datasetId stringlengths 2 117 | card stringlengths 19 1.01M |
|---|---|
gagan3012/hindawi_fonts | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': Noto_Sans_Arabic
'1': Readex_Pro
'2': Amiri
'3': Noto_Kufi_Arabic
'4': Reem_Kufi_Fun
'5': Lateef
'6': Changa
'7': Kufam
'8': ElMessiri
'9': Reem_Kufi
'10': Noto_Naskh_Arabic
'11': Reem_Kufi_Ink
'12': Tajawal
'13': Aref_Ruqaa_Ink
'14': Markazi_Text
'15': IBM_Plex_Sans_Arabic
'16': Vazirmatn
'17': Harmattan
'18': Gulzar
'19': Scheherazade_New
'20': Cairo
'21': Amiri_Quran
'22': Noto_Nastaliq_Urdu
'23': Mada
'24': Aref_Ruqaa
'25': Almarai
'26': Alkalami
'27': Qahiri
- name: text
dtype: string
splits:
- name: train
num_bytes: 4209517973.992
num_examples: 64624
- name: validation
num_bytes: 471903903.624
num_examples: 7196
- name: test
num_bytes: 471903903.624
num_examples: 7196
download_size: 5057297184
dataset_size: 5153325781.24
---
# Dataset Card for "hindawi_fonts"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
botp/RyokoAI_ShareGPT52K | ---
license: cc0-1.0
language:
- en
- es
- de
- multilingual
pretty_name: ShareGPT 90K Conversations
task_categories:
- text-generation
tags:
- conversation
- rlhf
- chatgpt
- gpt-3.5
size_categories:
- 10K<n<100K
duplicated_from: RyokoAI/ShareGPT52K
---
# Dataset Card for ShareGPT~~52K~~90K
## Dataset Description
- **Homepage: N/A**
- **Repository: N/A**
- **Paper: N/A**
- **Leaderboard: N/A**
- **Point of Contact:** Ronsor Labs <ronsor@ronsor.com>
### Dataset Summary
This dataset is a collection of approximately ~~52,000~~**90,000** conversations scraped via the ShareGPT API before it was shut down.
These conversations include both user prompts and responses from OpenAI's ChatGPT.
This repository now contains the new 90K conversations version. The previous 52K may
be found in the `old/` directory.
### Supported Tasks and Leaderboards
* text-generation
### Languages
This dataset is expected to primarily consist of messages in English and other Western languages.
## Dataset Structure
### Data Instances
```json
{
"id": "2wtLdZm",
"conversations": [
{
"from": "human",
"value": "Are you familiar with the SAS programming language?"
},
{
"from": "gpt",
"value": "<div class=\"markdown prose w-full break-words dark:prose-invert light\"><p>Yes, I am familiar with the SAS programming language. SAS is a statistical software suite that is widely used in data analysis, data management, and business intelligence applications. It has its own programming language, which is used to manipulate and analyze data, generate reports, and automate data-driven workflows. The language is known for its ease of use and powerful capabilities, and it is commonly used in industries such as healthcare, finance, and government.</p></div>"
},
{
"from": "human",
"value": "Can you write a SAS proc that imports a dataset from a CSV saved on my local machine?"
}
]
}
```
### Data Fields
* **id**: conversation id
* **conversations**: conversation session array
* **from**: ChatGPT ("gpt") or the user ("human")
* **value**: message contents as raw HTML
### Data Splits
N/A
## Dataset Creation
### Curation Rationale
This is a decently large dataset of realistic human-AI conversations which I believe should be released
to the research community.
### Source Data
#### Initial Data Collection and Normalization
This data was collected using the ShareGPT API.
#### Who are the source language producers?
ShareGPT users and OpenAI ChatGPT.
### Annotations
#### Annotation process
N/A
#### Who are the annotators?
N/A
### Personal and Sensitive Information
This dataset *may* contain personal information, if ShareGPT users were sending such information to
ChatGPT. ChatGPT warns users not to submit personal information to it, however, so without further
evaluation, we believe that this dataset should contain little or no personal information.
## Considerations for Using the Data
### Social Impact of Dataset
This dataset may be used to train models that are competitive with OpenAI's ChatGPT. Please filter
this dataset first, as it may contain canned responses, raw HTML, and other undesirable information.
### Discussion of Biases
This dataset exhibits all the biases of OpenAI's ChatGPT models (GPT-3.5 and GPT-4) as well as the
biases of the users who uploaded the conversations.
### Other Known Limitations
N/A
## Additional Information
### Dataset Curators
None.
### Licensing Information
**CC0: No Rights Reserved.**
The output of machine learning algorithms is uncopyrightable in the United States and other jurisdictions.
**Additionally, the OpenAI terms of service do not apply to this dataset as users of this dataset
are not accessing the OpenAI service.**
### Citation Information
TODO
### Contributions
These conversations were allegedly scraped by an anonymous user on 4chan.
The 90K version was sourced from [this post](https://boards.4channel.org/g/thread/92487155/lmg-local-models-general-snail-edition#p92490887).
Thanks, anon!
|
autoevaluate/autoeval-eval-inverse-scaling__41-inverse-scaling__41-150015-1682059402 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- inverse-scaling/41
eval_info:
task: text_zero_shot_classification
model: inverse-scaling/opt-66b_eval
metrics: []
dataset_name: inverse-scaling/41
dataset_config: inverse-scaling--41
dataset_split: train
col_mapping:
text: prompt
classes: classes
target: answer_index
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Zero-Shot Text Classification
* Model: inverse-scaling/opt-66b_eval
* Dataset: inverse-scaling/41
* Config: inverse-scaling--41
* Split: train
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@MicPie](https://huggingface.co/MicPie) for evaluating this model. |
Fael123/ModelMCPOZErodo | ---
license: openrail
---
|
foilfoilfoil/lamini512 | ---
license: other
---
|
CyberHarem/hakuryu_azurlane | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of hakuryu/白龍/白龙 (Azur Lane)
This is the dataset of hakuryu/白龍/白龙 (Azur Lane), containing 103 images and their tags.
The core tags of this character are `horns, breasts, long_hair, asymmetrical_horns, large_breasts, earrings, white_hair, white_eyes, red_horns, grey_hair, hair_between_eyes, grey_eyes, white_horns, 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 | 103 | 181.50 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hakuryu_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 103 | 89.69 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hakuryu_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 259 | 195.97 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hakuryu_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 103 | 153.80 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hakuryu_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 259 | 294.02 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hakuryu_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/hakuryu_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 | 10 |  |  |  |  |  | 1girl, holding_sword, ootachi, solo, japanese_armor, looking_at_viewer, pleated_dress, skindentation, jewelry, black_gloves, coat, from_below, sakuramon, sheath, thigh_strap, cleavage, full_body, short_dress, white_background, white_sash, black_footwear, simple_background, black_dress, wide_sleeves |
| 1 | 9 |  |  |  |  |  | 1girl, black_choker, cleavage, holding_sword, looking_at_viewer, ootachi, pleated_dress, solo, jewelry, coat, simple_background, huge_breasts, japanese_armor, sakuramon, white_background, black_gloves, sheath, wide_sleeves, smile, thigh_strap, white_sash |
| 2 | 19 |  |  |  |  |  | 1girl, center_opening, necktie_between_breasts, official_alternate_costume, black_necktie, jewelry, looking_at_viewer, maid_headdress, solo, black_dress, multiple_horns, white_apron, puffy_short_sleeves, white_gloves, hair_on_horn, mole_on_breast, black_pantyhose, cleavage, huge_breasts, open_mouth, sharp_teeth, holding, sword_behind_back, simple_background, white_background, high_ponytail, indoors |
| 3 | 11 |  |  |  |  |  | looking_at_viewer, 1girl, bare_shoulders, jewelry, mole_on_breast, solo, cleavage, dress, official_alternate_costume, smile, bangs, blush, sitting, braid, collarbone, indoors, multiple_horns, thighs, artist_name, black_footwear, floral_print, full_body, hair_on_horn, high_heels, holding, instrument, japanese_clothes, open_mouth, tassel |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | holding_sword | ootachi | solo | japanese_armor | looking_at_viewer | pleated_dress | skindentation | jewelry | black_gloves | coat | from_below | sakuramon | sheath | thigh_strap | cleavage | full_body | short_dress | white_background | white_sash | black_footwear | simple_background | black_dress | wide_sleeves | black_choker | huge_breasts | smile | center_opening | necktie_between_breasts | official_alternate_costume | black_necktie | maid_headdress | multiple_horns | white_apron | puffy_short_sleeves | white_gloves | hair_on_horn | mole_on_breast | black_pantyhose | open_mouth | sharp_teeth | holding | sword_behind_back | high_ponytail | indoors | bare_shoulders | dress | bangs | blush | sitting | braid | collarbone | thighs | artist_name | floral_print | high_heels | instrument | japanese_clothes | tassel |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:----------------|:----------|:-------|:-----------------|:--------------------|:----------------|:----------------|:----------|:---------------|:-------|:-------------|:------------|:---------|:--------------|:-----------|:------------|:--------------|:-------------------|:-------------|:-----------------|:--------------------|:--------------|:---------------|:---------------|:---------------|:--------|:-----------------|:--------------------------|:-----------------------------|:----------------|:-----------------|:-----------------|:--------------|:----------------------|:---------------|:---------------|:-----------------|:------------------|:-------------|:--------------|:----------|:--------------------|:----------------|:----------|:-----------------|:--------|:--------|:--------|:----------|:--------|:-------------|:---------|:--------------|:---------------|:-------------|:-------------|:-------------------|:---------|
| 0 | 10 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 9 |  |  |  |  |  | X | X | X | X | X | X | X | | X | X | X | | X | X | X | X | | | X | X | | X | | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 19 |  |  |  |  |  | X | | | X | | X | | | X | | | | | | | X | | | X | | | X | X | | | X | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | |
| 3 | 11 |  |  |  |  |  | X | | | X | | X | | | X | | | | | | | X | X | | | | X | | | | | | X | | | X | | | X | | | | X | X | | X | | X | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
|
vishnu42574/maheshbabu_dataset | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 1925009.0
num_examples: 10
download_size: 1926502
dataset_size: 1925009.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
ZurichNLP/mlit-guanaco | ---
dataset_info:
- config_name: guanaco
features:
- name: text
dtype: string
- name: lang
dtype: string
- name: confidence
dtype: float64
- name: id
dtype: int64
splits:
- name: train
num_bytes: 16084303
num_examples: 9846
- name: test
num_bytes: 854470
num_examples: 518
download_size: 9851098
dataset_size: 16938773
- config_name: ml1
features:
- name: text
dtype: string
- name: lang
dtype: string
- name: confidence
dtype: float64
- name: id
dtype: int64
splits:
- name: train
num_bytes: 5768604
num_examples: 3200
- name: test
num_bytes: 854470
num_examples: 518
download_size: 3725469
dataset_size: 6623074
- config_name: ml2
features:
- name: text
dtype: string
- name: lang
dtype: string
- name: confidence
dtype: float64
- name: id
dtype: int64
splits:
- name: train
num_bytes: 5710678
num_examples: 3200
- name: test
num_bytes: 854470
num_examples: 518
download_size: 3697276
dataset_size: 6565148
- config_name: ml3
features:
- name: text
dtype: string
- name: lang
dtype: string
- name: confidence
dtype: float64
- name: id
dtype: int64
splits:
- name: train
num_bytes: 5869588
num_examples: 3200
- name: test
num_bytes: 854470
num_examples: 518
download_size: 3740699
dataset_size: 6724058
- config_name: ml4
features:
- name: text
dtype: string
- name: lang
dtype: string
- name: confidence
dtype: float64
- name: id
dtype: int64
splits:
- name: train
num_bytes: 5793432
num_examples: 3200
- name: test
num_bytes: 854470
num_examples: 518
download_size: 3704436
dataset_size: 6647902
- config_name: ml5
features:
- name: text
dtype: string
- name: lang
dtype: string
- name: confidence
dtype: float64
- name: id
dtype: int64
splits:
- name: train
num_bytes: 5727523
num_examples: 3200
- name: test
num_bytes: 854470
num_examples: 518
download_size: 3676576
dataset_size: 6581993
- config_name: ml6
features:
- name: text
dtype: string
- name: lang
dtype: string
- name: confidence
dtype: float64
- name: id
dtype: int64
splits:
- name: train
num_bytes: 5617451
num_examples: 3200
- name: test
num_bytes: 854470
num_examples: 518
download_size: 3625589
dataset_size: 6471921
- config_name: mtml2
features:
- name: text
dtype: string
- name: lang
dtype: string
- name: confidence
dtype: float64
- name: id
dtype: int64
splits:
- name: train
num_bytes: 5813848
num_examples: 3200
- name: test
num_bytes: 854470
num_examples: 518
download_size: 3717965
dataset_size: 6668318
- config_name: mtml3
features:
- name: text
dtype: string
- name: lang
dtype: string
- name: confidence
dtype: float64
- name: id
dtype: int64
splits:
- name: train
num_bytes: 6105089
num_examples: 3200
- name: test
num_bytes: 854470
num_examples: 518
download_size: 3823447
dataset_size: 6959559
- config_name: mtml4
features:
- name: text
dtype: string
- name: lang
dtype: string
- name: confidence
dtype: float64
- name: id
dtype: int64
splits:
- name: train
num_bytes: 6155284
num_examples: 3200
- name: test
num_bytes: 854470
num_examples: 518
download_size: 3848827
dataset_size: 7009754
- config_name: mtml5
features:
- name: text
dtype: string
- name: lang
dtype: string
- name: confidence
dtype: float64
- name: id
dtype: int64
splits:
- name: train
num_bytes: 6104544
num_examples: 3200
- name: test
num_bytes: 854470
num_examples: 518
download_size: 3836022
dataset_size: 6959014
- config_name: mtml6
features:
- name: text
dtype: string
- name: lang
dtype: string
- name: confidence
dtype: float64
- name: id
dtype: int64
splits:
- name: train
num_bytes: 6174923
num_examples: 3200
- name: test
num_bytes: 854470
num_examples: 518
download_size: 3859467
dataset_size: 7029393
configs:
- config_name: guanaco
data_files:
- split: train
path: guanaco/train-*
- split: test
path: guanaco/test-*
- config_name: ml1
data_files:
- split: train
path: ml1/train-*
- split: test
path: ml1/test-*
- config_name: ml2
data_files:
- split: train
path: ml2/train-*
- split: test
path: ml2/test-*
- config_name: ml3
data_files:
- split: train
path: ml3/train-*
- split: test
path: ml3/test-*
- config_name: ml4
data_files:
- split: train
path: ml4/train-*
- split: test
path: ml4/test-*
- config_name: ml5
data_files:
- split: train
path: ml5/train-*
- split: test
path: ml5/test-*
- config_name: ml6
data_files:
- split: train
path: ml6/train-*
- split: test
path: ml6/test-*
- config_name: mtml2
data_files:
- split: train
path: mtml2/train-*
- split: test
path: mtml2/test-*
- config_name: mtml3
data_files:
- split: train
path: mtml3/train-*
- split: test
path: mtml3/test-*
- config_name: mtml4
data_files:
- split: train
path: mtml4/train-*
- split: test
path: mtml4/test-*
- config_name: mtml5
data_files:
- split: train
path: mtml5/train-*
- split: test
path: mtml5/test-*
- config_name: mtml6
data_files:
- split: train
path: mtml6/train-*
- split: test
path: mtml6/test-*
---
# Description
Guanaco dataset subsets used for experiments in the paper [Turning English-centric LLMs Into Polyglots: How Much Multilinguality Is Needed?](https://arxiv.org/abs/2312.12683)
We extend the original Guanaco dataset with language tags, with languages identified using [OpenLID](https://github.com/laurieburchell/open-lid-dataset).
The following subsets were used to train our experimental models:
| config name | languages |
|-------------|----------------------------------------------------------------|
| ml1 | en |
| ml2, mtml2 | en, es |
| ml3, mtml3 | en, es, ru |
| ml4, mtml4 | en, es, ru, de |
| ml5, mtml5 | en, es, ru, de, zh |
| ml6, mtml6 | en, es, ru, de, zh, fr |
| guanaco | en, es, ru, de, zh, fr, ca, th, pt, it, uk, eu, jp + many more |
Note:
- `ml` indicates that the non-English examples are taken directly from OpenAssistant and assumed to be native non-English.
- `mtml` indicates that the non-English examples are translated from original English ones using `gpt-3.5-tubo-16k`.
# Usage
```python
from datasets import load_dataset
ds = load_dataset('ZurichNLP/mlit-guanaco', 'ml2')
print(ds)
>>> DatasetDict({
train: Dataset({
features: ['text', 'lang', 'confidence', 'id'],
num_rows: 3200
})
test: Dataset({
features: ['text', 'lang', 'confidence', 'id'],
num_rows: 518
})
})
```
# Citation
```
@misc{dettmers2023qlora,
title={QLoRA: Efficient Finetuning of Quantized LLMs},
author={Tim Dettmers and Artidoro Pagnoni and Ari Holtzman and Luke Zettlemoyer},
year={2023},
eprint={2305.14314},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
```
@misc{kew2023turning,
title={Turning English-centric LLMs Into Polyglots: How Much Multilinguality Is Needed?},
author={Tannon Kew and Florian Schottmann and Rico Sennrich},
year={2023},
eprint={2312.12683},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
---
dataset_info:
- config_name: guanaco
features:
- name: text
dtype: string
- name: lang
dtype: string
- name: confidence
dtype: float64
- name: id
dtype: int64
splits:
- name: train
num_bytes: 16084303
num_examples: 9846
- name: test
num_bytes: 854470
num_examples: 518
download_size: 9851098
dataset_size: 16938773
- config_name: ml1
features:
- name: text
dtype: string
- name: lang
dtype: string
- name: confidence
dtype: float64
- name: id
dtype: int64
splits:
- name: train
num_bytes: 5768604
num_examples: 3200
- name: test
num_bytes: 854470
num_examples: 518
download_size: 3725469
dataset_size: 6623074
- config_name: ml2
features:
- name: text
dtype: string
- name: lang
dtype: string
- name: confidence
dtype: float64
- name: id
dtype: int64
splits:
- name: train
num_bytes: 5710678
num_examples: 3200
- name: test
num_bytes: 854470
num_examples: 518
download_size: 3697276
dataset_size: 6565148
- config_name: ml3
features:
- name: text
dtype: string
- name: lang
dtype: string
- name: confidence
dtype: float64
- name: id
dtype: int64
splits:
- name: train
num_bytes: 5869588
num_examples: 3200
- name: test
num_bytes: 854470
num_examples: 518
download_size: 3740699
dataset_size: 6724058
- config_name: ml4
features:
- name: text
dtype: string
- name: lang
dtype: string
- name: confidence
dtype: float64
- name: id
dtype: int64
splits:
- name: train
num_bytes: 5793432
num_examples: 3200
- name: test
num_bytes: 854470
num_examples: 518
download_size: 3704436
dataset_size: 6647902
- config_name: ml5
features:
- name: text
dtype: string
- name: lang
dtype: string
- name: confidence
dtype: float64
- name: id
dtype: int64
splits:
- name: train
num_bytes: 5727523
num_examples: 3200
- name: test
num_bytes: 854470
num_examples: 518
download_size: 3676576
dataset_size: 6581993
- config_name: ml6
features:
- name: text
dtype: string
- name: lang
dtype: string
- name: confidence
dtype: float64
- name: id
dtype: int64
splits:
- name: train
num_bytes: 5617451
num_examples: 3200
- name: test
num_bytes: 854470
num_examples: 518
download_size: 3625589
dataset_size: 6471921
- config_name: mtml2
features:
- name: text
dtype: string
- name: lang
dtype: string
- name: confidence
dtype: float64
- name: id
dtype: int64
splits:
- name: train
num_bytes: 5813848
num_examples: 3200
- name: test
num_bytes: 854470
num_examples: 518
download_size: 3717965
dataset_size: 6668318
- config_name: mtml3
features:
- name: text
dtype: string
- name: lang
dtype: string
- name: confidence
dtype: float64
- name: id
dtype: int64
splits:
- name: train
num_bytes: 6105089
num_examples: 3200
- name: test
num_bytes: 854470
num_examples: 518
download_size: 3823447
dataset_size: 6959559
- config_name: mtml4
features:
- name: text
dtype: string
- name: lang
dtype: string
- name: confidence
dtype: float64
- name: id
dtype: int64
splits:
- name: train
num_bytes: 6155284
num_examples: 3200
- name: test
num_bytes: 854470
num_examples: 518
download_size: 3848827
dataset_size: 7009754
- config_name: mtml5
features:
- name: text
dtype: string
- name: lang
dtype: string
- name: confidence
dtype: float64
- name: id
dtype: int64
splits:
- name: train
num_bytes: 6104544
num_examples: 3200
- name: test
num_bytes: 854470
num_examples: 518
download_size: 3836022
dataset_size: 6959014
- config_name: mtml6
features:
- name: text
dtype: string
- name: lang
dtype: string
- name: confidence
dtype: float64
- name: id
dtype: int64
splits:
- name: train
num_bytes: 6174923
num_examples: 3200
- name: test
num_bytes: 854470
num_examples: 518
download_size: 3859467
dataset_size: 7029393
configs:
- config_name: guanaco
data_files:
- split: train
path: guanaco/train-*
- split: test
path: guanaco/test-*
- config_name: ml1
data_files:
- split: train
path: ml1/train-*
- split: test
path: ml1/test-*
- config_name: ml2
data_files:
- split: train
path: ml2/train-*
- split: test
path: ml2/test-*
- config_name: ml3
data_files:
- split: train
path: ml3/train-*
- split: test
path: ml3/test-*
- config_name: ml4
data_files:
- split: train
path: ml4/train-*
- split: test
path: ml4/test-*
- config_name: ml5
data_files:
- split: train
path: ml5/train-*
- split: test
path: ml5/test-*
- config_name: ml6
data_files:
- split: train
path: ml6/train-*
- split: test
path: ml6/test-*
- config_name: mtml2
data_files:
- split: train
path: mtml2/train-*
- split: test
path: mtml2/test-*
- config_name: mtml3
data_files:
- split: train
path: mtml3/train-*
- split: test
path: mtml3/test-*
- config_name: mtml4
data_files:
- split: train
path: mtml4/train-*
- split: test
path: mtml4/test-*
- config_name: mtml5
data_files:
- split: train
path: mtml5/train-*
- split: test
path: mtml5/test-*
- config_name: mtml6
data_files:
- split: train
path: mtml6/train-*
- split: test
path: mtml6/test-*
---
|
ChanceFocus/en-fpb | ---
dataset_info:
features:
- name: id
dtype: string
- name: query
dtype: string
- name: answer
dtype: string
- name: text
dtype: string
- name: choices
sequence: string
- name: gold
dtype: int64
splits:
- name: train
num_bytes: 1520799
num_examples: 3100
- name: valid
num_bytes: 381025
num_examples: 776
- name: test
num_bytes: 475173
num_examples: 970
download_size: 0
dataset_size: 2376997
license: mit
task_categories:
- text-classification
language:
- en
tags:
- finance
size_categories:
- n<1K
---
# Dataset Card for "flare-fpb"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Aspik101/translated_polish_alpaca | ---
license: cc-by-4.0
language:
- pl
tags:
- instruction-finetuning
pretty_name: Translated Polish Alpaca
task_categories:
- text-generation
---
The dataset was translated into Polish using this model: "gsarti/opus-mt-tc-en-pl"
### How to use
```python
from datasets import load_dataset
dataset = load_dataset("Aspik101/translated_polish_alpaca")
```
|
atmallen/quirky_bookrating_alice_easy | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
dataset_info:
features:
- name: alice_label
dtype: bool
- name: bob_label
dtype: bool
- name: difficulty
dtype: float64
- name: statement
dtype: string
- name: choices
sequence: string
- name: character
dtype: string
- name: label
dtype: bool
splits:
- name: train
num_bytes: 96081.48302415121
num_examples: 714
- name: validation
num_bytes: 65429.5725
num_examples: 486
- name: test
num_bytes: 68424.7355
num_examples: 506
download_size: 78831
dataset_size: 229935.7910241512
---
# Dataset Card for "quirky_bookrating_alice_easy"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
open-llm-leaderboard/details_Writer__palmyra-med-20b | ---
pretty_name: Evaluation run of Writer/palmyra-med-20b
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Writer/palmyra-med-20b](https://huggingface.co/Writer/palmyra-med-20b) 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 3 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_Writer__palmyra-med-20b\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-27T07:43:51.319096](https://huggingface.co/datasets/open-llm-leaderboard/details_Writer__palmyra-med-20b/blob/main/results_2023-10-27T07-43-51.319096.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\": 0.05851510067114094,\n\
\ \"em_stderr\": 0.0024037002515447264,\n \"f1\": 0.11879089765100655,\n\
\ \"f1_stderr\": 0.002692444748823806,\n \"acc\": 0.34002374380150946,\n\
\ \"acc_stderr\": 0.008900409699475408\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.05851510067114094,\n \"em_stderr\": 0.0024037002515447264,\n\
\ \"f1\": 0.11879089765100655,\n \"f1_stderr\": 0.002692444748823806\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.026535253980288095,\n \
\ \"acc_stderr\": 0.004427045987265168\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.6535122336227308,\n \"acc_stderr\": 0.013373773411685648\n\
\ }\n}\n```"
repo_url: https://huggingface.co/Writer/palmyra-med-20b
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_09_11T17_21_21.677448
path:
- '**/details_harness|arc:challenge|25_2023-09-11T17-21-21.677448.parquet'
- split: 2023_09_12T21_53_25.718910
path:
- '**/details_harness|arc:challenge|25_2023-09-12T21-53-25.718910.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-09-12T21-53-25.718910.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_10_27T07_43_51.319096
path:
- '**/details_harness|drop|3_2023-10-27T07-43-51.319096.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-27T07-43-51.319096.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_27T07_43_51.319096
path:
- '**/details_harness|gsm8k|5_2023-10-27T07-43-51.319096.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-27T07-43-51.319096.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_09_11T17_21_21.677448
path:
- '**/details_harness|hellaswag|10_2023-09-11T17-21-21.677448.parquet'
- split: 2023_09_12T21_53_25.718910
path:
- '**/details_harness|hellaswag|10_2023-09-12T21-53-25.718910.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-09-12T21-53-25.718910.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_09_11T17_21_21.677448
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-11T17-21-21.677448.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-11T17-21-21.677448.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-11T17-21-21.677448.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-11T17-21-21.677448.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-11T17-21-21.677448.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-11T17-21-21.677448.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-11T17-21-21.677448.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-11T17-21-21.677448.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-11T17-21-21.677448.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-11T17-21-21.677448.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-11T17-21-21.677448.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-11T17-21-21.677448.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-11T17-21-21.677448.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-11T17-21-21.677448.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-11T17-21-21.677448.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-11T17-21-21.677448.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-11T17-21-21.677448.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-11T17-21-21.677448.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-11T17-21-21.677448.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-11T17-21-21.677448.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-11T17-21-21.677448.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-11T17-21-21.677448.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-11T17-21-21.677448.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-11T17-21-21.677448.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-11T17-21-21.677448.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-11T17-21-21.677448.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-11T17-21-21.677448.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-11T17-21-21.677448.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-11T17-21-21.677448.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-11T17-21-21.677448.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-11T17-21-21.677448.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-11T17-21-21.677448.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-11T17-21-21.677448.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-11T17-21-21.677448.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-09-11T17-21-21.677448.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-11T17-21-21.677448.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-11T17-21-21.677448.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-11T17-21-21.677448.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-09-11T17-21-21.677448.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-09-11T17-21-21.677448.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-11T17-21-21.677448.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-11T17-21-21.677448.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-11T17-21-21.677448.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-11T17-21-21.677448.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-11T17-21-21.677448.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-11T17-21-21.677448.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-11T17-21-21.677448.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-11T17-21-21.677448.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-11T17-21-21.677448.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-11T17-21-21.677448.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-11T17-21-21.677448.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-11T17-21-21.677448.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-11T17-21-21.677448.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-09-11T17-21-21.677448.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-11T17-21-21.677448.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-09-11T17-21-21.677448.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-11T17-21-21.677448.parquet'
- split: 2023_09_12T21_53_25.718910
