datasetId stringlengths 2 117 | card stringlengths 19 1.01M |
|---|---|
minwook/NovelImg | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 9932475788.685
num_examples: 63839
download_size: 9249007048
dataset_size: 9932475788.685
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "NovelImg"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
elissilva/sheldon | ---
license: openrail
---
|
davanstrien/autogenerated-dataset-card | ---
dataset_info:
features:
- name: file
dtype: string
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': text-only
'1': illustrations
- name: pub_date
dtype: timestamp[ns]
- name: page_seq_num
dtype: int64
- name: edition_seq_num
dtype: int64
- name: batch
dtype: string
- name: lccn
dtype: string
- name: box
sequence: float32
- name: score
dtype: float64
- name: ocr
dtype: string
- name: place_of_publication
dtype: string
- name: geographic_coverage
dtype: string
- name: name
dtype: string
- name: publisher
dtype: string
- name: url
dtype: string
- name: page_url
dtype: string
splits:
- name: train
num_bytes: 48233952
num_examples: 549
download_size: 48027719
dataset_size: 48233952
size_categories:
- n<1K
---
# Dataset Card for "test_dataset_cogapp"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
The first row of the dataset looks like
<!-- [[[cog
from datasets import load_dataset
import json
ds = load_dataset("davanstrien/test_dataset_cogapp")
data = ds['train'][0]
example = json.dumps({k: str(v) for k,v in data.items()}, indent=2)
cog.out(
"```\n{}\n```".format(example))
)]]] -->
```
{
"file": "pst_fenske_ver02_data_sn84026497_00280776129_1880042101_0834_002_6_96.jpg",
"image": "<PIL.JpegImagePlugin.JpegImageFile image mode=L size=388x395 at 0x11AF00990>",
"label": "0",
"pub_date": "1880-04-21 00:00:00",
"page_seq_num": "834",
"edition_seq_num": "1",
"batch": "pst_fenske_ver02",
"lccn": "sn84026497",
"box": "[0.649412214756012, 0.6045778393745422, 0.8002520799636841, 0.7152365446090698]",
"score": "0.9609346985816956",
"ocr": "H. II. IIASLKT & SOXN, Dealers in General Merchandise In New Store Room nt HASLET'S COS ITERS, 'JTionoMtii, ln. .Tau'y 1st, 1?0.",
"place_of_publication": "Tionesta, Pa.",
"geographic_coverage": "['Pennsylvania--Forest--Tionesta']",
"name": "The Forest Republican. [volume]",
"publisher": "Ed. W. Smiley",
"url": "https://news-navigator.labs.loc.gov/data/pst_fenske_ver02/data/sn84026497/00280776129/1880042101/0834/002_6_96.jpg",
"page_url": "https://chroniclingamerica.loc.gov/data/batches/pst_fenske_ver02/data/sn84026497/00280776129/1880042101/0834.jp2"
}
```
<!-- [[[end]]] -->
<!-- [[[cog
from auto_dataset_card.core import generate_label_breakdown_tables, get_label_counts
ds = load_dataset("davanstrien/test_dataset_cogapp")
data = get_label_counts(ds)
cog.out(
f"""
# Label breakdowns \n
```
{data}
```
""")
]]] -->
# Label breakdowns
```
{'train': {'text-only': 376, 'illustrations': 173}}
```
<!-- [[[end]]] -->
<!-- [[[cog
from auto_dataset_card.core import generate_label_breakdown_tables, get_label_counts
ds = load_dataset("davanstrien/test_dataset_cogapp")
data = get_label_counts(ds)
tables = generate_label_breakdown_tables(data)
split = tables[0][0]
table = tables[0][1]
cog.out(
f"""
# Label breakdown table for split: {split} \n
{table}
""")
]]] -->
# Label breakdown table for split: train
| Label | Count | Percentage |
|---------------|---------|--------------|
| text-only | 376 | 68.49% |
| illustrations | 173 | 31.51% |
<!-- [[[end]]] --> |
AdapterOcean/med_alpaca_standardized_cluster_45_alpaca | ---
dataset_info:
features:
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 14788736
num_examples: 8299
download_size: 7360208
dataset_size: 14788736
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "med_alpaca_standardized_cluster_45_alpaca"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
TokenBender/e5_FT_sentence_retrieval_task_Hindi_mini | ---
license: apache-2.0
---
|
CyberHarem/maryland_azurlane | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of maryland/メリーランド/马里兰 (Azur Lane)
This is the dataset of maryland/メリーランド/马里兰 (Azur Lane), containing 16 images and their tags.
The core tags of this character are `long_hair, ponytail, red_eyes, red_hair, breasts, large_breasts, bangs, hair_between_eyes, very_long_hair`, 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 | 16 | 15.30 MiB | [Download](https://huggingface.co/datasets/CyberHarem/maryland_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 16 | 9.25 MiB | [Download](https://huggingface.co/datasets/CyberHarem/maryland_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 36 | 18.65 MiB | [Download](https://huggingface.co/datasets/CyberHarem/maryland_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 16 | 14.07 MiB | [Download](https://huggingface.co/datasets/CyberHarem/maryland_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 36 | 25.55 MiB | [Download](https://huggingface.co/datasets/CyberHarem/maryland_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/maryland_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 | 16 |  |  |  |  |  | 1girl, looking_at_viewer, smile, solo, black_gloves, dress, thighhighs, cleavage, thigh_boots |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | looking_at_viewer | smile | solo | black_gloves | dress | thighhighs | cleavage | thigh_boots |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------------------|:--------|:-------|:---------------|:--------|:-------------|:-----------|:--------------|
| 0 | 16 |  |  |  |  |  | X | X | X | X | X | X | X | X | X |
|
mowoe/rico-captions-blip-large-conditioned | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 7488526713.052
num_examples: 66261
download_size: 6338706182
dataset_size: 7488526713.052
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Lakshmi12/Resume_Dataset | ---
license: openrail
task_categories:
- text-classification
language:
- en
--- |
DataLinguistic/Data_OpenSet | ---
license: bigscience-openrail-m
---
|
FranklinWillemen/mapa_plus | ---
license: mit
dataset_info:
features:
- name: language
dtype: string
- name: tokens
sequence: string
- name: coarse_grained
sequence: string
- name: fine_grained
sequence: string
- name: pos
sequence: string
- name: dep
sequence: string
- name: heads
sequence: string
splits:
- name: train
num_bytes: 16067776
num_examples: 8341
- name: validation
num_bytes: 2354667
num_examples: 1040
- name: test
num_bytes: 5313344
num_examples: 3081
download_size: 1026065
dataset_size: 23735787
---
|
roa7n/patched_test_p_80_f_UCH_m1_predictions | ---
dataset_info:
features:
- name: id
dtype: string
- name: sequence_str
dtype: string
- name: label
dtype: int64
- name: m1_preds
dtype: float32
splits:
- name: train
num_bytes: 45068001
num_examples: 100012
download_size: 4294815
dataset_size: 45068001
---
# Dataset Card for "patched_test_p_80_f_UCH_m1_predictions"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
ruliad/stack-v2-python-with-content-test | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 1614825
num_examples: 783
download_size: 597860
dataset_size: 1614825
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
lsb/enwiki20230101-minilml6v2-avgembeddings | ---
dataset_info:
features:
- name: id
dtype: string
- name: url
dtype: string
- name: title
dtype: string
- name: text
dtype: string
- name: avg_embed
sequence: float32
splits:
- name: train
num_bytes: 31116288935
num_examples: 6593739
download_size: 26588145966
dataset_size: 31116288935
---
# Dataset Card for "enwiki20230101-minilml6v2-avgembeddings"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
TwoAbove/gpt4v-emotion-dataset-test | ---
dataset_info:
features:
- name: caption
dtype: string
- name: image
dtype: image
- name: link
dtype: string
- name: message_id
dtype: string
- name: timestamp
dtype: string
splits:
- name: train
num_bytes: 0
num_examples: 0
download_size: 0
dataset_size: 0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
Use the Edit dataset card button to edit. |
rohitp1/librispeech_asr_clean | ---
license: cc-by-4.0
---
This dataset contains only 100 hours train data of librispeech_clean. Functionality of librispeech-other and test-clean and dev-clean is unchanged |
bibinsee/meme.me | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: content
dtype: string
- name: response
dtype: string
splits:
- name: train
num_bytes: 31723
num_examples: 57
download_size: 20324
dataset_size: 31723
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
gtfintechlab/finer-ord-bio | ---
dataset_info:
features:
- name: tokens
sequence: string
- name: tags
sequence: int64
splits:
- name: train
num_bytes: 1359318
num_examples: 3262
- name: validation
num_bytes: 171403
num_examples: 402
- name: test
num_bytes: 433606
num_examples: 1075
download_size: 413643
dataset_size: 1964327
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
|
liuyanchen1015/MULTI_VALUE_sst2_regularized_reflexives_object_pronouns | ---
dataset_info:
features:
- name: sentence
dtype: string
- name: label
dtype: int64
- name: idx
dtype: int64
- name: score
dtype: int64
splits:
- name: dev
num_bytes: 420
num_examples: 2
- name: test
num_bytes: 1430
num_examples: 9
- name: train
num_bytes: 11660
num_examples: 107
download_size: 11678
dataset_size: 13510
---
# Dataset Card for "MULTI_VALUE_sst2_regularized_reflexives_object_pronouns"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Falah/stable_diffusion_prompts_dataset | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 29273899
num_examples: 195001
download_size: 8194320
dataset_size: 29273899
---
# Dataset Card for "stable_diffusion_prompts_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
do-me/SemanticFinder | ---
license: mit
tags:
- transformers.js
- transformers
- semanticsearch
- SemanticFinder
---
<p align="center">
<a href="https://do-me.github.io/SemanticFinder/">
<img src="https://github.com/do-me/SemanticFinder/assets/47481567/4522ab9d-08f4-4f4c-92db-dbf14ccb2b70" width="320" alt="SemanticFinder">
</a>
<h1 align="center">Frontend-only live semantic search with transformers.js</h1>
</p>
- **App: [SemanticFinder](https://do-me.github.io/SemanticFinder/)**
- **GitHub: [do-me/SemanticFinder](https://github.com/do-me/SemanticFinder)**
This is the HF data repo for indexed texts, ready-to-import in SemanticFinder. The files contain the original text, text chunks and their embeddings.
### Catalogue
| filesize | textTitle | textAuthor | textYear | textLanguage | URL | modelName | quantized | splitParam | splitType | characters | chunks | wordsToAvoidAll | wordsToCheckAll | wordsToAvoidAny | wordsToCheckAny | exportDecimals | lines | textNotes | textSourceURL | filename |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| 100.96 | Collection of 100 books | Various Authors | 1890 | en | https://do-me.github.io/SemanticFinder/?hf=Collection_of_100_books_dd80b04b | Xenova/bge-small-en-v1.5 | True | 100 | Words | 55705582 | 158957 | | | | | 2 | 1085035 | US Public Domain Books (English) | https://huggingface.co/datasets/storytracer/US-PD-Books/tree/main/data | Collection_of_100_books_dd80b04b.json.gz |
| 4.78 | Das Kapital | Karl Marx | 1867 | de | https://do-me.github.io/SemanticFinder/?hf=Das_Kapital_c1a84fba | Xenova/multilingual-e5-small | True | 80 | Words | 2003807 | 3164 | | | | | 5 | 28673 | | https://ia601605.us.archive.org/13/items/KarlMarxDasKapitalpdf/KAPITAL1.pdf | Das_Kapital_c1a84fba.json.gz |
| 2.58 | Divina Commedia | Dante | 1321 | it | https://do-me.github.io/SemanticFinder/?hf=Divina_Commedia_d5a0fa67 | Xenova/multilingual-e5-base | True | 50 | Words | 383782 | 1179 | | | | | 5 | 6225 | | http://www.letteratura-italiana.com/pdf/divina%20commedia/08%20Inferno%20in%20versione%20italiana.pdf | Divina_Commedia_d5a0fa67.json.gz |
| 11.92 | Don Quijote | Miguel de Cervantes | 1605 | es | https://do-me.github.io/SemanticFinder/?hf=Don_Quijote_14a0b44 | Xenova/multilingual-e5-base | True | 25 | Words | 1047150 | 7186 | | | | | 4 | 12005 | | https://parnaseo.uv.es/lemir/revista/revista19/textos/quijote_1.pdf | Don_Quijote_14a0b44.json.gz |
| 0.06 | Hansel and Gretel | Brothers Grimm | 1812 | en | https://do-me.github.io/SemanticFinder/?hf=Hansel_and_Gretel_4de079eb | TaylorAI/gte-tiny | True | 100 | Chars | 5304 | 55 | | | | | 5 | 9 | | https://www.grimmstories.com/en/grimm_fairy-tales/hansel_and_gretel | Hansel_and_Gretel_4de079eb.json.gz |
| 13.52 | Iliad | Homer | -750 | gr | https://do-me.github.io/SemanticFinder/?hf=Iliad_8de5d1ea | Xenova/multilingual-e5-small | True | 20 | Words | 1597139 | 11848 | | | | | 5 | 32659 | Including modern interpretation | https://www.stipsi.gr/homer/iliada.pdf | Iliad_8de5d1ea.json.gz |
| 1.74 | IPCC Report 2023 | IPCC | 2023 | en | https://do-me.github.io/SemanticFinder/?hf=IPCC_Report_2023_2b260928 | Supabase/bge-small-en | True | 200 | Chars | 307811 | 1566 | | | | | 5 | 3230 | state of knowledge of climate change | https://report.ipcc.ch/ar6syr/pdf/IPCC_AR6_SYR_LongerReport.pdf | IPCC_Report_2023_2b260928.json.gz |
| 25.56 | King James Bible | | None | en | https://do-me.github.io/SemanticFinder/?hf=King_James_Bible_24f6dc4c | TaylorAI/gte-tiny | True | 200 | Chars | 4556163 | 23056 | | | | | 5 | 80496 | | https://www.holybooks.com/wp-content/uploads/2010/05/The-Holy-Bible-King-James-Version.pdf | King_James_Bible_24f6dc4c.json.gz |
| 11.45 | King James Bible | | None | en | https://do-me.github.io/SemanticFinder/?hf=King_James_Bible_6434a78d | TaylorAI/gte-tiny | True | 200 | Chars | 4556163 | 23056 | | | | | 2 | 80496 | | https://www.holybooks.com/wp-content/uploads/2010/05/The-Holy-Bible-King-James-Version.pdf | King_James_Bible_6434a78d.json.gz |
| 39.32 | Les Misérables | Victor Hugo | 1862 | fr | https://do-me.github.io/SemanticFinder/?hf=Les_Misérables_2239df51 | Xenova/multilingual-e5-base | True | 25 | Words | 3236941 | 19463 | | | | | 5 | 74491 | All five acts included | https://beq.ebooksgratuits.com/vents/Hugo-miserables-1.pdf | Les_Misérables_2239df51.json.gz |
| 8.67 | List of the Most Common English Words | Dolph | 2012 | en | https://do-me.github.io/SemanticFinder/?hf=List_of_the_Most_Common_English_Words_0d1e28dc | Xenova/bge-small-en-v1.5 | True | \n | Regex | 210518 | 25322 | | | | | 2 | 25323 | GitHub Repo | https://raw.githubusercontent.com/dolph/dictionary/master/popular.txt | List_of_the_Most_Common_English_Words_0d1e28dc.json.gz |
| 15.61 | List of the Most Common English Words | Dolph | 2012 | en | https://do-me.github.io/SemanticFinder/?hf=List_of_the_Most_Common_English_Words_70320cde | Xenova/multilingual-e5-base | True | \n | Regex | 210518 | 25322 | | | | | 2 | 25323 | GitHub Repo | https://raw.githubusercontent.com/dolph/dictionary/master/popular.txt | List_of_the_Most_Common_English_Words_70320cde.json.gz |
| 0.46 | REGULATION (EU) 2023/138 | European Commission | 2022 | en | https://do-me.github.io/SemanticFinder/?hf=REGULATION_(EU)_2023_138_c00e7ff6 | Supabase/bge-small-en | True | 25 | Words | 76809 | 424 | | | | | 5 | 1323 | | https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:32023R0138&qid=1704492501351 | REGULATION_(EU)_2023_138_c00e7ff6.json.gz |
| 0.07 | Universal Declaration of Human Rights | United Nations | 1948 | en | https://do-me.github.io/SemanticFinder/?hf=Universal_Declaration_of_Human_Rights_0a7da79a | TaylorAI/gte-tiny | True | \nArticle | Regex | 8623 | 63 | | | | | 5 | 109 | 30 articles | https://www.un.org/en/about-us/universal-declaration-of-human-rights | Universal_Declaration_of_Human_Rights_0a7da79a.json.gz |
### Example
Once loaded in SemanticFinder it takes around 2 seconds to search through the whole bible! Try it out.
