-23T18:30:58.468070.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_08_23T18_30_58.468070
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-23T18:30:58.468070.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-23T18:30:58.468070.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_08_23T18_30_58.468070
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-23T18:30:58.468070.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-23T18:30:58.468070.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_08_23T18_30_58.468070
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-23T18:30:58.468070.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-23T18:30:58.468070.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_08_23T18_30_58.468070
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-23T18:30:58.468070.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-23T18:30:58.468070.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_08_23T18_30_58.468070
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-23T18:30:58.468070.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-23T18:30:58.468070.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_08_23T18_30_58.468070
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-23T18:30:58.468070.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-23T18:30:58.468070.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_08_23T18_30_58.468070
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-23T18:30:58.468070.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-23T18:30:58.468070.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_08_23T18_30_58.468070
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-23T18:30:58.468070.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-23T18:30:58.468070.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_08_23T18_30_58.468070
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-23T18:30:58.468070.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-23T18:30:58.468070.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_08_23T18_30_58.468070
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-23T18:30:58.468070.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-23T18:30:58.468070.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_08_23T18_30_58.468070
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-23T18:30:58.468070.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-23T18:30:58.468070.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_08_23T18_30_58.468070
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-23T18:30:58.468070.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-23T18:30:58.468070.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_19T17_02_54.174662
path:
- '**/details_harness|winogrande|5_2023-10-19T17-02-54.174662.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-19T17-02-54.174662.parquet'
- config_name: results
data_files:
- split: 2023_10_19T17_02_54.174662
path:
- results_2023-10-19T17-02-54.174662.parquet
- split: latest
path:
- results_2023-10-19T17-02-54.174662.parquet
---
# Dataset Card for Evaluation run of ehartford/Samantha-1.11-70b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/ehartford/Samantha-1.11-70b
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** clementine@hf.co
### Dataset Summary
Dataset automatically created during the evaluation run of model [ehartford/Samantha-1.11-70b](https://huggingface.co/ehartford/Samantha-1.11-70b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 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_ehartford__Samantha-1.11-70b",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-19T17:02:54.174662](https://huggingface.co/datasets/open-llm-leaderboard/details_ehartford__Samantha-1.11-70b/blob/main/results_2023-10-19T17-02-54.174662.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": 0.5320889261744967,
"em_stderr": 0.0051099120270992685,
"f1": 0.5767973993288609,
"f1_stderr": 0.004860619911447506,
"acc": 0.5660724533007654,
"acc_stderr": 0.011553454771173869
},
"harness|drop|3": {
"em": 0.5320889261744967,
"em_stderr": 0.0051099120270992685,
"f1": 0.5767973993288609,
"f1_stderr": 0.004860619911447506
},
"harness|gsm8k|5": {
"acc": 0.29946929492039426,
"acc_stderr": 0.012616300735519658
},
"harness|winogrande|5": {
"acc": 0.8326756116811366,
"acc_stderr": 0.010490608806828079
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] |
lshowway/wikipedia.reorder.osv.de | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 2385745587
num_examples: 1137317
download_size: 1065735715
dataset_size: 2385745587
---