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-12T21-53-25.718910.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-12T21-53-25.718910.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-12T21-53-25.718910.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-12T21-53-25.718910.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-12T21-53-25.718910.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-12T21-53-25.718910.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-12T21-53-25.718910.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-12T21-53-25.718910.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-12T21-53-25.718910.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-12T21-53-25.718910.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-12T21-53-25.718910.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-12T21-53-25.718910.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-12T21-53-25.718910.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-12T21-53-25.718910.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-12T21-53-25.718910.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-12T21-53-25.718910.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-12T21-53-25.718910.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-12T21-53-25.718910.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-12T21-53-25.718910.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-12T21-53-25.718910.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-12T21-53-25.718910.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-12T21-53-25.718910.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-12T21-53-25.718910.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-12T21-53-25.718910.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-12T21-53-25.718910.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-12T21-53-25.718910.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-12T21-53-25.718910.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-12T21-53-25.718910.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-12T21-53-25.718910.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-12T21-53-25.718910.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-12T21-53-25.718910.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-12T21-53-25.718910.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-12T21-53-25.718910.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-12T21-53-25.718910.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-09-12T21-53-25.718910.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-12T21-53-25.718910.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-12T21-53-25.718910.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-12T21-53-25.718910.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-09-12T21-53-25.718910.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-09-12T21-53-25.718910.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-12T21-53-25.718910.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-12T21-53-25.718910.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-12T21-53-25.718910.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-12T21-53-25.718910.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-12T21-53-25.718910.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-12T21-53-25.718910.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-12T21-53-25.718910.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-12T21-53-25.718910.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-12T21-53-25.718910.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-12T21-53-25.718910.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-12T21-53-25.718910.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-12T21-53-25.718910.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-12T21-53-25.718910.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-09-12T21-53-25.718910.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-12T21-53-25.718910.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-09-12T21-53-25.718910.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-12T21-53-25.718910.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-12T21-53-25.718910.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-12T21-53-25.718910.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-12T21-53-25.718910.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-12T21-53-25.718910.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-12T21-53-25.718910.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-12T21-53-25.718910.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-12T21-53-25.718910.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-12T21-53-25.718910.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-12T21-53-25.718910.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-12T21-53-25.718910.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-12T21-53-25.718910.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-12T21-53-25.718910.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-12T21-53-25.718910.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-12T21-53-25.718910.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-12T21-53-25.718910.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-12T21-53-25.718910.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-12T21-53-25.718910.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-12T21-53-25.718910.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-12T21-53-25.718910.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-12T21-53-25.718910.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-12T21-53-25.718910.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-12T21-53-25.718910.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-12T21-53-25.718910.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-12T21-53-25.718910.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-12T21-53-25.718910.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-12T21-53-25.718910.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-12T21-53-25.718910.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-12T21-53-25.718910.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-12T21-53-25.718910.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-12T21-53-25.718910.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-12T21-53-25.718910.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-12T21-53-25.718910.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-12T21-53-25.718910.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-12T21-53-25.718910.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-09-12T21-53-25.718910.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-12T21-53-25.718910.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-12T21-53-25.718910.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-12T21-53-25.718910.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-09-12T21-53-25.718910.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-09-12T21-53-25.718910.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-12T21-53-25.718910.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-12T21-53-25.718910.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-12T21-53-25.718910.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-12T21-53-25.718910.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-12T21-53-25.718910.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-12T21-53-25.718910.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-12T21-53-25.718910.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-12T21-53-25.718910.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-12T21-53-25.718910.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-12T21-53-25.718910.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-12T21-53-25.718910.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-12T21-53-25.718910.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-12T21-53-25.718910.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-09-12T21-53-25.718910.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-12T21-53-25.718910.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-09-12T21-53-25.718910.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-12T21-53-25.718910.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_09_11T17_21_21.677448
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-11T17-21-21.677448.parquet'
- split: 2023_09_12T21_53_25.718910
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-12T21-53-25.718910.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-12T21-53-25.718910.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_09_11T17_21_21.677448
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-11T17-21-21.677448.parquet'
- split: 2023_09_12T21_53_25.718910
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-12T21-53-25.718910.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-12T21-53-25.718910.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_09_11T17_21_21.677448
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-11T17-21-21.677448.parquet'
- split: 2023_09_12T21_53_25.718910
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-12T21-53-25.718910.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-12T21-53-25.718910.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_09_11T17_21_21.677448
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-11T17-21-21.677448.parquet'
- split: 2023_09_12T21_53_25.718910
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-12T21-53-25.718910.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-12T21-53-25.718910.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_09_11T17_21_21.677448
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-11T17-21-21.677448.parquet'
- split: 2023_09_12T21_53_25.718910
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-12T21-53-25.718910.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-12T21-53-25.718910.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_09_11T17_21_21.677448
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-11T17-21-21.677448.parquet'
- split: 2023_09_12T21_53_25.718910
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-12T21-53-25.718910.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-12T21-53-25.718910.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_09_11T17_21_21.677448
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-11T17-21-21.677448.parquet'
- split: 2023_09_12T21_53_25.718910
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-12T21-53-25.718910.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-12T21-53-25.718910.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_09_11T17_21_21.677448
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-11T17-21-21.677448.parquet'
- split: 2023_09_12T21_53_25.718910
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-12T21-53-25.718910.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-12T21-53-25.718910.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_09_11T17_21_21.677448
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-11T17-21-21.677448.parquet'
- split: 2023_09_12T21_53_25.718910
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-12T21-53-25.718910.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-12T21-53-25.718910.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_09_11T17_21_21.677448
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-11T17-21-21.677448.parquet'
- split: 2023_09_12T21_53_25.718910
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-12T21-53-25.718910.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-12T21-53-25.718910.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_09_11T17_21_21.677448
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-11T17-21-21.677448.parquet'
- split: 2023_09_12T21_53_25.718910
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-12T21-53-25.718910.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-12T21-53-25.718910.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_09_11T17_21_21.677448
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-11T17-21-21.677448.parquet'
- split: 2023_09_12T21_53_25.718910
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-12T21-53-25.718910.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-12T21-53-25.718910.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_09_11T17_21_21.677448
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-11T17-21-21.677448.parquet'
- split: 2023_09_12T21_53_25.718910
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-12T21-53-25.718910.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-12T21-53-25.718910.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_09_11T17_21_21.677448
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-11T17-21-21.677448.parquet'
- split: 2023_09_12T21_53_25.718910
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-12T21-53-25.718910.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-12T21-53-25.718910.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_09_11T17_21_21.677448
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-11T17-21-21.677448.parquet'
- split: 2023_09_12T21_53_25.718910
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-12T21-53-25.718910.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-12T21-53-25.718910.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_09_11T17_21_21.677448
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-11T17-21-21.677448.parquet'
- split: 2023_09_12T21_53_25.718910
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-12T21-53-25.718910.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-12T21-53-25.718910.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_09_11T17_21_21.677448
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-11T17-21-21.677448.parquet'
- split: 2023_09_12T21_53_25.718910
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-12T21-53-25.718910.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-12T21-53-25.718910.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_09_11T17_21_21.677448
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-11T17-21-21.677448.parquet'
- split: 2023_09_12T21_53_25.718910
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-12T21-53-25.718910.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-12T21-53-25.718910.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_09_11T17_21_21.677448
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-11T17-21-21.677448.parquet'
- split: 2023_09_12T21_53_25.718910
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-12T21-53-25.718910.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-12T21-53-25.718910.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_09_11T17_21_21.677448
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-11T17-21-21.677448.parquet'
- split: 2023_09_12T21_53_25.718910
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-12T21-53-25.718910.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-12T21-53-25.718910.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_09_11T17_21_21.677448
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-11T17-21-21.677448.parquet'
- split: 2023_09_12T21_53_25.718910
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-12T21-53-25.718910.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-12T21-53-25.718910.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_09_11T17_21_21.677448
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-11T17-21-21.677448.parquet'
- split: 2023_09_12T21_53_25.718910
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-12T21-53-25.718910.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-12T21-53-25.718910.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_09_11T17_21_21.677448
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-11T17-21-21.677448.parquet'
- split: 2023_09_12T21_53_25.718910
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-12T21-53-25.718910.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-12T21-53-25.718910.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_09_11T17_21_21.677448
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-11T17-21-21.677448.parquet'
- split: 2023_09_12T21_53_25.718910
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-12T21-53-25.718910.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-12T21-53-25.718910.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_09_11T17_21_21.677448
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-11T17-21-21.677448.parquet'
- split: 2023_09_12T21_53_25.718910
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-12T21-53-25.718910.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-12T21-53-25.718910.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_09_11T17_21_21.677448
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-11T17-21-21.677448.parquet'
- split: 2023_09_12T21_53_25.718910
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-12T21-53-25.718910.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-12T21-53-25.718910.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_09_11T17_21_21.677448
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-11T17-21-21.677448.parquet'
- split: 2023_09_12T21_53_25.718910
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-12T21-53-25.718910.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-12T21-53-25.718910.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_09_11T17_21_21.677448
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-11T17-21-21.677448.parquet'
- split: 2023_09_12T21_53_25.718910
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-12T21-53-25.718910.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-12T21-53-25.718910.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_09_11T17_21_21.677448
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-11T17-21-21.677448.parquet'
- split: 2023_09_12T21_53_25.718910
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-12T21-53-25.718910.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-12T21-53-25.718910.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_09_11T17_21_21.677448
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-11T17-21-21.677448.parquet'
- split: 2023_09_12T21_53_25.718910
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-12T21-53-25.718910.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-12T21-53-25.718910.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_09_11T17_21_21.677448
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-11T17-21-21.677448.parquet'
- split: 2023_09_12T21_53_25.718910
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-12T21-53-25.718910.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-12T21-53-25.718910.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_09_11T17_21_21.677448
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-11T17-21-21.677448.parquet'
- split: 2023_09_12T21_53_25.718910
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-12T21-53-25.718910.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-12T21-53-25.718910.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_09_11T17_21_21.677448
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-11T17-21-21.677448.parquet'
- split: 2023_09_12T21_53_25.718910
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-12T21-53-25.718910.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-12T21-53-25.718910.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_09_11T17_21_21.677448
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-11T17-21-21.677448.parquet'
- split: 2023_09_12T21_53_25.718910
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-12T21-53-25.718910.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-12T21-53-25.718910.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_09_11T17_21_21.677448
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-09-11T17-21-21.677448.parquet'
- split: 2023_09_12T21_53_25.718910
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-09-12T21-53-25.718910.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-09-12T21-53-25.718910.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_09_11T17_21_21.677448
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-11T17-21-21.677448.parquet'
- split: 2023_09_12T21_53_25.718910
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-12T21-53-25.718910.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-12T21-53-25.718910.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_09_11T17_21_21.677448
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-11T17-21-21.677448.parquet'
- split: 2023_09_12T21_53_25.718910
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-12T21-53-25.718910.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-12T21-53-25.718910.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_09_11T17_21_21.677448
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-11T17-21-21.677448.parquet'
- split: 2023_09_12T21_53_25.718910
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-12T21-53-25.718910.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-12T21-53-25.718910.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_09_11T17_21_21.677448
path:
- '**/details_harness|hendrycksTest-management|5_2023-09-11T17-21-21.677448.parquet'
- split: 2023_09_12T21_53_25.718910
path:
- '**/details_harness|hendrycksTest-management|5_2023-09-12T21-53-25.718910.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-09-12T21-53-25.718910.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_09_11T17_21_21.677448
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-09-11T17-21-21.677448.parquet'
- split: 2023_09_12T21_53_25.718910
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-09-12T21-53-25.718910.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-09-12T21-53-25.718910.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_09_11T17_21_21.677448
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-11T17-21-21.677448.parquet'
- split: 2023_09_12T21_53_25.718910
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-12T21-53-25.718910.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-12T21-53-25.718910.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_09_11T17_21_21.677448
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-11T17-21-21.677448.parquet'
- split: 2023_09_12T21_53_25.718910
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-12T21-53-25.718910.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-12T21-53-25.718910.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_09_11T17_21_21.677448
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-11T17-21-21.677448.parquet'
- split: 2023_09_12T21_53_25.718910
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-12T21-53-25.718910.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-12T21-53-25.718910.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_09_11T17_21_21.677448
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-11T17-21-21.677448.parquet'
- split: 2023_09_12T21_53_25.718910
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-12T21-53-25.718910.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-12T21-53-25.718910.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_09_11T17_21_21.677448
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-11T17-21-21.677448.parquet'
- split: 2023_09_12T21_53_25.718910
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-12T21-53-25.718910.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-12T21-53-25.718910.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_09_11T17_21_21.677448
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-11T17-21-21.677448.parquet'
- split: 2023_09_12T21_53_25.718910
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-12T21-53-25.718910.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-12T21-53-25.718910.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_09_11T17_21_21.677448
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-11T17-21-21.677448.parquet'
- split: 2023_09_12T21_53_25.718910
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-12T21-53-25.718910.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-12T21-53-25.718910.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_09_11T17_21_21.677448
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-11T17-21-21.677448.parquet'
- split: 2023_09_12T21_53_25.718910
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-12T21-53-25.718910.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-12T21-53-25.718910.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_09_11T17_21_21.677448
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-11T17-21-21.677448.parquet'
- split: 2023_09_12T21_53_25.718910
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-12T21-53-25.718910.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-12T21-53-25.718910.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_09_11T17_21_21.677448
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-11T17-21-21.677448.parquet'
- split: 2023_09_12T21_53_25.718910
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-12T21-53-25.718910.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-12T21-53-25.718910.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_09_11T17_21_21.677448
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-11T17-21-21.677448.parquet'
- split: 2023_09_12T21_53_25.718910
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-12T21-53-25.718910.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-12T21-53-25.718910.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_09_11T17_21_21.677448
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-11T17-21-21.677448.parquet'
- split: 2023_09_12T21_53_25.718910
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-12T21-53-25.718910.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-12T21-53-25.718910.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_09_11T17_21_21.677448
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-11T17-21-21.677448.parquet'
- split: 2023_09_12T21_53_25.718910
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-12T21-53-25.718910.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-12T21-53-25.718910.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_09_11T17_21_21.677448
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-09-11T17-21-21.677448.parquet'
- split: 2023_09_12T21_53_25.718910
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-09-12T21-53-25.718910.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-09-12T21-53-25.718910.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_09_11T17_21_21.677448
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-11T17-21-21.677448.parquet'
- split: 2023_09_12T21_53_25.718910
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-12T21-53-25.718910.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-12T21-53-25.718910.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_09_11T17_21_21.677448
path:
- '**/details_harness|hendrycksTest-virology|5_2023-09-11T17-21-21.677448.parquet'
- split: 2023_09_12T21_53_25.718910
path:
- '**/details_harness|hendrycksTest-virology|5_2023-09-12T21-53-25.718910.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-09-12T21-53-25.718910.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_09_11T17_21_21.677448
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-11T17-21-21.677448.parquet'
- split: 2023_09_12T21_53_25.718910
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-12T21-53-25.718910.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-12T21-53-25.718910.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_09_11T17_21_21.677448
path:
- '**/details_harness|truthfulqa:mc|0_2023-09-11T17-21-21.677448.parquet'
- split: 2023_09_12T21_53_25.718910
path:
- '**/details_harness|truthfulqa:mc|0_2023-09-12T21-53-25.718910.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-09-12T21-53-25.718910.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_27T07_43_51.319096
path:
- '**/details_harness|winogrande|5_2023-10-27T07-43-51.319096.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-27T07-43-51.319096.parquet'
- config_name: results
data_files:
- split: 2023_09_11T17_21_21.677448
path:
- results_2023-09-11T17-21-21.677448.parquet
- split: 2023_09_12T21_53_25.718910
path:
- results_2023-09-12T21-53-25.718910.parquet
- split: 2023_10_27T07_43_51.319096
path:
- results_2023-10-27T07-43-51.319096.parquet
- split: latest
path:
- results_2023-10-27T07-43-51.319096.parquet
---
# Dataset Card for Evaluation run of Writer/palmyra-med-20b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Writer/palmyra-med-20b
- **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 [Writer/palmyra-med-20b](https://huggingface.co/Writer/palmyra-med-20b) 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 3 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_Writer__palmyra-med-20b",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-27T07:43:51.319096](https://huggingface.co/datasets/open-llm-leaderboard/details_Writer__palmyra-med-20b/blob/main/results_2023-10-27T07-43-51.319096.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": 0.05851510067114094,
"em_stderr": 0.0024037002515447264,
"f1": 0.11879089765100655,
"f1_stderr": 0.002692444748823806,
"acc": 0.34002374380150946,
"acc_stderr": 0.008900409699475408
},
"harness|drop|3": {
"em": 0.05851510067114094,
"em_stderr": 0.0024037002515447264,
"f1": 0.11879089765100655,
"f1_stderr": 0.002692444748823806
},
"harness|gsm8k|5": {
"acc": 0.026535253980288095,
"acc_stderr": 0.004427045987265168
},
"harness|winogrande|5": {
"acc": 0.6535122336227308,
"acc_stderr": 0.013373773411685648
}
}
```
### 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] |
phellonchen/video | ---
license: cc-by-3.0
---
|
atmallen/qm_grader_first_1.0e | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
dataset_info:
features:
- name: alice_label
dtype: bool
- name: bob_label
dtype: bool
- name: difficulty
dtype: int64
- name: statement
dtype: string
- name: choices
sequence: string
- name: character
dtype: string
- name: label
dtype:
class_label:
names:
'0': 'False'
'1': 'True'
splits:
- name: train
num_bytes: 35940088
num_examples: 400000
- name: validation
num_bytes: 3602836
num_examples: 40000
- name: test
num_bytes: 3604340
num_examples: 40000
download_size: 0
dataset_size: 43147264
---
# Dataset Card for "qm_grader_first_1.0e"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
christophsonntag/sst2-constituency | ---
dataset_info:
features:
- name: idx
dtype: int32
- name: sentence
dtype: string
- name: label
dtype:
class_label:
names:
'0': negative
'1': positive
- name: constituency_tree
dtype: string
splits:
- name: train
num_bytes: 15168877
num_examples: 67349
download_size: 7927354
dataset_size: 15168877
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
DBQ/Saint.Laurent.Product.prices.South.Korea | ---
annotations_creators:
- other
language_creators:
- other
language:
- en
license:
- unknown
multilinguality:
- monolingual
source_datasets:
- original
task_categories:
- text-classification
- image-classification
- feature-extraction
- image-segmentation
- image-to-image
- image-to-text
- object-detection
- summarization
- zero-shot-image-classification
pretty_name: South Korea - Saint Laurent - Product-level price list
tags:
- webscraping
- ecommerce
- Saint Laurent
- fashion
- fashion product
- image
- fashion image
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: website_name
dtype: string
- name: competence_date
dtype: string
- name: country_code
dtype: string
- name: currency_code
dtype: string
- name: brand
dtype: string
- name: category1_code
dtype: string
- name: category2_code
dtype: string
- name: category3_code
dtype: string
- name: product_code
dtype: string
- name: title
dtype: string
- name: itemurl
dtype: string
- name: imageurl
dtype: string
- name: full_price
dtype: float64
- name: price
dtype: float64
- name: full_price_eur
dtype: float64
- name: price_eur
dtype: float64
- name: flg_discount
dtype: int64
splits:
- name: train
num_bytes: 1548448
num_examples: 3018
download_size: 468846
dataset_size: 1548448
---
# Saint Laurent web scraped data
## About the website
The **fashion industry** in the **Asia Pacific**, particularly in **South Korea**, is thriving and recognized for its dynamic adaptability to global styling trends. Central to this growth is the luxury segment that includes renowned brands such as **Saint Laurent**. Rapid digital transformation has occurred in this regions retail sector, most notably the rise of **Ecommerce**. Today, online retail portals serve as a significant sales channel for fashion brands. The dataset observed involves **Ecommerce product-list page (PLP)** data on Saint Laurent in South Korea which grants valuable insights into the online retail trends and consumer behaviors of the luxury brand in this region.