1. Click on one of the example URLs of your choice.
2. Once the index loaded, simply enter something you want to search for and hit "Find". The results will appear almost instantly.
### Create SemanticFinder files
1. Just use SemanticFinder as usual and run at least one search so that the index is created. This might take a while if your input is large. E.g. indexing the bible with 200 chars results in ~23k embeddings and takes 15-30 mins with a quantized gte-tiny model.
2. Add the metadata (so other people can find your index) and export the file. Note that you have the freedom to reduce decimals to reduce file size; usually 3 is more than enough depending on the model. Less than 3 will also do in most cases but if you need highest accuracy go with 5 or more.
3. Create a PR here if you want to see it added in the official collection! Just make sure to run `create_meta_data_csv_md.py` once to update the csv/md file. For now, the `readme.md` table here needs to be updated with the meta_data.md manually.
### Privacy
- This repo is public and shares documents of public interest or documents in the public domain.
- If you have sensitive documents you can still create the index with SemanticFinder and use it only locally.
Either you can load the index from disk each time or you host it in your local network and add the URL in SemanticFinder.
### Use cases
#### Standard use case
Search for most similar words/sentences/paragraphs/pages in any text. Just imagine CTRL+F could find related words and not only the exact same one you used!
If you're working on the same text repeatedly you can save the index and reuse it.
Also, there is the option of summarizing the results with generative AI like Qwen models right in your browser or connecting a heavy Llama2 instance with Ollama.
#### Advanced use cases
- [Translate words with multilingual embeddings](https://do-me.github.io/SemanticFinder/?hf=List_of_the_Most_Common_English_Words_70320cde&firstOnly=true&inferencingActive=False) or see which words out of a given list are most similar to your input word. Using e.g. the index of ~30k English words you can use more than 100 input languages to query! Note that here the expert settings change so that only the first match is displayed.
- [English synonym finder](https://do-me.github.io/SemanticFinder/?hf=List_of_the_Most_Common_English_Words_0d1e28dc&firstOnly=true&inferencingActive=False), using again the index of ~30k English words but with slightly better (and smaller) English-only embeddings. Same expert settings here.
- The [universal index idea](https://github.com/do-me/SemanticFinder/discussions/48), i.e. use the 30k English words index and do not inference for any new words. In this way you can perform **instant** semantic search on unknown / unseen / not indexed texts! Use [this URL](https://do-me.github.io/SemanticFinder/?hf=List_of_the_Most_Common_English_Words_0d1e28dc&inferencingActive=False&universalIndexSettingsWordLevel) and add then copy and paste any text of your choice into the text field. Inferencing any new words is turned off for speed gains.
- A hybrid version of the universal index where you use the 30k English words as start index but then "fill up" with all the additional words the index doesn't know yet. For this option just use [this URL](https://do-me.github.io/SemanticFinder/?hf=List_of_the_Most_Common_English_Words_0d1e28dc&inferencingActive=True&universalIndexSettingsWordLevel) where the inferencing is turned on again. This yields best results and might be a good compromise assuming that new texts generally don't have that many new words. Even if it's a couple of hundreds (like in a particular research paper in a niche domain) inferencing is quite fast.
## If you have any feedback/ideas/feature requests please open an issue or create a PR in the GitHub repo.
## ⭐Stars very welcome to spread the word and democratize semantic search!⭐ |
CyberHarem/yorck_azurlane | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of yorck/ヨルク/约克DE (Azur Lane)
This is the dataset of yorck/ヨルク/约克DE (Azur Lane), containing 51 images and their tags.
The core tags of this character are `breasts, long_hair, large_breasts, white_hair, bangs, red_eyes, hat, black_headwear, hair_ornament`, 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 | 51 | 97.05 MiB | [Download](https://huggingface.co/datasets/CyberHarem/yorck_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 51 | 45.53 MiB | [Download](https://huggingface.co/datasets/CyberHarem/yorck_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 135 | 104.20 MiB | [Download](https://huggingface.co/datasets/CyberHarem/yorck_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 51 | 80.73 MiB | [Download](https://huggingface.co/datasets/CyberHarem/yorck_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 135 | 160.30 MiB | [Download](https://huggingface.co/datasets/CyberHarem/yorck_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/yorck_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 | 21 |  |  |  |  |  | 1girl, looking_at_viewer, solo, cleavage, official_alternate_costume, black_dress, black_thighhighs, choker, thighs, horns, very_long_hair, bare_shoulders, sitting, smile, brown_thighhighs, blush, thigh_strap, closed_mouth, evening_gown |
| 1 | 19 |  |  |  |  |  | 1girl, looking_at_viewer, solo, cleavage, black_gloves, bare_shoulders, black_dress, smile, blush, fishnets, iron_cross, earrings, military_hat, white_thighhighs, closed_mouth, simple_background, white_background, peaked_cap |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | looking_at_viewer | solo | cleavage | official_alternate_costume | black_dress | black_thighhighs | choker | thighs | horns | very_long_hair | bare_shoulders | sitting | smile | brown_thighhighs | blush | thigh_strap | closed_mouth | evening_gown | black_gloves | fishnets | iron_cross | earrings | military_hat | white_thighhighs | simple_background | white_background | peaked_cap |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------------------|:-------|:-----------|:-----------------------------|:--------------|:-------------------|:---------|:---------|:--------|:-----------------|:-----------------|:----------|:--------|:-------------------|:--------|:--------------|:---------------|:---------------|:---------------|:-----------|:-------------|:-----------|:---------------|:-------------------|:--------------------|:-------------------|:-------------|
| 0 | 21 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | |
| 1 | 19 |  |  |  |  |  | X | X | X | X | | X | | | | | | X | | X | | X | | X | | X | X | X | X | X | X | X | X | X |
|
rewicks/paradocs | ---
license: apache-2.0
---
|
gmaijoe-emailchaser/emailchaser-llm-subject-data-v0.0.1 | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 512963
num_examples: 713
download_size: 70894
dataset_size: 512963
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "emailchaser-llm-subject-data-v0.0.3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
KatMarie/eu_corpora_parliament_processed | ---
dataset_info:
features: []
splits:
- name: train
download_size: 323
dataset_size: 0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "eu_corpora_parliament_processed"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
atmallen/neg_facts_azaria_mitchell | ---
dataset_info:
features:
- name: statement
dtype: string
- name: label
dtype:
class_label:
names:
'0': 'false'
'1': 'true'
splits:
- name: train
num_bytes: 62255.95721925134
num_examples: 897
- name: test
num_bytes: 15616.042780748663
num_examples: 225
download_size: 0
dataset_size: 77872.0
---
# Dataset Card for "neg_facts_azaria_mitchell"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
tobiaslee/VEC | ---
license: apache-2.0
---
|
Pedro712/gedu | ---
license: openrail
---
|
autoevaluate/autoeval-staging-eval-project-6fbfec76-7855034 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- samsum
eval_info:
task: summarization
model: henryu-lin/t5-3b-samsum-deepspeed
metrics: []
dataset_name: samsum
dataset_config: samsum
dataset_split: test
col_mapping:
text: dialogue
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: henryu-lin/t5-3b-samsum-deepspeed
* Dataset: samsum
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. |
Artzy7/Cazuza | ---
license: cc-by-3.0
---
|
zolak/twitter_dataset_78_1713090996 | ---
dataset_info:
features:
- name: id
dtype: string
- name: tweet_content
dtype: string
- name: user_name
dtype: string
- name: user_id
dtype: string
- name: created_at
dtype: string
- name: url
dtype: string
- name: favourite_count
dtype: int64
- name: scraped_at
dtype: string
- name: image_urls
dtype: string
splits:
- name: train
num_bytes: 2613092
num_examples: 6351
download_size: 1320768
dataset_size: 2613092
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
qazisaad/llama_2-product-titles-esci-sft-test-2 | ---
dataset_info:
features:
- name: index
dtype: int64
- name: query
dtype: string
- name: text
dtype: string
- name: label
dtype: string
- name: preds
dtype: string
- name: average_score
dtype: float64
- name: total_score
dtype: float64
- name: max_score
dtype: float64
- name: min_score
dtype: float64
- name: best_title
dtype: string
- name: clean_preds
dtype: string
- name: new_score
dtype: float64
- name: good_pred
dtype: bool
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 1617480.0
num_examples: 1677
download_size: 828108
dataset_size: 1617480.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "llama_2-product-titles-esci-sft-test-2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
diszep/rose | ---
license: openrail
---
|
omarelsayeed/UAE_bayan_news_0.xml | ---
dataset_info:
features:
- name: text
dtype: string
- name: summary
dtype: string
- name: summary_word_count_ratio
dtype: float64
- name: text_word_count_ratio
dtype: float64
- name: compression_ratio
dtype: float64
splits:
- name: train
num_bytes: 64880688
num_examples: 26692
download_size: 31814427
dataset_size: 64880688
---
# Dataset Card for "UAE_bayan_news_0.xml"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
mstz/titanic | ---
language:
- en
tags:
- titanic
- tabular_classification
- binary_classification
pretty_name: Titanic
size_categories:
- n<1K
task_categories:
- tabular-classification
configs:
- survival
license: cc
---
# Titanic
The [Titanic dataset](https://www.kaggle.com/datasets/vinicius150987/titanic3) from [Kaggle](https://www.kaggle.com/).
# Configurations and tasks
| **Configuration** | **Task** | **Description** |
|-------------------|---------------------------|----------------------------|
| survival | Binary classification | Has the passanger survived?|
# Usage
```python
from datasets import load_dataset
dataset = load_dataset("mstz/titanic")["train"]
``` |
Vinnyyw/Santanosoy | ---
license: openrail
---
|
result-kand2-sdxl-wuerst-karlo/92580e64 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 168
num_examples: 10
download_size: 1306
dataset_size: 168
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "92580e64"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Corianas/GPT_Tasks | ---
license: apache-2.0
---
This is a synthetic database of questions for testing InstructGPTs on.
It came about as I couldn't think of good examples when asked and got a bit out of hand. |
carnival13/rbrt_uda_trn | ---
dataset_info:
features:
- name: domain_label
dtype: int64
- name: pass_label
dtype: int64
- name: input
dtype: string
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
splits:
- name: train
num_bytes: 1115662838
num_examples: 755110
download_size: 352431197
dataset_size: 1115662838
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "rbrt_uda_trn"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
BhabhaAI/DEITA-Quality | ---
license: apache-2.0
---
|
Taraassss/sentiment_analysis_IT_dataset | ---
language:
- it
task_categories:
- text-classification
---
# Dataset: sentiment_analysis-IT-dataset
## Dataset Description
Our data has been collected by annotating tweets on Italian language from a broad range of topics. In total, we have 2037 tweets annotated with an emotion label. More details can be found in our paper (https://aclanthology.org/2021.wassa-1.8/).
### Languages
The BCP-47 code for the dataset's language is it.
## Dataset Structure
### Data Instances
@inproceedings{bianchi2021feel,
title = {{"Sentiment Classification for the Italian Language"}},
author = "Bianchi, Federico and Nozza, Debora and Hovy, Dirk",
booktitle = "Proceedings of the 11th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
year = "2021",
publisher = "Association for Computational Linguistics",
}
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"text": "Value(dtype='string', id=None)",
"feat_id_noticia": "Value(dtype='int16', id=None)",
"feat_target": "Value(dtype='string', id=None)",
"target": "ClassLabel(names=['NEG', 'NEU', 'POS'], id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 1096 |
| valid | 275 | |
gigant/m-ailabs_speech_dataset_fr | ---
language:
- fr
license: cc
size_categories:
fr:
- 10K<n<100K
task_categories:
- automatic-speech-recognition
task_ids: []
pretty_name: M-AILABS Speech Dataset (French)
---
## Dataset Description
- **Homepage:** https://www.caito.de/2019/01/the-m-ailabs-speech-dataset/
### Dataset Summary
The M-AILABS Speech Dataset is the first large dataset that we are providing free-of-charge, freely usable as training data for speech recognition and speech synthesis.
Most of the data is based on LibriVox and Project Gutenberg. The training data consist of nearly thousand hours of audio and the text-files in prepared format.
A transcription is provided for each clip. Clips vary in length from 1 to 20 seconds and have a total length of approximately shown in the list (and in the respective info.txt-files) below.
The texts were published between 1884 and 1964, and are in the public domain. The audio was recorded by the LibriVox project and is also in the public domain – except for Ukrainian.
Ukrainian audio was kindly provided either by Nash Format or Gwara Media for machine learning purposes only (please check the data info.txt files for details).
### Languages
French
## Dataset Structure
### Data Instances
A typical data point comprises the path to the audio file, called audio and its sentence.
### Data Fields
- audio: A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, *i.e.* `dataset[0]["audio"]` should **always** be preferred over `dataset["audio"][0]`.
- sentence: The sentence the user was prompted to speak
### Data Splits
The speech material has not been subdivided into portions, everything is in the "train" split.
The train split consists of 82825 audio clips and the related sentences.
### Contributions
[@gigant](https://huggingface.co/gigant) added this dataset. |
fsuarez/autotrain-data-logo-identifier-v2-short | ---
task_categories:
- image-classification
---
# AutoTrain Dataset for project: logo-identifier-v2-short
## Dataset Description
This dataset has been automatically processed by AutoTrain for project logo-identifier-v2-short.