# Dataset Card for "wikipedia.reorder.osv.de"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
BubbleJoe/bootstrap_sms_v2 | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 2007757
num_examples: 5390
download_size: 651361
dataset_size: 2007757
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
HumanCompatibleAI/ppo-seals-Ant-v1 | ---
dataset_info:
features:
- name: obs
sequence:
sequence: float64
- name: acts
sequence:
sequence: float32
- name: infos
sequence: string
- name: terminal
dtype: bool
- name: rews
sequence: float32
splits:
- name: train
num_bytes: 141011280
num_examples: 104
download_size: 41078990
dataset_size: 141011280
---
# Dataset Card for "ppo-seals-Ant-v1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
kheopss/lettre_admin_gpt | ---
dataset_info:
features:
- name: Input
dtype: string
- name: Response
dtype: string
- name: conversations
list:
- name: from
dtype: string
- name: value
dtype: string
- name: input
dtype: string
- name: response
dtype: string
- name: text
dtype: string
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 12876967
num_examples: 2657
download_size: 4610121
dataset_size: 12876967
---
# Dataset Card for "lettre_admin_gpt"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
dar5654/masked5-dataset-test | ---
dataset_info:
features:
- name: image
dtype: image
- name: annotation
dtype: image
- name: scene_category
dtype: int64
splits:
- name: train
num_bytes: 684055.0
num_examples: 10
download_size: 697135
dataset_size: 684055.0
---
# Dataset Card for "masked5-dataset-test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Tann-dev/conversation-zizi-sexting | ---
dataset_info:
features:
- name: He
dtype: string
- name: She
dtype: string
splits:
- name: train
num_bytes: 250620
num_examples: 2318
download_size: 55776
dataset_size: 250620
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
qbhy/dataset-example | ---
license: afl-3.0
---
|
InstaDeepAI/ms_proteometools | ---
license: cc0-1.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
dataset_info:
features:
- name: experiment_name
dtype: string
- name: evidence_index
dtype: int64
- name: scan_number
dtype: int64
- name: sequence
dtype: string
- name: modified_sequence
dtype: string
- name: precursor_mz
dtype: float64
- name: precursor_recalibrated_mz
dtype: float64
- name: precursor_mass
dtype: float64
- name: precursor_charge
dtype: int64
- name: retention_time
dtype: float64
- name: mz_array
sequence: float32
- name: intensity_array
sequence: float32
splits:
- name: train
num_bytes: 3370985593
num_examples: 2132847
- name: validation
num_bytes: 413243959
num_examples: 257187
- name: test
num_bytes: 421581021
num_examples: 265369
download_size: 3944832530
dataset_size: 4205810573
---
# Dataset Card for High-Confidence ProteomeTools
Dataset used to train, validate and test InstaNovo and InstaNovo+.
## Dataset Description
- **Repository:** [InstaNovo](https://github.com/instadeepai/InstaNovo)
- **Paper:** [De novo peptide sequencing with InstaNovo: Accurate, database-free peptide identification for large scale proteomics experiments](https://www.biorxiv.org/content/10.1101/2023.08.30.555055v1)
### Dataset Summary
This dataset consists of the highest-confidence peptide-spectral matches from three parts of the [ProteomeTools](https://www.proteometools.org/) datasets. The original datasets may be found in the PRIDE repository with identifiers:
- `PXD004732` (Part I)
- `PXD010595` (Part II)
- `PXD021013` (Part III)
The dataset has been split on unique peptides with the following ratio:
- 80% train
- 10% validation
- 10% test
## Dataset Structure
The dataset is tabular, where each row corresponds to a labelled MS2 spectra.
- `sequence (string)` \
The target peptide sequence excluding post-translational modifications
- `modified_sequence (string)` \
The target peptide sequence including post-translational modifications
- `precursor_mz (float64)` \
The mass-to-charge of the precursor (from MS1)
- `charge (int64)` \
The charge of the precursor (from MS1)
- `mz_array (list[float64])` \
The mass-to-charge values of the MS2 spectrum
- `mz_array (list[float32])` \
The intensity values of the MS2 spectrum
MaxQuant additional columns:
- `experiment_name (string)`
- `evidence_index (in64)`
- `scan_number (in64)`
- `precursor_recalibrated_mz (float64)`
## Citation Information
If you use this dataset, please cite the original authors.
The original [ProteomeTools](https://www.proteometools.org/) data is available on [PRIDE](https://www.ebi.ac.uk/pride/) with identifiers `PXD004732` (Part I), `PXD010595` (Part II), and `PXD021013` (Part III).