## Link to **dataset**
[South Korea - Saint Laurent - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Saint%20Laurent%20Product-prices%20South%20Korea/r/recnrJOE5NE4dFDeY)
|
sdiazlor/evol-instruct-4-10samples | ---
dataset_info:
features:
- name: input
dtype: string
- name: generation_model
sequence: string
- name: generation_prompt
list:
list:
- name: content
dtype: string
- name: role
dtype: string
- name: raw_generation_responses
sequence: string
- name: instructions
sequence: string
splits:
- name: train
num_bytes: 23576
num_examples: 10
download_size: 25197
dataset_size: 23576
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
CyberHarem/bianca_zeshia_maougakuinnofutekigousha | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of Bianca Zeshia/ゼシア・ビアンカ (Maou Gakuin no Futekigousha)
This is the dataset of Bianca Zeshia/ゼシア・ビアンカ (Maou Gakuin no Futekigousha), containing 148 images and their tags.
The core tags of this character are `long_hair, hair_between_eyes, black_hair, purple_hair, red_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 | 148 | 97.98 MiB | [Download](https://huggingface.co/datasets/CyberHarem/bianca_zeshia_maougakuinnofutekigousha/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 1200 | 148 | 97.94 MiB | [Download](https://huggingface.co/datasets/CyberHarem/bianca_zeshia_maougakuinnofutekigousha/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 242 | 149.36 MiB | [Download](https://huggingface.co/datasets/CyberHarem/bianca_zeshia_maougakuinnofutekigousha/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/bianca_zeshia_maougakuinnofutekigousha',
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 | 13 |  |  |  |  |  | 1girl, profile, red_jacket, solo_focus, from_side, open_mouth, 1boy, closed_mouth |
| 1 | 20 |  |  |  |  |  | 1girl, solo, closed_mouth, portrait, looking_at_viewer, brown_eyes |
| 2 | 5 |  |  |  |  |  | 1girl, sword, solo, uniform, very_long_hair, white_gloves, boots, half_updo, brown_eyes |
| 3 | 7 |  |  |  |  |  | 1girl, white_gloves, brown_eyes, solo, belt, day, holding_sword, sky, very_long_hair, cloud, outdoors, red_pants |
| 4 | 5 |  |  |  |  |  | 2girls, very_long_hair, smile, solo_focus, brown_eyes, hands_on_another's_shoulders, head_out_of_frame, 1boy, 1girl, belt, large_breasts, long_sleeves |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | profile | red_jacket | solo_focus | from_side | open_mouth | 1boy | closed_mouth | solo | portrait | looking_at_viewer | brown_eyes | sword | uniform | very_long_hair | white_gloves | boots | half_updo | belt | day | holding_sword | sky | cloud | outdoors | red_pants | 2girls | smile | hands_on_another's_shoulders | head_out_of_frame | large_breasts | long_sleeves |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:----------|:-------------|:-------------|:------------|:-------------|:-------|:---------------|:-------|:-----------|:--------------------|:-------------|:--------|:----------|:-----------------|:---------------|:--------|:------------|:-------|:------|:----------------|:------|:--------|:-----------|:------------|:---------|:--------|:-------------------------------|:--------------------|:----------------|:---------------|
| 0 | 13 |  |  |  |  |  | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 20 |  |  |  |  |  | X | | | | | | | X | X | X | X | X | | | | | | | | | | | | | | | | | | | |
| 2 | 5 |  |  |  |  |  | X | | | | | | | | X | | | X | X | X | X | X | X | X | | | | | | | | | | | | | |
| 3 | 7 |  |  |  |  |  | X | | | | | | | | X | | | X | | | X | X | | | X | X | X | X | X | X | X | | | | | | |
| 4 | 5 |  |  |  |  |  | X | | | X | | | X | | | | | X | | | X | | | | X | | | | | | | X | X | X | X | X | X |
|
teowu/DIVIDE-MaxWell | ---
license: apache-2.0
---
|
ryzzlestrizzle/sqwiki-clustering-p2p | ---
license: apache-2.0
language:
- sq
--- |
nergis/deneme | ---
dataset_info:
features:
- name: label
dtype: string
- name: sign1
dtype: string
- name: sign2
dtype: string
- name: sign3
dtype: string
- name: review_length
dtype: int64
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 6147164
num_examples: 2452
download_size: 347074
dataset_size: 6147164
---
# Dataset Card for "deneme"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Nexdata/500_Drivers_7_Expression_Recognition_Data | ---
license: cc-by-nc-nd-4.0
---
## Description
Seven facial expressions recognition data of 500 drivers cover multiple ages, multiple time periods and multiple expressions. In terms of acquisition equipment, visible and infrared binocular cameras are used. This set of driver expression recognition data can be used for driver expression recognition analysis and other tasks.
For more details, please refer to the link: https://www.nexdata.ai/dataset/1293?source=Huggingface
## Data size
500 people
## Population distribution
gender distribution: male, female; race distribution: Vietnam; age distribution: 18~45 years old, 46~60 years old, over 60 years old
## Collecting environment
in-car Cameras
## Data diversity
multiple expressions, multiple ages, multiple time periods
## Device
visible light and infrared binocular camera, resolution 1,920x1,080
## Shooting position
the center of the inside rear view mirror of the car, above the center console in the car, above the left A-pillar in the car, steering wheel position
## Collecting time
day, evening, night
## Vehicle Type
car, SUV, MVP, truck, coach
## Data Format
the video data format is .mp4
## Accuracy
according to the accuracy of each person's acquisition expression, the accuracy exceeds 95%;the accuracy of label annotation is not less than 95%
# Licensing Information
Commercial License
|
jaryong/dataset_itaz_240405 | ---
license: mit
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 1643
num_examples: 10
download_size: 2542
dataset_size: 1643
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Atsushi/fungi_trait_circus_database | ---
annotations_creators:
- other
language:
- en
- ja
multilinguality:
- multilingual
license:
- cc-by-4.0
source_datasets:
- original
size_categories:
- 100K<n<1M
---
fungi_trait_circus_database
大菌輪「Trait Circus」データセット（統制形質）
最終更新日：2023/12/29
====
### Languages
Japanese and English
Please do not use this dataset for academic purposes for the time being. (casual use only)
当面の間仮公開とします。学術目的での使用はご遠慮ください。
# 概要
Atsushi Nakajima（中島淳志）が個人で運営しているWebサイト[大菌輪](http://mycoscouter.coolblog.jp/daikinrin/) では、菌類の記載文を自然言語処理の手法を利用して半自動的に処理し、菌類の形態、生態などに関する様々な「形質 (traits)」データを抽出して、集計や解析の便宜を図るために、あらかじめ設定された「統制語 (controlled term)」の形でまとめています。
抽出手法については「ニッチェ・ライフ」誌の[こちらの記事](https://media.niche-life.com/series/008/Niche008_06.pdf)（査読なし）で報告しています。
自動抽出という性質上、ある程度の誤りが含まれる可能性があることをご承知おきください。
2023/12/29に全ての掲載内容を見直し、ほぼ一から収集し直したデータに差し替えました。
統制語は「要素 (element）」「属性（attribute）」「値（value）」の3つ組からなります。
例えば「傘_色_黒」はそれぞれ「傘」「色」「黒」の要素/属性/値を持っています。一部の統制語では要素と属性が同一となっています（「生息環境」など）
参考までに、データ数上位3件は「要素」で「子実体」「傘」「胞子」、「属性」で「色」「形状」「表面性状」、「値」で「褐」「平滑」「黄」です。
また、菌類分類学の学習および同定支援の目的で、そのデータを基にしたインタラクティブな可視化Webアプリ「[Trait Circus](https://tinyurl.com/nrhcfksu)」を提供しています。
本データセットは、そのWebアプリの生データに相当し、容量の都合等でWebアプリに反映されていない情報も含まれています。
## 関連データセット
「論文3行まとめ」
[Atsushi/fungi_indexed_mycological_papers_japanese](https://huggingface.co/datasets/Atsushi/fungi_indexed_mycological_papers_japanese)
「識別形質まとめ」
[Atsushi/fungi_diagnostic_chars_comparison_japanese](https://huggingface.co/datasets/Atsushi/fungi_diagnostic_chars_comparison_japanese)
## 各カラムの説明
* source … 各情報の出典のURLです。多くは学術文献またはMycoBankの記載文データベースを参照しています。
* hit_term … 抽出された形質の出典中における表現です。
* current_name … その形質を有する菌の現行学名です。MycoBankを参照していますが、最新の情報ではない可能性があります。
* original_name … その形質を有する菌の出典で使用されていた学名です。（2023/12/29版から追加）
* element_j … 「要素」の日本語表記です。
* attribute_j … 「属性」の日本語表記です。
* value_j … 「値」の日本語表記です。
* element … 「要素」の英語表記です。
* attribute … 「属性」の英語表記です。
* value … 「値」の英語表記です。 |
Yaoshixuexi/wulizhishi | ---
license: unknown
---
|
yeajinmin/News-NER-dataset-ForKCBERT | ---
dataset_info:
features:
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 67780919.01423985
num_examples: 120113
- name: test
num_bytes: 16945652.98576015
num_examples: 30029
download_size: 17567617
dataset_size: 84726572.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
qed | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- extended|natural_questions
task_categories:
- question-answering
task_ids:
- extractive-qa
paperswithcode_id: qed
pretty_name: QED
tags:
- explanations-in-question-answering
dataset_info:
features:
- name: example_id
dtype: int64
- name: title_text
dtype: string
- name: url
dtype: string
- name: question
dtype: string
- name: paragraph_text
dtype: string
- name: sentence_starts
sequence: int32
- name: original_nq_answers
list:
- name: start
dtype: int32
- name: end
dtype: int32
- name: string
dtype: string
- name: annotation
struct:
- name: referential_equalities
list:
- name: question_reference
struct:
- name: start
dtype: int32
- name: end
dtype: int32
- name: string
dtype: string
- name: sentence_reference
struct:
- name: start
dtype: int32
- name: end
dtype: int32
- name: bridge
dtype: string
- name: string
dtype: string
- name: answer
list:
- name: sentence_reference
struct:
- name: start
dtype: int32
- name: end
dtype: int32
- name: bridge
dtype: string
- name: string
dtype: string
- name: paragraph_reference
struct:
- name: start
dtype: int32
- name: end
dtype: int32
- name: string
dtype: string
- name: explanation_type
dtype: string
- name: selected_sentence
struct:
- name: start
dtype: int32
- name: end
dtype: int32
- name: string
dtype: string
config_name: qed
splits:
- name: train
num_bytes: 8602094
num_examples: 7638
- name: validation
num_bytes: 1584139
num_examples: 1355
download_size: 14083968
dataset_size: 10186233
---
# Dataset Card for QED
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** N/A
- **Repository:** [GitHub](https://github.com/google-research-datasets/QED)
- **Paper:** [QED: A Framework and Dataset for Explanations in Question Answering](https://arxiv.org/abs/2009.06354)
- **Leaderboard:** N/A
- **Point of Contact:** -
### Dataset Summary
[More Information Needed]
### 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
[More Information Needed]
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
[More Information Needed]
#### 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
Thanks to [@patil-suraj](https://github.com/patil-suraj) for adding this dataset. |
mask-distilled-one-sec-cv12/chunk_246 | ---
dataset_info:
features:
- name: logits
sequence: float32
- name: mfcc
sequence:
sequence: float64
splits:
- name: train
num_bytes: 827714784
num_examples: 162552
download_size: 844586452
dataset_size: 827714784
---
# Dataset Card for "chunk_246"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
nlplabtdtu/review_edu_data | ---
dataset_info:
features:
- name: id
dtype: int64
- name: date
dtype: string
- name: alias
dtype: string
- name: university
dtype: string
- name: content
dtype: string
splits:
- name: train
num_bytes: 9075720
num_examples: 684
download_size: 3649707
dataset_size: 9075720
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
matmat13/resume_parse | ---
dataset_info:
features:
- name: image
dtype: image
- name: ground_truth
dtype: string
splits:
- name: train
num_bytes: 92827045.0
num_examples: 8
- name: validation
num_bytes: 23204290.0
num_examples: 2
- name: test
num_bytes: 11601842.0
num_examples: 1
download_size: 12167269
dataset_size: 127633177.0
---
# Dataset Card for "resume_parse"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
BoooomNing/pix-to-pix-blip-viton-resized-dataset | ---
dataset_info:
features:
- name: original_image
dtype: image
- name: edit_prompt
dtype: string
- name: cartoonized_image
dtype: image
splits:
- name: train
num_bytes: 411553735.61
num_examples: 11262
download_size: 368397140
dataset_size: 411553735.61
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
TimKoornstra/financial-tweets-sentiment | ---
language:
- en
license: mit
size_categories:
- 10K<n<100K
task_categories:
- text-classification
pretty_name: Financial Tweets with Sentiment class
dataset_info:
features:
- name: tweet
dtype: string
- name: sentiment
dtype:
class_label:
names:
'0': neutral
'1': bullish
'2': bearish
- name: url
dtype: string
splits:
- name: train
num_bytes: 6848991
num_examples: 38091
download_size: 2648082
dataset_size: 6848991
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
tags:
- sentiment
- twitter
- finance
- crypto
- stocks
- tweet
- collection
---
# Financial Sentiment Analysis Dataset
## Overview
This dataset is a comprehensive collection of tweets focused on financial topics, meticulously curated to assist in sentiment analysis in the domain of finance and stock markets. It serves as a valuable resource for training machine learning models to understand and predict sentiment trends based on social media discourse, particularly within the financial sector.
## Data Description
The dataset comprises tweets related to financial markets, stocks, and economic discussions. Each tweet is labeled with a sentiment value, where '1' denotes a positive sentiment, '2' signifies a negative sentiment, and '0' indicates a neutral sentiment. The dataset has undergone thorough preprocessing, including sentiment mapping and the removal of duplicate entries, to ensure data quality and consistency.
### Dataset Structure
- **Tweet**: The text of the tweet, providing insights into financial discussions.
- **Sentiment**: A numerical label indicating the sentiment of the tweet (1 for bullish, 2 for bearish, and 0 for neutral).
## Dataset Size
- **Bullish Sentiments**: 17,368
- **Bearish Sentiments**: 8,542
- **Neutral Sentiments**: 12,181
## Sources
This dataset is an amalgamation of data from various reputable sources, each contributing a unique perspective on financial sentiment:
- [FIQA Sentiment Classification](https://huggingface.co/datasets/ChanceFocus/fiqa-sentiment-classification): A sentiment analysis dataset with 721 positive, 379 negative, and 11 neutral sentiments.
- [Stock Market Tweets Data](https://ieee-dataport.org/open-access/stock-market-tweets-data): A collection of tweets with 523 positive, 420 neutral, and 341 negative sentiments.
- [Stock Related Tweet Sentiment](https://www.kaggle.com/datasets/mattgilgo/stock-related-tweet-sentiment): A dataset featuring 5005 positive, 741 neutral, and 736 negative sentiments.
- [Master Thesis Data](https://github.com/moritzwilksch/MasterThesis/tree/main): Includes 3711 positive, 2784 neutral, and 2167 negative sentiments.
- [Twitter Stock Sentiment](https://github.com/poojathakoor/twitter-stock-sentiment): Comprises 702 positive, 595 negative, and 481 neutral sentiments.
- [Crypto Sentiment](https://github.com/surge-ai/crypto-sentiment/tree/main): Sentiment data for cryptocurrency-related tweets with 296 positive and 256 negative sentiments.
- [Stock Sentiment](https://github.com/surge-ai/stock-sentiment/tree/main): Sentiment analysis on stock-related tweets, including 327 positive and 173 negative sentiments.
- [Stockmarket Sentiment Dataset](https://www.kaggle.com/datasets/yash612/stockmarket-sentiment-dataset): Features 3685 positive and 2106 negative sentiments.
- [Twitter Financial News Sentiment](https://huggingface.co/datasets/zeroshot/twitter-financial-news-sentiment): Contains 2398 positive, 1789 negative, and 7744 neutral sentiments.
## Usage
This dataset is ideal for training and evaluating machine learning models for sentiment analysis, especially those focused on understanding market trends and investor sentiment. It can be used for academic research, financial market analysis, and developing AI tools for financial institutions.
## Acknowledgments
We extend our heartfelt gratitude to all the authors and contributors of the original datasets. Their efforts in data collection and curation have been pivotal in creating this comprehensive resource.
## License
This dataset is made available under the MIT license, adhering to the licensing terms of the original datasets. |
Unbabel/TowerBlocks-v0.1 | ---
language:
- en
- de
- fr
- zh
- pt
- nl
- ru
- ko
- it
- es
size_categories:
- 100K<n<1M
task_categories:
- conversational
dataset_info:
features:
- name: conversations
list:
- name: from
dtype: string
- name: value
dtype: string
- name: lang
dtype: string
- name: split
dtype: string
- name: dataset
dtype: string
- name: task
dtype: string
splits:
- name: train
num_bytes: 1568822476
num_examples: 637495
download_size: 730580350
dataset_size: 1568822476
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for TowerBlocks
TowerBlocks is the dataset used to train [TowerInstruct-v0.1](https://huggingface.co/Unbabel/TowerInstruct-7B-v0.1), a language model specialized for translation tasks such as machine translation (e.g. general, document, terminology-aware or context-aware translation), automatic post edition, named-entity recognition, gramatical error correction, and paraphrase generation.
- **Curated by:** Unbabel, Instituto Superior Técnico, CentraleSupélec, University of Paris-Saclay;
- **Language(s) (NLP):** English, Portuguese, Spanish, French, German, Dutch, Italian, Korean, Chinese, Russian;
- **License:** TowerBlocks contains data from many sources. We refer to the respective data sources below for information regarding licensing of the data.
## Dataset Details
TowerBlocks is a conversational dataset for translation related tasks created from a diverse set of high quality data sources:
| Data Source | Task(s) |
| -------------- | ----------- |
| [WMT14 to WMT21](https://www.statmt.org/wmt22/results.html) | General Translation |
| [WMT22](https://github.com/microsoft/gpt-MT) | Few-shot General Translation w/ Quality Shots |
| [NTREX](https://github.com/MicrosoftTranslator/NTREX) | General Translation |
| [Flores Dev](https://github.com/facebookresearch/flores) | General Translation |
| [FRMT](https://github.com/google-research/google-research/tree/master/frmt) | General Translation |
| [QT21](https://lindat.mff.cuni.cz/repository/xmlui/handle/11372/LRT-2390) | General Translation, Automatic Post Edition |
| [ApeQuest](https://apequest.wordpress.com/) | General Translation, Automatic Post Edition |
| [OPUS (Quality Filtered)](https://opus.nlpl.eu/) | General Translation |
| [MT-GenEval](https://github.com/amazon-science/machine-translation-gender-eval) | General Translation, Context-Aware Translation |
| [WMT20 to WMT22 Metrics MQM](https://www.statmt.org/wmt22/results.html) | Machine Translation Evaluation |
| [WMT17 to WMT22 Metrics Direct Assessments](https://www.statmt.org/wmt22/results.html) | Machine Translation Evaluation |
| [WMT21 Terminology Dev (filtered)](https://www.statmt.org/wmt21/terminology-task.html) | Terminology-aware Translation |
| [Tatoeba Dev (filtered)](https://github.com/Helsinki-NLP/Tatoeba-Challenge) | Multi-reference Translation |
| [MultiCoNER 2022 and 2023 Dev](https://registry.opendata.aws/multiconer/) | Named-entity Recognition |
| [PAWS-X Dev](https://github.com/google-research-datasets/paws) | Paraphrase Generation |
| [UltraChat 200k (filtered)](https://huggingface.co/datasets/HuggingFaceH4/ultrachat_200k) | Synthetic Chat data |
| [Glaive Code Assistant (filtered)](https://huggingface.co/datasets/glaiveai/glaive-code-assistant) | Code instructions |
The dataset was built by generating user instructions with records from each data source using a set of zero- and few-shot templates (with the exception of UltraChat 200k and Glaive Code Assistant which already contain user instructions).
### Dataset features
* `conversations` - The user and assistant dialog turns;
* `dataset` - Original dataset for the record;
* `lang` - Either the language or language pair of the original dataset;
* `task` - Task for the record (Can be used to identify the training templates for each task);
* `split` - Split of the original dataset from which the record was taken.
## Intended uses and limitations
TowerBlocks is intended for specializing language models towards translation related tasks via supervised finetuning.
## Citation
```bibtex
@misc{tower_llm_2024,
title={Tower: An Open Multilingual Large Language Model for Translation-Related Tasks},
author={Duarte M. Alves and José Pombal and Nuno M. Guerreiro and Pedro H. Martins and João Alves and Amin Farajian and Ben Peters and Ricardo Rei and Patrick Fernandes and Sweta Agrawal and Pierre Colombo and José G. C. de Souza and André F. T. Martins},
year={2024},
eprint={2402.17733},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` |
dlproject/msp_val_hubert | ---
dataset_info:
features:
- name: input_values
sequence:
sequence:
sequence: float32
- name: labels
dtype: int64
splits:
- name: train
num_bytes: 1895848620
num_examples: 5213
download_size: 1773614710
dataset_size: 1895848620
---
# Dataset Card for "msp_val_hubert"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
koutch/staqc | ---
dataset_info:
- config_name: mca_python
features:
- name: id
dtype: int32
- name: question_id
dtype: int32
- name: question
dtype: string
- name: snippet
sequence: string
splits:
- name: train
num_bytes: 23286786
num_examples: 40391
download_size: 72054260
dataset_size: 23286786
- config_name: mca_sql
features:
- name: id
dtype: int32
- name: question_id
dtype: int32
- name: question
dtype: string
- name: snippet
sequence: string
splits:
- name: train
num_bytes: 15164206
num_examples: 26052
download_size: 50304531
dataset_size: 15164206
- config_name: sca_python
features:
- name: id
dtype: int32
- name: question_id
dtype: int32
- name: question
dtype: string
- name: snippet
dtype: string
splits:
- name: train
num_bytes: 39678168
num_examples: 85294
download_size: 47378850
dataset_size: 39678168
- config_name: sca_sql
features:
- name: id
dtype: int32
- name: question_id
dtype: int32
- name: question
dtype: string
- name: snippet
dtype: string
splits:
- name: train
num_bytes: 28656467
num_examples: 75637
download_size: 34194025
dataset_size: 28656467
- config_name: man_python
features:
- name: id
dtype: int32
- name: question_id
dtype: int32
- name: question
dtype: string
- name: snippet
sequence:
- name: text
dtype: string
- name: is_sda
dtype: bool
splits:
- name: train
num_bytes: 1445103
num_examples: 2052
download_size: 71250225
dataset_size: 1445103
- config_name: man_sql
features:
- name: id
dtype: int32
- name: question_id
dtype: int32
- name: question
dtype: string
- name: snippet
sequence:
- name: text
dtype: string
- name: is_sda
dtype: bool
splits:
- name: train
num_bytes: 1123721
num_examples: 1587
download_size: 49745860
dataset_size: 1123721
license: cc-by-4.0
task_categories:
- question-answering
language:
- en
tags:
- code
pretty_name: staqc
size_categories:
- 10K<n<100K
---
# Dataset Card for StaQC (A Systematically Mined Question-Code Dataset from Stack Overflow)
## Dataset Description
- **Homepage: [GitHub](https://github.com/LittleYUYU/StackOverflow-Question-Code-Dataset)**
- **Paper: [StaQC: A Systematically Mined Question-Code Dataset from Stack Overflow](https://arxiv.org/abs/1803.09371)**
### Dataset Summary
StaQC (Stack Overflow Question-Code pairs) is a large dataset of around 148K Python and 120K SQL domain question-code pairs,
which are automatically mined from Stack Overflow using a Bi-View Hierarchical Neural Network. StaQC is collected from three sources: multi-code answer posts, single-code answer posts, and manual annotations on multi-code answer posts.