### Languages
The BCP-47 code for the dataset's language is unk.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"image": "<100x100 RGB PIL image>",
"target": 98
},
{
"image": "<100x100 RGB PIL image>",
"target": 3
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"image": "Image(decode=True, id=None)",
"target": "ClassLabel(names=['20thTelevision', '3M', '7Eleven', 'Acer', 'AmericanExpress', 'Amul', 'Anthem', 'ApolloHospitals', 'Apple', 'Armani', 'Asahi', 'Asus', 'Atari', 'Audi', 'Avon', 'Booking', 'Bosch', 'Bridgestone', 'British Airways', 'Budweiser', 'Burberry', 'BurgerKing', 'BuzzFeed', 'Canon', 'CocaColaZero', 'Coleman', 'Coles', 'Converse', 'CornFlakes', 'Corona', 'CostcoWholesale', 'Crayola', 'Credit Agricole', 'Crocs', 'Crunchyroll', 'Ctrip', 'Dropbox', 'Ducati', 'DunkinDonuts', 'Duracell', 'Dyson', 'Ethereum', 'Etsy', 'Excel', 'ExxonMobil', 'FoxNews', 'FreddieMac', 'Fujitsu', 'Goodyear', 'Google', 'Grubhub', 'Gucci', 'Huawei', 'Hudson Bay Company', 'HugoBoss', 'Hulu', 'Hyundai', 'Instagram', 'Intel', 'John Lewis & Partners', 'Johnson&Johnson', 'Kingston', 'LouisVuitton', 'Lowes', 'Lufthansa', 'Lululemon', 'Luxottica', 'MorganStanley', 'Motorola', 'MountainDew', 'Moutai', 'Movistar', 'Msci', 'Muji', 'Nike', 'Nissan', 'Nokia', 'Nvidia', 'Orange', 'Oreo', 'Porsche', 'Power China', 'Prada', 'Pringles', 'Publix', 'Puma', 'Purina', 'PwC', 'Qualcomm', 'Rolex', 'Rolls-Royce', 'RoyalCaribbean', 'Spotify', 'Sprite', 'Starbucks', 'StateBankofIndia', 'StateGrid', 'Subaru', 'Subway', 'SumitomoGroup', 'Suning', 'Supreme', 'Suzuki', 'Toshiba', 'Total SA', 'TotalEnergies', 'Toyota', 'TripAdvisor', 'Twitch', 'Twitter', 'UnitedHealthCare', 'Universal', 'Volkswagen', 'Volvo', 'Wikipedia', 'Wipro', 'Wuliangye', 'Xiaomi', 'Youtube', 'Zoom', 'hennessy', 'iHeartRadio', 'koolAid'], id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 7168 |
| valid | 1859 |
|
goodcoffee/covidQA_eval_v2 | ---
dataset_info:
features:
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
- name: start_positions
dtype: int64
- name: end_positions
dtype: int64
splits:
- name: train
num_bytes: 782952
num_examples: 303
download_size: 0
dataset_size: 782952
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "covidQA_eval_v2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
seank0602/gpteacher_rp | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: response
dtype: string
splits:
- name: train
num_bytes: 1507005
num_examples: 1923
download_size: 941833
dataset_size: 1507005
---
# Dataset Card for "gpteacher_rp"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
mHossain/final_train_v4_test_380000 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: 'Unnamed: 0'
dtype: int64
- name: input_text
dtype: string
- name: target_text
dtype: string
- name: prefix
dtype: string
splits:
- name: train
num_bytes: 6729122.7
num_examples: 18000
- name: test
num_bytes: 747680.3
num_examples: 2000
download_size: 3220998
dataset_size: 7476803.0
---
# Dataset Card for "final_train_v4_test_380000"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
open-llm-leaderboard/details_amazingvince__zephyr-smol_llama-100m-dpo-full | ---
pretty_name: Evaluation run of amazingvince/zephyr-smol_llama-100m-dpo-full
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [amazingvince/zephyr-smol_llama-100m-dpo-full](https://huggingface.co/amazingvince/zephyr-smol_llama-100m-dpo-full)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 1 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the aggregated 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_amazingvince__zephyr-smol_llama-100m-dpo-full\"\
,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\
\ are the [latest results from run 2023-12-03T16:25:51.768387](https://huggingface.co/datasets/open-llm-leaderboard/details_amazingvince__zephyr-smol_llama-100m-dpo-full/blob/main/results_2023-12-03T16-25-51.768387.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 \"acc\": 0.006823351023502654,\n\
\ \"acc_stderr\": 0.002267537102254512\n },\n \"harness|gsm8k|5\":\
\ {\n \"acc\": 0.006823351023502654,\n \"acc_stderr\": 0.002267537102254512\n\
\ }\n}\n```"
repo_url: https://huggingface.co/amazingvince/zephyr-smol_llama-100m-dpo-full
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_03T16_25_51.768387
path:
- '**/details_harness|gsm8k|5_2023-12-03T16-25-51.768387.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-03T16-25-51.768387.parquet'
- config_name: results
data_files:
- split: 2023_12_03T16_25_51.768387
path:
- results_2023-12-03T16-25-51.768387.parquet
- split: latest
path:
- results_2023-12-03T16-25-51.768387.parquet
---
# Dataset Card for Evaluation run of amazingvince/zephyr-smol_llama-100m-dpo-full
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/amazingvince/zephyr-smol_llama-100m-dpo-full
- **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 [amazingvince/zephyr-smol_llama-100m-dpo-full](https://huggingface.co/amazingvince/zephyr-smol_llama-100m-dpo-full) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 1 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated 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_amazingvince__zephyr-smol_llama-100m-dpo-full",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-03T16:25:51.768387](https://huggingface.co/datasets/open-llm-leaderboard/details_amazingvince__zephyr-smol_llama-100m-dpo-full/blob/main/results_2023-12-03T16-25-51.768387.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": {
"acc": 0.006823351023502654,
"acc_stderr": 0.002267537102254512
},
"harness|gsm8k|5": {
"acc": 0.006823351023502654,
"acc_stderr": 0.002267537102254512
}
}
```
### 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] |
chanind/logiclm_bookcorpus_dataset | ---
dataset_info:
features:
- name: input_ids
sequence: int32
- name: token_type_ids
sequence: int8
- name: attention_mask
sequence: int8
- name: special_tokens_mask
sequence: int8
splits:
- name: train
num_bytes: 9635097081.0
num_examples: 72888253
download_size: 3280936965
dataset_size: 9635097081.0
---
# Dataset Card for "logiclm_bookcorpus_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Muennighoff/P3 | ---
annotations_creators:
- crowdsourced
- expert-generated
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: P3
size_categories:
- 100M<n<1B
task_categories:
- other
---
This is a repreprocessed version of [P3](https://huggingface.co/datasets/bigscience/P3) with any updates that have been made to the P3 datasets since the release of the original P3. It is used for the finetuning of [bloomz-p3](https://huggingface.co/bigscience/bloomz-p3) & [mt0-xxl-p3](https://huggingface.co/bigscience/mt0-xxl-p3). The script is available [here](https://github.com/bigscience-workshop/bigscience/blob/638e66e40395dbfab9fa08a662d43b317fb2eb38/data/p3/prepare_p3.py).
|
mstz/hill | ---
language:
- en
tags:
- hill
- tabular_classification
- binary_classification
- UCI
pretty_name: Hill
size_categories:
- n<1K
task_categories:
- tabular-classification
configs:
- hill
license: cc
---
# Hill
The [Hill dataset](https://archive.ics.uci.edu/ml/datasets/Hill) from the [UCI ML repository](https://archive.ics.uci.edu/ml/datasets).
Do the plotted coordinates draw a hill?
# Configurations and tasks
| **Configuration** | **Task** | **Description** |
|-------------------|---------------------------|------------------------------------------|
| hill | Binary classification | Do the plotted coordinates draw a hill? |
# Usage
```python
from datasets import load_dataset
dataset = load_dataset("mstz/hill")["train"]
```
# Features
Features are the coordinates of the drawn point. Feature `X{i}` is the `y` coordinate of the point `(i, X{i})`. |
yezhengli9/wmt20-en-zh | ---
dataset_info:
features:
- name: id (string)
dtype: string
- name: translation (translation)
dtype: string
splits:
- name: train
num_bytes: 811187
num_examples: 1418
download_size: 444205
dataset_size: 811187
---
# Dataset Card for "wmt20-en-zh"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
bh8648/split_dataset_2 | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: output
dtype: string
- name: page_num
dtype: int64
splits:
- name: train
num_bytes: 713287
num_examples: 212
download_size: 374432
dataset_size: 713287
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "split_dataset_2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Nexdata/70846_Images_Human_Face_Segmentation_Data | ---
license: cc-by-nc-nd-4.0
---
## Description
Human Face Segmentation Data from 70,846 Images. Pure color backgrounds, interior and exterior scene types are all included in the data. Both males and females are included in the data. Asian, Black, and Caucasian races are represented in the race distribution. The age ranges from young children to elderly people. Simple and complex facial expressions can be found in the data (large-angle tilt of face, closing eye, glower, pucker, opening mouth, etc.). We used pixel-by-pixel segmentation annotations to annotate the human face, the five sense organs, the body, and appendages. The information can be applied to tasks like facial Recon Related Tasks
For more details, please refer to the link: https://www.nexdata.ai/dataset/945?source=Huggingface
# Specifications
## Data size
70,846 images, there is only one face in an image
## Population distribution
race distribution: 32,235 images of Asian, 29,501 images of Caucasian, 9,110 images of black race; gender distribution: 34,044 male images and 36,802 female images; age distribution: baby, teenager, young, midlife and senior
## Collection environment
including pure color background, indoor scenes and outdoor scenes
## Data diversity
multiple scenes, multiple ages, multiple races, complicated expressions (closing eye, glower, pucker, opening mouth, etc.), and multiple appendages
## Image Parameter
Data format: the image data is in .jpg or .png format, the annotation file is in .json or .psd format; the human face resolution is not lower than 128*128, and pupillary distance is not less than 60 pixels
## Annotation content
segmentation annotation of human face, the five sense organs, body and appendages
## Accuracy
the mask edge location errors in x and y directions are less than 3 pixels, which is considered as a qualified annotation; the annotation part (id) is regarded as the unit, the accuracy rate of segmentation annotation shall be more than 97%
# Licensing Information
Commercial License
|
open-llm-leaderboard/details_vicgalle__gpt2-alpaca | ---
pretty_name: Evaluation run of vicgalle/gpt2-alpaca
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [vicgalle/gpt2-alpaca](https://huggingface.co/vicgalle/gpt2-alpaca) 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_vicgalle__gpt2-alpaca\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-09-22T17:31:40.228869](https://huggingface.co/datasets/open-llm-leaderboard/details_vicgalle__gpt2-alpaca/blob/main/results_2023-09-22T17-31-40.228869.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\": 0.009542785234899329,\n\
\ \"em_stderr\": 0.0009956233793266855,\n \"f1\": 0.05457529362416121,\n\
\ \"f1_stderr\": 0.001605303697316422,\n \"acc\": 0.2533543804262036,\n\
\ \"acc_stderr\": 0.0070256103461651745\n },\n \"harness|drop|3\":\
\ {\n \"em\": 0.009542785234899329,\n \"em_stderr\": 0.0009956233793266855,\n\
\ \"f1\": 0.05457529362416121,\n \"f1_stderr\": 0.001605303697316422\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \"acc_stderr\"\
: 0.0\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.5067087608524072,\n\
\ \"acc_stderr\": 0.014051220692330349\n }\n}\n```"
repo_url: https://huggingface.co/vicgalle/gpt2-alpaca
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_07_19T10_35_22.548714
path:
- '**/details_harness|arc:challenge|25_2023-07-19T10:35:22.548714.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-07-19T10:35:22.548714.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_09_22T17_31_40.228869
path:
- '**/details_harness|drop|3_2023-09-22T17-31-40.228869.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-22T17-31-40.228869.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_22T17_31_40.228869
path:
- '**/details_harness|gsm8k|5_2023-09-22T17-31-40.228869.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-09-22T17-31-40.228869.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_07_19T10_35_22.548714
path:
- '**/details_harness|hellaswag|10_2023-07-19T10:35:22.548714.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-07-19T10:35:22.548714.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_07_19T10_35_22.548714
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T10:35:22.548714.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T10:35:22.548714.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T10:35:22.548714.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T10:35:22.548714.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T10:35:22.548714.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T10:35:22.548714.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T10:35:22.548714.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T10:35:22.548714.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T10:35:22.548714.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T10:35:22.548714.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T10:35:22.548714.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T10:35:22.548714.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T10:35:22.548714.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T10:35:22.548714.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T10:35:22.548714.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T10:35:22.548714.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T10:35:22.548714.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T10:35:22.548714.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T10:35:22.548714.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T10:35:22.548714.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T10:35:22.548714.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T10:35:22.548714.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T10:35:22.548714.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T10:35:22.548714.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T10:35:22.548714.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T10:35:22.548714.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T10:35:22.548714.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T10:35:22.548714.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T10:35:22.548714.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T10:35:22.548714.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T10:35:22.548714.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T10:35:22.548714.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T10:35:22.548714.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T10:35:22.548714.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-07-19T10:35:22.548714.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T10:35:22.548714.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T10:35:22.548714.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T10:35:22.548714.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-07-19T10:35:22.548714.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-07-19T10:35:22.548714.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T10:35:22.548714.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T10:35:22.548714.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T10:35:22.548714.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T10:35:22.548714.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T10:35:22.548714.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T10:35:22.548714.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T10:35:22.548714.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T10:35:22.548714.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T10:35:22.548714.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T10:35:22.548714.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T10:35:22.548714.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T10:35:22.548714.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T10:35:22.548714.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T10:35:22.548714.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T10:35:22.548714.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T10:35:22.548714.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T10:35:22.548714.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T10:35:22.548714.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T10:35:22.548714.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T10:35:22.548714.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T10:35:22.548714.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T10:35:22.548714.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T10:35:22.548714.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T10:35:22.548714.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T10:35:22.548714.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T10:35:22.548714.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T10:35:22.548714.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T10:35:22.548714.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T10:35:22.548714.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T10:35:22.548714.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T10:35:22.548714.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T10:35:22.548714.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T10:35:22.548714.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T10:35:22.548714.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T10:35:22.548714.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T10:35:22.548714.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T10:35:22.548714.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T10:35:22.548714.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T10:35:22.548714.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T10:35:22.548714.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T10:35:22.548714.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T10:35:22.548714.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T10:35:22.548714.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T10:35:22.548714.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T10:35:22.548714.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T10:35:22.548714.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T10:35:22.548714.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T10:35:22.548714.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T10:35:22.548714.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T10:35:22.548714.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T10:35:22.548714.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-07-19T10:35:22.548714.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T10:35:22.548714.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T10:35:22.548714.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T10:35:22.548714.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-07-19T10:35:22.548714.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-07-19T10:35:22.548714.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T10:35:22.548714.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T10:35:22.548714.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T10:35:22.548714.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T10:35:22.548714.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T10:35:22.548714.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T10:35:22.548714.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T10:35:22.548714.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T10:35:22.548714.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T10:35:22.548714.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T10:35:22.548714.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T10:35:22.548714.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T10:35:22.548714.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T10:35:22.548714.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T10:35:22.548714.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T10:35:22.548714.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T10:35:22.548714.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T10:35:22.548714.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_07_19T10_35_22.548714
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T10:35:22.548714.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T10:35:22.548714.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_07_19T10_35_22.548714
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T10:35:22.548714.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T10:35:22.548714.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_07_19T10_35_22.548714
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T10:35:22.548714.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T10:35:22.548714.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_07_19T10_35_22.548714
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T10:35:22.548714.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T10:35:22.548714.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_07_19T10_35_22.548714
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T10:35:22.548714.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T10:35:22.548714.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_07_19T10_35_22.548714
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T10:35:22.548714.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T10:35:22.548714.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_07_19T10_35_22.548714
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T10:35:22.548714.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T10:35:22.548714.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_07_19T10_35_22.548714
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T10:35:22.548714.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T10:35:22.548714.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_07_19T10_35_22.548714
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T10:35:22.548714.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T10:35:22.548714.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_07_19T10_35_22.548714
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T10:35:22.548714.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T10:35:22.548714.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_07_19T10_35_22.548714
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T10:35:22.548714.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T10:35:22.548714.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_07_19T10_35_22.548714
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T10:35:22.548714.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T10:35:22.548714.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_07_19T10_35_22.548714
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T10:35:22.548714.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T10:35:22.548714.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_07_19T10_35_22.548714
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T10:35:22.548714.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T10:35:22.548714.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_07_19T10_35_22.548714
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T10:35:22.548714.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T10:35:22.548714.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_07_19T10_35_22.548714
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T10:35:22.548714.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T10:35:22.548714.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_07_19T10_35_22.548714
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T10:35:22.548714.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T10:35:22.548714.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_07_19T10_35_22.548714
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T10:35:22.548714.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T10:35:22.548714.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_07_19T10_35_22.548714
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T10:35:22.548714.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T10:35:22.548714.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_07_19T10_35_22.548714
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T10:35:22.548714.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T10:35:22.548714.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_07_19T10_35_22.548714
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T10:35:22.548714.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T10:35:22.548714.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_07_19T10_35_22.548714
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T10:35:22.548714.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T10:35:22.548714.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_07_19T10_35_22.548714
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T10:35:22.548714.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T10:35:22.548714.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_07_19T10_35_22.548714
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T10:35:22.548714.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T10:35:22.548714.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_07_19T10_35_22.548714
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T10:35:22.548714.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T10:35:22.548714.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_07_19T10_35_22.548714
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T10:35:22.548714.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T10:35:22.548714.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_07_19T10_35_22.548714
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T10:35:22.548714.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T10:35:22.548714.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_07_19T10_35_22.548714
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T10:35:22.548714.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T10:35:22.548714.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_07_19T10_35_22.548714
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T10:35:22.548714.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T10:35:22.548714.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_07_19T10_35_22.548714
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T10:35:22.548714.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T10:35:22.548714.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_07_19T10_35_22.548714
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T10:35:22.548714.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T10:35:22.548714.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_07_19T10_35_22.548714
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T10:35:22.548714.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T10:35:22.548714.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_07_19T10_35_22.548714
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T10:35:22.548714.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T10:35:22.548714.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_07_19T10_35_22.548714
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T10:35:22.548714.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T10:35:22.548714.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_07_19T10_35_22.548714
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-07-19T10:35:22.548714.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-07-19T10:35:22.548714.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_07_19T10_35_22.548714
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T10:35:22.548714.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T10:35:22.548714.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_07_19T10_35_22.548714
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T10:35:22.548714.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T10:35:22.548714.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_07_19T10_35_22.548714
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T10:35:22.548714.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T10:35:22.548714.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_07_19T10_35_22.548714
path:
- '**/details_harness|hendrycksTest-management|5_2023-07-19T10:35:22.548714.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-07-19T10:35:22.548714.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_07_19T10_35_22.548714
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-07-19T10:35:22.548714.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-07-19T10:35:22.548714.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_07_19T10_35_22.548714
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T10:35:22.548714.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T10:35:22.548714.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_07_19T10_35_22.548714
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T10:35:22.548714.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T10:35:22.548714.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_07_19T10_35_22.548714
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T10:35:22.548714.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T10:35:22.548714.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_07_19T10_35_22.548714
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T10:35:22.548714.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T10:35:22.548714.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_07_19T10_35_22.548714
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T10:35:22.548714.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T10:35:22.548714.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_07_19T10_35_22.548714
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T10:35:22.548714.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T10:35:22.548714.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_07_19T10_35_22.548714
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T10:35:22.548714.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T10:35:22.548714.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_07_19T10_35_22.548714
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T10:35:22.548714.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T10:35:22.548714.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_07_19T10_35_22.548714