Please also cite InstaNovo:
```bibtex
@article{eloff_kalogeropoulos_2023_instanovo,
title = {De novo peptide sequencing with InstaNovo: Accurate, database-free peptide identification for large scale proteomics experiments},
author = {Kevin Eloff and Konstantinos Kalogeropoulos and Oliver Morell and Amandla Mabona and Jakob Berg Jespersen and Wesley Williams and Sam van Beljouw and Marcin Skwark and Andreas Hougaard Laustsen and Stan J. J. Brouns and Anne Ljungars and Erwin Marten Schoof and Jeroen Van Goey and Ulrich auf dem Keller and Karim Beguir and Nicolas Lopez Carranza and Timothy Patrick Jenkins},
year = {2023},
doi = {10.1101/2023.08.30.555055},
publisher = {Cold Spring Harbor Laboratory},
URL = {https://www.biorxiv.org/content/10.1101/2023.08.30.555055v1},
journal = {bioRxiv}
}
```
|
open-llm-leaderboard/details_jondurbin__airoboros-l2-7b-2.1 | ---
pretty_name: Evaluation run of jondurbin/airoboros-l2-7b-2.1
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [jondurbin/airoboros-l2-7b-2.1](https://huggingface.co/jondurbin/airoboros-l2-7b-2.1)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 64 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_jondurbin__airoboros-l2-7b-2.1\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-22T19:19:26.603130](https://huggingface.co/datasets/open-llm-leaderboard/details_jondurbin__airoboros-l2-7b-2.1/blob/main/results_2023-10-22T19-19-26.603130.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\": 0.3058934563758389,\n\
\ \"em_stderr\": 0.004718867836387577,\n \"f1\": 0.36892197986577313,\n\
\ \"f1_stderr\": 0.004645489671001802,\n \"acc\": 0.38155355549664816,\n\
\ \"acc_stderr\": 0.008174839284551696\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.3058934563758389,\n \"em_stderr\": 0.004718867836387577,\n\
\ \"f1\": 0.36892197986577313,\n \"f1_stderr\": 0.004645489671001802\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.021986353297952996,\n \
\ \"acc_stderr\": 0.004039162758110015\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7411207576953434,\n \"acc_stderr\": 0.012310515810993378\n\
\ }\n}\n```"
repo_url: https://huggingface.co/jondurbin/airoboros-l2-7b-2.1
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_08_30T21_48_31.608881
path:
- '**/details_harness|arc:challenge|25_2023-08-30T21:48:31.608881.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-08-30T21:48:31.608881.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_10_22T19_19_26.603130
path:
- '**/details_harness|drop|3_2023-10-22T19-19-26.603130.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-22T19-19-26.603130.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_22T19_19_26.603130
path:
- '**/details_harness|gsm8k|5_2023-10-22T19-19-26.603130.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-22T19-19-26.603130.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_08_30T21_48_31.608881
path:
- '**/details_harness|hellaswag|10_2023-08-30T21:48:31.608881.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-08-30T21:48:31.608881.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_08_30T21_48_31.608881
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-30T21:48:31.608881.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-30T21:48:31.608881.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-30T21:48:31.608881.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-30T21:48:31.608881.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-30T21:48:31.608881.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-30T21:48:31.608881.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-30T21:48:31.608881.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-30T21:48:31.608881.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-30T21:48:31.608881.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-30T21:48:31.608881.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-30T21:48:31.608881.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-30T21:48:31.608881.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-30T21:48:31.608881.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-30T21:48:31.608881.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-30T21:48:31.608881.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-30T21:48:31.608881.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-30T21:48:31.608881.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-30T21:48:31.608881.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-30T21:48:31.608881.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-30T21:48:31.608881.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-30T21:48:31.608881.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-30T21:48:31.608881.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-30T21:48:31.608881.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-30T21:48:31.608881.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-30T21:48:31.608881.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-30T21:48:31.608881.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-30T21:48:31.608881.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-30T21:48:31.608881.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-30T21:48:31.608881.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-30T21:48:31.608881.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-30T21:48:31.608881.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-30T21:48:31.608881.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-30T21:48:31.608881.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-30T21:48:31.608881.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-30T21:48:31.608881.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-30T21:48:31.608881.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-30T21:48:31.608881.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-30T21:48:31.608881.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-30T21:48:31.608881.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-30T21:48:31.608881.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-30T21:48:31.608881.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-30T21:48:31.608881.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-30T21:48:31.608881.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-30T21:48:31.608881.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-30T21:48:31.608881.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-30T21:48:31.608881.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-30T21:48:31.608881.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-30T21:48:31.608881.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-30T21:48:31.608881.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-30T21:48:31.608881.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-30T21:48:31.608881.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-30T21:48:31.608881.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-30T21:48:31.608881.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-30T21:48:31.608881.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-30T21:48:31.608881.