The dataset was originally released by the main authors on [GitHub](https://github.com/LittleYUYU/StackOverflow-Question-Code-Dataset). This version is a *non-modified* redistributed copy (under the [license](#licensing-information) permission) made available on the hub for easier access.
#### Standalone solutions
As noted in the paper, the authors *define a code snippet as a code solution when the
questioner can solve the problem solely based on it (also named as
“standalone” solution).*
#### Manual annotations
The manual annotations are the collection of multi-code answer posts for which each code snippet was annotated with a boolean indicating whether or not the snippet is a *standalone solution* to the question.
#### Multi-code answer posts
A *Multi-code answer post* is an (accepted) answer post that contains multiple code snippets,
some of which may not be a *standalone* code solution to the question (see Section 1 in [paper](http://web.cse.ohio-state.edu/~sun.397/docs/StaQC-www18.pdf)).
For example, in [this multi-code answer post](https://stackoverflow.com/a/5996949),
the third code snippet is not a code solution to the question "How to limit a number to be within a specified range? (Python)".
Note: the multi-code answer posts contain also the manual annotations.
#### Single-code answer posts
A *Single-code answer post* is an (accepted) answer post that contains only one code snippet.
We pair such code snippets with the question title as a question-code pair.
### Supported Tasks and Leaderboards
This dataset can be used for Natural Language to Code Generation tasks.
### Languages
Python, SQL, English
## Dataset Structure
### Data Instances
Each configuration correspond to one of the three parts, in a given programming language.
There are three parts for the dataset:
- mca (Multi-code answer posts)
- sca (Single-code answer posts)
- man (Manual annotations)
And two programming/query languages:
- python
- sql
One can obtain obtain a configuration as a combination of a part in a programing language. For instance, one can obtain the automatically mined multi-code answers in python using:
```python
dataset = load_dataset("koutch/staqc", 'mca_python')
DatasetDict({
train: Dataset({
features: ['id', 'question_id', 'question', 'snippet'],
num_rows: 40391
})
})
```
or the manual annotations using:
```python
dataset = load_dataset("koutch/staqc", 'man_sql')
DatasetDict({
train: Dataset({
features: ['id', 'question_id', 'question', 'snippet'],
num_rows: 1587
})
})
```
#### Manual annotations
The manual annotations contain, for a given stackoverflow questions, for each individual code block in the accepted answer of that post, information on whether or not the given code block is a *standalone* solution to the question asked (the question title).
```
{
'question_id': 5947137,
'question': 'How can I use a list comprehension to extend a list in python?',
'snippet': {'text': ['import itertools as it\n\nreturn sum(it.imap(doSomething, originalList), [])\n',
'return sum(map(doSomething, originalList), [])\n',
'return sum((doSomething(x) for x in originalList), [])\n',
'accumulationList = []\nfor x in originalList:\n accumulationList.extend(doSomething(x))\nreturn accumulationList\n'],
'is_sda': [True, True, True, True]}
}
```
#### Multi-code answer posts
```
{
'question_id': 35349290,
'question': 'Python: Generating YYMM string between two dates',
'snippet': ['start_year = 2005\nend_year = 2007\nstart_month = 3\nend_month = 2\nyymm = [(yy, mm) for yy in range(start_year, end_year + 1) for mm in range(1, 13)\n if (start_year, start_month) <= (yy, mm) <= (end_year, end_month)]\n',
"formatted_yymm = ['{:>02}{:>02}.mat'.format(yy % 100, mm) for yy, mm in yymm]\n"]
}
```
#### Single-code answer posts
```
{
'question_id': 19387200,
'question': 'Python: get OS language',
'snippet': "import locale\nloc = locale.getlocale() # get current locale\nlocale.getdefaultlocale() # Tries to determine the default locale settings and returns them as a tuple of the form (language code, encoding); e.g, ('en_US', 'UTF-8').\n"
}
```
### Data Fields
- `question_id`: id of the stackoverflow question
- `question`: title of the stackoverflow question repurposed as the natural language intent
- `snippet`: mined or annotated standalone solution(s) (potentially) answerring the question
- `is_sda`: for the manual annotations, whether or not the given code snippet is a standalone solution to the question.
### Data Splits
Each configuration of the dataset contains only a training split.
## Dataset Creation
### Source Data
StackOverflow data dump.
### Annotations
See section 2.3 "Annotating QC Pairs for Model Training" of the [paper](https://arxiv.org/abs/1803.09371)
## Additional Information
### Licensing Information
<a rel="license" href="http://creativecommons.org/licenses/by/4.0/"><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by/4.0/88x31.png" /></a><br />This work is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a>.
### Citation Information
If you use the dataset or the code in your research, please cite the following paper:
```
@inproceedings{yao2018staqc,
title={StaQC: A Systematically Mined Question-Code Dataset from Stack Overflow},
author={Yao, Ziyu and Weld, Daniel S and Chen, Wei-Peng and Sun, Huan},
booktitle={Proceedings of the 2018 World Wide Web Conference on World Wide Web},
pages={1693--1703},
year={2018},
organization={International World Wide Web Conferences Steering Committee}
}
```
### Contributions information
I did *not* contribute to the *creation* of this dataset, only to the redistribution. All credits should be attributed to the original authors. |
BangumiBase/kobayashisanchinomaidragon | ---
license: mit
tags:
- art
size_categories:
- 1K<n<10K
---
# Bangumi Image Base of Kobayashi-san Chi No Maidragon
This is the image base of bangumi Kobayashi-san Chi no Maidragon, we detected 33 characters, 3524 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|
| 0 | 497 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 31 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 53 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 29 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 13 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 561 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 13 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 9 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 18 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 170 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 375 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 133 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 57 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 150 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 46 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 134 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 137 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 68 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 71 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 20 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 12 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 11 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 12 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 15 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 11 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| 25 | 11 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| 26 | 171 | [Download](26/dataset.zip) |  |  |  |  |  |  |  |  |
| 27 | 14 | [Download](27/dataset.zip) |  |  |  |  |  |  |  |  |
| 28 | 167 | [Download](28/dataset.zip) |  |  |  |  |  |  |  |  |
| 29 | 64 | [Download](29/dataset.zip) |  |  |  |  |  |  |  |  |
| 30 | 7 | [Download](30/dataset.zip) |  |  |  |  |  |  |  | N/A |
| 31 | 11 | [Download](31/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 433 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
Blockit/jaomic | ---
license: openrail
---
|
gzguevara/mr_potato_head_masked | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: image
dtype: image
- name: mask_0
dtype: image
- name: mask_1
dtype: image
- name: mask_2
dtype: image
splits:
- name: train
num_bytes: 1233151.0
num_examples: 4
- name: test
num_bytes: 561828.0
num_examples: 2
download_size: 1883534
dataset_size: 1794979.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
AdapterOcean/data-standardized_cluster_1_std | ---
dataset_info:
features:
- name: message
dtype: string
- name: message_type
dtype: string
- name: message_id
dtype: int64
- name: conversation_id
dtype: int64
- name: cluster
dtype: float64
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 8711883
num_examples: 7946
download_size: 3834980
dataset_size: 8711883
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "data-standardized_cluster_1_std"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
CyberHarem/konoe_kanata_loveliveschoolidolfestivalallstars | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of konoe_kanata/近江彼方/코노에카나타 (Love Live! School Idol Festival ALL STARS)
This is the dataset of konoe_kanata/近江彼方/코노에카나타 (Love Live! School Idol Festival ALL STARS), containing 500 images and their tags.
The core tags of this character are `long_hair, purple_eyes, bangs, orange_hair, breasts, hair_ornament, wavy_hair, brown_hair, large_breasts`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:-------------------------------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 500 | 888.58 MiB | [Download](https://huggingface.co/datasets/CyberHarem/konoe_kanata_loveliveschoolidolfestivalallstars/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 500 | 405.36 MiB | [Download](https://huggingface.co/datasets/CyberHarem/konoe_kanata_loveliveschoolidolfestivalallstars/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 1316 | 944.12 MiB | [Download](https://huggingface.co/datasets/CyberHarem/konoe_kanata_loveliveschoolidolfestivalallstars/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 500 | 736.37 MiB | [Download](https://huggingface.co/datasets/CyberHarem/konoe_kanata_loveliveschoolidolfestivalallstars/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 1316 | 1.50 GiB | [Download](https://huggingface.co/datasets/CyberHarem/konoe_kanata_loveliveschoolidolfestivalallstars/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/konoe_kanata_loveliveschoolidolfestivalallstars',
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 | 6 |  |  |  |  |  | 1girl, looking_at_viewer, midriff, navel, smile, solo, birthday, crown, detached_sleeves, double_bun, earrings, shorts, medium_breasts |
| 1 | 5 |  |  |  |  |  | 1girl, earrings, looking_at_viewer, smile, solo, witch_hat, black_gloves, blush, open_mouth, star_(symbol), upper_body |
| 2 | 9 |  |  |  |  |  | 1girl, looking_at_viewer, solo, blush, smile, upper_body, hat |
| 3 | 7 |  |  |  |  |  | 1girl, holding_umbrella, looking_at_viewer, smile, solo, white_background, blush, hair_between_eyes, simple_background, upper_body, closed_mouth, ribbon, white_dress, hair_bun, long_sleeves, wristwatch |
| 4 | 7 |  |  |  |  |  | 1girl, looking_at_viewer, nijigasaki_academy_school_uniform, simple_background, solo, white_background, blush, long_sleeves, open_mouth, plaid_skirt, smile, black_jacket, white_shirt, white_skirt, blazer, hairclip |
| 5 | 10 |  |  |  |  |  | 1girl, looking_at_viewer, nijigasaki_academy_school_uniform, solo, jacket, pillow_hug, blush, one_eye_closed, skirt, birthday, smile |
| 6 | 7 |  |  |  |  |  | 1girl, looking_at_viewer, solo, bikini, cleavage, hair_flower, twin_braids, cloud, earrings, navel, outdoors, sky, ocean, smile, upper_body |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | looking_at_viewer | midriff | navel | smile | solo | birthday | crown | detached_sleeves | double_bun | earrings | shorts | medium_breasts | witch_hat | black_gloves | blush | open_mouth | star_(symbol) | upper_body | hat | holding_umbrella | white_background | hair_between_eyes | simple_background | closed_mouth | ribbon | white_dress | hair_bun | long_sleeves | wristwatch | nijigasaki_academy_school_uniform | plaid_skirt | black_jacket | white_shirt | white_skirt | blazer | hairclip | jacket | pillow_hug | one_eye_closed | skirt | bikini | cleavage | hair_flower | twin_braids | cloud | outdoors | sky | ocean |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------------------|:----------|:--------|:--------|:-------|:-----------|:--------|:-------------------|:-------------|:-----------|:---------|:-----------------|:------------|:---------------|:--------|:-------------|:----------------|:-------------|:------|:-------------------|:-------------------|:--------------------|:--------------------|:---------------|:---------|:--------------|:-----------|:---------------|:-------------|:------------------------------------|:--------------|:---------------|:--------------|:--------------|:---------|:-----------|:---------|:-------------|:-----------------|:--------|:---------|:-----------|:--------------|:--------------|:--------|:-----------|:------|:--------|
| 0 | 6 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 5 |  |  |  |  |  | X | X | | | X | X | | | | | X | | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 9 |  |  |  |  |  | X | X | | | X | X | | | | | | | | | | X | | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 7 |  |  |  |  |  | X | X | | | X | X | | | | | | | | | | X | | | X | | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | |
| 4 | 7 |  |  |  |  |  | X | X | | | X | X | | | | | | | | | | X | X | | | | | X | | X | | | | | X | | X | X | X | X | X | X | X | | | | | | | | | | | | |
| 5 | 10 |  |  |  |  |  | X | X | | | X | X | X | | | | | | | | | X | | | | | | | | | | | | | | | X | | | | | | | X | X | X | X | | | | | | | | |
| 6 | 7 |  |  |  |  |  | X | X | | X | X | X | | | | | X | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X |
|
kgr123/quality_counter_1536_4_uniq | ---
dataset_info:
features:
- name: context
dtype: string
- name: word
dtype: string
- name: claim
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_bytes: 550553150
num_examples: 20000
- name: validation
num_bytes: 226698164
num_examples: 8000
- name: test
num_bytes: 56238416
num_examples: 2300
download_size: 26414757
dataset_size: 833489730
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
|
allenai/ms2_dense_mean | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|other-MS^2
- extended|other-Cochrane
task_categories:
- summarization
- text2text-generation
paperswithcode_id: multi-document-summarization
pretty_name: MSLR Shared Task
---
This is a copy of the [MS^2](https://huggingface.co/datasets/allenai/mslr2022) dataset, except the input source documents of its `train`, `validation` and `test` splits have been replaced by a __dense__ retriever. The retrieval pipeline used:
- __query__: The `background` field of each example
- __corpus__: The union of all documents in the `train`, `validation` and `test` splits. A document is the concatenation of the `title` and `abstract`.
- __retriever__: [`facebook/contriever-msmarco`](https://huggingface.co/facebook/contriever-msmarco) via [PyTerrier](https://pyterrier.readthedocs.io/en/latest/) with default settings
- __top-k strategy__: `"max"`, i.e. the number of documents retrieved, `k`, is set as the maximum number of documents seen across examples in this dataset, in this case `k==17`
Retrieval results on the `train` set:
| Recall@100 | Rprec | Precision@k | Recall@k |
| ----------- | ----------- | ----------- | ----------- |
| 0.4764 | 0.2395 | 0.2271 | 0.2418 |
Retrieval results on the `validation` set:
| Recall@100 | Rprec | Precision@k | Recall@k |
| ----------- | ----------- | ----------- | ----------- |
| 0.4364 | 0.2125 | 0.2131 | 0.2074 |
Retrieval results on the `test` set:
| Recall@100 | Rprec | Precision@k | Recall@k |
| ----------- | ----------- | ----------- | ----------- |
| 0.4481 | 0.2224 | 0.2254 | 0.2100 | |
DopeorNope/SSL_dup | ---
dataset_info:
features:
- name: input
dtype: string
- name: output
dtype: string
- name: instruction
dtype: string
- name: tag
dtype: string
splits:
- name: train
num_bytes: 2010363944
num_examples: 486065
download_size: 996251879
dataset_size: 2010363944
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
aertit/xglmV2 | ---
dataset_info:
features:
- name: text
dtype: string
- name: nb_token
dtype: int64
- name: metadata
dtype: string
splits:
- name: train
num_bytes: 14007291.2
num_examples: 8000
- name: test
num_bytes: 3501822.8
num_examples: 2000
download_size: 6807588
dataset_size: 17509114.0
---
# Dataset Card for "xglmV2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Atipico1/NQ-colbert | ---
dataset_info:
features:
- name: question
dtype: string
- name: answers
sequence: string
- name: ctxs
list:
- name: hasanswer
dtype: bool
- name: score
dtype: float64
- name: text
dtype: string
- name: title
dtype: string
splits:
- name: train
num_bytes: 290367640
num_examples: 87925
- name: test
num_bytes: 12000594
num_examples: 3610
download_size: 176894459
dataset_size: 302368234
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
CyberHarem/highmore_arknights | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of highmore/ハイモア/海沫 (Arknights)
This is the dataset of highmore/ハイモア/海沫 (Arknights), containing 67 images and their tags.
The core tags of this character are `long_hair, black_hair, pointy_ears, blue_eyes, breasts, very_long_hair, multicolored_hair, horns`, 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 | 67 | 145.37 MiB | [Download](https://huggingface.co/datasets/CyberHarem/highmore_arknights/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 1200 | 67 | 115.24 MiB | [Download](https://huggingface.co/datasets/CyberHarem/highmore_arknights/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 181 | 217.60 MiB | [Download](https://huggingface.co/datasets/CyberHarem/highmore_arknights/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/highmore_arknights',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 30 |  |  |  |  |  | 1girl, solo, bare_shoulders, looking_at_viewer, white_dress, long_sleeves, black_dress, white_thighhighs, garter_straps, detached_sleeves, bubble, holding_scythe, holding_weapon, white_background |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | bare_shoulders | looking_at_viewer | white_dress | long_sleeves | black_dress | white_thighhighs | garter_straps | detached_sleeves | bubble | holding_scythe | holding_weapon | white_background |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:-----------------|:--------------------|:--------------|:---------------|:--------------|:-------------------|:----------------|:-------------------|:---------|:-----------------|:-----------------|:-------------------|
| 0 | 30 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
|
ylacombe/cml-tts | ---
language:
- nl
- fr
- de
- it
- pl
- pt
- es
license: cc-by-4.0
size_categories:
- 1M<n<10M
task_categories:
- text-to-speech
- text-to-audio
pretty_name: CML-TTS
dataset_info:
- config_name: dutch
features:
- name: audio
dtype: audio
- name: wav_filesize
dtype: int64
- name: text
dtype: string
- name: transcript_wav2vec
dtype: string
- name: levenshtein
dtype: float64
- name: duration
dtype: float64
- name: num_words
dtype: int64
- name: speaker_id
dtype: int64
splits:
- name: train
num_bytes: 186374683541.98
num_examples: 309785
- name: dev
num_bytes: 2912063172.928
num_examples: 4834
- name: test
num_bytes: 2757891736.78
num_examples: 4570
download_size: 132987704971
dataset_size: 192044638451.68802
- config_name: french
features:
- name: audio
dtype: audio
- name: wav_filesize
dtype: int64
- name: text
dtype: string
- name: transcript_wav2vec
dtype: string
- name: levenshtein
dtype: float64
- name: duration
dtype: float64
- name: num_words
dtype: int64
- name: speaker_id
dtype: int64
splits:
- name: train
num_bytes: 64984002840.768
num_examples: 107598
- name: dev
num_bytes: 2257393207.796
num_examples: 3739
- name: test
num_bytes: 2281630546.306
num_examples: 3763
download_size: 48345998335
dataset_size: 69523026594.87
- config_name: german
features:
- name: audio
dtype: audio
- name: wav_filesize
dtype: int64
- name: text
dtype: string
- name: transcript_wav2vec
dtype: string
- name: levenshtein
dtype: float64
- name: duration
dtype: float64
- name: num_words
dtype: int64
- name: speaker_id
dtype: int64
splits:
- name: train
num_bytes: 369052038020.872
num_examples: 608296
- name: dev
num_bytes: 3197115278.604
num_examples: 5314
- name: test
num_bytes: 3288183839.092
num_examples: 5466
download_size: 280438261836
dataset_size: 375537337138.568
- config_name: italian
features:
- name: audio
dtype: audio
- name: wav_filesize
dtype: int64
- name: text
dtype: string
- name: transcript_wav2vec
dtype: string
- name: levenshtein
dtype: float64
- name: duration
dtype: float64
- name: num_words
dtype: int64
- name: speaker_id
dtype: int64
splits:
- name: train
num_bytes: 30242801015.92
num_examples: 50345
- name: dev
num_bytes: 938644924.81
num_examples: 1765
- name: test
num_bytes: 979116355.51
num_examples: 1835
download_size: 21996805791
dataset_size: 32160562296.239998
- config_name: polish
features:
- name: audio
dtype: audio
- name: wav_filesize
dtype: int64
- name: text
dtype: string
- name: transcript_wav2vec
dtype: string
- name: levenshtein
dtype: float64
- name: duration
dtype: float64
- name: num_words
dtype: int64
- name: speaker_id
dtype: int64
splits:
- name: train
num_bytes: 11127461686.356
num_examples: 18719
- name: dev
num_bytes: 356048249
num_examples: 853
- name: test
num_bytes: 367796887
num_examples: 814
download_size: 8114633186
dataset_size: 11851306822.356
- config_name: portuguese
features:
- name: audio
dtype: audio
- name: wav_filesize
dtype: int64
- name: text
dtype: string
- name: transcript_wav2vec
dtype: string
- name: levenshtein
dtype: float64
- name: duration
dtype: float64
- name: num_words
dtype: int64
- name: speaker_id
dtype: int64
splits:
- name: train
num_bytes: 20722423371.0
num_examples: 34265
- name: dev
num_bytes: 622824524.224
num_examples: 1134
- name: test
num_bytes: 673141068.9
num_examples: 1297
download_size: 14421097659
dataset_size: 22018388964.124
- config_name: spanish
features:
- name: audio
dtype: audio
- name: wav_filesize
dtype: int64
- name: text
dtype: string
- name: transcript_wav2vec
dtype: string
- name: levenshtein
dtype: float64
- name: duration
dtype: float64
- name: num_words
dtype: int64
- name: speaker_id
dtype: int64
splits:
- name: train
num_bytes: 101377452063.176
num_examples: 168524
- name: dev
num_bytes: 1882729515.184
num_examples: 3148
- name: test
num_bytes: 1851592818.0
num_examples: 3080
download_size: 73687756096
dataset_size: 105111774396.36
configs:
- config_name: dutch
data_files:
- split: train
path: dutch/train-*
- split: dev
path: dutch/dev-*
- split: test
path: dutch/test-*
- config_name: french
data_files:
- split: train
path: french/train-*
- split: dev
path: french/dev-*
- split: test
path: french/test-*
- config_name: german
data_files:
- split: train
path: german/train-*
- split: dev
path: german/dev-*
- split: test
path: german/test-*
- config_name: italian
data_files:
- split: train
path: italian/train-*
- split: dev
path: italian/dev-*
- split: test
path: italian/test-*
- config_name: polish
data_files:
- split: train
path: polish/train-*
- split: dev
path: polish/dev-*
- split: test
path: polish/test-*
- config_name: portuguese
data_files:
- split: train
path: portuguese/train-*
- split: dev
path: portuguese/dev-*
- split: test
path: portuguese/test-*
- config_name: spanish
data_files:
- split: train
path: spanish/train-*
- split: dev
path: spanish/dev-*
- split: test
path: spanish/test-*
---
# Dataset Card for CML-TTS
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks)
- [Languages](#languages)
- [How to use](#how-to-use)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Data Statistics](#data-statistics)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [MultiLingual LibriSpeech ASR corpus](https://www.openslr.org/146/)
- **Repository:** [CML-TTS-Dataset](https://github.com/freds0/CML-TTS-Dataset)
- **Paper:** [CML-TTS A Multilingual Dataset for Speech Synthesis in Low-Resource Languages](https://arxiv.org/abs/2306.10097)
### Dataset Summary
CML-TTS is a recursive acronym for CML-Multi-Lingual-TTS, a Text-to-Speech (TTS) dataset developed at the Center of Excellence in Artificial Intelligence (CEIA) of the Federal University of Goias (UFG).