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T10:35:22.548714.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T10:35:22.548714.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_07_19T10_35_22.548714
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T10:35:22.548714.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T10:35:22.548714.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_07_19T10_35_22.548714
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T10:35:22.548714.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T10:35:22.548714.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_07_19T10_35_22.548714
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T10:35:22.548714.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T10:35:22.548714.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_07_19T10_35_22.548714
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T10:35:22.548714.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T10:35:22.548714.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_07_19T10_35_22.548714
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T10:35:22.548714.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T10:35:22.548714.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_07_19T10_35_22.548714
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T10:35:22.548714.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T10:35:22.548714.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_07_19T10_35_22.548714
path:
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T10:35:22.548714.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T10:35:22.548714.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_07_19T10_35_22.548714
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T10:35:22.548714.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T10:35:22.548714.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_07_19T10_35_22.548714
path:
- '**/details_harness|truthfulqa:mc|0_2023-07-19T10:35:22.548714.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-07-19T10:35:22.548714.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_09_22T17_31_40.228869
path:
- '**/details_harness|winogrande|5_2023-09-22T17-31-40.228869.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-09-22T17-31-40.228869.parquet'
- config_name: results
data_files:
- split: 2023_07_19T10_35_22.548714
path:
- results_2023-07-19T10:35:22.548714.parquet
- split: 2023_09_22T17_31_40.228869
path:
- results_2023-09-22T17-31-40.228869.parquet
- split: latest
path:
- results_2023-09-22T17-31-40.228869.parquet
---
# Dataset Card for Evaluation run of vicgalle/gpt2-alpaca
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/vicgalle/gpt2-alpaca
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** clementine@hf.co
### Dataset Summary
Dataset automatically created during the evaluation run of model [vicgalle/gpt2-alpaca](https://huggingface.co/vicgalle/gpt2-alpaca) 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_vicgalle__gpt2-alpaca",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-22T17:31:40.228869](https://huggingface.co/datasets/open-llm-leaderboard/details_vicgalle__gpt2-alpaca/blob/main/results_2023-09-22T17-31-40.228869.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": 0.009542785234899329,
"em_stderr": 0.0009956233793266855,
"f1": 0.05457529362416121,
"f1_stderr": 0.001605303697316422,
"acc": 0.2533543804262036,
"acc_stderr": 0.0070256103461651745
},
"harness|drop|3": {
"em": 0.009542785234899329,
"em_stderr": 0.0009956233793266855,
"f1": 0.05457529362416121,
"f1_stderr": 0.001605303697316422
},
"harness|gsm8k|5": {
"acc": 0.0,
"acc_stderr": 0.0
},
"harness|winogrande|5": {
"acc": 0.5067087608524072,
"acc_stderr": 0.014051220692330349
}
}
```
### 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] |
LozanoJohan/Sasha | ---
license: openrail
---
|
LIAGM/LPFF_dataset | ---
license: apache-2.0
---
|
shariqfarooq/cs323_densepred_seg256 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: val
path: data/val-*
dataset_info:
features:
- name: image
dtype: image
- name: mask
dtype: image
splits:
- name: train
num_bytes: 187512341.0
num_examples: 1464
- name: val
num_bytes: 187805177.75
num_examples: 1449
download_size: 375496804
dataset_size: 375317518.75
---
# Dataset Card for "cs323_densepred_seg256"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
siyufan/github-lll | ---
dataset_info:
features:
- name: 'Unnamed: 0'
dtype: int64
- name: fname
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 72915525
num_examples: 156824
download_size: 26658308
dataset_size: 72915525
---
# Dataset Card for "github-lll"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
surabhiMV/qrcode_val_n | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype: image
- name: bbox
sequence:
sequence:
sequence: float64
splits:
- name: train
num_bytes: 2138785.0
num_examples: 60
download_size: 2038537
dataset_size: 2138785.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "qrcode_val_n"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
open-llm-leaderboard/details_Sao10K__BrainDerp2 | ---
pretty_name: Evaluation run of Sao10K/BrainDerp2
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Sao10K/BrainDerp2](https://huggingface.co/Sao10K/BrainDerp2) 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_Sao10K__BrainDerp2\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-25T19:44:45.310953](https://huggingface.co/datasets/open-llm-leaderboard/details_Sao10K__BrainDerp2/blob/main/results_2023-10-25T19-44-45.310953.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\": 0.02139261744966443,\n\
\ \"em_stderr\": 0.0014817531449682906,\n \"f1\": 0.14337038590604004,\n\
\ \"f1_stderr\": 0.002432995569296514,\n \"acc\": 0.42474686941447715,\n\
\ \"acc_stderr\": 0.009953548160337068\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.02139261744966443,\n \"em_stderr\": 0.0014817531449682906,\n\
\ \"f1\": 0.14337038590604004,\n \"f1_stderr\": 0.002432995569296514\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.09021986353297953,\n \
\ \"acc_stderr\": 0.007891537108449961\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7592738752959748,\n \"acc_stderr\": 0.012015559212224174\n\
\ }\n}\n```"
repo_url: https://huggingface.co/Sao10K/BrainDerp2
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_10_04T07_46_51.716254
path:
- '**/details_harness|arc:challenge|25_2023-10-04T07-46-51.716254.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-10-04T07-46-51.716254.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_10_25T19_44_45.310953
path:
- '**/details_harness|drop|3_2023-10-25T19-44-45.310953.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-25T19-44-45.310953.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_25T19_44_45.310953
path:
- '**/details_harness|gsm8k|5_2023-10-25T19-44-45.310953.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-25T19-44-45.310953.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_10_04T07_46_51.716254
path:
- '**/details_harness|hellaswag|10_2023-10-04T07-46-51.716254.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-10-04T07-46-51.716254.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_10_04T07_46_51.716254
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-04T07-46-51.716254.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-04T07-46-51.716254.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-04T07-46-51.716254.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-04T07-46-51.716254.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-04T07-46-51.716254.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-04T07-46-51.716254.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-04T07-46-51.716254.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-04T07-46-51.716254.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-04T07-46-51.716254.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-04T07-46-51.716254.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-04T07-46-51.716254.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-04T07-46-51.716254.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-04T07-46-51.716254.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-04T07-46-51.716254.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-04T07-46-51.716254.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-04T07-46-51.716254.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-04T07-46-51.716254.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-04T07-46-51.716254.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-04T07-46-51.716254.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-04T07-46-51.716254.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-04T07-46-51.716254.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-04T07-46-51.716254.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-04T07-46-51.716254.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-04T07-46-51.716254.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-04T07-46-51.716254.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-04T07-46-51.716254.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-04T07-46-51.716254.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-04T07-46-51.716254.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-04T07-46-51.716254.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-04T07-46-51.716254.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-04T07-46-51.716254.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-04T07-46-51.716254.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-04T07-46-51.716254.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-04T07-46-51.716254.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-10-04T07-46-51.716254.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-04T07-46-51.716254.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-04T07-46-51.716254.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-04T07-46-51.716254.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-10-04T07-46-51.716254.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-10-04T07-46-51.716254.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-04T07-46-51.716254.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-04T07-46-51.716254.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-04T07-46-51.716254.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-04T07-46-51.716254.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-04T07-46-51.716254.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-04T07-46-51.716254.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-04T07-46-51.716254.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-04T07-46-51.716254.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-04T07-46-51.716254.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-04T07-46-51.716254.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-04T07-46-51.716254.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-04T07-46-51.716254.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-04T07-46-51.716254.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-10-04T07-46-51.716254.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-04T07-46-51.716254.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-10-04T07-46-51.716254.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-04T07-46-51.716254.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-04T07-46-51.716254.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-04T07-46-51.716254.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-04T07-46-51.716254.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-04T07-46-51.716254.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-04T07-46-51.716254.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-04T07-46-51.716254.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-04T07-46-51.716254.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-04T07-46-51.716254.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-04T07-46-51.716254.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-04T07-46-51.716254.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-04T07-46-51.716254.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-04T07-46-51.716254.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-04T07-46-51.716254.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-04T07-46-51.716254.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-04T07-46-51.716254.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-04T07-46-51.716254.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-04T07-46-51.716254.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-04T07-46-51.716254.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-04T07-46-51.716254.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-04T07-46-51.716254.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-04T07-46-51.716254.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-04T07-46-51.716254.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-04T07-46-51.716254.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-04T07-46-51.716254.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-04T07-46-51.716254.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-04T07-46-51.716254.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-04T07-46-51.716254.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-04T07-46-51.716254.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-04T07-46-51.716254.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-04T07-46-51.716254.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-04T07-46-51.716254.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-04T07-46-51.716254.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-04T07-46-51.716254.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-04T07-46-51.716254.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-10-04T07-46-51.716254.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-04T07-46-51.716254.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-04T07-46-51.716254.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-04T07-46-51.716254.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-10-04T07-46-51.716254.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-10-04T07-46-51.716254.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-04T07-46-51.716254.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-04T07-46-51.716254.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-04T07-46-51.716254.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-04T07-46-51.716254.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-04T07-46-51.716254.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-04T07-46-51.716254.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-04T07-46-51.716254.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-04T07-46-51.716254.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-04T07-46-51.716254.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-04T07-46-51.716254.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-04T07-46-51.716254.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-04T07-46-51.716254.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-04T07-46-51.716254.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-10-04T07-46-51.716254.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-04T07-46-51.716254.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-10-04T07-46-51.716254.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-04T07-46-51.716254.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_10_04T07_46_51.716254
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-04T07-46-51.716254.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-04T07-46-51.716254.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_10_04T07_46_51.716254
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-04T07-46-51.716254.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-04T07-46-51.716254.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_10_04T07_46_51.716254
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-04T07-46-51.716254.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-04T07-46-51.716254.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_10_04T07_46_51.716254
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-04T07-46-51.716254.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-04T07-46-51.716254.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_10_04T07_46_51.716254
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-04T07-46-51.716254.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-04T07-46-51.716254.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_10_04T07_46_51.716254
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-04T07-46-51.716254.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-04T07-46-51.716254.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_10_04T07_46_51.716254
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-04T07-46-51.716254.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-04T07-46-51.716254.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_10_04T07_46_51.716254
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-04T07-46-51.716254.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-04T07-46-51.716254.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_10_04T07_46_51.716254
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-04T07-46-51.716254.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-04T07-46-51.716254.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_10_04T07_46_51.716254
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-04T07-46-51.716254.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-04T07-46-51.716254.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_10_04T07_46_51.716254
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-04T07-46-51.716254.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-04T07-46-51.716254.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_10_04T07_46_51.716254
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-04T07-46-51.716254.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-04T07-46-51.716254.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_10_04T07_46_51.716254
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-04T07-46-51.716254.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-04T07-46-51.716254.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_10_04T07_46_51.716254
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-04T07-46-51.716254.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-04T07-46-51.716254.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_10_04T07_46_51.716254
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-04T07-46-51.716254.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-04T07-46-51.716254.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_10_04T07_46_51.716254
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-04T07-46-51.716254.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-04T07-46-51.716254.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_10_04T07_46_51.716254
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-04T07-46-51.716254.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-04T07-46-51.716254.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_10_04T07_46_51.716254
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-04T07-46-51.716254.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-04T07-46-51.716254.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_10_04T07_46_51.716254
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-04T07-46-51.716254.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-04T07-46-51.716254.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_10_04T07_46_51.716254
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-04T07-46-51.716254.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-04T07-46-51.716254.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_10_04T07_46_51.716254
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-04T07-46-51.716254.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-04T07-46-51.716254.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_10_04T07_46_51.716254
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-04T07-46-51.716254.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-04T07-46-51.716254.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_10_04T07_46_51.716254
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-04T07-46-51.716254.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-04T07-46-51.716254.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_10_04T07_46_51.716254
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-04T07-46-51.716254.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-04T07-46-51.716254.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_10_04T07_46_51.716254
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-04T07-46-51.716254.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-04T07-46-51.716254.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_10_04T07_46_51.716254
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-04T07-46-51.716254.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-04T07-46-51.716254.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_10_04T07_46_51.716254
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-04T07-46-51.716254.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-04T07-46-51.716254.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_10_04T07_46_51.716254
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-04T07-46-51.716254.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-04T07-46-51.716254.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_10_04T07_46_51.716254
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-04T07-46-51.716254.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-04T07-46-51.716254.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_10_04T07_46_51.716254
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-04T07-46-51.716254.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-04T07-46-51.716254.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_10_04T07_46_51.716254
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-04T07-46-51.716254.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-04T07-46-51.716254.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_10_04T07_46_51.716254
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-04T07-46-51.716254.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-04T07-46-51.716254.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_10_04T07_46_51.716254
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-04T07-46-51.716254.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-04T07-46-51.716254.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_10_04T07_46_51.716254
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-04T07-46-51.716254.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-04T07-46-51.716254.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_10_04T07_46_51.716254
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-10-04T07-46-51.716254.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-10-04T07-46-51.716254.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_10_04T07_46_51.716254
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-04T07-46-51.716254.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-04T07-46-51.716254.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_10_04T07_46_51.716254
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-04T07-46-51.716254.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-04T07-46-51.716254.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_10_04T07_46_51.716254
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-04T07-46-51.716254.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-04T07-46-51.716254.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_10_04T07_46_51.716254
path:
- '**/details_harness|hendrycksTest-management|5_2023-10-04T07-46-51.716254.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-10-04T07-46-51.716254.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_10_04T07_46_51.716254
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-10-04T07-46-51.716254.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-10-04T07-46-51.716254.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_10_04T07_46_51.716254
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-04T07-46-51.716254.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-04T07-46-51.716254.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_10_04T07_46_51.716254
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-04T07-46-51.716254.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-04T07-46-51.716254.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_10_04T07_46_51.716254
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-04T07-46-51.716254.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-04T07-46-51.716254.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_10_04T07_46_51.716254
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-04T07-46-51.716254.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-04T07-46-51.716254.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_10_04T07_46_51.716254
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-04T07-46-51.716254.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-04T07-46-51.716254.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_10_04T07_46_51.716254
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-04T07-46-51.716254.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-04T07-46-51.716254.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_10_04T07_46_51.716254
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-04T07-46-51.716254.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-04T07-46-51.716254.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_10_04T07_46_51.716254
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-04T07-46-51.716254.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-04T07-46-51.716254.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_10_04T07_46_51.716254
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-04T07-46-51.716254.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-04T07-46-51.716254.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_10_04T07_46_51.716254
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-04T07-46-51.716254.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-04T07-46-51.716254.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_10_04T07_46_51.716254
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-04T07-46-51.716254.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-04T07-46-51.716254.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_10_04T07_46_51.716254
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-04T07-46-51.716254.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-04T07-46-51.716254.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_10_04T07_46_51.716254
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-04T07-46-51.716254.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-04T07-46-51.716254.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_10_04T07_46_51.716254
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-10-04T07-46-51.716254.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-10-04T07-46-51.716254.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_10_04T07_46_51.716254
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-04T07-46-51.716254.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-04T07-46-51.716254.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_10_04T07_46_51.716254
path:
- '**/details_harness|hendrycksTest-virology|5_2023-10-04T07-46-51.716254.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-10-04T07-46-51.716254.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_10_04T07_46_51.716254
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-04T07-46-51.716254.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-04T07-46-51.716254.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_10_04T07_46_51.716254
path:
- '**/details_harness|truthfulqa:mc|0_2023-10-04T07-46-51.716254.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-10-04T07-46-51.716254.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_25T19_44_45.310953
path:
- '**/details_harness|winogrande|5_2023-10-25T19-44-45.310953.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-25T19-44-45.310953.parquet'
- config_name: results
data_files:
- split: 2023_10_04T07_46_51.716254
path:
- results_2023-10-04T07-46-51.716254.parquet
- split: 2023_10_25T19_44_45.310953
path:
- results_2023-10-25T19-44-45.310953.parquet
- split: latest
path:
- results_2023-10-25T19-44-45.310953.parquet
---
# Dataset Card for Evaluation run of Sao10K/BrainDerp2
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Sao10K/BrainDerp2
- **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 [Sao10K/BrainDerp2](https://huggingface.co/Sao10K/BrainDerp2) 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_Sao10K__BrainDerp2",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-25T19:44:45.310953](https://huggingface.co/datasets/open-llm-leaderboard/details_Sao10K__BrainDerp2/blob/main/results_2023-10-25T19-44-45.310953.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": 0.02139261744966443,
"em_stderr": 0.0014817531449682906,
"f1": 0.14337038590604004,
"f1_stderr": 0.002432995569296514,
"acc": 0.42474686941447715,
"acc_stderr": 0.009953548160337068
},
"harness|drop|3": {
"em": 0.02139261744966443,
"em_stderr": 0.0014817531449682906,
"f1": 0.14337038590604004,
"f1_stderr": 0.002432995569296514
},
"harness|gsm8k|5": {
"acc": 0.09021986353297953,
"acc_stderr": 0.007891537108449961
},
"harness|winogrande|5": {
"acc": 0.7592738752959748,
"acc_stderr": 0.012015559212224174
}
}
```
### 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] |
Junr-syl/movie_review_test | ---
dataset_info:
features:
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 7334957
num_examples: 5000
download_size: 0
dataset_size: 7334957
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "movie_review_test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Solshine/CollegeOfAgricultureAndForestry_Agricultural_Crop_Dataset | ---
license: cc
---
Context:
This data originally came from the College of Agriculture and Forestry
Originally posted to Kaggle by AGRICULTURAL INNOVATIONS with the following description
"Precision agriculture is in trend nowadays. It helps the farmers to get informed decision about the farming strategy. Here, we present to you a dataset which would allow the users to build a predictive model to recommend the most suitable crops to grow in a particular farm based on various parameters."