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-30T21:48:31.608881.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-30T21:48:31.608881.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-30T21:48:31.608881.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-30T21:48:31.608881.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-30T21:48:31.608881.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-30T21:48:31.608881.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-30T21:48:31.608881.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-30T21:48:31.608881.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-30T21:48:31.608881.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-30T21:48:31.608881.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-30T21:48:31.608881.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-30T21:48:31.608881.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-30T21:48:31.608881.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-30T21:48:31.608881.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-30T21:48:31.608881.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-30T21:48:31.608881.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-30T21:48:31.608881.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-30T21:48:31.608881.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-30T21:48:31.608881.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-30T21:48:31.608881.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-30T21:48:31.608881.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-30T21:48:31.608881.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-30T21:48:31.608881.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-30T21:48:31.608881.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-30T21:48:31.608881.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-30T21:48:31.608881.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-30T21:48:31.608881.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-30T21:48:31.608881.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-30T21:48:31.608881.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-30T21:48:31.608881.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-30T21:48:31.608881.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-30T21:48:31.608881.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-30T21:48:31.608881.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-30T21:48:31.608881.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-30T21:48:31.608881.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-30T21:48:31.608881.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-30T21:48:31.608881.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-30T21:48:31.608881.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-30T21:48:31.608881.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-30T21:48:31.608881.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-30T21:48:31.608881.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-30T21:48:31.608881.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-30T21:48:31.608881.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-30T21:48:31.608881.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-30T21:48:31.608881.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-30T21:48:31.608881.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-30T21:48:31.608881.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-30T21:48:31.608881.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-30T21:48:31.608881.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-30T21:48:31.608881.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-30T21:48:31.608881.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-30T21:48:31.608881.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-30T21:48:31.608881.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-30T21:48:31.608881.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-30T21:48:31.608881.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-30T21:48:31.608881.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-30T21:48:31.608881.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-30T21:48:31.608881.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-30T21:48:31.608881.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_08_30T21_48_31.608881
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-30T21:48:31.608881.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-30T21:48:31.608881.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_08_30T21_48_31.608881
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-30T21:48:31.608881.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-30T21:48:31.608881.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_08_30T21_48_31.608881
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-30T21:48:31.608881.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-30T21:48:31.608881.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_08_30T21_48_31.608881
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-30T21:48:31.608881.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-30T21:48:31.608881.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_08_30T21_48_31.608881
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-30T21:48:31.608881.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-30T21:48:31.608881.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_08_30T21_48_31.608881
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-30T21:48:31.608881.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-30T21:48:31.608881.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_08_30T21_48_31.608881
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-30T21:48:31.608881.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-30T21:48:31.608881.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_08_30T21_48_31.608881
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-30T21:48:31.608881.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-30T21:48:31.608881.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_08_30T21_48_31.608881
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-30T21:48:31.608881.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-30T21:48:31.608881.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_08_30T21_48_31.608881
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-30T21:48:31.608881.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-30T21:48:31.608881.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_08_30T21_48_31.608881
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-30T21:48:31.608881.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-30T21:48:31.608881.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_08_30T21_48_31.608881
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-30T21:48:31.608881.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-30T21:48:31.608881.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_08_30T21_48_31.608881