CML-TTS is a dataset comprising audiobooks sourced from the public domain books of Project Gutenberg, read by volunteers from the LibriVox project. The dataset includes recordings in Dutch, German, French, Italian, Polish, Portuguese, and Spanish, all at a sampling rate of 24kHz.
The data archives were restructured from the original ones from [OpenSLR](http://www.openslr.org/146) to make it easier to stream.
### Supported Tasks
- `text-to-speech`, `text-to-audio`: The dataset can also be used to train a model for Text-To-Speech (TTS).
### Languages
The dataset includes recordings in Dutch, German, French, Italian, Polish, Portuguese, and Spanish, all at a sampling rate of 24kHz.
### How to use
The `datasets` library allows you to load and pre-process your dataset in pure Python, at scale. The dataset can be downloaded and prepared in one call to your local drive by using the `load_dataset` function.
For example, to download the German config, simply specify the corresponding language config name (i.e., "german" for German):
```python
from datasets import load_dataset
mls = load_dataset("ylacombe/cml-tts", "german", split="train")
```
Using the datasets library, you can also stream the dataset on-the-fly by adding a `streaming=True` argument to the `load_dataset` function call. Loading a dataset in streaming mode loads individual samples of the dataset at a time, rather than downloading the entire dataset to disk.
```python
from datasets import load_dataset
mls = load_dataset("ylacombe/cml-tts", "german", split="train", streaming=True)
print(next(iter(mls)))
```
#### *Bonus*
You can create a [PyTorch dataloader](https://huggingface.co/docs/datasets/use_with_pytorch) directly with your own datasets (local/streamed).
**Local:**
```python
from datasets import load_dataset
from torch.utils.data.sampler import BatchSampler, RandomSampler
mls = load_dataset("ylacombe/cml-tts", "german", split="train")
batch_sampler = BatchSampler(RandomSampler(mls), batch_size=32, drop_last=False)
dataloader = DataLoader(mls, batch_sampler=batch_sampler)
```
**Streaming:**
```python
from datasets import load_dataset
from torch.utils.data import DataLoader
mls = load_dataset("ylacombe/cml-tts", "german", split="train", streaming=True)
dataloader = DataLoader(mls, batch_size=32)
```
To find out more about loading and preparing audio datasets, head over to [hf.co/blog/audio-datasets](https://huggingface.co/blog/audio-datasets).
## Dataset Structure
### Data Instances
A typical data point comprises the path to the audio file, usually called `file` and its transcription, called `text`. Some additional information about the speaker and the passage which contains the transcription is provided.
```
{'audio': {'path': '6892_8912_000729.wav', 'array': array([-1.52587891e-...7344e-05]), 'sampling_rate': 24000}, 'wav_filesize': 601964, 'text': 'Proszę pana, tu pano... zdziwiony', 'transcript_wav2vec': 'proszę pana tu panow... zdziwiony', 'levenshtein': 0.96045197740113, 'duration': 13.648979591836737, 'num_words': 29, 'speaker_id': 6892}
```
### Data Fields
- audio: A dictionary containing the audio filename, 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]`.
- text: the transcription of the audio file.
- speaker_id: unique id of the speaker. The same speaker id can be found for multiple data samples.
- transcript_wav2vec: the transcription of the audio file using the wav2vec model. Has been used to curate the dataset.
- wav_filesize: The size of the audio waveform file. Has been used to curate the dataset.
- levenshtein: The [Levenshtein distance](https://en.wikipedia.org/wiki/Levenshtein_distance) between the wav2vec transcription and the original transcription. Has been used to curate the dataset.
- duration: The duration of the audio in seconds.
- num_words: The number of words of the transcription.
### Data Splits
| # Samples | Train | Dev | Test |
|------------|--------|------|------|
| german | 608296 | 5314 | 5466 |
| dutch | 309785 | 4834 | 4570 |
| french | 107598 | 3739 | 3763 |
| spanish | 168524 | 3148 | 3080 |
| italian | 50345 | 1765 | 1835 |
| portuguese | 34265 | 1134 | 1297 |
| polish | 18719 | 853 | 814 |
### Data Statistics
| Language | Duration (Train) | Duration (Test) | Duration (Dev) | Speakers (Train) | Speakers (Test) | Speakers (Dev) |
|------------|-------------------|------------------|----------------|------------------|-----------------|----------------|
| | M | F | M | F | M | F | M | F | M | F | M | F |
| Dutch | 482.82 | 162.17 | 2.46 | 1.29 | 2.24 | 1.67 | 8 | 27 | 3 | 3 | 2 | 4 |
| French | 260.08 | 24.04 | 2.48 | 3.55 | 3.31 | 2.72 | 25 | 20 | 8 | 9 | 10 | 8 |
| German | 1128.96 | 436.64 | 3.75 | 5.27 | 4.31 | 5.03 | 78 | 90 | 13 | 17 | 13 | 15 |
| Italian | 73.78 | 57.51 | 1.47 | 0.85 | 0.40 | 1.52 | 23 | 38 | 5 | 5 | 4 | 6 |
| Polish | 30.61 | 8.32 | 0.70 | 0.90 | 0.56 | 0.80 | 4 | 4 | 2 | 2 | 2 | 2 |
| Portuguese | 23.14 | 44.81 | 0.28 | 0.24 | 0.68 | 0.20 | 20 | 10 | 5 | 4 | 6 | 3 |
| Spanish | 279.15 | 164.08 | 2.77 | 2.06 | 3.40 | 2.34 | 35 | 42 | 10 | 8 | 11 | 9 |
| Total | 3,176.13| | 28.11 | | 29.19 | | 424 | | 94 | | 95 | |
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in this dataset.
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
Public Domain, Creative Commons Attribution 4.0 International Public License ([CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/legalcode))
### Citation Information
```
@misc{oliveira2023cmltts,
title={CML-TTS A Multilingual Dataset for Speech Synthesis in Low-Resource Languages},
author={Frederico S. Oliveira and Edresson Casanova and Arnaldo Cândido Júnior and Anderson S. Soares and Arlindo R. Galvão Filho},
year={2023},
eprint={2306.10097},
archivePrefix={arXiv},
primaryClass={eess.AS}
}
```
### Contributions
Thanks to [@ylacombe](https://github.com/ylacombe) for adding this dataset.
|
AdapterOcean/med_alpaca_standardized_cluster_67_alpaca | ---
dataset_info:
features:
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 15463853
num_examples: 7726
download_size: 9140061
dataset_size: 15463853
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "med_alpaca_standardized_cluster_67_alpaca"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
RikoteMaster/dataset_dair_ai_4_llama2 | ---
dataset_info:
features:
- name: Text_processed
dtype: string
- name: Emotion
dtype: string
- name: text
dtype: string
- name: Augmented
dtype: bool
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 7397641
num_examples: 17640
download_size: 0
dataset_size: 7397641
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "dataset_dair_ai_4_llama2_v2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
open-llm-leaderboard/details_xxyyy123__mc_data_30k_from_platpus_orca_7b_10k_v1_lora_qkvo_rank14_v2 | ---
pretty_name: Evaluation run of xxyyy123/mc_data_30k_from_platpus_orca_7b_10k_v1_lora_qkvo_rank14_v2
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [xxyyy123/mc_data_30k_from_platpus_orca_7b_10k_v1_lora_qkvo_rank14_v2](https://huggingface.co/xxyyy123/mc_data_30k_from_platpus_orca_7b_10k_v1_lora_qkvo_rank14_v2)\
\ 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_xxyyy123__mc_data_30k_from_platpus_orca_7b_10k_v1_lora_qkvo_rank14_v2\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-09-17T16:41:24.154084](https://huggingface.co/datasets/open-llm-leaderboard/details_xxyyy123__mc_data_30k_from_platpus_orca_7b_10k_v1_lora_qkvo_rank14_v2/blob/main/results_2023-09-17T16-41-24.154084.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\": 0.272126677852349,\n\
\ \"em_stderr\": 0.004557777416899833,\n \"f1\": 0.34851929530201453,\n\
\ \"f1_stderr\": 0.004500795514577557,\n \"acc\": 0.36653625926220684,\n\
\ \"acc_stderr\": 0.007090302750388251\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.272126677852349,\n \"em_stderr\": 0.004557777416899833,\n\
\ \"f1\": 0.34851929530201453,\n \"f1_stderr\": 0.004500795514577557\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0037907505686125853,\n \
\ \"acc_stderr\": 0.0016927007401502038\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7292817679558011,\n \"acc_stderr\": 0.012487904760626299\n\
\ }\n}\n```"
repo_url: https://huggingface.co/xxyyy123/mc_data_30k_from_platpus_orca_7b_10k_v1_lora_qkvo_rank14_v2
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_09_03T18_33_19.019825
path:
- '**/details_harness|arc:challenge|25_2023-09-03T18:33:19.019825.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-09-03T18:33:19.019825.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_09_17T16_41_24.154084
path:
- '**/details_harness|drop|3_2023-09-17T16-41-24.154084.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-17T16-41-24.154084.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_17T16_41_24.154084
path:
- '**/details_harness|gsm8k|5_2023-09-17T16-41-24.154084.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-09-17T16-41-24.154084.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_09_03T18_33_19.019825
path:
- '**/details_harness|hellaswag|10_2023-09-03T18:33:19.019825.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-09-03T18:33:19.019825.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_09_03T18_33_19.019825
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-03T18:33:19.019825.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-03T18:33:19.019825.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-03T18:33:19.019825.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-03T18:33:19.019825.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-03T18:33:19.019825.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-03T18:33:19.019825.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-03T18:33:19.019825.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-03T18:33:19.019825.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-03T18:33:19.019825.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-03T18:33:19.019825.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-03T18:33:19.019825.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-03T18:33:19.019825.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-03T18:33:19.019825.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-03T18:33:19.019825.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-03T18:33:19.019825.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-03T18:33:19.019825.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-03T18:33:19.019825.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-03T18:33:19.019825.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-03T18:33:19.019825.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-03T18:33:19.019825.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-03T18:33:19.019825.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-03T18:33:19.019825.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-03T18:33:19.019825.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-03T18:33:19.019825.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-03T18:33:19.019825.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-03T18:33:19.019825.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-03T18:33:19.019825.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-03T18:33:19.019825.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-03T18:33:19.019825.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-03T18:33:19.019825.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-03T18:33:19.019825.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-03T18:33:19.019825.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-03T18:33:19.019825.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-03T18:33:19.019825.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-09-03T18:33:19.019825.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-03T18:33:19.019825.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-03T18:33:19.019825.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-03T18:33:19.019825.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-09-03T18:33:19.019825.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-09-03T18:33:19.019825.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-03T18:33:19.019825.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-03T18:33:19.019825.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-03T18:33:19.019825.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-03T18:33:19.019825.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-03T18:33:19.019825.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-03T18:33:19.019825.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-03T18:33:19.019825.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-03T18:33:19.019825.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-03T18:33:19.019825.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-03T18:33:19.019825.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-03T18:33:19.019825.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-03T18:33:19.019825.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-03T18:33:19.019825.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-09-03T18:33:19.019825.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-03T18:33:19.019825.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-09-03T18:33:19.019825.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-03T18:33:19.019825.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-03T18:33:19.019825.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-03T18:33:19.019825.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-03T18:33:19.019825.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-03T18:33:19.019825.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-03T18:33:19.019825.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-03T18:33:19.019825.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-03T18:33:19.019825.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-03T18:33:19.019825.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-03T18:33:19.019825.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-03T18:33:19.019825.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-03T18:33:19.019825.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-03T18:33:19.019825.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-03T18:33:19.019825.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-03T18:33:19.019825.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-03T18:33:19.019825.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-03T18:33:19.019825.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-03T18:33:19.019825.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-03T18:33:19.019825.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-03T18:33:19.019825.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-03T18:33:19.019825.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-03T18:33:19.019825.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-03T18:33:19.019825.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-03T18:33:19.019825.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-03T18:33:19.019825.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-03T18:33:19.019825.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-03T18:33:19.019825.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-03T18:33:19.019825.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-03T18:33:19.019825.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-03T18:33:19.019825.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-03T18:33:19.019825.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-03T18:33:19.019825.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-03T18:33:19.019825.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-03T18:33:19.019825.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-03T18:33:19.019825.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-09-03T18:33:19.019825.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-03T18:33:19.019825.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-03T18:33:19.019825.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-03T18:33:19.019825.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-09-03T18:33:19.019825.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-09-03T18:33:19.019825.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-03T18:33:19.019825.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-03T18:33:19.019825.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-03T18:33:19.019825.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-03T18:33:19.019825.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-03T18:33:19.019825.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-03T18:33:19.019825.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-03T18:33:19.019825.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-03T18:33:19.019825.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-03T18:33:19.019825.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-03T18:33:19.019825.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-03T18:33:19.019825.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-03T18:33:19.019825.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-03T18:33:19.019825.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-09-03T18:33:19.019825.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-03T18:33:19.019825.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-09-03T18:33:19.019825.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-03T18:33:19.019825.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_09_03T18_33_19.019825
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-03T18:33:19.019825.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-03T18:33:19.019825.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_09_03T18_33_19.019825
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-03T18:33:19.019825.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-03T18:33:19.019825.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_09_03T18_33_19.019825
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-03T18:33:19.019825.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-03T18:33:19.019825.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_09_03T18_33_19.019825
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-03T18:33:19.019825.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-03T18:33:19.019825.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_09_03T18_33_19.019825
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-03T18:33:19.019825.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-03T18:33:19.019825.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_09_03T18_33_19.019825
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-03T18:33:19.019825.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-03T18:33:19.019825.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_09_03T18_33_19.019825
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-03T18:33:19.019825.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-03T18:33:19.019825.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_09_03T18_33_19.019825
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-03T18:33:19.019825.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-03T18:33:19.019825.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_09_03T18_33_19.019825
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-03T18:33:19.019825.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-03T18:33:19.019825.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_09_03T18_33_19.019825
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-03T18:33:19.019825.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-03T18:33:19.019825.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_09_03T18_33_19.019825
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-03T18:33:19.019825.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-03T18:33:19.019825.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_09_03T18_33_19.019825
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-03T18:33:19.019825.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-03T18:33:19.019825.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_09_03T18_33_19.019825
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-03T18:33:19.019825.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-03T18:33:19.019825.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_09_03T18_33_19.019825
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-03T18:33:19.019825.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-03T18:33:19.019825.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_09_03T18_33_19.019825
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-03T18:33:19.019825.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-03T18:33:19.019825.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_09_03T18_33_19.019825
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-03T18:33:19.019825.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-03T18:33:19.019825.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_09_03T18_33_19.019825
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-03T18:33:19.019825.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-03T18:33:19.019825.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_09_03T18_33_19.019825
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-03T18:33:19.019825.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-03T18:33:19.019825.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_09_03T18_33_19.019825
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-03T18:33:19.019825.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-03T18:33:19.019825.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_09_03T18_33_19.019825
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-03T18:33:19.019825.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-03T18:33:19.019825.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_09_03T18_33_19.019825
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-03T18:33:19.019825.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-03T18:33:19.019825.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_09_03T18_33_19.019825
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-03T18:33:19.019825.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-03T18:33:19.019825.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_09_03T18_33_19.019825
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-03T18:33:19.019825.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-03T18:33:19.019825.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_09_03T18_33_19.019825
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-03T18:33:19.019825.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-03T18:33:19.019825.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_09_03T18_33_19.019825
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-03T18:33:19.019825.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-03T18:33:19.019825.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_09_03T18_33_19.019825
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-03T18:33:19.019825.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-03T18:33:19.019825.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_09_03T18_33_19.019825
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-03T18:33:19.019825.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-03T18:33:19.019825.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_09_03T18_33_19.019825
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-03T18:33:19.019825.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-03T18:33:19.019825.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_09_03T18_33_19.019825
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-03T18:33:19.019825.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-03T18:33:19.019825.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_09_03T18_33_19.019825
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-03T18:33:19.019825.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-03T18:33:19.019825.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_09_03T18_33_19.019825
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-03T18:33:19.019825.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-03T18:33:19.019825.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_09_03T18_33_19.019825
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-03T18:33:19.019825.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-03T18:33:19.019825.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_09_03T18_33_19.019825
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-03T18:33:19.019825.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-03T18:33:19.019825.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_09_03T18_33_19.019825
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-03T18:33:19.019825.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-03T18:33:19.019825.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_09_03T18_33_19.019825
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-09-03T18:33:19.019825.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-09-03T18:33:19.019825.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_09_03T18_33_19.019825
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-03T18:33:19.019825.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-03T18:33:19.019825.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_09_03T18_33_19.019825
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-03T18:33:19.019825.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-03T18:33:19.019825.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_09_03T18_33_19.019825
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-03T18:33:19.019825.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-03T18:33:19.019825.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_09_03T18_33_19.019825
path:
- '**/details_harness|hendrycksTest-management|5_2023-09-03T18:33:19.019825.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-09-03T18:33:19.019825.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_09_03T18_33_19.019825
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-09-03T18:33:19.019825.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-09-03T18:33:19.019825.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_09_03T18_33_19.019825
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-03T18:33:19.019825.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-03T18:33:19.019825.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_09_03T18_33_19.019825
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-03T18:33:19.019825.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-03T18:33:19.019825.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_09_03T18_33_19.019825
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-03T18:33:19.019825.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-03T18:33:19.019825.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_09_03T18_33_19.019825
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-03T18:33:19.019825.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-03T18:33:19.019825.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_09_03T18_33_19.019825
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-03T18:33:19.019825.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-03T18:33:19.019825.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_09_03T18_33_19.019825
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-03T18:33:19.019825.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-03T18:33:19.019825.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_09_03T18_33_19.019825
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-03T18:33:19.019825.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-03T18:33:19.019825.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_09_03T18_33_19.019825
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-03T18:33:19.019825.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-03T18:33:19.019825.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_09_03T18_33_19.019825
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-03T18:33:19.019825.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-03T18:33:19.019825.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_09_03T18_33_19.019825
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-03T18:33:19.019825.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-03T18:33:19.019825.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_09_03T18_33_19.019825
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-03T18:33:19.019825.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-03T18:33:19.019825.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_09_03T18_33_19.019825
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-03T18:33:19.019825.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-03T18:33:19.019825.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_09_03T18_33_19.019825
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-03T18:33:19.019825.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-03T18:33:19.019825.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_09_03T18_33_19.019825
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-09-03T18:33:19.019825.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-09-03T18:33:19.019825.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_09_03T18_33_19.019825
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-03T18:33:19.019825.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-03T18:33:19.019825.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_09_03T18_33_19.019825
path:
- '**/details_harness|hendrycksTest-virology|5_2023-09-03T18:33:19.019825.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-09-03T18:33:19.019825.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_09_03T18_33_19.019825
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-03T18:33:19.019825.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-03T18:33:19.019825.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_09_03T18_33_19.019825
path:
- '**/details_harness|truthfulqa:mc|0_2023-09-03T18:33:19.019825.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-09-03T18:33:19.019825.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_09_17T16_41_24.154084
path:
- '**/details_harness|winogrande|5_2023-09-17T16-41-24.154084.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-09-17T16-41-24.154084.parquet'
- config_name: results
data_files:
- split: 2023_09_03T18_33_19.019825
path:
- results_2023-09-03T18:33:19.019825.parquet
- split: 2023_09_17T16_41_24.154084
path:
- results_2023-09-17T16-41-24.154084.parquet
- split: latest
path:
- results_2023-09-17T16-41-24.154084.parquet
---
# Dataset Card for Evaluation run of xxyyy123/mc_data_30k_from_platpus_orca_7b_10k_v1_lora_qkvo_rank14_v2
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/xxyyy123/mc_data_30k_from_platpus_orca_7b_10k_v1_lora_qkvo_rank14_v2
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** clementine@hf.co
### Dataset Summary
Dataset automatically created during the evaluation run of model [xxyyy123/mc_data_30k_from_platpus_orca_7b_10k_v1_lora_qkvo_rank14_v2](https://huggingface.co/xxyyy123/mc_data_30k_from_platpus_orca_7b_10k_v1_lora_qkvo_rank14_v2) 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_xxyyy123__mc_data_30k_from_platpus_orca_7b_10k_v1_lora_qkvo_rank14_v2",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-17T16:41:24.154084](https://huggingface.co/datasets/open-llm-leaderboard/details_xxyyy123__mc_data_30k_from_platpus_orca_7b_10k_v1_lora_qkvo_rank14_v2/blob/main/results_2023-09-17T16-41-24.154084.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": 0.272126677852349,
"em_stderr": 0.004557777416899833,
"f1": 0.34851929530201453,
"f1_stderr": 0.004500795514577557,
"acc": 0.36653625926220684,
"acc_stderr": 0.007090302750388251
},
"harness|drop|3": {
"em": 0.272126677852349,
"em_stderr": 0.004557777416899833,
"f1": 0.34851929530201453,
"f1_stderr": 0.004500795514577557
},
"harness|gsm8k|5": {
"acc": 0.0037907505686125853,
"acc_stderr": 0.0016927007401502038
},
"harness|winogrande|5": {
"acc": 0.7292817679558011,
"acc_stderr": 0.012487904760626299
}
}
```
### 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] |
CyberHarem/kaenbyou_rin_touhou | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of kaenbyou_rin/火焔猫燐/카엔뵤린 (Touhou)
This is the dataset of kaenbyou_rin/火焔猫燐/카엔뵤린 (Touhou), containing 500 images and their tags.
The core tags of this character are `red_hair, animal_ears, cat_ears, red_eyes, braid, twin_braids, bow, hair_bow, tail, cat_tail, multiple_tails, long_hair, extra_ears, ribbon, twintails`, 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 | 500 | 604.89 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kaenbyou_rin_touhou/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 500 | 377.72 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kaenbyou_rin_touhou/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 1105 | 735.85 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kaenbyou_rin_touhou/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 500 | 545.21 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kaenbyou_rin_touhou/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 1105 | 984.83 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kaenbyou_rin_touhou/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/kaenbyou_rin_touhou',
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 | 6 |  |  |  |  |  | 1girl, bangs, black_bow, looking_at_viewer, simple_background, solo, white_background, :d, blush, breasts, green_dress, juliet_sleeves, open_mouth, animal_ear_fluff, fang, nekomata, two_tails, paw_pose |
| 1 | 18 |  |  |  |  |  | 1girl, solo, skull, smile, dress |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | bangs | black_bow | looking_at_viewer | simple_background | solo | white_background | :d | blush | breasts | green_dress | juliet_sleeves | open_mouth | animal_ear_fluff | fang | nekomata | two_tails | paw_pose | skull | smile | dress |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------|:------------|:--------------------|:--------------------|:-------|:-------------------|:-----|:--------|:----------|:--------------|:-----------------|:-------------|:-------------------|:-------|:-----------|:------------|:-----------|:--------|:--------|:--------|
| 0 | 6 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | |
| 1 | 18 |  |  |  |  |  | X | | | | | X | | | | | | | | | | | | | X | X | X |
|
Redwood0/pippa_custom | ---
license: apache-2.0
task_categories:
- conversational
language:
- en
tags:
- not-for-all-audiences
- conversational
- roleplay
- custom-format
viewer: true
---
This custom pippa dataset is from Undi of TheBloke Discord |
dmrau/cqudupstack-programmers | ---
configs:
- config_name: default
data_files:
- split: queries
path: data/queries-*
- split: corpus
path: data/corpus-*
dataset_info:
features:
- name: _id
dtype: string
- name: text
dtype: string
- name: title
dtype: string
splits:
- name: queries
num_bytes: 63785
num_examples: 876
- name: corpus
num_bytes: 32727262
num_examples: 32176
download_size: 19360000
dataset_size: 32791047
---
# Dataset Card for "cqudupstack-programmers"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
CarperAI/pilev2-dev | ---
annotations_creators:
- no-annotation
language:
- en
- code
language_creators:
- crowdsourced
- machine-generated
license: []
multilinguality:
- multilingual
pretty_name: Pile V2
size_categories:
- n>1T
source_datasets:
- extended|the_pile
tags:
- code
task_categories:
- text-generation
task_ids:
- language-modeling
---
# Dataset Card for [Dataset Name]
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
The PileV2 is a larger and more diverse collection of text data mostly focused on English text. Specifically, it is a collection of roughly 40 different data subsets. This includes the original 22 subsets from the original pile plus a heavy focus on additional software engineering specific data subsets including the newly released "The Stack" from bigcode, various programming competition sources, and a number of programmer oriented discussion groups such as Discourse, programming Subreddits, and Stack Exchange. This portion of the PileV2 we've named the CodePile to hopefully improve language model for the domain of Software Engineering that goes beyond simplily coding.