Includes recommendations for the following needs of plants:
N,
P,
K,
temperature,
humidity,
ph,
rainfall
This may also be useful for training models in reccomending nutrition for crops based on environmental conditions. |
nos1de/redis-commit-bugfixes | ---
dataset_info:
features:
- name: commit_msg
dtype: string
- name: sha
dtype: string
- name: remote_url
dtype: string
- name: date
dtype: string
- name: labels
dtype:
class_label:
names:
'0': non-bugfix
'1': bugfix
splits:
- name: train
num_bytes: 199969.95985155195
num_examples: 521
download_size: 161029
dataset_size: 199969.95985155195
---
# Dataset Card for "redis-commit-bugfixes"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
nick-lebesis/autotrain-data-gabba_v1 | ---
license: apache-2.0
dataset_info:
features:
- name: autotrain_text
dtype: string
splits:
- name: train
num_bytes: 1550475
num_examples: 378
- name: validation
num_bytes: 1550475
num_examples: 378
download_size: 361088
dataset_size: 3100950
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
---
|
mgorin1985/tcga-rcc | ---
license: apache-2.0
---
|
ibivibiv/alpaca_tiny9 | ---
dataset_info:
features:
- name: output
dtype: string
- name: instruction
dtype: string
- name: input
dtype: string
splits:
- name: train
num_bytes: 460769197
num_examples: 290901
download_size: 266516179
dataset_size: 460769197
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
anan-2024/twitter_dataset_1713151073 | ---
dataset_info:
features:
- name: id
dtype: string
- name: tweet_content
dtype: string
- name: user_name
dtype: string
- name: user_id
dtype: string
- name: created_at
dtype: string
- name: url
dtype: string
- name: favourite_count
dtype: int64
- name: scraped_at
dtype: string
- name: image_urls
dtype: string
splits:
- name: train
num_bytes: 286007
num_examples: 774
download_size: 154518
dataset_size: 286007
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
cseamaoo/gpt-assignment | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 1654448
num_examples: 1000
download_size: 966694
dataset_size: 1654448
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
argilla/emotion | ---
size_categories: 10K<n<100K
tags:
- rlfh
- argilla
- human-feedback
---
# Dataset Card for emotion
This dataset has been created with [Argilla](https://docs.argilla.io).
As shown in the sections below, this dataset can be loaded into Argilla as explained in [Load with Argilla](#load-with-argilla), or used directly with the `datasets` library in [Load with `datasets`](#load-with-datasets).
## Dataset Description
- **Homepage:** https://argilla.io
- **Repository:** https://github.com/argilla-io/argilla
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset contains:
* A dataset configuration file conforming to the Argilla dataset format named `argilla.yaml`. This configuration file will be used to configure the dataset when using the `FeedbackDataset.from_huggingface` method in Argilla.
* Dataset records in a format compatible with HuggingFace `datasets`. These records will be loaded automatically when using `FeedbackDataset.from_huggingface` and can be loaded independently using the `datasets` library via `load_dataset`.
* The [annotation guidelines](#annotation-guidelines) that have been used for building and curating the dataset, if they've been defined in Argilla.
### Load with Argilla
To load with Argilla, you'll just need to install Argilla as `pip install argilla --upgrade` and then use the following code:
```python
import argilla as rg
ds = rg.FeedbackDataset.from_huggingface("argilla/emotion")
```
### Load with `datasets`
To load this dataset with `datasets`, you'll just need to install `datasets` as `pip install datasets --upgrade` and then use the following code:
```python
from datasets import load_dataset
ds = load_dataset("argilla/emotion")
```
### Supported Tasks and Leaderboards
This dataset can contain [multiple fields, questions and responses](https://docs.argilla.io/en/latest/guides/llms/conceptual_guides/data_model.html) so it can be used for different NLP tasks, depending on the configuration. The dataset structure is described in the [Dataset Structure section](#dataset-structure).
There are no leaderboards associated with this dataset.
### Languages
[More Information Needed]
## Dataset Structure
### Data in Argilla
The dataset is created in Argilla with: **fields**, **questions**, **suggestions**, and **guidelines**.
The **fields** are the dataset records themselves, for the moment just text fields are suppported. These are the ones that will be used to provide responses to the questions.
| Field Name | Title | Type | Required | Markdown |
| ---------- | ----- | ---- | -------- | -------- |
| text | Text | TextField | True | False |
The **questions** are the questions that will be asked to the annotators. They can be of different types, such as rating, text, single choice, or multiple choice.
| Question Name | Title | Type | Required | Description | Values/Labels |
| ------------- | ----- | ---- | -------- | ----------- | ------------- |
| label | Label | LabelQuestion | True | N/A | ['0', '1', '2', '3', '4', '5'] |
**✨ NEW** Additionally, we also have **suggestions**, which are linked to the existing questions, and so on, named appending "-suggestion" and "-suggestion-metadata" to those, containing the value/s of the suggestion and its metadata, respectively. So on, the possible values are the same as in the table above.
Finally, the **guidelines** are just a plain string that can be used to provide instructions to the annotators. Find those in the [annotation guidelines](#annotation-guidelines) section.
### Data Instances
An example of a dataset instance in Argilla looks as follows:
```json
{
"fields": {
"text": "i didnt feel humiliated"
},
"metadata": {
"split": "train"
},
"responses": [
{
"status": "submitted",
"values": {
"label": {
"value": "0"
}
}
}
],
"suggestions": []
}
```
While the same record in HuggingFace `datasets` looks as follows:
```json
{
"external_id": null,
"label": [
{
"status": "submitted",
"user_id": null,
"value": "0"
}
],
"label-suggestion": null,
"label-suggestion-metadata": {
"agent": null,
"score": null,
"type": null
},
"metadata": "{\"split\": \"train\"}",
"text": "i didnt feel humiliated"
}
```
### Data Fields
Among the dataset fields, we differentiate between the following:
* **Fields:** These are the dataset records themselves, for the moment just text fields are suppported. These are the ones that will be used to provide responses to the questions.
* **text** is of type `TextField`.
* **Questions:** These are the questions that will be asked to the annotators. They can be of different types, such as `RatingQuestion`, `TextQuestion`, `LabelQuestion`, `MultiLabelQuestion`, and `RankingQuestion`.
* **label** is of type `LabelQuestion` with the following allowed values ['0', '1', '2', '3', '4', '5'].
* **✨ NEW** **Suggestions:** As of Argilla 1.13.0, the suggestions have been included to provide the annotators with suggestions to ease or assist during the annotation process. Suggestions are linked to the existing questions, are always optional, and contain not just the suggestion itself, but also the metadata linked to it, if applicable.
* (optional) **label-suggestion** is of type `label_selection` with the following allowed values ['0', '1', '2', '3', '4', '5'].
Additionally, we also have one more field which is optional and is the following:
* **external_id:** This is an optional field that can be used to provide an external ID for the dataset record. This can be useful if you want to link the dataset record to an external resource, such as a database or a file.
### Data Splits
The dataset contains a single split, which is `train`.
## 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 guidelines
Argilla port of [dair-ai/emotion](https://huggingface.co/datasets/dair-ai/emotion).
#### 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] |
Mitsuki-Sakamoto/alfa-deberta-re-pref-64-fil_self_1.4b_bo16_2_64_mix_50_kl_0.1_prm_160m_thr_0.0_seed_2_t_1.0 | ---
dataset_info:
config_name: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500
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: 43723892
num_examples: 18928
- name: epoch_1
num_bytes: 43813083
num_examples: 18928
- name: epoch_2
num_bytes: 43704431
num_examples: 18928
- name: epoch_3
num_bytes: 43595373
num_examples: 18928
- name: epoch_4
num_bytes: 43525569
num_examples: 18928
download_size: 116169229
dataset_size: 218362348
configs:
- config_name: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500
data_files:
- split: epoch_0
path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_0-*
- split: epoch_1
path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_1-*
- split: epoch_2
path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_2-*
- split: epoch_3
path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_3-*
- split: epoch_4
path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_4-*
---
|
liuyanchen1015/MULTI_VALUE_stsb_negative_inversion | ---
dataset_info:
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: score
dtype: float64
- name: idx
dtype: int64
- name: value_score
dtype: int64
splits:
- name: dev
num_bytes: 360
num_examples: 2
- name: test
num_bytes: 574
num_examples: 2
- name: train
num_bytes: 1004
num_examples: 5
download_size: 10778
dataset_size: 1938
---
# Dataset Card for "MULTI_VALUE_stsb_negative_inversion"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
polymath707/indollama2-without-emoji | ---
license: apache-2.0
---
|
MaCoCu/parallel_data | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- bs
- bg
- en
- is
- hr
- cnr
- mk
- mt
- sl
- sr
- sq
- tr
license:
- cc0-1.0
multilinguality:
- translation
pretty_name: MaCoCu_parallel
size_categories:
- 10M<n<100M
source_datasets:
- original
task_categories:
- translation
task_ids: []
dataset_info:
- config_name: enis
features:
- name: translation
dtype:
translation:
languages:
- is
- en
splits:
- name: train
num_bytes: 133883139
num_examples: 546172
download_size: 133883139
dataset_size: 133883139
- config_name: enbg
features:
- name: translation
dtype:
translation:
languages:
- bg
- en
splits:
- name: train
num_bytes: 133883139
num_examples: 546172
download_size: 133883139
dataset_size: 133883139
---
license: cc0-1.0
---
### Dataset Summary
The collection of MaCoCu parallel corpora have been crawled and consist of pairs of source and target segments (one or several sentences) and additional metadata. The following metadata is included:
- "src_url" and "trg_url": source and target document URL;
- "src_text" and "trg_text": text in non-English language and in English Language;
- "bleualign_score": similarity score as provided by the sentence alignment tool Bleualign (value between 0 and 1);
- "src_deferred_hash" and "trg_deferred_hash": hash identifier for the corresponding segment;
- "src_paragraph_id" and "trg_paragraph_id": identifier of the paragraph where the segment appears in the original document;
- "src_doc_title" and "trg_doc_title": title of the documents from which segments where obtained;
- "src_crawl_date" and "trg_crawl_date": date and time when source and target documents where donwoaded;
- "src_file_type" and "trg_file_type": type of the original documents (usually HTML format);
- "src_boilerplate" and "trg_boilerplate": are source or target segments boilerplates?
- "bifixer_hash": hash identifier for the segment pair;
- "bifixer_score": score that indicates how likely are segments to be correct in their corresponding language;
- "bicleaner_ai_score": score that indicates how likely are segments to be parallel;
- "biroamer_entities_detected": do any of the segments contain personal information?
- "dsi": a DSI class (“dsi”): information whether the segment is connected to any of Digital Service Infrastructure (DSI) classes (e.g., cybersecurity, e-health, e-justice, open-data-portal), defined by the Connecting Europe Facility (https://github.com/RikVN/DSI);
- "translation_direction": translation direction and machine translation identification ("translation-direction"): the source segment in each segment pair was identified by using a probabilistic model (https://github.com/RikVN/TranslationDirection), which also determines if the translation has been produced by a machine-translation system;
- "en_document_level_variant": the language variant of English (British or American, using a lexicon-based English variety classifier - https://pypi.org/project/abclf/) was identified on document and domain level;
- "domain_en": name of the web domain for the English document;
- "en_domain_level_variant": language variant for English at the level of the web domain.