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-30T21:48:31.608881.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-30T21:48:31.608881.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_08_30T21_48_31.608881
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-30T21:48:31.608881.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-30T21:48:31.608881.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_08_30T21_48_31.608881
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-30T21:48:31.608881.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-30T21:48:31.608881.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_08_30T21_48_31.608881
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-30T21:48:31.608881.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-30T21:48:31.608881.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_08_30T21_48_31.608881
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-30T21:48:31.608881.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-30T21:48:31.608881.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_08_30T21_48_31.608881
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-30T21:48:31.608881.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-30T21:48:31.608881.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_08_30T21_48_31.608881
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-30T21:48:31.608881.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-30T21:48:31.608881.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_08_30T21_48_31.608881
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-30T21:48:31.608881.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-30T21:48:31.608881.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_08_30T21_48_31.608881
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-30T21:48:31.608881.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-30T21:48:31.608881.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_08_30T21_48_31.608881
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-30T21:48:31.608881.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-30T21:48:31.608881.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_08_30T21_48_31.608881
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-30T21:48:31.608881.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-30T21:48:31.608881.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_08_30T21_48_31.608881
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-30T21:48:31.608881.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-30T21:48:31.608881.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_08_30T21_48_31.608881
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-30T21:48:31.608881.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-30T21:48:31.608881.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_08_30T21_48_31.608881
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-30T21:48:31.608881.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-30T21:48:31.608881.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_08_30T21_48_31.608881
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-30T21:48:31.608881.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-30T21:48:31.608881.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_08_30T21_48_31.608881
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-30T21:48:31.608881.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-30T21:48:31.608881.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_08_30T21_48_31.608881
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-30T21:48:31.608881.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-30T21:48:31.608881.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_08_30T21_48_31.608881
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-30T21:48:31.608881.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-30T21:48:31.608881.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_08_30T21_48_31.608881
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-30T21:48:31.608881.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-30T21:48:31.608881.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_08_30T21_48_31.608881
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-30T21:48:31.608881.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-30T21:48:31.608881.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_08_30T21_48_31.608881
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-30T21:48:31.608881.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-30T21:48:31.608881.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_08_30T21_48_31.608881
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-30T21:48:31.608881.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-30T21:48:31.608881.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_08_30T21_48_31.608881
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-30T21:48:31.608881.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-30T21:48:31.608881.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_08_30T21_48_31.608881
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-30T21:48:31.608881.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-30T21:48:31.608881.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_08_30T21_48_31.608881
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-30T21:48:31.608881.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-30T21:48:31.608881.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_08_30T21_48_31.608881
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-30T21:48:31.608881.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-30T21:48:31.608881.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_08_30T21_48_31.608881
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-30T21:48:31.608881.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-30T21:48:31.608881.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_08_30T21_48_31.608881
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-30T21:48:31.608881.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-30T21:48:31.608881.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_08_30T21_48_31.608881
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-30T21:48:31.608881.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-30T21:48:31.608881.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_08_30T21_48_31.608881
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-30T21:48:31.608881.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-30T21:48:31.608881.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_08_30T21_48_31.608881
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-30T21:48:31.608881.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-30T21:48:31.608881.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_08_30T21_48_31.608881
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-30T21:48:31.608881.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-30T21:48:31.608881.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_08_30T21_48_31.608881