### 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
The PileV2:
* ArXiv - https://arxiv.org/
* PubMed Central - https://www.ncbi.nlm.nih.gov/pmc/
* Books3 - https://the-eye.eu/public/AI/pile_preliminary_components/books3.tar.gz
* Project Gutenberg - https://www.gutenberg.org/
* Free Law Project - https://free.law/
* Wikipedia (en) - https://dumps.wikimedia.org/enwiki/
* EuroParl - https://www.statmt.org/europarl/
* (Hendryks) SEC - https://www.sec.gov/
* (Hendryks) AMPS - https://www.amps.org/
* USPTO - https://www.uspto.gov/
* Hacker News - https://news.ycombinator.com/
* OpenWebText2 - https://skylion007.github.io/OpenWebTextCorpus/
* Pile-CC -
* Pile of Law - https://www.pileoflaw.com/
* Case.Law - https://case.law/
* Multi Session -
* Reddit - https://files.pushshift.io/reddit/
The CodePile:
* The Stack - https://www.bigcode-project.org/docs/about/the-stack/
* Ubuntu IRC - https://irclogs.ubuntu.com/
* Stack Exchange - https://archive.org/details/stackexchange
* DM Mathematics - https://www.kaggle.com/c/learn-ai-bowl-2020/data
* Apache Software Foundation Public Mail Archives - https://mail-archives.apache.org/
* Arduino Forum - https://forum.arduino.cc/
* GitLab - https://gitlab.com/
* Bitbucket diffs - https://bitbucket.org/
* Bitbucket code - https://bitbucket.org/
* Programming Competition Data - https://www.kaggle.com/c/learn-ai-bowl-2020/data
* Discourse - https://meta.discourse.org/t/discourse-data-explorer/112497
* Reddit Programming Subthreads - https://files.pushshift.io/reddit/
* Programming Books - https://www.kaggle.com/gyani95/380000-lyrics-from-metrolyrics
* UseNet - https://archive.org/details/usenet
* Mailing Lists - https://www.kaggle.com/wcukierski/enron-email-dataset
* Gitter Discussions - https://gitter.im/
* Zulip - https://zulipchat.com/
* AI4Code Notebooks -
* LinusTechTips forums - https://linustechtips.com/
* GitHub diffs -
* GitHub Issues -
* Leetcode - https://leetcode.com/
[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
Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset. |
CyberHarem/i_168_kantaicollection | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of i_168/伊168/伊168 (Kantai Collection)
This is the dataset of i_168/伊168/伊168 (Kantai Collection), containing 500 images and their tags.
The core tags of this character are `ponytail, long_hair, red_hair, red_eyes, hair_ribbon, ribbon, ahoge`, 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 | 500 | 357.74 MiB | [Download](https://huggingface.co/datasets/CyberHarem/i_168_kantaicollection/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 500 | 264.00 MiB | [Download](https://huggingface.co/datasets/CyberHarem/i_168_kantaicollection/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 1021 | 521.66 MiB | [Download](https://huggingface.co/datasets/CyberHarem/i_168_kantaicollection/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 500 | 339.13 MiB | [Download](https://huggingface.co/datasets/CyberHarem/i_168_kantaicollection/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 1021 | 638.48 MiB | [Download](https://huggingface.co/datasets/CyberHarem/i_168_kantaicollection/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/i_168_kantaicollection',
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 | 10 |  |  |  |  |  | 1girl, one-piece_swimsuit, school_swimsuit, solo, open_mouth, blush, looking_at_viewer, innertube, medium_breasts, torn_swimsuit, name_tag |
| 1 | 6 |  |  |  |  |  | 1girl, looking_at_viewer, one-piece_swimsuit, school_swimsuit, serafuku, solo, swimsuit_under_clothes, blush, innertube, open_mouth, ass |
| 2 | 6 |  |  |  |  |  | 1girl, one-piece_swimsuit, school_swimsuit, serafuku, solo, swimsuit_under_clothes |
| 3 | 6 |  |  |  |  |  | 1girl, one-piece_swimsuit, school_swimsuit, serafuku, smartphone, solo, swimsuit_under_clothes, smile |
| 4 | 8 |  |  |  |  |  | 1girl, fake_animal_ears, playboy_bunny, rabbit_ears, solo, wrist_cuffs, detached_collar, simple_background, strapless_leotard, white_background, black_leotard, looking_at_viewer, alternate_costume, pantyhose, rabbit_tail, blue_leotard, blush, bowtie, cleavage, dated |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | one-piece_swimsuit | school_swimsuit | solo | open_mouth | blush | looking_at_viewer | innertube | medium_breasts | torn_swimsuit | name_tag | serafuku | swimsuit_under_clothes | ass | smartphone | smile | fake_animal_ears | playboy_bunny | rabbit_ears | wrist_cuffs | detached_collar | simple_background | strapless_leotard | white_background | black_leotard | alternate_costume | pantyhose | rabbit_tail | blue_leotard | bowtie | cleavage | dated |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:---------------------|:------------------|:-------|:-------------|:--------|:--------------------|:------------|:-----------------|:----------------|:-----------|:-----------|:-------------------------|:------|:-------------|:--------|:-------------------|:----------------|:--------------|:--------------|:------------------|:--------------------|:--------------------|:-------------------|:----------------|:--------------------|:------------|:--------------|:---------------|:---------|:-----------|:--------|
| 0 | 10 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | |
| 1 | 6 |  |  |  |  |  | X | X | X | X | X | X | X | X | | | | X | X | X | | | | | | | | | | | | | | | | | | |
| 2 | 6 |  |  |  |  |  | X | X | X | X | | | | | | | | X | X | | | | | | | | | | | | | | | | | | | |
| 3 | 6 |  |  |  |  |  | X | X | X | X | | | | | | | | X | X | | X | X | | | | | | | | | | | | | | | | |
| 4 | 8 |  |  |  |  |  | X | | | X | | X | X | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
|
kaleemWaheed/twitter_dataset_1713195687 | ---
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: 32926
num_examples: 80
download_size: 19345
dataset_size: 32926
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
liuyanchen1015/MULTI_VALUE_mnli_fronting_pobj | ---
dataset_info:
features:
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: label
dtype: int64
- name: idx
dtype: int64
- name: score
dtype: int64
splits:
- name: dev_matched
num_bytes: 1286510
num_examples: 6081
- name: dev_mismatched
num_bytes: 1439100
num_examples: 6572
- name: test_matched
num_bytes: 1267540
num_examples: 6010
- name: test_mismatched
num_bytes: 1410080
num_examples: 6433
- name: train
num_bytes: 51114007
num_examples: 239789
download_size: 37269539
dataset_size: 56517237
---
# Dataset Card for "MULTI_VALUE_mnli_fronting_pobj"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
yardeny/tokenized_bert_context_len_64 | ---
dataset_info:
features:
- name: input_ids
sequence: int32
- name: token_type_ids
sequence: int8
- name: attention_mask
sequence: int8
splits:
- name: train
num_bytes: 10826495952
num_examples: 80462898
download_size: 3641089943
dataset_size: 10826495952
---
# Dataset Card for "tokenized_bert_context_len_64"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
d0rj/alpaca-cleaned-ru | ---
dataset_info:
features:
- name: input
dtype: string
- name: instruction
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 74829755.0
num_examples: 51760
download_size: 36596664
dataset_size: 74829755.0
license: cc-by-4.0
language:
- ru
multilinguality:
- monolingual
tags:
- instruction-finetuning
pretty_name: Alpaca-Cleaned (ru)
task_categories:
- text-generation
size_categories:
- 10K<n<100K
source_datasets:
- yahma/alpaca-cleaned
language_creators:
- translated
---
# alpaca-cleaned-ru
Translated version of [yahma/alpaca-cleaned](https://huggingface.co/datasets/yahma/alpaca-cleaned) into Russian.
## Dataset Description
- **Repository:** https://github.com/gururise/AlpacaDataCleaned |
florentgbelidji/running-science | ---
dataset_info:
features:
- name: filename
dtype: string
- name: text
dtype: string
- name: metadata
dtype: string
- name: id
dtype: string
splits:
- name: train
num_bytes: 2419733
num_examples: 196
download_size: 1296408
dataset_size: 2419733
---
# Dataset Card for "running-science"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Ar4ikov/iemocap_audio_text_splitted | ---
dataset_info:
features:
- name: _id
dtype: string
- name: activation
dtype: float64
- name: dominance
dtype: float64
- name: emotion
dtype: string
- name: end_time
dtype: float64
- name: start_time
dtype: float64
- name: titre
dtype: string
- name: to_translate
dtype: string
- name: translated
dtype: string
- name: valence
dtype: float64
- name: audio
dtype:
audio:
sampling_rate: 16000
splits:
- name: train
num_bytes: 1148478491.1463113
num_examples: 8031
- name: test
num_bytes: 287155695.4826887
num_examples: 2008
download_size: 1409847521
dataset_size: 1435634186.629
---
# Dataset Card for "iemocap_audio_text_splitted"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
open-llm-leaderboard/details_TheBloke__Wizard-Vicuna-30B-Uncensored-fp16 | ---
pretty_name: Evaluation run of TheBloke/Wizard-Vicuna-30B-Uncensored-fp16
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [TheBloke/Wizard-Vicuna-30B-Uncensored-fp16](https://huggingface.co/TheBloke/Wizard-Vicuna-30B-Uncensored-fp16)\
\ 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_TheBloke__Wizard-Vicuna-30B-Uncensored-fp16\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-19T13:45:18.299512](https://huggingface.co/datasets/open-llm-leaderboard/details_TheBloke__Wizard-Vicuna-30B-Uncensored-fp16/blob/main/results_2023-10-19T13-45-18.299512.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\": 0.18162751677852348,\n\
\ \"em_stderr\": 0.0039482621737543045,\n \"f1\": 0.2674087667785243,\n\
\ \"f1_stderr\": 0.004012090110572664,\n \"acc\": 0.46353130406008236,\n\
\ \"acc_stderr\": 0.01059244186586655\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.18162751677852348,\n \"em_stderr\": 0.0039482621737543045,\n\
\ \"f1\": 0.2674087667785243,\n \"f1_stderr\": 0.004012090110572664\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.1425322213798332,\n \
\ \"acc_stderr\": 0.009629588445673819\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7845303867403315,\n \"acc_stderr\": 0.011555295286059279\n\
\ }\n}\n```"
repo_url: https://huggingface.co/TheBloke/Wizard-Vicuna-30B-Uncensored-fp16
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_19T22_48_26.116631
path:
- '**/details_harness|arc:challenge|25_2023-07-19T22:48:26.116631.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-07-19T22:48:26.116631.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_10_19T13_45_18.299512
path:
- '**/details_harness|drop|3_2023-10-19T13-45-18.299512.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-19T13-45-18.299512.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_19T13_45_18.299512
path:
- '**/details_harness|gsm8k|5_2023-10-19T13-45-18.299512.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-19T13-45-18.299512.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_07_19T22_48_26.116631
path:
- '**/details_harness|hellaswag|10_2023-07-19T22:48:26.116631.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-07-19T22:48:26.116631.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_07_19T22_48_26.116631
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T22:48:26.116631.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T22:48:26.116631.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T22:48:26.116631.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T22:48:26.116631.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T22:48:26.116631.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T22:48:26.116631.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T22:48:26.116631.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T22:48:26.116631.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T22:48:26.116631.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T22:48:26.116631.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T22:48:26.116631.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T22:48:26.116631.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T22:48:26.116631.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T22:48:26.116631.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T22:48:26.116631.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T22:48:26.116631.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T22:48:26.116631.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T22:48:26.116631.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T22:48:26.116631.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T22:48:26.116631.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T22:48:26.116631.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T22:48:26.116631.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T22:48:26.116631.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T22:48:26.116631.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T22:48:26.116631.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T22:48:26.116631.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T22:48:26.116631.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T22:48:26.116631.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T22:48:26.116631.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T22:48:26.116631.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T22:48:26.116631.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T22:48:26.116631.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T22:48:26.116631.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T22:48:26.116631.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-07-19T22:48:26.116631.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T22:48:26.116631.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T22:48:26.116631.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T22:48:26.116631.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-07-19T22:48:26.116631.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-07-19T22:48:26.116631.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T22:48:26.116631.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T22:48:26.116631.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T22:48:26.116631.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T22:48:26.116631.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T22:48:26.116631.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T22:48:26.116631.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T22:48:26.116631.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T22:48:26.116631.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T22:48:26.116631.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T22:48:26.116631.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T22:48:26.116631.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T22:48:26.116631.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T22:48:26.116631.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T22:48:26.116631.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T22:48:26.116631.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T22:48:26.116631.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T22:48:26.116631.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T22:48:26.116631.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T22:48:26.116631.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T22:48:26.116631.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T22:48:26.116631.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T22:48:26.116631.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T22:48:26.116631.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T22:48:26.116631.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T22:48:26.116631.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T22:48:26.116631.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T22:48:26.116631.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T22:48:26.116631.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T22:48:26.116631.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T22:48:26.116631.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T22:48:26.116631.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T22:48:26.116631.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T22:48:26.116631.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T22:48:26.116631.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T22:48:26.116631.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T22:48:26.116631.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T22:48:26.116631.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T22:48:26.116631.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T22:48:26.116631.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T22:48:26.116631.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T22:48:26.116631.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T22:48:26.116631.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T22:48:26.116631.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T22:48:26.116631.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T22:48:26.116631.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T22:48:26.116631.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T22:48:26.116631.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T22:48:26.116631.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T22:48:26.116631.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T22:48:26.116631.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T22:48:26.116631.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-07-19T22:48:26.116631.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T22:48:26.116631.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T22:48:26.116631.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T22:48:26.116631.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-07-19T22:48:26.116631.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-07-19T22:48:26.116631.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T22:48:26.116631.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T22:48:26.116631.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T22:48:26.116631.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T22:48:26.116631.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T22:48:26.116631.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T22:48:26.116631.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T22:48:26.116631.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T22:48:26.116631.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T22:48:26.116631.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T22:48:26.116631.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T22:48:26.116631.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T22:48:26.116631.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T22:48:26.116631.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T22:48:26.116631.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T22:48:26.116631.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T22:48:26.116631.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T22:48:26.116631.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_07_19T22_48_26.116631
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T22:48:26.116631.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T22:48:26.116631.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_07_19T22_48_26.116631
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T22:48:26.116631.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T22:48:26.116631.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_07_19T22_48_26.116631
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T22:48:26.116631.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T22:48:26.116631.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_07_19T22_48_26.116631
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T22:48:26.116631.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T22:48:26.116631.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_07_19T22_48_26.116631
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T22:48:26.116631.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T22:48:26.116631.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_07_19T22_48_26.116631
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T22:48:26.116631.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T22:48:26.116631.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_07_19T22_48_26.116631
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T22:48:26.116631.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T22:48:26.116631.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_07_19T22_48_26.116631
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T22:48:26.116631.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T22:48:26.116631.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_07_19T22_48_26.116631
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T22:48:26.116631.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T22:48:26.116631.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_07_19T22_48_26.116631
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T22:48:26.116631.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T22:48:26.116631.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_07_19T22_48_26.116631
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T22:48:26.116631.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T22:48:26.116631.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_07_19T22_48_26.116631
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T22:48:26.116631.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T22:48:26.116631.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_07_19T22_48_26.116631
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T22:48:26.116631.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T22:48:26.116631.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_07_19T22_48_26.116631
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T22:48:26.116631.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T22:48:26.116631.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_07_19T22_48_26.116631
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T22:48:26.116631.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T22:48:26.116631.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_07_19T22_48_26.116631
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T22:48:26.116631.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T22:48:26.116631.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_07_19T22_48_26.116631
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T22:48:26.116631.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T22:48:26.116631.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_07_19T22_48_26.116631
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T22:48:26.116631.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T22:48:26.116631.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_07_19T22_48_26.116631
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T22:48:26.116631.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T22:48:26.116631.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_07_19T22_48_26.116631
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T22:48:26.116631.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T22:48:26.116631.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_07_19T22_48_26.116631
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T22:48:26.116631.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T22:48:26.116631.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_07_19T22_48_26.116631
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T22:48:26.116631.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T22:48:26.116631.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_07_19T22_48_26.116631
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T22:48:26.116631.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T22:48:26.116631.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_07_19T22_48_26.116631
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T22:48:26.116631.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T22:48:26.116631.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_07_19T22_48_26.116631
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T22:48:26.116631.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T22:48:26.116631.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_07_19T22_48_26.116631
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T22:48:26.116631.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T22:48:26.116631.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_07_19T22_48_26.116631
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T22:48:26.116631.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T22:48:26.116631.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_07_19T22_48_26.116631
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T22:48:26.116631.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T22:48:26.116631.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_07_19T22_48_26.116631
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T22:48:26.116631.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T22:48:26.116631.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_07_19T22_48_26.116631
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T22:48:26.116631.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T22:48:26.116631.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_07_19T22_48_26.116631
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T22:48:26.116631.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T22:48:26.116631.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_07_19T22_48_26.116631
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T22:48:26.116631.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T22:48:26.116631.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_07_19T22_48_26.116631
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T22:48:26.116631.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T22:48:26.116631.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_07_19T22_48_26.116631
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T22:48:26.116631.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T22:48:26.116631.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_07_19T22_48_26.116631
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-07-19T22:48:26.116631.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-07-19T22:48:26.116631.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_07_19T22_48_26.116631
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T22:48:26.116631.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T22:48:26.116631.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_07_19T22_48_26.116631
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T22:48:26.116631.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T22:48:26.116631.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_07_19T22_48_26.116631
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T22:48:26.116631.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T22:48:26.116631.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_07_19T22_48_26.116631
path:
- '**/details_harness|hendrycksTest-management|5_2023-07-19T22:48:26.116631.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-07-19T22:48:26.116631.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_07_19T22_48_26.116631
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-07-19T22:48:26.116631.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-07-19T22:48:26.116631.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_07_19T22_48_26.116631
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T22:48:26.116631.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T22:48:26.116631.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_07_19T22_48_26.116631
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T22:48:26.116631.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T22:48:26.116631.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_07_19T22_48_26.116631
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T22:48:26.116631.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T22:48:26.116631.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_07_19T22_48_26.116631
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T22:48:26.116631.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T22:48:26.116631.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_07_19T22_48_26.116631
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T22:48:26.116631.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T22:48:26.116631.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_07_19T22_48_26.116631
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T22:48:26.116631.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T22:48:26.116631.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_07_19T22_48_26.116631
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T22:48:26.116631.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T22:48:26.116631.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_07_19T22_48_26.116631
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T22:48:26.116631.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T22:48:26.116631.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_07_19T22_48_26.116631
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T22:48:26.116631.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T22:48:26.116631.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_07_19T22_48_26.116631
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T22:48:26.116631.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T22:48:26.116631.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_07_19T22_48_26.116631
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T22:48:26.116631.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T22:48:26.116631.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_07_19T22_48_26.116631
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T22:48:26.116631.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T22:48:26.116631.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_07_19T22_48_26.116631
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T22:48:26.116631.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T22:48:26.116631.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_07_19T22_48_26.116631
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T22:48:26.116631.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T22:48:26.116631.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_07_19T22_48_26.116631
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T22:48:26.116631.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T22:48:26.116631.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_07_19T22_48_26.116631
path:
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T22:48:26.116631.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T22:48:26.116631.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_07_19T22_48_26.116631
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T22:48:26.116631.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T22:48:26.116631.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_07_19T22_48_26.116631
path:
- '**/details_harness|truthfulqa:mc|0_2023-07-19T22:48:26.116631.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-07-19T22:48:26.116631.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_19T13_45_18.299512
path:
- '**/details_harness|winogrande|5_2023-10-19T13-45-18.299512.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-19T13-45-18.299512.parquet'
- config_name: results
data_files:
- split: 2023_07_19T22_48_26.116631
path:
- results_2023-07-19T22:48:26.116631.parquet
- split: 2023_10_19T13_45_18.299512
path:
- results_2023-10-19T13-45-18.299512.parquet
- split: latest
path:
- results_2023-10-19T13-45-18.299512.parquet
---
# Dataset Card for Evaluation run of TheBloke/Wizard-Vicuna-30B-Uncensored-fp16
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/TheBloke/Wizard-Vicuna-30B-Uncensored-fp16
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** clementine@hf.co
### Dataset Summary
Dataset automatically created during the evaluation run of model [TheBloke/Wizard-Vicuna-30B-Uncensored-fp16](https://huggingface.co/TheBloke/Wizard-Vicuna-30B-Uncensored-fp16) 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_TheBloke__Wizard-Vicuna-30B-Uncensored-fp16",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-19T13:45:18.299512](https://huggingface.co/datasets/open-llm-leaderboard/details_TheBloke__Wizard-Vicuna-30B-Uncensored-fp16/blob/main/results_2023-10-19T13-45-18.299512.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": 0.18162751677852348,
"em_stderr": 0.0039482621737543045,
"f1": 0.2674087667785243,
"f1_stderr": 0.004012090110572664,
"acc": 0.46353130406008236,
"acc_stderr": 0.01059244186586655
},
"harness|drop|3": {
"em": 0.18162751677852348,
"em_stderr": 0.0039482621737543045,
"f1": 0.2674087667785243,
"f1_stderr": 0.004012090110572664
},
"harness|gsm8k|5": {
"acc": 0.1425322213798332,
"acc_stderr": 0.009629588445673819
},
"harness|winogrande|5": {
"acc": 0.7845303867403315,
"acc_stderr": 0.011555295286059279
}
}
```
### 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] |
ziozzang/Human_Gen_Trans_DEKO | ---
license: cc-by-nc-4.0
task_categories:
- translation
language:
- de
- ko
---
# TL;DR
Human Generated/Translated Quaility Trnaslation dataset
- Paired Sentances/Corpus between DE/German-KO/Korean
# Source
- goethe_institute.json : Goethe Institute articles / https://www.goethe.de/de/index.html
- gitbook.json : GitBook / https://www.git-scm.com/book/en/v2
# Curated
Curated by Jioh L. Jung (ziozzang@gmail.com)
---
license: cc-by-nc-4.0
--- |
NickyNicky/distilabel-intel-orca-dpo-pairs_gemma_chatml | ---
dataset_info:
features:
- name: chosen
dtype: string
- name: rejected
dtype: string
- name: prompt
dtype: string
splits:
- name: train
num_bytes: 36354371
num_examples: 12859
download_size: 19642371
dataset_size: 36354371
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
manu/french_bench_hellaswag | ---
dataset_info:
features:
- name: ind
dtype: int32
- name: activity_label
dtype: string
- name: ctx_a
dtype: string
- name: ctx_b
dtype: string
- name: ctx
dtype: string
- name: endings
sequence: string
- name: source_id
dtype: string
- name: split
dtype: string
- name: split_type
dtype: string
- name: label
dtype: string
splits:
- name: validation
num_bytes: 12527721
num_examples: 9338
download_size: 7040656
dataset_size: 12527721
configs:
- config_name: default
data_files:
- split: validation
path: data/validation-*
---
|
Ubaidbhat/stock_market_basics | ---
dataset_info:
features:
- name: context
dtype: string
- name: question
dtype: string
- name: answer
dtype: string
- name: source_doc
dtype: string
- name: groundedness_score
dtype: int64
- name: groundedness_eval
dtype: string
- name: relevance_score
dtype: int64
- name: relevance_eval
dtype: string
splits:
- name: train
num_bytes: 14467
num_examples: 6
download_size: 23670
dataset_size: 14467
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
rubend18/DALL-E-Prompts-OpenAI-ChatGPT | ---
task_categories:
- text-generation
- feature-extraction
- zero-shot-classification
language:
- en
tags:
- DALL-E
- Prompt
- Dataset
- Compilation
- OpenAI
- image
- images
pretty_name: DALL-E Prompt Dataset Compilation
size_categories:
- 1B<n<10B
---
# Dataset Card for Dataset Name
## Dataset Description
- **Autor:** Rubén Darío Jaramillo
- **Email:** rubend18@hotmail.com
- **WhatsApp:** +593 93 979 6676
### Dataset Summary
This dataset has been generated using [Prompt Generator for OpenAI's DALL-E](https://huggingface.co/spaces/rubend18/Prompt-Generator-for-OpenAI-DALL-E).