To load a language pair just indicate the dataset and the pair of languages with English first
```python
dataset = load_dataset("MaCoCu/parallel_data", "en-is")
```
|
merlinyx/pose-controlnet | ---
license: mit
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': gt
'1': pose
'2': st
- name: caption
dtype: string
- name: gtimage
dtype: image
- name: stimage
dtype: image
splits:
- name: train
num_bytes: 1702123872.04
num_examples: 15764
- name: test
num_bytes: 144819992.92
num_examples: 1346
download_size: 1762884199
dataset_size: 1846943864.96
---
### Dataset Summary
The data is based on DeepFashion; turned into image pairs of the same person in same garment with different poses.
This won't preserve the person/garment at all but just want to process the data first and see what kind of controlnet it can train as an exercise for training a controlnet.
The controlnet_aux's openpose detector sometimes return black images for occluded human images so there won't be a lot of valid image pairs. |
crumb/textfiles | ---
language:
- en
size_categories:
- 10K<n<100K
---
backup of textfiles.com for easy download purposes
Disclaimer
```
THANK YOU FOR READING THE DISCLAIMER
TEXTFILES.COM is meant to be a historical archive, collecting textfiles written on BBSes in the 1980's. The reason for creating such an archive is because the potential for an important piece of history in human culture was in danger of being forgotten and inaccessible. It stands to reason that many of the textfiles written during the Golden Age of Phone-based Bulletin Board Systems would have a relatively small distribution compared to the multimedia of the modern day; but this makes what was written no less important.
In fact, this recent phenomenon of a world-linked network computers makes the availability of this past history more important than ever. While the present-day mainstream grapples with the issues and events of bringing life online, many of these very same issues were addressed in BBS culture and were in some cases either solved or quantified, and could provide important research for those who are unaware of the roots of many of the online world's most sacred rites and traditions.
As the ability of the Internet to transmit and provide information grows at an ever-increasing rate, the TEXTFILES.COM project endeavors to provide examples of the 1980's BBS culture by putting up as many examples of it as possible. This includes transcriptions of message bases, specific reports or essays about many different subjects, and fiction. Generally, if it was ASCII-based and posted on a Bulletin Board System, this site has attempted to procure examples or, with luck, complete specimens.
As is the nature with such an all-encompassing subject, there are a lot of files on this site that do not strictly fall into the realm of ASCII posted on Bulletin Boards of the 1980-1989 era. Some of the files are much older, and some of the files are very, very recent. This is because the nature of the culture being archived is very fluid; and it is better to err on the side of completeness than that of strict adherence to the "official" theme. In some cases, a trend started in the 1980's has continued to flourish into the present day, and it would be much more effort to cut off the example files at an arbitrary date than to just provide a complete collection. In other cases, the trend extends before the 1980's, and throwing away the earlier files would be an inappropriate way to demonstrate the importance of history.
TEXTFILES.COM considers itself a library or an archive in the sense that we are not selling or providing a commercial collection of the texts we make available. The site operates at a loss, and is being done solely to be a clearinghouse for these important pieces in our online culture. We do not necessarily agree with the opinions, instructions, allegations, or presented information in any of our textfiles. We do not have the resources to track down the truthfulness or accuracy of any text on the site, including our own written histories or essays.
We ask with all our hearts that you do not follow instructions or steps in any textfile on this site without consulting professionals in the field or established reference materials; some of these files have no basis in reality whatsoever, and should not be construed as appropriate for any purpose other than historical research.
TEXTFILES.COM broaches a wide array of subjects, concepts, points of view, and writing styles. We ask that you respect the law in your jurisdiction regarding what texts are appropriate for you to access, and that if you are unsure as to your right to browse this site, that you leave this site immediately and consult your local law enforcement, facility supervisor, or caretakers. TEXTFILES.COM supports filtering technologies such as NetNanny and Cyberpatrol to allow parents to decide what sites their children should browse; we have asked these companies to ban us completely because we cannot guarantee that all of our content is appropriate for all minors worldwide. If you have any doubts about the nature of our content, we ask that you leave the site immediately. We stress that we do not have the facilities to ensure that our content is appropriate for everyone who browses our site and additionally, we cannot guarantee that all textfiles on this site are appropriately labelled or classified.
It is the nature of this site that literately thousands and thousands of textfiles are being added by the staff, with only a cursory glance at the beginning of the files to create a one-line description before placing it online. In some cases, the one-line descriptions are being generated and placed online with no human intervention whatsoever. Because of this, we ask for your assistance in helping us make more accurate descriptions, less doubled files, and, where justified, to request that we remove the file in question if it was mistakenly provided to us under false pretenses or was originally put online in a different, unmodified form.
Our policy in terms of removal of textfiles from this site is to allow the creator/writers of the files to determine the fate of their own files. Requests to have files removed from the site will be granted, although we do reserve the right to notate that the file has been removed, so that other users will not waste our time and theirs trying to replace it. If the author has a more complete collection of their files and wishes to upload that collection to us, we will gladly replace the modified files with the originals.
TEXTFILES.COM is strongly against censorship and will not remove files because of questions of taste, truthfulness, obsolesence, or need. We are not seeking to have the most up-to-date information on a given technical subject; we only wish to present how the subject was perceived by the BBS world, long before new features or corporate interest changed the subject. We actively pursue the opinions of the writers of these textfiles regarding the context in which they were originally created, and provide a historical essay section for the writers to pontificate about these contexts and facts. Again, we make no guarantees about the accuracy or truthfulness of these opinion-centric essays by textfile writers.
Finally, TEXTFILES.COM maintains absolutely NO COPYRIGHT OR OWNERSHIP on any part of the site, including our own descriptions and introductions. TEXTFILES.COM as an organization is only interested in distribution of these texts, and makes no claim on them. We hope you enjoy the site, and that you will help to make it the best archive of these texts on the Internet.
-- Jason Scott of TEXTFILES.COM
``` |
gokuls/glue_augmented_qnli | ---
license: apache-2.0
---
# Dataset Card for glue_augmented_qnli
## Dataset Description
Augmented QNLI dataset
**Reference:** https://huggingface.co/datasets/glue |
enimai/MuST-C-ru | ---
license: afl-3.0
---
|
mmnga/wikipedia-ja-20230720-2k | ---
dataset_info:
features:
- name: curid
dtype: string
- name: title
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 5492016.948562663
num_examples: 2048
download_size: 3161030
dataset_size: 5492016.948562663
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "wikipedia-ja-20230720-2k"
This is data extracted randomly from [izumi-lab/wikipedia-ja-20230720](https://huggingface.co/datasets/izumi-lab/wikipedia-ja-20230720), consisting of 2,048 records.
[izumi-lab/wikipedia-ja-20230720](https://huggingface.co/datasets/izumi-lab/wikipedia-ja-20230720)からデータを2k分ランダムに抽出したデータです。
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
open-llm-leaderboard/details_Weyaxi__SlimOpenOrca-Mistral-7B | ---
pretty_name: Evaluation run of Weyaxi/SlimOpenOrca-Mistral-7B
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Weyaxi/SlimOpenOrca-Mistral-7B](https://huggingface.co/Weyaxi/SlimOpenOrca-Mistral-7B)\
\ 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_Weyaxi__SlimOpenOrca-Mistral-7B\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-24T00:40:26.410334](https://huggingface.co/datasets/open-llm-leaderboard/details_Weyaxi__SlimOpenOrca-Mistral-7B/blob/main/results_2023-10-24T00-40-26.410334.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\": 0.004404362416107382,\n\
\ \"em_stderr\": 0.0006781451620479603,\n \"f1\": 0.0900964765100671,\n\
\ \"f1_stderr\": 0.001791740655538585,\n \"acc\": 0.494413205574767,\n\
\ \"acc_stderr\": 0.011528615182477716\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.004404362416107382,\n \"em_stderr\": 0.0006781451620479603,\n\
\ \"f1\": 0.0900964765100671,\n \"f1_stderr\": 0.001791740655538585\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.21455648218347234,\n \
\ \"acc_stderr\": 0.011307604104052887\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7742699289660616,\n \"acc_stderr\": 0.011749626260902547\n\
\ }\n}\n```"
repo_url: https://huggingface.co/Weyaxi/SlimOpenOrca-Mistral-7B
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_10_11T10_04_43.187576
path:
- '**/details_harness|arc:challenge|25_2023-10-11T10-04-43.187576.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-10-11T10-04-43.187576.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_10_24T00_40_26.410334
path:
- '**/details_harness|drop|3_2023-10-24T00-40-26.410334.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-24T00-40-26.410334.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_24T00_40_26.410334
path:
- '**/details_harness|gsm8k|5_2023-10-24T00-40-26.410334.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-24T00-40-26.410334.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_10_11T10_04_43.187576
path:
- '**/details_harness|hellaswag|10_2023-10-11T10-04-43.187576.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-10-11T10-04-43.187576.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_10_11T10_04_43.187576
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-11T10-04-43.187576.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-11T10-04-43.187576.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-11T10-04-43.187576.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-11T10-04-43.187576.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-11T10-04-43.187576.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-11T10-04-43.187576.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-11T10-04-43.187576.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-11T10-04-43.187576.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-11T10-04-43.187576.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-11T10-04-43.187576.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-11T10-04-43.187576.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-11T10-04-43.187576.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-11T10-04-43.187576.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-11T10-04-43.187576.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-11T10-04-43.187576.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-11T10-04-43.187576.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-11T10-04-43.187576.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-11T10-04-43.187576.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-11T10-04-43.187576.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-11T10-04-43.187576.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-11T10-04-43.187576.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-11T10-04-43.187576.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-11T10-04-43.187576.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-11T10-04-43.187576.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-11T10-04-43.187576.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-11T10-04-43.187576.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-11T10-04-43.187576.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-11T10-04-43.187576.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-11T10-04-43.187576.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-11T10-04-43.187576.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-11T10-04-43.187576.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-11T10-04-43.187576.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-11T10-04-43.187576.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-11T10-04-43.187576.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-10-11T10-04-43.187576.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-11T10-04-43.187576.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-11T10-04-43.187576.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-11T10-04-43.187576.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-10-11T10-04-43.187576.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-10-11T10-04-43.187576.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-11T10-04-43.187576.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-11T10-04-43.187576.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-11T10-04-43.187576.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-11T10-04-43.187576.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-11T10-04-43.187576.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-11T10-04-43.187576.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-11T10-04-43.187576.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-11T10-04-43.187576.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-11T10-04-43.187576.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-11T10-04-43.187576.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-11T10-04-43.187576.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-11T10-04-43.187576.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-11T10-04-43.187576.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-10-11T10-04-43.187576.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-11T10-04-43.187576.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-10-11T10-04-43.187576.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-11T10-04-43.187576.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-11T10-04-43.187576.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-11T10-04-43.187576.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-11T10-04-43.187576.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-11T10-04-43.187576.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-11T10-04-43.187576.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-11T10-04-43.187576.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-11T10-04-43.187576.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-11T10-04-43.187576.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-11T10-04-43.187576.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-11T10-04-43.187576.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-11T10-04-43.187576.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-11T10-04-43.187576.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-11T10-04-43.187576.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-11T10-04-43.187576.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-11T10-04-43.187576.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-11T10-04-43.187576.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-11T10-04-43.187576.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-11T10-04-43.187576.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-11T10-04-43.187576.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-11T10-04-43.187576.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-11T10-04-43.187576.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-11T10-04-43.187576.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-11T10-04-43.187576.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-11T10-04-43.187576.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-11T10-04-43.187576.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-11T10-04-43.187576.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-11T10-04-43.187576.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-11T10-04-43.187576.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-11T10-04-43.187576.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-11T10-04-43.187576.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-11T10-04-43.187576.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-11T10-04-43.187576.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-11T10-04-43.187576.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-11T10-04-43.187576.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-10-11T10-04-43.187576.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-11T10-04-43.187576.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-11T10-04-43.187576.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-11T10-04-43.187576.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-10-11T10-04-43.187576.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-10-11T10-04-43.187576.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-11T10-04-43.187576.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-11T10-04-43.187576.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-11T10-04-43.187576.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-11T10-04-43.187576.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-11T10-04-43.187576.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-11T10-04-43.187576.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-11T10-04-43.187576.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-11T10-04-43.187576.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-11T10-04-43.187576.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-11T10-04-43.187576.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-11T10-04-43.187576.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-11T10-04-43.187576.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-11T10-04-43.187576.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-10-11T10-04-43.187576.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-11T10-04-43.187576.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-10-11T10-04-43.187576.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-11T10-04-43.187576.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_10_11T10_04_43.187576
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-11T10-04-43.187576.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-11T10-04-43.187576.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_10_11T10_04_43.187576
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-11T10-04-43.187576.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-11T10-04-43.187576.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_10_11T10_04_43.187576
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-11T10-04-43.187576.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-11T10-04-43.187576.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_10_11T10_04_43.187576
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-11T10-04-43.187576.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-11T10-04-43.187576.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_10_11T10_04_43.187576
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-11T10-04-43.187576.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-11T10-04-43.187576.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_10_11T10_04_43.187576
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-11T10-04-43.187576.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-11T10-04-43.187576.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_10_11T10_04_43.187576
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-11T10-04-43.187576.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-11T10-04-43.187576.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_10_11T10_04_43.187576
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-11T10-04-43.187576.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-11T10-04-43.187576.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_10_11T10_04_43.187576
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-11T10-04-43.187576.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-11T10-04-43.187576.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_10_11T10_04_43.187576