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-30T21:48:31.608881.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-30T21:48:31.608881.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_08_30T21_48_31.608881
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-30T21:48:31.608881.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-30T21:48:31.608881.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_08_30T21_48_31.608881
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-30T21:48:31.608881.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-30T21:48:31.608881.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_08_30T21_48_31.608881
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-30T21:48:31.608881.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-30T21:48:31.608881.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_08_30T21_48_31.608881
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-30T21:48:31.608881.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-30T21:48:31.608881.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_08_30T21_48_31.608881
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-30T21:48:31.608881.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-30T21:48:31.608881.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_08_30T21_48_31.608881
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-30T21:48:31.608881.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-30T21:48:31.608881.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_08_30T21_48_31.608881
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-30T21:48:31.608881.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-30T21:48:31.608881.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_08_30T21_48_31.608881
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-30T21:48:31.608881.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-30T21:48:31.608881.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_08_30T21_48_31.608881
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-30T21:48:31.608881.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-30T21:48:31.608881.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_08_30T21_48_31.608881
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-30T21:48:31.608881.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-30T21:48:31.608881.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_08_30T21_48_31.608881
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-30T21:48:31.608881.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-30T21:48:31.608881.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_08_30T21_48_31.608881
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-30T21:48:31.608881.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-30T21:48:31.608881.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_08_30T21_48_31.608881
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-30T21:48:31.608881.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-30T21:48:31.608881.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_22T19_19_26.603130
path:
- '**/details_harness|winogrande|5_2023-10-22T19-19-26.603130.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-22T19-19-26.603130.parquet'
- config_name: results
data_files:
- split: 2023_08_30T21_48_31.608881
path:
- results_2023-08-30T21:48:31.608881.parquet
- split: 2023_10_22T19_19_26.603130
path:
- results_2023-10-22T19-19-26.603130.parquet
- split: latest
path:
- results_2023-10-22T19-19-26.603130.parquet
---
# Dataset Card for Evaluation run of jondurbin/airoboros-l2-7b-2.1
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/jondurbin/airoboros-l2-7b-2.1
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** clementine@hf.co
### Dataset Summary
Dataset automatically created during the evaluation run of model [jondurbin/airoboros-l2-7b-2.1](https://huggingface.co/jondurbin/airoboros-l2-7b-2.1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 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_jondurbin__airoboros-l2-7b-2.1",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-22T19:19:26.603130](https://huggingface.co/datasets/open-llm-leaderboard/details_jondurbin__airoboros-l2-7b-2.1/blob/main/results_2023-10-22T19-19-26.603130.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": 0.3058934563758389,
"em_stderr": 0.004718867836387577,
"f1": 0.36892197986577313,
"f1_stderr": 0.004645489671001802,
"acc": 0.38155355549664816,
"acc_stderr": 0.008174839284551696
},
"harness|drop|3": {
"em": 0.3058934563758389,
"em_stderr": 0.004718867836387577,
"f1": 0.36892197986577313,
"f1_stderr": 0.004645489671001802
},
"harness|gsm8k|5": {
"acc": 0.021986353297952996,
"acc_stderr": 0.004039162758110015
},
"harness|winogrande|5": {
"acc": 0.7411207576953434,
"acc_stderr": 0.012310515810993378
}
}
```
### 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] |
irds/nyt_trec-core-2017 | ---
pretty_name: '`nyt/trec-core-2017`'
viewer: false
source_datasets: ['irds/nyt']
task_categories:
- text-retrieval
---
# Dataset Card for `nyt/trec-core-2017`
The `nyt/trec-core-2017` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/nyt#nyt/trec-core-2017).
# Data
This dataset provides:
- `queries` (i.e., topics); count=50
- `qrels`: (relevance assessments); count=30,030
- For `docs`, use [`irds/nyt`](https://huggingface.co/datasets/irds/nyt)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/nyt_trec-core-2017', 'queries')
for record in queries:
record # {'query_id': ..., 'title': ..., 'description': ..., 'narrative': ...}
qrels = load_dataset('irds/nyt_trec-core-2017', 'qrels')
for record in qrels:
record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@inproceedings{Allan2017TrecCore,
author = {James Allan and Donna Harman and Evangelos Kanoulas and Dan Li and Christophe Van Gysel and Ellen Vorhees},
title = {TREC 2017 Common Core Track Overview},
booktitle = {TREC},
year = {2017}
}
@article{Sandhaus2008Nyt,
title={The new york times annotated corpus},
author={Sandhaus, Evan},
journal={Linguistic Data Consortium, Philadelphia},
volume={6},
number={12},
pages={e26752},
year={2008}
}
```
|
Dzeniks/hover | ---
license: mit
task_categories:
- text-classification
---
# Hover Dataset
The Hover dataset is a collection of labeled examples for many-hop fact extraction and claim verification tasks. It contains claims, with each claim labeled as either "Supports" or "Refutes". The dataset was created by Yichen Jiang, Shikha Bordia, Zheng Zhong, Charles Dognin, Maneesh Singh, and Mohit Bansal, and was presented in their paper "HoVer: A Dataset for Many-Hop Fact Extraction and Claim Verification" at the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) [Hover page](https://hover-nlp.github.io/).
## Format
The Hover dataset is formatted as a TSV file, with each line containing the following fields:
- **Claim:** The text of the claim to be verified.
- **Label:** The label for the claim, either "0" for "Supports" or "1" for "Refutes".
- **Explanation:** A sentence or phrase explaining why the claim is labeled as such.
- **Evidence:** Evidence supporting or refuting the claim, if available. This may be a URL or a short text snippet.