### Languages
English
## Dataset Structure
1.000.000 Prompts |
BelalElhossany/mgb2_audios_transcriptions_preprocessed | ---
dataset_info:
features:
- name: path
dtype: string
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: sentence
dtype: string
splits:
- name: train
num_bytes: 1146187887.0
num_examples: 5842
download_size: 1141969416
dataset_size: 1146187887.0
---
# Dataset Card for "mgb2_audios_transcriptions_preprocessed"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
xcopa | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- et
- ht
- id
- it
- qu
- sw
- ta
- th
- tr
- vi
- zh
license:
- cc-by-4.0
multilinguality:
- multilingual
size_categories:
- unknown
source_datasets:
- extended|copa
task_categories:
- question-answering
task_ids:
- multiple-choice-qa
paperswithcode_id: xcopa
pretty_name: XCOPA
dataset_info:
- config_name: et
features:
- name: premise
dtype: string
- name: choice1
dtype: string
- name: choice2
dtype: string
- name: question
dtype: string
- name: label
dtype: int32
- name: idx
dtype: int32
- name: changed
dtype: bool
splits:
- name: validation
num_bytes: 11669
num_examples: 100
- name: test
num_bytes: 56471
num_examples: 500
download_size: 54200
dataset_size: 68140
- config_name: ht
features:
- name: premise
dtype: string
- name: choice1
dtype: string
- name: choice2
dtype: string
- name: question
dtype: string
- name: label
dtype: int32
- name: idx
dtype: int32
- name: changed
dtype: bool
splits:
- name: validation
num_bytes: 11957
num_examples: 100
- name: test
num_bytes: 58437
num_examples: 500
download_size: 50346
dataset_size: 70394
- config_name: id
features:
- name: premise
dtype: string
- name: choice1
dtype: string
- name: choice2
dtype: string
- name: question
dtype: string
- name: label
dtype: int32
- name: idx
dtype: int32
- name: changed
dtype: bool
splits:
- name: validation
num_bytes: 13855
num_examples: 100
- name: test
num_bytes: 63189
num_examples: 500
download_size: 55608
dataset_size: 77044
- config_name: it
features:
- name: premise
dtype: string
- name: choice1
dtype: string
- name: choice2
dtype: string
- name: question
dtype: string
- name: label
dtype: int32
- name: idx
dtype: int32
- name: changed
dtype: bool
splits:
- name: validation
num_bytes: 13324
num_examples: 100
- name: test
num_bytes: 64909
num_examples: 500
download_size: 59602
dataset_size: 78233
- config_name: qu
features:
- name: premise
dtype: string
- name: choice1
dtype: string
- name: choice2
dtype: string
- name: question
dtype: string
- name: label
dtype: int32
- name: idx
dtype: int32
- name: changed
dtype: bool
splits:
- name: validation
num_bytes: 13941
num_examples: 100
- name: test
num_bytes: 68569
num_examples: 500
download_size: 56734
dataset_size: 82510
- config_name: sw
features:
- name: premise
dtype: string
- name: choice1
dtype: string
- name: choice2
dtype: string
- name: question
dtype: string
- name: label
dtype: int32
- name: idx
dtype: int32
- name: changed
dtype: bool
splits:
- name: validation
num_bytes: 12666
num_examples: 100
- name: test
num_bytes: 60533
num_examples: 500
download_size: 53862
dataset_size: 73199
- config_name: ta
features:
- name: premise
dtype: string
- name: choice1
dtype: string
- name: choice2
dtype: string
- name: question
dtype: string
- name: label
dtype: int32
- name: idx
dtype: int32
- name: changed
dtype: bool
splits:
- name: validation
num_bytes: 36995
num_examples: 100
- name: test
num_bytes: 176112
num_examples: 500
download_size: 91348
dataset_size: 213107
- config_name: th
features:
- name: premise
dtype: string
- name: choice1
dtype: string
- name: choice2
dtype: string
- name: question
dtype: string
- name: label
dtype: int32
- name: idx
dtype: int32
- name: changed
dtype: bool
splits:
- name: validation
num_bytes: 21817
num_examples: 100
- name: test
num_bytes: 104023
num_examples: 500
download_size: 65925
dataset_size: 125840
- config_name: tr
features:
- name: premise
dtype: string
- name: choice1
dtype: string
- name: choice2
dtype: string
- name: question
dtype: string
- name: label
dtype: int32
- name: idx
dtype: int32
- name: changed
dtype: bool
splits:
- name: validation
num_bytes: 11899
num_examples: 100
- name: test
num_bytes: 57599
num_examples: 500
download_size: 53677
dataset_size: 69498
- config_name: translation-et
features:
- name: premise
dtype: string
- name: choice1
dtype: string
- name: choice2
dtype: string
- name: question
dtype: string
- name: label
dtype: int32
- name: idx
dtype: int32
- name: changed
dtype: bool
splits:
- name: validation
num_bytes: 11881
num_examples: 100
- name: test
num_bytes: 57327
num_examples: 500
download_size: 52078
dataset_size: 69208
- config_name: translation-ht
features:
- name: premise
dtype: string
- name: choice1
dtype: string
- name: choice2
dtype: string
- name: question
dtype: string
- name: label
dtype: int32
- name: idx
dtype: int32
- name: changed
dtype: bool
splits:
- name: validation
num_bytes: 12130
num_examples: 100
- name: test
num_bytes: 58019
num_examples: 500
download_size: 52823
dataset_size: 70149
- config_name: translation-id
features:
- name: premise
dtype: string
- name: choice1
dtype: string
- name: choice2
dtype: string
- name: question
dtype: string
- name: label
dtype: int32
- name: idx
dtype: int32
- name: changed
dtype: bool
splits:
- name: validation
num_bytes: 12457
num_examples: 100
- name: test
num_bytes: 58406
num_examples: 500
download_size: 53701
dataset_size: 70863
- config_name: translation-it
features:
- name: premise
dtype: string
- name: choice1
dtype: string
- name: choice2
dtype: string
- name: question
dtype: string
- name: label
dtype: int32
- name: idx
dtype: int32
- name: changed
dtype: bool
splits:
- name: validation
num_bytes: 12382
num_examples: 100
- name: test
num_bytes: 58936
num_examples: 500
download_size: 53410
dataset_size: 71318
- config_name: translation-sw
features:
- name: premise
dtype: string
- name: choice1
dtype: string
- name: choice2
dtype: string
- name: question
dtype: string
- name: label
dtype: int32
- name: idx
dtype: int32
- name: changed
dtype: bool
splits:
- name: validation
num_bytes: 12180
num_examples: 100
- name: test
num_bytes: 58607
num_examples: 500
download_size: 52888
dataset_size: 70787
- config_name: translation-ta
features:
- name: premise
dtype: string
- name: choice1
dtype: string
- name: choice2
dtype: string
- name: question
dtype: string
- name: label
dtype: int32
- name: idx
dtype: int32
- name: changed
dtype: bool
splits:
- name: validation
num_bytes: 12372
num_examples: 100
- name: test
num_bytes: 59442
num_examples: 500
download_size: 54488
dataset_size: 71814
- config_name: translation-th
features:
- name: premise
dtype: string
- name: choice1
dtype: string
- name: choice2
dtype: string
- name: question
dtype: string
- name: label
dtype: int32
- name: idx
dtype: int32
- name: changed
dtype: bool
splits:
- name: validation
num_bytes: 11347
num_examples: 100
- name: test
num_bytes: 54758
num_examples: 500
download_size: 52243
dataset_size: 66105
- config_name: translation-tr
features:
- name: premise
dtype: string
- name: choice1
dtype: string
- name: choice2
dtype: string
- name: question
dtype: string
- name: label
dtype: int32
- name: idx
dtype: int32
- name: changed
dtype: bool
splits:
- name: validation
num_bytes: 11879
num_examples: 100
- name: test
num_bytes: 57599
num_examples: 500
download_size: 52223
dataset_size: 69478
- config_name: translation-vi
features:
- name: premise
dtype: string
- name: choice1
dtype: string
- name: choice2
dtype: string
- name: question
dtype: string
- name: label
dtype: int32
- name: idx
dtype: int32
- name: changed
dtype: bool
splits:
- name: validation
num_bytes: 11604
num_examples: 100
- name: test
num_bytes: 55797
num_examples: 500
download_size: 52087
dataset_size: 67401
- config_name: translation-zh
features:
- name: premise
dtype: string
- name: choice1
dtype: string
- name: choice2
dtype: string
- name: question
dtype: string
- name: label
dtype: int32
- name: idx
dtype: int32
- name: changed
dtype: bool
splits:
- name: validation
num_bytes: 12001
num_examples: 100
- name: test
num_bytes: 57895
num_examples: 500
download_size: 52896
dataset_size: 69896
- config_name: vi
features:
- name: premise
dtype: string
- name: choice1
dtype: string
- name: choice2
dtype: string
- name: question
dtype: string
- name: label
dtype: int32
- name: idx
dtype: int32
- name: changed
dtype: bool
splits:
- name: validation
num_bytes: 15093
num_examples: 100
- name: test
num_bytes: 70169
num_examples: 500
download_size: 59132
dataset_size: 85262
- config_name: zh
features:
- name: premise
dtype: string
- name: choice1
dtype: string
- name: choice2
dtype: string
- name: question
dtype: string
- name: label
dtype: int32
- name: idx
dtype: int32
- name: changed
dtype: bool
splits:
- name: validation
num_bytes: 11604
num_examples: 100
- name: test
num_bytes: 55134
num_examples: 500
download_size: 52634
dataset_size: 66738
configs:
- config_name: et
data_files:
- split: validation
path: et/validation-*
- split: test
path: et/test-*
- config_name: ht
data_files:
- split: validation
path: ht/validation-*
- split: test
path: ht/test-*
- config_name: id
data_files:
- split: validation
path: id/validation-*
- split: test
path: id/test-*
- config_name: it
data_files:
- split: validation
path: it/validation-*
- split: test
path: it/test-*
- config_name: qu
data_files:
- split: validation
path: qu/validation-*
- split: test
path: qu/test-*
- config_name: sw
data_files:
- split: validation
path: sw/validation-*
- split: test
path: sw/test-*
- config_name: ta
data_files:
- split: validation
path: ta/validation-*
- split: test
path: ta/test-*
- config_name: th
data_files:
- split: validation
path: th/validation-*
- split: test
path: th/test-*
- config_name: tr
data_files:
- split: validation
path: tr/validation-*
- split: test
path: tr/test-*
- config_name: translation-et
data_files:
- split: validation
path: translation-et/validation-*
- split: test
path: translation-et/test-*
- config_name: translation-ht
data_files:
- split: validation
path: translation-ht/validation-*
- split: test
path: translation-ht/test-*
- config_name: translation-id
data_files:
- split: validation
path: translation-id/validation-*
- split: test
path: translation-id/test-*
- config_name: translation-it
data_files:
- split: validation
path: translation-it/validation-*
- split: test
path: translation-it/test-*
- config_name: translation-sw
data_files:
- split: validation
path: translation-sw/validation-*
- split: test
path: translation-sw/test-*
- config_name: translation-ta
data_files:
- split: validation
path: translation-ta/validation-*
- split: test
path: translation-ta/test-*
- config_name: translation-th
data_files:
- split: validation
path: translation-th/validation-*
- split: test
path: translation-th/test-*
- config_name: translation-tr
data_files:
- split: validation
path: translation-tr/validation-*
- split: test
path: translation-tr/test-*
- config_name: translation-vi
data_files:
- split: validation
path: translation-vi/validation-*
- split: test
path: translation-vi/test-*
- config_name: translation-zh
data_files:
- split: validation
path: translation-zh/validation-*
- split: test
path: translation-zh/test-*
- config_name: vi
data_files:
- split: validation
path: vi/validation-*
- split: test
path: vi/test-*
- config_name: zh
data_files:
- split: validation
path: zh/validation-*
- split: test
path: zh/test-*
---
# Dataset Card for "xcopa"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://github.com/cambridgeltl/xcopa](https://github.com/cambridgeltl/xcopa)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of downloaded dataset files:** 4.08 MB
- **Size of the generated dataset:** 1.02 MB
- **Total amount of disk used:** 5.10 MB
### Dataset Summary
XCOPA: A Multilingual Dataset for Causal Commonsense Reasoning
The Cross-lingual Choice of Plausible Alternatives dataset is a benchmark to evaluate the ability of machine learning models to transfer commonsense reasoning across
languages. The dataset is the translation and reannotation of the English COPA (Roemmele et al. 2011) and covers 11 languages from 11 families and several areas around
the globe. The dataset is challenging as it requires both the command of world knowledge and the ability to generalise to new languages. All the details about the
creation of XCOPA and the implementation of the baselines are available in the paper.
Xcopa language et
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
- et
- ht
- id
- it
- qu
- sw
- ta
- th
- tr
- vi
- zh
## Dataset Structure
### Data Instances
#### et
- **Size of downloaded dataset files:** 0.37 MB
- **Size of the generated dataset:** 0.07 MB
- **Total amount of disk used:** 0.44 MB
An example of 'validation' looks as follows.
```
{
"changed": false,
"choice1": "Ta kallas piima kaussi.",
"choice2": "Ta kaotas oma isu.",
"idx": 1,
"label": 1,
"premise": "Tüdruk leidis oma helveste seest putuka.",
"question": "effect"
}
```
#### ht
- **Size of downloaded dataset files:** 0.37 MB
- **Size of the generated dataset:** 0.07 MB
- **Total amount of disk used:** 0.44 MB
An example of 'validation' looks as follows.
```
{
"changed": false,
"choice1": "Ta kallas piima kaussi.",
"choice2": "Ta kaotas oma isu.",
"idx": 1,
"label": 1,
"premise": "Tüdruk leidis oma helveste seest putuka.",
"question": "effect"
}
```
#### id
- **Size of downloaded dataset files:** 0.37 MB
- **Size of the generated dataset:** 0.07 MB
- **Total amount of disk used:** 0.45 MB
An example of 'validation' looks as follows.
```
{
"changed": false,
"choice1": "Ta kallas piima kaussi.",
"choice2": "Ta kaotas oma isu.",
"idx": 1,
"label": 1,
"premise": "Tüdruk leidis oma helveste seest putuka.",
"question": "effect"
}
```
#### it
- **Size of downloaded dataset files:** 0.37 MB
- **Size of the generated dataset:** 0.08 MB
- **Total amount of disk used:** 0.45 MB
An example of 'validation' looks as follows.
```
{
"changed": false,
"choice1": "Ta kallas piima kaussi.",
"choice2": "Ta kaotas oma isu.",
"idx": 1,
"label": 1,
"premise": "Tüdruk leidis oma helveste seest putuka.",
"question": "effect"
}
```
#### qu
- **Size of downloaded dataset files:** 0.37 MB
- **Size of the generated dataset:** 0.08 MB
- **Total amount of disk used:** 0.45 MB
An example of 'validation' looks as follows.
```
{
"changed": false,
"choice1": "Ta kallas piima kaussi.",
"choice2": "Ta kaotas oma isu.",
"idx": 1,
"label": 1,
"premise": "Tüdruk leidis oma helveste seest putuka.",
"question": "effect"
}
```
### Data Fields
The data fields are the same among all splits.
#### et
- `premise`: a `string` feature.
- `choice1`: a `string` feature.
- `choice2`: a `string` feature.
- `question`: a `string` feature.
- `label`: a `int32` feature.
- `idx`: a `int32` feature.
- `changed`: a `bool` feature.
#### ht
- `premise`: a `string` feature.
- `choice1`: a `string` feature.
- `choice2`: a `string` feature.
- `question`: a `string` feature.
- `label`: a `int32` feature.
- `idx`: a `int32` feature.
- `changed`: a `bool` feature.
#### id
- `premise`: a `string` feature.
- `choice1`: a `string` feature.
- `choice2`: a `string` feature.
- `question`: a `string` feature.
- `label`: a `int32` feature.
- `idx`: a `int32` feature.
- `changed`: a `bool` feature.
#### it
- `premise`: a `string` feature.
- `choice1`: a `string` feature.
- `choice2`: a `string` feature.
- `question`: a `string` feature.
- `label`: a `int32` feature.
- `idx`: a `int32` feature.
- `changed`: a `bool` feature.
#### qu
- `premise`: a `string` feature.
- `choice1`: a `string` feature.
- `choice2`: a `string` feature.
- `question`: a `string` feature.
- `label`: a `int32` feature.
- `idx`: a `int32` feature.
- `changed`: a `bool` feature.
### Data Splits
|name|validation|test|
|----|---------:|---:|
|et | 100| 500|
|ht | 100| 500|
|id | 100| 500|
|it | 100| 500|
|qu | 100| 500|
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/).