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-11T10-04-43.187576.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-11T10-04-43.187576.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_10_11T10_04_43.187576
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-11T10-04-43.187576.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-11T10-04-43.187576.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_10_11T10_04_43.187576
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-11T10-04-43.187576.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-11T10-04-43.187576.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_10_11T10_04_43.187576
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-11T10-04-43.187576.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-11T10-04-43.187576.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_10_11T10_04_43.187576
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-11T10-04-43.187576.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-11T10-04-43.187576.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_10_11T10_04_43.187576
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-11T10-04-43.187576.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-11T10-04-43.187576.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_10_11T10_04_43.187576
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-11T10-04-43.187576.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-11T10-04-43.187576.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_10_11T10_04_43.187576
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-11T10-04-43.187576.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-11T10-04-43.187576.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_10_11T10_04_43.187576
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-11T10-04-43.187576.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-11T10-04-43.187576.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_10_11T10_04_43.187576
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-11T10-04-43.187576.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-11T10-04-43.187576.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_10_11T10_04_43.187576
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-11T10-04-43.187576.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-11T10-04-43.187576.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_10_11T10_04_43.187576
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-11T10-04-43.187576.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-11T10-04-43.187576.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_10_11T10_04_43.187576
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-11T10-04-43.187576.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-11T10-04-43.187576.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_10_11T10_04_43.187576
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-11T10-04-43.187576.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-11T10-04-43.187576.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_10_11T10_04_43.187576
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-11T10-04-43.187576.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-11T10-04-43.187576.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_10_11T10_04_43.187576
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-11T10-04-43.187576.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-11T10-04-43.187576.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_10_11T10_04_43.187576
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-11T10-04-43.187576.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-11T10-04-43.187576.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_10_11T10_04_43.187576
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-11T10-04-43.187576.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-11T10-04-43.187576.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_10_11T10_04_43.187576
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-11T10-04-43.187576.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-11T10-04-43.187576.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_10_11T10_04_43.187576
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-11T10-04-43.187576.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-11T10-04-43.187576.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_10_11T10_04_43.187576
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-11T10-04-43.187576.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-11T10-04-43.187576.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_10_11T10_04_43.187576
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-11T10-04-43.187576.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-11T10-04-43.187576.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_10_11T10_04_43.187576
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-11T10-04-43.187576.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-11T10-04-43.187576.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_10_11T10_04_43.187576
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-11T10-04-43.187576.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-11T10-04-43.187576.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_10_11T10_04_43.187576
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-11T10-04-43.187576.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-11T10-04-43.187576.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_10_11T10_04_43.187576
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-10-11T10-04-43.187576.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-10-11T10-04-43.187576.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_10_11T10_04_43.187576
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-11T10-04-43.187576.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-11T10-04-43.187576.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_10_11T10_04_43.187576
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-11T10-04-43.187576.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-11T10-04-43.187576.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_10_11T10_04_43.187576
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-11T10-04-43.187576.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-11T10-04-43.187576.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_10_11T10_04_43.187576
path:
- '**/details_harness|hendrycksTest-management|5_2023-10-11T10-04-43.187576.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-10-11T10-04-43.187576.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_10_11T10_04_43.187576
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-10-11T10-04-43.187576.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-10-11T10-04-43.187576.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_10_11T10_04_43.187576
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-11T10-04-43.187576.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-11T10-04-43.187576.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_10_11T10_04_43.187576
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-11T10-04-43.187576.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-11T10-04-43.187576.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_10_11T10_04_43.187576
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-11T10-04-43.187576.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-11T10-04-43.187576.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_10_11T10_04_43.187576
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-11T10-04-43.187576.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-11T10-04-43.187576.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_10_11T10_04_43.187576
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-11T10-04-43.187576.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-11T10-04-43.187576.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_10_11T10_04_43.187576
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-11T10-04-43.187576.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-11T10-04-43.187576.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_10_11T10_04_43.187576
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-11T10-04-43.187576.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-11T10-04-43.187576.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_10_11T10_04_43.187576
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-11T10-04-43.187576.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-11T10-04-43.187576.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_10_11T10_04_43.187576
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-11T10-04-43.187576.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-11T10-04-43.187576.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_10_11T10_04_43.187576
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-11T10-04-43.187576.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-11T10-04-43.187576.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_10_11T10_04_43.187576
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-11T10-04-43.187576.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-11T10-04-43.187576.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_10_11T10_04_43.187576
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-11T10-04-43.187576.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-11T10-04-43.187576.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_10_11T10_04_43.187576
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-11T10-04-43.187576.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-11T10-04-43.187576.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_10_11T10_04_43.187576
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-10-11T10-04-43.187576.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-10-11T10-04-43.187576.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_10_11T10_04_43.187576
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-11T10-04-43.187576.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-11T10-04-43.187576.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_10_11T10_04_43.187576
path:
- '**/details_harness|hendrycksTest-virology|5_2023-10-11T10-04-43.187576.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-10-11T10-04-43.187576.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_10_11T10_04_43.187576
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-11T10-04-43.187576.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-11T10-04-43.187576.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_10_11T10_04_43.187576
path:
- '**/details_harness|truthfulqa:mc|0_2023-10-11T10-04-43.187576.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-10-11T10-04-43.187576.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_24T00_40_26.410334
path:
- '**/details_harness|winogrande|5_2023-10-24T00-40-26.410334.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-24T00-40-26.410334.parquet'
- config_name: results
data_files:
- split: 2023_10_11T10_04_43.187576
path:
- results_2023-10-11T10-04-43.187576.parquet
- split: 2023_10_24T00_40_26.410334
path:
- results_2023-10-24T00-40-26.410334.parquet
- split: latest
path:
- results_2023-10-24T00-40-26.410334.parquet
---
# Dataset Card for Evaluation run of Weyaxi/SlimOpenOrca-Mistral-7B
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Weyaxi/SlimOpenOrca-Mistral-7B
- **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 [Weyaxi/SlimOpenOrca-Mistral-7B](https://huggingface.co/Weyaxi/SlimOpenOrca-Mistral-7B) 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_Weyaxi__SlimOpenOrca-Mistral-7B",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-24T00:40:26.410334](https://huggingface.co/datasets/open-llm-leaderboard/details_Weyaxi__SlimOpenOrca-Mistral-7B/blob/main/results_2023-10-24T00-40-26.410334.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": 0.004404362416107382,
"em_stderr": 0.0006781451620479603,
"f1": 0.0900964765100671,
"f1_stderr": 0.001791740655538585,
"acc": 0.494413205574767,
"acc_stderr": 0.011528615182477716
},
"harness|drop|3": {
"em": 0.004404362416107382,
"em_stderr": 0.0006781451620479603,
"f1": 0.0900964765100671,
"f1_stderr": 0.001791740655538585
},
"harness|gsm8k|5": {
"acc": 0.21455648218347234,
"acc_stderr": 0.011307604104052887
},
"harness|winogrande|5": {
"acc": 0.7742699289660616,
"acc_stderr": 0.011749626260902547
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] |
autoevaluate/autoeval-staging-eval-project-emotion-a7ced70d-13715889 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- emotion
eval_info:
task: multi_class_classification
model: autoevaluate/multi-class-classification
metrics: []
dataset_name: emotion
dataset_config: default
dataset_split: test
col_mapping:
text: text
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 Text Classification
* Model: autoevaluate/multi-class-classification
* Dataset: emotion
* Config: default
* Split: test
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. |
xic051/Inspired | ---
license: unknown
---
|
HumanDynamics/reward_modeling_dataset | ---
dataset_info:
features:
- name: chosen
dtype: string
- name: rejected
dtype: string
splits:
- name: train
num_bytes: 23728942
num_examples: 10000
download_size: 10959215
dataset_size: 23728942
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "reward_modeling_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
davanstrien/fuego-20230502-133307-164708 | ---
tags:
- fuego
fuego:
id: 20230502-133307-164708
status: done
script: script.py
requirements_file: requirements.txt
space_id: davanstrien/fuego-20230502-133307-164708
space_hardware: cpu-basic
---
|
CyberHarem/cathy_graham_idolmastercinderellagirls | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of cathy_graham/キャシー・グラハム (THE iDOLM@STER: Cinderella Girls)
This is the dataset of cathy_graham/キャシー・グラハム (THE iDOLM@STER: Cinderella Girls), containing 20 images and their tags.
The core tags of this character are `short_hair, earrings, brown_hair, blue_eyes, blonde_hair, thick_eyebrows, ahoge, aqua_eyes`, 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 | 20 | 14.28 MiB | [Download](https://huggingface.co/datasets/CyberHarem/cathy_graham_idolmastercinderellagirls/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 20 | 12.27 MiB | [Download](https://huggingface.co/datasets/CyberHarem/cathy_graham_idolmastercinderellagirls/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 33 | 18.81 MiB | [Download](https://huggingface.co/datasets/CyberHarem/cathy_graham_idolmastercinderellagirls/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 20 | 14.25 MiB | [Download](https://huggingface.co/datasets/CyberHarem/cathy_graham_idolmastercinderellagirls/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 33 | 21.88 MiB | [Download](https://huggingface.co/datasets/CyberHarem/cathy_graham_idolmastercinderellagirls/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/cathy_graham_idolmastercinderellagirls',
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 | 8 |  |  |  |  |  | 1girl, smile, solo, jewelry, card_(medium), character_name, hair_ornament, one_eye_closed, open_mouth, sun_symbol, thighhighs, dress, flower, hat, orange_background, skates, skirt |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | smile | solo | jewelry | card_(medium) | character_name | hair_ornament | one_eye_closed | open_mouth | sun_symbol | thighhighs | dress | flower | hat | orange_background | skates | skirt |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------|:-------|:----------|:----------------|:-----------------|:----------------|:-----------------|:-------------|:-------------|:-------------|:--------|:---------|:------|:--------------------|:---------|:--------|
| 0 | 8 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
|
saifkha/fitze_chat | ---
license: apache-2.0
---
|
Tamnemtf/hcmue_qa | ---
language:
- vi
pretty_name: v
dataset_info:
features:
- name: concept
dtype: string
- name: description
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 472263
num_examples: 1145
download_size: 187152
dataset_size: 472263
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
freshpearYoon/v3_train_free_4 | ---
dataset_info:
features:
- name: input_features
sequence:
sequence: float32
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 15366786360
num_examples: 10000
download_size: 2250340482
dataset_size: 15366786360
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
War420da/Tesy | ---
license: openrail
---
|
reaganjlee/truthful_qa_mc_pt | ---
dataset_info:
features:
- name: question
dtype: string
- name: choices
sequence: string
- name: label
dtype:
class_label:
names:
'0': A
'1': B
'2': C
'3': D
splits:
- name: train
num_bytes: 106899.5
num_examples: 342
- name: validation
num_bytes: 106899.5
num_examples: 342
download_size: 113452
dataset_size: 213799.0
---
# Dataset Card for "truthful_qa_mc_pt"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
lsr42/flickr30k-blip-dense | ---
license: apache-2.0
---
|
rvenie/mybert | ---
license: apache-2.0
task_categories:
- table-question-answering
language:
- ru
--- |
SGBTalha/MyModels | ---
license: openrail
---
|
AvishayDev/gutendex-dataset | ---
language:
- en
size_categories:
- 10K<n<100K
task_categories:
- text-classification
- token-classification
- text-generation
- fill-mask
pretty_name: Books For All!
dataset_info:
features:
- name: title
dtype: string
- name: authors
list:
- name: birth_year
dtype: int64
- name: death_year
dtype: int64
- name: name
dtype: string
- name: translators
list:
- name: birth_year
dtype: int64
- name: death_year
dtype: int64
- name: name
dtype: string
- name: subjects
sequence: string
- name: bookshelves
sequence: string
- name: languages
sequence: string
- name: copyright
dtype: bool
- name: download_count
dtype: int64
- name: text
dtype: string
splits:
- name: train
num_bytes: 11768240138
num_examples: 26138
download_size: 7164356350
dataset_size: 11768240138
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
tags:
- books
- text
---
# Welcome to the World of Gutendex English Books!
**This repository brings you a curated collection of English books from the vast Gutendex library, hosted on the amazing Hugging Face platform. Dive into a plethora of classics, timeless gems, and lesser-known treasures, all waiting to be explored and rediscovered.**
## ✨ What's inside?
+ **Thousands of English books:** We've meticulously handpicked and processed a diverse selection of English books, spanning various genres and eras. From adventure novels and historical sagas to philosophical treatises and poetry anthologies, you'll find something for every taste.
+ **Clean and accessible text:** Each book is carefully cleaned and converted to plain text format, making it readily available for natural language processing (NLP) tasks and analysis.
+ **Easy-to-use metadata:** Each book comes with rich metadata, including author, title, publication date, genre, and language. This makes it simple to filter, search, and organize your literary adventures.
+ **Open and shareable:** This repository is built on the collaborative spirit of Hugging Face. Feel free to fork, contribute, and build upon this collection to create your own unique literary playground.
## Getting started:
+ **Explore the dataset:** Browse the list of available books or use the provided metadata to find specific titles or genres.
+ **⬇️ Download & Use:** Each book is readily available for download in plain text format. Feel free to integrate them into your NLP projects, build custom applications, or simply enjoy reading them in their digital form.
+ **Contribute & share:** This is just the beginning! If you find a missing book, have suggestions for improvement, or want to create new features, we encourage you to contribute and be part of the growing community.
## Beyond the bookshelf:
This repository is not just a collection of books, it's a springboard for creativity and innovation. Here are some potential applications:
+ **Train your own NLP models:** Use this dataset to train language models on diverse writing styles and historical texts.
+ **Build research projects:** Analyze literary trends, explore author similarities, or develop new methods for text analysis.
+ **Spark creativity:** Draw inspiration from timeless stories and craft your own literary masterpieces.
+ **Share your love of books:** Create engaging applications, interactive experiences, or simply recommend hidden gems to fellow bookworms.
## Let the adventure begin!
We hope this repository opens up a world of possibilities for readers, researchers, and NLP enthusiasts alike. So grab your bookworm hat, embark on a literary journey, and don't hesitate to leave your mark on this ever-growing treasure trove of words.
## Happy reading!
**P.S.** Feel free to reach out if you have any questions, suggestions, or simply want to share your favorite book from the collection. We're always happy to hear from fellow bibliophiles! |
ItsKazzle/baller-training-data | ---
license: gpl-3.0
---
|
CyberHarem/udagawa_tomoe_bangdream | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of udagawa_tomoe/宇田川巴 (BanG Dream!)
This is the dataset of udagawa_tomoe/宇田川巴 (BanG Dream!), containing 162 images and their tags.