|
kpriyanshu256/MultiTabQA-multitable_pretraining-Salesforce-codet5-base_train-latex-64000 | ---
dataset_info:
features:
- name: input_ids
sequence:
sequence: int32
- name: attention_mask
sequence:
sequence: int8
- name: labels
sequence:
sequence: int64
splits:
- name: train
num_bytes: 13336000
num_examples: 1000
download_size: 974725
dataset_size: 13336000
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
TinyPixel/airoboros_llama2 | ---
dataset_info:
features:
- name: id
dtype: string
- name: conversations
list:
- name: from
dtype: string
- name: value
dtype: string
- name: category
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 213067797
num_examples: 59277
download_size: 111592267
dataset_size: 213067797
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "airo"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
open-cn-llm-leaderboard/requests | ---
license: apache-2.0
---
|
Weni/zeroshot-3.2.0 | ---
dataset_info:
features:
- name: context
dtype: string
- name: all_classes
list:
- name: class
dtype: string
- name: context
dtype: string
- name: id
dtype: int64
- name: input
dtype: string
- name: output
dtype: string
- name: language
dtype:
class_label:
names:
'0': pt
'1': en
'2': es
- name: output_id
dtype: int64
splits:
- name: train
num_bytes: 29103170
num_examples: 28664
download_size: 9161188
dataset_size: 29103170
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Tverous/claim-amr | ---
dataset_info:
features:
- name: uid
dtype: string
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: label
dtype:
class_label:
names:
'0': entailment
'1': neutral
'2': contradiction
- name: reason
dtype: string
- name: claim_cleaned_amr
dtype: string
splits:
- name: train
num_bytes: 60227369
num_examples: 100459
- name: dev
num_bytes: 853786
num_examples: 1200
- name: test
num_bytes: 846997
num_examples: 1200
download_size: 21047805
dataset_size: 61928152
---
# Dataset Card for "claim-amr"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
danu9327/1 | ---
dataset_info:
features:
- name: pixel_values
dtype: image
- name: label
dtype: image
splits:
- name: train
num_bytes: 40572119.0
num_examples: 24
download_size: 3253281
dataset_size: 40572119.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
dbutt7/NTP_Treefall_Segmentation | ---
license: cc-by-nc-4.0
dataset_info:
features:
- name: x
dtype: image
- name: y
sequence:
sequence:
sequence: uint8
splits:
- name: train
num_bytes: 6183133760
num_examples: 7240
download_size: 1458099889
dataset_size: 6183133760
---
|
huggingartists/nervy | ---
language:
- en
tags:
- huggingartists
- lyrics
---
# Dataset Card for "huggingartists/nervy"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [How to use](#how-to-use)
- [Dataset Structure](#dataset-structure)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [About](#about)
## Dataset Description
- **Homepage:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
- **Repository:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of the generated dataset:** 0.290463 MB
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/690c7ea858696b779e94dc99b204f034.1000x1000x1.jpg')">
</div>
</div>
<a href="https://huggingface.co/huggingartists/nervy">
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
</a>
<div style="text-align: center; font-size: 16px; font-weight: 800">Нервы (Nervy)</div>
<a href="https://genius.com/artists/nervy">
<div style="text-align: center; font-size: 14px;">@nervy</div>
</a>
</div>
### Dataset Summary
The Lyrics dataset parsed from Genius. This dataset is designed to generate lyrics with HuggingArtists.
Model is available [here](https://huggingface.co/huggingartists/nervy).
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
en
## How to use
How to load this dataset directly with the datasets library:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/nervy")
```
## Dataset Structure
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"text": "Look, I was gonna go easy on you\nNot to hurt your feelings\nBut I'm only going to get this one chance\nSomething's wrong, I can feel it..."
}
```
### Data Fields
The data fields are the same among all splits.
- `text`: a `string` feature.
### Data Splits
| train |validation|test|
|------:|---------:|---:|
|132| -| -|
'Train' can be easily divided into 'train' & 'validation' & 'test' with few lines of code:
```python
from datasets import load_dataset, Dataset, DatasetDict
import numpy as np
datasets = load_dataset("huggingartists/nervy")
train_percentage = 0.9
validation_percentage = 0.07
test_percentage = 0.03
train, validation, test = np.split(datasets['train']['text'], [int(len(datasets['train']['text'])*train_percentage), int(len(datasets['train']['text'])*(train_percentage + validation_percentage))])
datasets = DatasetDict(
{
'train': Dataset.from_dict({'text': list(train)}),
'validation': Dataset.from_dict({'text': list(validation)}),
'test': Dataset.from_dict({'text': list(test)})
}
)
```
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@InProceedings{huggingartists,
author={Aleksey Korshuk}
year=2021
}
```
## About
*Built by Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
|
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