The core tags of this character are `red_hair, long_hair, bangs, blue_eyes, earrings`, 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 | 162 | 120.34 MiB | [Download](https://huggingface.co/datasets/CyberHarem/udagawa_tomoe_bangdream/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 162 | 96.47 MiB | [Download](https://huggingface.co/datasets/CyberHarem/udagawa_tomoe_bangdream/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 263 | 158.64 MiB | [Download](https://huggingface.co/datasets/CyberHarem/udagawa_tomoe_bangdream/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 162 | 112.44 MiB | [Download](https://huggingface.co/datasets/CyberHarem/udagawa_tomoe_bangdream/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 263 | 186.87 MiB | [Download](https://huggingface.co/datasets/CyberHarem/udagawa_tomoe_bangdream/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/udagawa_tomoe_bangdream',
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 | 22 |  |  |  |  |  | 1girl, solo, midriff, drumsticks, navel, crop_top, holding, looking_at_viewer, drum_set, choker, bracelet, jacket, grin, studded_belt, vest, blush, hair_ornament, pants, skirt |
| 1 | 7 |  |  |  |  |  | black_gloves, looking_at_viewer, 1girl, fingerless_gloves, long_sleeves, belt, crop_top, midriff, shirt, solo, :d, black_choker, black_shorts, blush, drumsticks, holding, open_jacket, open_mouth, torn_clothes, black_footwear, boots, fishnets, green_jacket, navel, necklace, pants, ponytail, short_shorts |
| 2 | 5 |  |  |  |  |  | grin, looking_at_viewer, white_shirt, 1girl, black_jacket, jewelry, long_sleeves, solo, upper_body, white_background, white_gloves, 2girls, black_necktie, green_eyes, hair_between_eyes, ponytail, sidelocks, simple_background, v-shaped_eyebrows |
| 3 | 11 |  |  |  |  |  | 1girl, school_uniform, solo, white_shirt, blush, collared_shirt, jacket, grin, :d, ^_^, open_mouth, striped_necktie, upper_body, fang, long_sleeves, plaid_skirt, pleated_skirt, white_outline |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | midriff | drumsticks | navel | crop_top | holding | looking_at_viewer | drum_set | choker | bracelet | jacket | grin | studded_belt | vest | blush | hair_ornament | pants | skirt | black_gloves | fingerless_gloves | long_sleeves | belt | shirt | :d | black_choker | black_shorts | open_jacket | open_mouth | torn_clothes | black_footwear | boots | fishnets | green_jacket | necklace | ponytail | short_shorts | white_shirt | black_jacket | jewelry | upper_body | white_background | white_gloves | 2girls | black_necktie | green_eyes | hair_between_eyes | sidelocks | simple_background | v-shaped_eyebrows | school_uniform | collared_shirt | ^_^ | striped_necktie | fang | plaid_skirt | pleated_skirt | white_outline |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:----------|:-------------|:--------|:-----------|:----------|:--------------------|:-----------|:---------|:-----------|:---------|:-------|:---------------|:-------|:--------|:----------------|:--------|:--------|:---------------|:--------------------|:---------------|:-------|:--------|:-----|:---------------|:---------------|:--------------|:-------------|:---------------|:-----------------|:--------|:-----------|:---------------|:-----------|:-----------|:---------------|:--------------|:---------------|:----------|:-------------|:-------------------|:---------------|:---------|:----------------|:-------------|:--------------------|:------------|:--------------------|:--------------------|:-----------------|:-----------------|:------|:------------------|:-------|:--------------|:----------------|:----------------|
| 0 | 22 |  |  |  |  |  | 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 | X | X | X | | | | | | | | | | | | | | | | | | | | | |
| 2 | 5 |  |  |  |  |  | X | X | | | | | | X | | | | | X | | | | | | | | | X | | | | | | | | | | | | | | X | | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | |
| 3 | 11 |  |  |  |  |  | X | X | | | | | | | | | | X | X | | | X | | | | | | X | | | X | | | | X | | | | | | | | | X | | | X | | | | | | | | | | X | X | X | X | X | X | X | X |
|
open-llm-leaderboard/details_Aspik101__Redmond-Puffin-13B-instruct-PL-lora_unload | ---
pretty_name: Evaluation run of Aspik101/Redmond-Puffin-13B-instruct-PL-lora_unload
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Aspik101/Redmond-Puffin-13B-instruct-PL-lora_unload](https://huggingface.co/Aspik101/Redmond-Puffin-13B-instruct-PL-lora_unload)\
\ 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_Aspik101__Redmond-Puffin-13B-instruct-PL-lora_unload\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-17T13:30:13.279131](https://huggingface.co/datasets/open-llm-leaderboard/details_Aspik101__Redmond-Puffin-13B-instruct-PL-lora_unload/blob/main/results_2023-10-17T13-30-13.279131.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.002936241610738255,\n\
\ \"em_stderr\": 0.0005541113054710113,\n \"f1\": 0.05736577181208053,\n\
\ \"f1_stderr\": 0.0013664047590611983,\n \"acc\": 0.43379799697577687,\n\
\ \"acc_stderr\": 0.010348919090911759\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.002936241610738255,\n \"em_stderr\": 0.0005541113054710113,\n\
\ \"f1\": 0.05736577181208053,\n \"f1_stderr\": 0.0013664047590611983\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.1106899166034875,\n \
\ \"acc_stderr\": 0.008642172551392473\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7569060773480663,\n \"acc_stderr\": 0.012055665630431044\n\
\ }\n}\n```"
repo_url: https://huggingface.co/Aspik101/Redmond-Puffin-13B-instruct-PL-lora_unload
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_09T11_16_00.382833
path:
- '**/details_harness|arc:challenge|25_2023-08-09T11:16:00.382833.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-08-09T11:16:00.382833.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_10_17T13_30_13.279131
path:
- '**/details_harness|drop|3_2023-10-17T13-30-13.279131.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-17T13-30-13.279131.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_17T13_30_13.279131
path:
- '**/details_harness|gsm8k|5_2023-10-17T13-30-13.279131.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-17T13-30-13.279131.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_08_09T11_16_00.382833
path:
- '**/details_harness|hellaswag|10_2023-08-09T11:16:00.382833.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-08-09T11:16:00.382833.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_08_09T11_16_00.382833
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-09T11:16:00.382833.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-09T11:16:00.382833.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-09T11:16:00.382833.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-09T11:16:00.382833.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-09T11:16:00.382833.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-09T11:16:00.382833.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-09T11:16:00.382833.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-09T11:16:00.382833.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-09T11:16:00.382833.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-09T11:16:00.382833.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-09T11:16:00.382833.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-09T11:16:00.382833.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-09T11:16:00.382833.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-09T11:16:00.382833.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-09T11:16:00.382833.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-09T11:16:00.382833.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-09T11:16:00.382833.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-09T11:16:00.382833.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-09T11:16:00.382833.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-09T11:16:00.382833.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-09T11:16:00.382833.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-09T11:16:00.382833.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-09T11:16:00.382833.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-09T11:16:00.382833.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-09T11:16:00.382833.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-09T11:16:00.382833.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-09T11:16:00.382833.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-09T11:16:00.382833.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-09T11:16:00.382833.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-09T11:16:00.382833.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-09T11:16:00.382833.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-09T11:16:00.382833.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-09T11:16:00.382833.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-09T11:16:00.382833.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-09T11:16:00.382833.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-09T11:16:00.382833.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-09T11:16:00.382833.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-09T11:16:00.382833.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-09T11:16:00.382833.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-09T11:16:00.382833.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-09T11:16:00.382833.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-09T11:16:00.382833.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-09T11:16:00.382833.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-09T11:16:00.382833.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-09T11:16:00.382833.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-09T11:16:00.382833.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-09T11:16:00.382833.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-09T11:16:00.382833.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-09T11:16:00.382833.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-09T11:16:00.382833.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-09T11:16:00.382833.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-09T11:16:00.382833.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-09T11:16:00.382833.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-09T11:16:00.382833.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-09T11:16:00.382833.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-09T11:16:00.382833.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-09T11:16:00.382833.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-09T11:16:00.382833.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-09T11:16:00.382833.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-09T11:16:00.382833.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-09T11:16:00.382833.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-09T11:16:00.382833.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-09T11:16:00.382833.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-09T11:16:00.382833.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-09T11:16:00.382833.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-09T11:16:00.382833.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-09T11:16:00.382833.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-09T11:16:00.382833.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-09T11:16:00.382833.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-09T11:16:00.382833.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-09T11:16:00.382833.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-09T11:16:00.382833.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-09T11:16:00.382833.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-09T11:16:00.382833.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-09T11:16:00.382833.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-09T11:16:00.382833.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-09T11:16:00.382833.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-09T11:16:00.382833.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-09T11:16:00.382833.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-09T11:16:00.382833.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-09T11:16:00.382833.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-09T11:16:00.382833.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-09T11:16:00.382833.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-09T11:16:00.382833.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-09T11:16:00.382833.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-09T11:16:00.382833.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-09T11:16:00.382833.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-09T11:16:00.382833.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-09T11:16:00.382833.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-09T11:16:00.382833.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-09T11:16:00.382833.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-09T11:16:00.382833.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-09T11:16:00.382833.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-09T11:16:00.382833.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-09T11:16:00.382833.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-09T11:16:00.382833.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-09T11:16:00.382833.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-09T11:16:00.382833.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-09T11:16:00.382833.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-09T11:16:00.382833.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-09T11:16:00.382833.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-09T11:16:00.382833.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-09T11:16:00.382833.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-09T11:16:00.382833.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-09T11:16:00.382833.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-09T11:16:00.382833.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-09T11:16:00.382833.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-09T11:16:00.382833.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-09T11:16:00.382833.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-09T11:16:00.382833.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-09T11:16:00.382833.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-09T11:16:00.382833.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-09T11:16:00.382833.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-09T11:16:00.382833.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_08_09T11_16_00.382833
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-09T11:16:00.382833.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-09T11:16:00.382833.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_08_09T11_16_00.382833
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-09T11:16:00.382833.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-09T11:16:00.382833.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_08_09T11_16_00.382833
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-09T11:16:00.382833.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-09T11:16:00.382833.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_08_09T11_16_00.382833
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-09T11:16:00.382833.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-09T11:16:00.382833.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_08_09T11_16_00.382833
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-09T11:16:00.382833.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-09T11:16:00.382833.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_08_09T11_16_00.382833
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-09T11:16:00.382833.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-09T11:16:00.382833.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_08_09T11_16_00.382833
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-09T11:16:00.382833.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-09T11:16:00.382833.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_08_09T11_16_00.382833
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-09T11:16:00.382833.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-09T11:16:00.382833.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_08_09T11_16_00.382833
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-09T11:16:00.382833.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-09T11:16:00.382833.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_08_09T11_16_00.382833
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-09T11:16:00.382833.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-09T11:16:00.382833.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_08_09T11_16_00.382833
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-09T11:16:00.382833.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-09T11:16:00.382833.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_08_09T11_16_00.382833
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-09T11:16:00.382833.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-09T11:16:00.382833.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_08_09T11_16_00.382833
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-09T11:16:00.382833.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-09T11:16:00.382833.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_08_09T11_16_00.382833
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-09T11:16:00.382833.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-09T11:16:00.382833.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_08_09T11_16_00.382833
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-09T11:16:00.382833.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-09T11:16:00.382833.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_08_09T11_16_00.382833
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-09T11:16:00.382833.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-09T11:16:00.382833.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_08_09T11_16_00.382833
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-09T11:16:00.382833.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-09T11:16:00.382833.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_08_09T11_16_00.382833
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-09T11:16:00.382833.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-09T11:16:00.382833.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_08_09T11_16_00.382833
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-09T11:16:00.382833.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-09T11:16:00.382833.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_08_09T11_16_00.382833
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-09T11:16:00.382833.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-09T11:16:00.382833.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_08_09T11_16_00.382833
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-09T11:16:00.382833.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-09T11:16:00.382833.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_08_09T11_16_00.382833
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-09T11:16:00.382833.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-09T11:16:00.382833.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_08_09T11_16_00.382833
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-09T11:16:00.382833.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-09T11:16:00.382833.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_08_09T11_16_00.382833
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-09T11:16:00.382833.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-09T11:16:00.382833.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_08_09T11_16_00.382833
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-09T11:16:00.382833.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-09T11:16:00.382833.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_08_09T11_16_00.382833
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-09T11:16:00.382833.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-09T11:16:00.382833.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_08_09T11_16_00.382833
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-09T11:16:00.382833.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-09T11:16:00.382833.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_08_09T11_16_00.382833
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-09T11:16:00.382833.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-09T11:16:00.382833.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_08_09T11_16_00.382833
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-09T11:16:00.382833.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-09T11:16:00.382833.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_08_09T11_16_00.382833
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-09T11:16:00.382833.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-09T11:16:00.382833.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_08_09T11_16_00.382833
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-09T11:16:00.382833.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-09T11:16:00.382833.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_08_09T11_16_00.382833
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-09T11:16:00.382833.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-09T11:16:00.382833.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_08_09T11_16_00.382833
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-09T11:16:00.382833.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-09T11:16:00.382833.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_08_09T11_16_00.382833
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-09T11:16:00.382833.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-09T11:16:00.382833.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_08_09T11_16_00.382833
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-09T11:16:00.382833.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-09T11:16:00.382833.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_08_09T11_16_00.382833
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-09T11:16:00.382833.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-09T11:16:00.382833.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_08_09T11_16_00.382833
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-09T11:16:00.382833.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-09T11:16:00.382833.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_08_09T11_16_00.382833
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-09T11:16:00.382833.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-09T11:16:00.382833.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_08_09T11_16_00.382833
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-09T11:16:00.382833.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-09T11:16:00.382833.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_08_09T11_16_00.382833
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-09T11:16:00.382833.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-09T11:16:00.382833.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_08_09T11_16_00.382833
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-09T11:16:00.382833.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-09T11:16:00.382833.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_08_09T11_16_00.382833
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-09T11:16:00.382833.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-09T11:16:00.382833.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_08_09T11_16_00.382833
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-09T11:16:00.382833.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-09T11:16:00.382833.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_08_09T11_16_00.382833
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-09T11:16:00.382833.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-09T11:16:00.382833.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_08_09T11_16_00.382833
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-09T11:16:00.382833.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-09T11:16:00.382833.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_08_09T11_16_00.382833
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-09T11:16:00.382833.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-09T11:16:00.382833.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_08_09T11_16_00.382833
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-09T11:16:00.382833.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-09T11:16:00.382833.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_08_09T11_16_00.382833
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-09T11:16:00.382833.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-09T11:16:00.382833.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_08_09T11_16_00.382833
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-09T11:16:00.382833.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-09T11:16:00.382833.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_08_09T11_16_00.382833
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-09T11:16:00.382833.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-09T11:16:00.382833.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_08_09T11_16_00.382833
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-09T11:16:00.382833.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-09T11:16:00.382833.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_08_09T11_16_00.382833
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-09T11:16:00.382833.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-09T11:16:00.382833.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_08_09T11_16_00.382833
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-09T11:16:00.382833.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-09T11:16:00.382833.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_08_09T11_16_00.382833
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-09T11:16:00.382833.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-09T11:16:00.382833.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_08_09T11_16_00.382833
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-09T11:16:00.382833.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-09T11:16:00.382833.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_08_09T11_16_00.382833
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-09T11:16:00.382833.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-09T11:16:00.382833.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_08_09T11_16_00.382833
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-09T11:16:00.382833.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-09T11:16:00.382833.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_08_09T11_16_00.382833
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-09T11:16:00.382833.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-09T11:16:00.382833.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_17T13_30_13.279131
path:
- '**/details_harness|winogrande|5_2023-10-17T13-30-13.279131.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-17T13-30-13.279131.parquet'
- config_name: results
data_files:
- split: 2023_08_09T11_16_00.382833
path:
- results_2023-08-09T11:16:00.382833.parquet
- split: 2023_10_17T13_30_13.279131
path:
- results_2023-10-17T13-30-13.279131.parquet
- split: latest
path:
- results_2023-10-17T13-30-13.279131.parquet
---
# Dataset Card for Evaluation run of Aspik101/Redmond-Puffin-13B-instruct-PL-lora_unload
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Aspik101/Redmond-Puffin-13B-instruct-PL-lora_unload
- **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 [Aspik101/Redmond-Puffin-13B-instruct-PL-lora_unload](https://huggingface.co/Aspik101/Redmond-Puffin-13B-instruct-PL-lora_unload) 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_Aspik101__Redmond-Puffin-13B-instruct-PL-lora_unload",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-17T13:30:13.279131](https://huggingface.co/datasets/open-llm-leaderboard/details_Aspik101__Redmond-Puffin-13B-instruct-PL-lora_unload/blob/main/results_2023-10-17T13-30-13.279131.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.002936241610738255,
"em_stderr": 0.0005541113054710113,
"f1": 0.05736577181208053,
"f1_stderr": 0.0013664047590611983,
"acc": 0.43379799697577687,
"acc_stderr": 0.010348919090911759
},
"harness|drop|3": {
"em": 0.002936241610738255,
"em_stderr": 0.0005541113054710113,
"f1": 0.05736577181208053,
"f1_stderr": 0.0013664047590611983
},
"harness|gsm8k|5": {
"acc": 0.1106899166034875,
"acc_stderr": 0.008642172551392473
},
"harness|winogrande|5": {
"acc": 0.7569060773480663,
"acc_stderr": 0.012055665630431044
}
}
```
### 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] |
wsin/tobasesentences | ---
dataset_info:
features:
- name: sentence
dtype: string
- name: base base_sentences
dtype: string
- name: base_sentences
dtype: string
splits:
- name: train
num_bytes: 1141
num_examples: 4
download_size: 4035
dataset_size: 1141
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "tobasesentences"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Doub7e/SDv2-Count-Repeated-1 | ---
dataset_info:
features:
- name: image
dtype: image
- name: prompt
dtype: string
- name: T5_last_hidden_states
sequence:
sequence:
sequence: float32
- name: style
dtype: string
splits:
- name: train
num_bytes: 1475468013.25
num_examples: 1150
download_size: 1283858164
dataset_size: 1475468013.25
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
hayesyang/un_corpus_content | ---
dataset_info:
features:
- name: id
dtype: int64
- name: url
dtype: string
- name: status
dtype: int64
- name: content
dtype: string
- name: hash
dtype: string
- name: is_duplicate
dtype: int64
splits:
- name: train
num_bytes: 125504913
num_examples: 2140
download_size: 39366870
dataset_size: 125504913
---
# Dataset Card for "un_corpus_content"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
JeremiahZ/mbxp_llvm_wasm | ---
dataset_info:
features:
- name: task_id
dtype: string
- name: language
dtype: string
- name: prompt
dtype: string
- name: description
dtype: string
- name: test
dtype: string
- name: entry_point
dtype: string
- name: canonical_solution
dtype: string
- name: llvm_ir
dtype: string
- name: wat
dtype: string
splits:
- name: test
num_bytes: 13548211
num_examples: 773
download_size: 2857975
dataset_size: 13548211
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
---
# Dataset Card for "hmbxp_llvm_wasm"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
jorgefer/jorgefer | ---
license: openrail
---
|
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