### Citation Information
```
@article{ponti2020xcopa,
title={{XCOPA: A} Multilingual Dataset for Causal Commonsense Reasoning},
author={Edoardo M. Ponti, Goran Glava
{s}, Olga Majewska, Qianchu Liu, Ivan Vuli'{c} and Anna Korhonen},
journal={arXiv preprint},
year={2020},
url={https://ducdauge.github.io/files/xcopa.pdf}
}
@inproceedings{roemmele2011choice,
title={Choice of plausible alternatives: An evaluation of commonsense causal reasoning},
author={Roemmele, Melissa and Bejan, Cosmin Adrian and Gordon, Andrew S},
booktitle={2011 AAAI Spring Symposium Series},
year={2011},
url={https://people.ict.usc.edu/~gordon/publications/AAAI-SPRING11A.PDF},
}
```
### Contributions
Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten), [@lewtun](https://github.com/lewtun), [@thomwolf](https://github.com/thomwolf) for adding this dataset. |
EgilKarlsen/Thunderbird_DistilRoBERTa_FT | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: '0'
dtype: float32
- name: '1'
dtype: float32
- name: '2'
dtype: float32
- name: '3'
dtype: float32
- name: '4'
dtype: float32
- name: '5'
dtype: float32
- name: '6'
dtype: float32
- name: '7'
dtype: float32
- name: '8'
dtype: float32
- name: '9'
dtype: float32
- name: '10'
dtype: float32
- name: '11'
dtype: float32
- name: '12'
dtype: float32
- name: '13'
dtype: float32
- name: '14'
dtype: float32
- name: '15'
dtype: float32
- name: '16'
dtype: float32
- name: '17'
dtype: float32
- name: '18'
dtype: float32
- name: '19'
dtype: float32
- name: '20'
dtype: float32
- name: '21'
dtype: float32
- name: '22'
dtype: float32
- name: '23'
dtype: float32
- name: '24'
dtype: float32
- name: '25'
dtype: float32
- name: '26'
dtype: float32
- name: '27'
dtype: float32
- name: '28'
dtype: float32
- name: '29'
dtype: float32
- name: '30'
dtype: float32
- name: '31'
dtype: float32
- name: '32'
dtype: float32
- name: '33'
dtype: float32
- name: '34'
dtype: float32
- name: '35'
dtype: float32
- name: '36'
dtype: float32
- name: '37'
dtype: float32
- name: '38'
dtype: float32
- name: '39'
dtype: float32
- name: '40'
dtype: float32
- name: '41'
dtype: float32
- name: '42'
dtype: float32
- name: '43'
dtype: float32
- name: '44'
dtype: float32
- name: '45'
dtype: float32
- name: '46'
dtype: float32
- name: '47'
dtype: float32
- name: '48'
dtype: float32
- name: '49'
dtype: float32
- name: '50'
dtype: float32
- name: '51'
dtype: float32
- name: '52'
dtype: float32
- name: '53'
dtype: float32
- name: '54'
dtype: float32
- name: '55'
dtype: float32
- name: '56'
dtype: float32
- name: '57'
dtype: float32
- name: '58'
dtype: float32
- name: '59'
dtype: float32
- name: '60'
dtype: float32
- name: '61'
dtype: float32
- name: '62'
dtype: float32
- name: '63'
dtype: float32
- name: '64'
dtype: float32
- name: '65'
dtype: float32
- name: '66'
dtype: float32
- name: '67'
dtype: float32
- name: '68'
dtype: float32
- name: '69'
dtype: float32
- name: '70'
dtype: float32
- name: '71'
dtype: float32
- name: '72'
dtype: float32
- name: '73'
dtype: float32
- name: '74'
dtype: float32
- name: '75'
dtype: float32
- name: '76'
dtype: float32
- name: '77'
dtype: float32
- name: '78'
dtype: float32
- name: '79'
dtype: float32
- name: '80'
dtype: float32
- name: '81'
dtype: float32
- name: '82'
dtype: float32
- name: '83'
dtype: float32
- name: '84'
dtype: float32
- name: '85'
dtype: float32
- name: '86'
dtype: float32
- name: '87'
dtype: float32
- name: '88'
dtype: float32
- name: '89'
dtype: float32
- name: '90'
dtype: float32
- name: '91'
dtype: float32
- name: '92'
dtype: float32
- name: '93'
dtype: float32
- name: '94'
dtype: float32
- name: '95'
dtype: float32
- name: '96'
dtype: float32
- name: '97'
dtype: float32
- name: '98'
dtype: float32
- name: '99'
dtype: float32
- name: '100'
dtype: float32
- name: '101'
dtype: float32
- name: '102'
dtype: float32
- name: '103'
dtype: float32
- name: '104'
dtype: float32
- name: '105'
dtype: float32
- name: '106'
dtype: float32
- name: '107'
dtype: float32
- name: '108'
dtype: float32
- name: '109'
dtype: float32
- name: '110'
dtype: float32
- name: '111'
dtype: float32
- name: '112'
dtype: float32
- name: '113'
dtype: float32
- name: '114'
dtype: float32
- name: '115'
dtype: float32
- name: '116'
dtype: float32
- name: '117'
dtype: float32
- name: '118'
dtype: float32
- name: '119'
dtype: float32
- name: '120'
dtype: float32
- name: '121'
dtype: float32
- name: '122'
dtype: float32
- name: '123'
dtype: float32
- name: '124'
dtype: float32
- name: '125'
dtype: float32
- name: '126'
dtype: float32
- name: '127'
dtype: float32
- name: '128'
dtype: float32
- name: '129'
dtype: float32
- name: '130'
dtype: float32
- name: '131'
dtype: float32
- name: '132'
dtype: float32
- name: '133'
dtype: float32
- name: '134'
dtype: float32
- name: '135'
dtype: float32
- name: '136'
dtype: float32
- name: '137'
dtype: float32
- name: '138'
dtype: float32
- name: '139'
dtype: float32
- name: '140'
dtype: float32
- name: '141'
dtype: float32
- name: '142'
dtype: float32
- name: '143'
dtype: float32
- name: '144'
dtype: float32
- name: '145'
dtype: float32
- name: '146'
dtype: float32
- name: '147'
dtype: float32
- name: '148'
dtype: float32
- name: '149'
dtype: float32
- name: '150'
dtype: float32
- name: '151'
dtype: float32
- name: '152'
dtype: float32
- name: '153'
dtype: float32
- name: '154'
dtype: float32
- name: '155'
dtype: float32
- name: '156'
dtype: float32
- name: '157'
dtype: float32
- name: '158'
dtype: float32
- name: '159'
dtype: float32
- name: '160'
dtype: float32
- name: '161'
dtype: float32
- name: '162'
dtype: float32
- name: '163'
dtype: float32
- name: '164'
dtype: float32
- name: '165'
dtype: float32
- name: '166'
dtype: float32
- name: '167'
dtype: float32
- name: '168'
dtype: float32
- name: '169'
dtype: float32
- name: '170'
dtype: float32
- name: '171'
dtype: float32
- name: '172'
dtype: float32
- name: '173'
dtype: float32
- name: '174'
dtype: float32
- name: '175'
dtype: float32
- name: '176'
dtype: float32
- name: '177'
dtype: float32
- name: '178'
dtype: float32
- name: '179'
dtype: float32
- name: '180'
dtype: float32
- name: '181'
dtype: float32
- name: '182'
dtype: float32
- name: '183'
dtype: float32
- name: '184'
dtype: float32
- name: '185'
dtype: float32
- name: '186'
dtype: float32
- name: '187'
dtype: float32
- name: '188'
dtype: float32
- name: '189'
dtype: float32
- name: '190'
dtype: float32
- name: '191'
dtype: float32
- name: '192'
dtype: float32
- name: '193'
dtype: float32
- name: '194'
dtype: float32
- name: '195'
dtype: float32
- name: '196'
dtype: float32
- name: '197'
dtype: float32
- name: '198'
dtype: float32
- name: '199'
dtype: float32
- name: '200'
dtype: float32
- name: '201'
dtype: float32
- name: '202'
dtype: float32
- name: '203'
dtype: float32
- name: '204'
dtype: float32
- name: '205'
dtype: float32
- name: '206'
dtype: float32
- name: '207'
dtype: float32
- name: '208'
dtype: float32
- name: '209'
dtype: float32
- name: '210'
dtype: float32
- name: '211'
dtype: float32
- name: '212'
dtype: float32
- name: '213'
dtype: float32
- name: '214'
dtype: float32
- name: '215'
dtype: float32
- name: '216'
dtype: float32
- name: '217'
dtype: float32
- name: '218'
dtype: float32
- name: '219'
dtype: float32
- name: '220'
dtype: float32
- name: '221'
dtype: float32
- name: '222'
dtype: float32
- name: '223'
dtype: float32
- name: '224'
dtype: float32
- name: '225'
dtype: float32
- name: '226'
dtype: float32
- name: '227'
dtype: float32
- name: '228'
dtype: float32
- name: '229'
dtype: float32
- name: '230'
dtype: float32
- name: '231'
dtype: float32
- name: '232'
dtype: float32
- name: '233'
dtype: float32
- name: '234'
dtype: float32
- name: '235'
dtype: float32
- name: '236'
dtype: float32
- name: '237'
dtype: float32
- name: '238'
dtype: float32
- name: '239'
dtype: float32
- name: '240'
dtype: float32
- name: '241'
dtype: float32
- name: '242'
dtype: float32
- name: '243'
dtype: float32
- name: '244'
dtype: float32
- name: '245'
dtype: float32
- name: '246'
dtype: float32
- name: '247'
dtype: float32
- name: '248'
dtype: float32
- name: '249'
dtype: float32
- name: '250'
dtype: float32
- name: '251'
dtype: float32
- name: '252'
dtype: float32
- name: '253'
dtype: float32
- name: '254'
dtype: float32
- name: '255'
dtype: float32
- name: '256'
dtype: float32
- name: '257'
dtype: float32
- name: '258'
dtype: float32
- name: '259'
dtype: float32
- name: '260'
dtype: float32
- name: '261'
dtype: float32
- name: '262'
dtype: float32
- name: '263'
dtype: float32
- name: '264'
dtype: float32
- name: '265'
dtype: float32
- name: '266'
dtype: float32
- name: '267'
dtype: float32
- name: '268'
dtype: float32
- name: '269'
dtype: float32
- name: '270'
dtype: float32
- name: '271'
dtype: float32
- name: '272'
dtype: float32
- name: '273'
dtype: float32
- name: '274'
dtype: float32
- name: '275'
dtype: float32
- name: '276'
dtype: float32
- name: '277'
dtype: float32
- name: '278'
dtype: float32
- name: '279'
dtype: float32
- name: '280'
dtype: float32
- name: '281'
dtype: float32
- name: '282'
dtype: float32
- name: '283'
dtype: float32
- name: '284'
dtype: float32
- name: '285'
dtype: float32
- name: '286'
dtype: float32
- name: '287'
dtype: float32
- name: '288'
dtype: float32
- name: '289'
dtype: float32
- name: '290'
dtype: float32
- name: '291'
dtype: float32
- name: '292'
dtype: float32
- name: '293'
dtype: float32
- name: '294'
dtype: float32
- name: '295'
dtype: float32
- name: '296'
dtype: float32
- name: '297'
dtype: float32
- name: '298'
dtype: float32
- name: '299'
dtype: float32
- name: '300'
dtype: float32
- name: '301'
dtype: float32
- name: '302'
dtype: float32
- name: '303'
dtype: float32
- name: '304'
dtype: float32
- name: '305'
dtype: float32
- name: '306'
dtype: float32
- name: '307'
dtype: float32
- name: '308'
dtype: float32
- name: '309'
dtype: float32
- name: '310'
dtype: float32
- name: '311'
dtype: float32
- name: '312'
dtype: float32
- name: '313'
dtype: float32
- name: '314'
dtype: float32
- name: '315'
dtype: float32
- name: '316'
dtype: float32
- name: '317'
dtype: float32
- name: '318'
dtype: float32
- name: '319'
dtype: float32
- name: '320'
dtype: float32
- name: '321'
dtype: float32
- name: '322'
dtype: float32
- name: '323'
dtype: float32
- name: '324'
dtype: float32
- name: '325'
dtype: float32
- name: '326'
dtype: float32
- name: '327'
dtype: float32
- name: '328'
dtype: float32
- name: '329'
dtype: float32
- name: '330'
dtype: float32
- name: '331'
dtype: float32
- name: '332'
dtype: float32
- name: '333'
dtype: float32
- name: '334'
dtype: float32
- name: '335'
dtype: float32
- name: '336'
dtype: float32
- name: '337'
dtype: float32
- name: '338'
dtype: float32
- name: '339'
dtype: float32
- name: '340'
dtype: float32
- name: '341'
dtype: float32
- name: '342'
dtype: float32
- name: '343'
dtype: float32
- name: '344'
dtype: float32
- name: '345'
dtype: float32
- name: '346'
dtype: float32
- name: '347'
dtype: float32
- name: '348'
dtype: float32
- name: '349'
dtype: float32
- name: '350'
dtype: float32
- name: '351'
dtype: float32
- name: '352'
dtype: float32
- name: '353'
dtype: float32
- name: '354'
dtype: float32
- name: '355'
dtype: float32
- name: '356'
dtype: float32
- name: '357'
dtype: float32
- name: '358'
dtype: float32
- name: '359'
dtype: float32
- name: '360'
dtype: float32
- name: '361'
dtype: float32
- name: '362'
dtype: float32
- name: '363'
dtype: float32
- name: '364'
dtype: float32
- name: '365'
dtype: float32
- name: '366'
dtype: float32
- name: '367'
dtype: float32
- name: '368'
dtype: float32
- name: '369'
dtype: float32
- name: '370'
dtype: float32
- name: '371'
dtype: float32
- name: '372'
dtype: float32
- name: '373'
dtype: float32
- name: '374'
dtype: float32
- name: '375'
dtype: float32
- name: '376'
dtype: float32
- name: '377'
dtype: float32
- name: '378'
dtype: float32
- name: '379'
dtype: float32
- name: '380'
dtype: float32
- name: '381'
dtype: float32
- name: '382'
dtype: float32
- name: '383'
dtype: float32
- name: '384'
dtype: float32
- name: '385'
dtype: float32
- name: '386'
dtype: float32
- name: '387'
dtype: float32
- name: '388'
dtype: float32
- name: '389'
dtype: float32
- name: '390'
dtype: float32
- name: '391'
dtype: float32
- name: '392'
dtype: float32
- name: '393'
dtype: float32
- name: '394'
dtype: float32
- name: '395'
dtype: float32
- name: '396'
dtype: float32
- name: '397'
dtype: float32
- name: '398'
dtype: float32
- name: '399'
dtype: float32
- name: '400'
dtype: float32
- name: '401'
dtype: float32
- name: '402'
dtype: float32
- name: '403'
dtype: float32
- name: '404'
dtype: float32
- name: '405'
dtype: float32
- name: '406'
dtype: float32
- name: '407'
dtype: float32
- name: '408'
dtype: float32
- name: '409'
dtype: float32
- name: '410'
dtype: float32
- name: '411'
dtype: float32
- name: '412'
dtype: float32
- name: '413'
dtype: float32
- name: '414'
dtype: float32
- name: '415'
dtype: float32
- name: '416'
dtype: float32
- name: '417'
dtype: float32
- name: '418'
dtype: float32
- name: '419'
dtype: float32
- name: '420'
dtype: float32
- name: '421'
dtype: float32
- name: '422'
dtype: float32
- name: '423'
dtype: float32
- name: '424'
dtype: float32
- name: '425'
dtype: float32
- name: '426'
dtype: float32
- name: '427'
dtype: float32
- name: '428'
dtype: float32
- name: '429'
dtype: float32
- name: '430'
dtype: float32
- name: '431'
dtype: float32
- name: '432'
dtype: float32
- name: '433'
dtype: float32
- name: '434'
dtype: float32
- name: '435'
dtype: float32
- name: '436'
dtype: float32
- name: '437'
dtype: float32
- name: '438'
dtype: float32
- name: '439'
dtype: float32
- name: '440'
dtype: float32
- name: '441'
dtype: float32
- name: '442'
dtype: float32
- name: '443'
dtype: float32
- name: '444'
dtype: float32
- name: '445'
dtype: float32
- name: '446'
dtype: float32
- name: '447'
dtype: float32
- name: '448'
dtype: float32
- name: '449'
dtype: float32
- name: '450'
dtype: float32
- name: '451'
dtype: float32
- name: '452'
dtype: float32
- name: '453'
dtype: float32
- name: '454'
dtype: float32
- name: '455'
dtype: float32
- name: '456'
dtype: float32
- name: '457'
dtype: float32
- name: '458'
dtype: float32
- name: '459'
dtype: float32
- name: '460'
dtype: float32
- name: '461'
dtype: float32
- name: '462'
dtype: float32
- name: '463'
dtype: float32
- name: '464'
dtype: float32
- name: '465'
dtype: float32
- name: '466'
dtype: float32
- name: '467'
dtype: float32
- name: '468'
dtype: float32
- name: '469'
dtype: float32
- name: '470'
dtype: float32
- name: '471'
dtype: float32
- name: '472'
dtype: float32
- name: '473'
dtype: float32
- name: '474'
dtype: float32
- name: '475'
dtype: float32
- name: '476'
dtype: float32
- name: '477'
dtype: float32
- name: '478'
dtype: float32
- name: '479'
dtype: float32
- name: '480'
dtype: float32
- name: '481'
dtype: float32
- name: '482'
dtype: float32
- name: '483'
dtype: float32
- name: '484'
dtype: float32
- name: '485'
dtype: float32
- name: '486'
dtype: float32
- name: '487'
dtype: float32
- name: '488'
dtype: float32
- name: '489'
dtype: float32
- name: '490'
dtype: float32
- name: '491'
dtype: float32
- name: '492'
dtype: float32
- name: '493'
dtype: float32
- name: '494'
dtype: float32
- name: '495'
dtype: float32
- name: '496'
dtype: float32
- name: '497'
dtype: float32
- name: '498'
dtype: float32
- name: '499'
dtype: float32
- name: '500'
dtype: float32
- name: '501'
dtype: float32
- name: '502'
dtype: float32
- name: '503'
dtype: float32
- name: '504'
dtype: float32
- name: '505'
dtype: float32
- name: '506'
dtype: float32
- name: '507'
dtype: float32
- name: '508'
dtype: float32
- name: '509'
dtype: float32
- name: '510'
dtype: float32
- name: '511'
dtype: float32
- name: '512'
dtype: float32
- name: '513'
dtype: float32
- name: '514'
dtype: float32
- name: '515'
dtype: float32
- name: '516'
dtype: float32
- name: '517'
dtype: float32
- name: '518'
dtype: float32
- name: '519'
dtype: float32
- name: '520'
dtype: float32
- name: '521'
dtype: float32
- name: '522'
dtype: float32
- name: '523'
dtype: float32
- name: '524'
dtype: float32
- name: '525'
dtype: float32
- name: '526'
dtype: float32
- name: '527'
dtype: float32
- name: '528'
dtype: float32
- name: '529'
dtype: float32
- name: '530'
dtype: float32
- name: '531'
dtype: float32
- name: '532'
dtype: float32
- name: '533'
dtype: float32
- name: '534'
dtype: float32
- name: '535'
dtype: float32
- name: '536'
dtype: float32
- name: '537'
dtype: float32
- name: '538'
dtype: float32
- name: '539'
dtype: float32
- name: '540'
dtype: float32
- name: '541'
dtype: float32
- name: '542'
dtype: float32
- name: '543'
dtype: float32
- name: '544'
dtype: float32
- name: '545'
dtype: float32
- name: '546'
dtype: float32
- name: '547'
dtype: float32
- name: '548'
dtype: float32
- name: '549'
dtype: float32
- name: '550'
dtype: float32
- name: '551'
dtype: float32
- name: '552'
dtype: float32
- name: '553'
dtype: float32
- name: '554'
dtype: float32
- name: '555'
dtype: float32
- name: '556'
dtype: float32
- name: '557'
dtype: float32
- name: '558'
dtype: float32
- name: '559'
dtype: float32
- name: '560'
dtype: float32
- name: '561'
dtype: float32
- name: '562'
dtype: float32
- name: '563'
dtype: float32
- name: '564'
dtype: float32
- name: '565'
dtype: float32
- name: '566'
dtype: float32
- name: '567'
dtype: float32
- name: '568'
dtype: float32
- name: '569'
dtype: float32
- name: '570'
dtype: float32
- name: '571'
dtype: float32
- name: '572'
dtype: float32
- name: '573'
dtype: float32
- name: '574'
dtype: float32
- name: '575'
dtype: float32
- name: '576'
dtype: float32
- name: '577'
dtype: float32
- name: '578'
dtype: float32
- name: '579'
dtype: float32
- name: '580'
dtype: float32
- name: '581'
dtype: float32
- name: '582'
dtype: float32
- name: '583'
dtype: float32
- name: '584'
dtype: float32
- name: '585'
dtype: float32
- name: '586'
dtype: float32
- name: '587'
dtype: float32
- name: '588'
dtype: float32
- name: '589'
dtype: float32
- name: '590'
dtype: float32
- name: '591'
dtype: float32
- name: '592'
dtype: float32
- name: '593'
dtype: float32
- name: '594'
dtype: float32
- name: '595'
dtype: float32
- name: '596'
dtype: float32
- name: '597'
dtype: float32
- name: '598'
dtype: float32
- name: '599'
dtype: float32
- name: '600'
dtype: float32
- name: '601'
dtype: float32
- name: '602'
dtype: float32
- name: '603'
dtype: float32
- name: '604'
dtype: float32
- name: '605'
dtype: float32
- name: '606'
dtype: float32
- name: '607'
dtype: float32
- name: '608'
dtype: float32
- name: '609'
dtype: float32
- name: '610'
dtype: float32
- name: '611'
dtype: float32
- name: '612'
dtype: float32
- name: '613'
dtype: float32
- name: '614'
dtype: float32
- name: '615'
dtype: float32
- name: '616'
dtype: float32
- name: '617'
dtype: float32
- name: '618'
dtype: float32
- name: '619'
dtype: float32
- name: '620'
dtype: float32
- name: '621'
dtype: float32
- name: '622'
dtype: float32
- name: '623'
dtype: float32
- name: '624'
dtype: float32
- name: '625'
dtype: float32
- name: '626'
dtype: float32
- name: '627'
dtype: float32
- name: '628'
dtype: float32
- name: '629'
dtype: float32
- name: '630'
dtype: float32
- name: '631'
dtype: float32
- name: '632'
dtype: float32
- name: '633'
dtype: float32
- name: '634'
dtype: float32
- name: '635'
dtype: float32
- name: '636'
dtype: float32
- name: '637'
dtype: float32
- name: '638'
dtype: float32
- name: '639'
dtype: float32
- name: '640'
dtype: float32
- name: '641'
dtype: float32
- name: '642'
dtype: float32
- name: '643'
dtype: float32
- name: '644'
dtype: float32
- name: '645'
dtype: float32
- name: '646'
dtype: float32
- name: '647'
dtype: float32
- name: '648'
dtype: float32
- name: '649'
dtype: float32
- name: '650'
dtype: float32
- name: '651'
dtype: float32
- name: '652'
dtype: float32
- name: '653'
dtype: float32
- name: '654'
dtype: float32
- name: '655'
dtype: float32
- name: '656'
dtype: float32
- name: '657'
dtype: float32
- name: '658'
dtype: float32
- name: '659'
dtype: float32
- name: '660'
dtype: float32
- name: '661'
dtype: float32
- name: '662'
dtype: float32
- name: '663'
dtype: float32
- name: '664'
dtype: float32
- name: '665'
dtype: float32
- name: '666'
dtype: float32
- name: '667'
dtype: float32
- name: '668'
dtype: float32
- name: '669'
dtype: float32
- name: '670'
dtype: float32
- name: '671'
dtype: float32
- name: '672'
dtype: float32
- name: '673'
dtype: float32
- name: '674'
dtype: float32
- name: '675'
dtype: float32
- name: '676'
dtype: float32
- name: '677'
dtype: float32
- name: '678'
dtype: float32
- name: '679'
dtype: float32
- name: '680'
dtype: float32
- name: '681'
dtype: float32
- name: '682'
dtype: float32
- name: '683'
dtype: float32
- name: '684'
dtype: float32
- name: '685'
dtype: float32
- name: '686'
dtype: float32
- name: '687'
dtype: float32
- name: '688'
dtype: float32
- name: '689'
dtype: float32
- name: '690'
dtype: float32
- name: '691'
dtype: float32
- name: '692'
dtype: float32
- name: '693'
dtype: float32
- name: '694'
dtype: float32
- name: '695'
dtype: float32
- name: '696'
dtype: float32
- name: '697'
dtype: float32
- name: '698'
dtype: float32
- name: '699'
dtype: float32
- name: '700'
dtype: float32
- name: '701'
dtype: float32
- name: '702'
dtype: float32
- name: '703'
dtype: float32
- name: '704'
dtype: float32
- name: '705'
dtype: float32
- name: '706'
dtype: float32
- name: '707'
dtype: float32
- name: '708'
dtype: float32
- name: '709'
dtype: float32
- name: '710'
dtype: float32
- name: '711'
dtype: float32
- name: '712'
dtype: float32
- name: '713'
dtype: float32
- name: '714'
dtype: float32
- name: '715'
dtype: float32
- name: '716'
dtype: float32
- name: '717'
dtype: float32
- name: '718'
dtype: float32
- name: '719'
dtype: float32
- name: '720'
dtype: float32
- name: '721'
dtype: float32
- name: '722'
dtype: float32
- name: '723'
dtype: float32
- name: '724'
dtype: float32
- name: '725'
dtype: float32
- name: '726'
dtype: float32
- name: '727'
dtype: float32
- name: '728'
dtype: float32
- name: '729'
dtype: float32
- name: '730'
dtype: float32
- name: '731'
dtype: float32
- name: '732'
dtype: float32
- name: '733'
dtype: float32
- name: '734'
dtype: float32
- name: '735'
dtype: float32
- name: '736'
dtype: float32
- name: '737'
dtype: float32
- name: '738'
dtype: float32
- name: '739'
dtype: float32
- name: '740'
dtype: float32
- name: '741'
dtype: float32
- name: '742'
dtype: float32
- name: '743'
dtype: float32
- name: '744'
dtype: float32
- name: '745'
dtype: float32
- name: '746'
dtype: float32
- name: '747'
dtype: float32
- name: '748'
dtype: float32
- name: '749'
dtype: float32
- name: '750'
dtype: float32
- name: '751'
dtype: float32
- name: '752'
dtype: float32
- name: '753'
dtype: float32
- name: '754'
dtype: float32
- name: '755'
dtype: float32
- name: '756'
dtype: float32
- name: '757'
dtype: float32
- name: '758'
dtype: float32
- name: '759'
dtype: float32
- name: '760'
dtype: float32
- name: '761'
dtype: float32
- name: '762'
dtype: float32
- name: '763'
dtype: float32
- name: '764'
dtype: float32
- name: '765'
dtype: float32
- name: '766'
dtype: float32
- name: '767'
dtype: float32
- name: label
dtype: string
splits:
- name: train
num_bytes: 115576722
num_examples: 37500
- name: test
num_bytes: 38525585
num_examples: 12500
download_size: 211881897
dataset_size: 154102307
---
# Dataset Card for "Thunderbird_DistilRoBERTa_FT"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
DigitalClockwork/ultra-feedback-js-instruct | ---
license: mit
task_categories:
- text-generation
language:
- en
tags:
- code
pretty_name: Ultra Feedback JS
size_categories:
- 1K<n<10K
---
# Ultra Feedback JS Instruct
A subset of the wonderful and elegant [Ultra Feedback](https://huggingface.co/datasets/coseal/CodeUltraFeedback) dataset. Ratings are 1-5 ( inclusive )
## Example
```javascript
{
"inst": "Refine the subsequent JavaScript code snippet to obtain the cumulative total of elements within an array:\nlet numbers = [1, 2, 3]",
"author": "codellama-34b-instruct",
"fun": "let numbers = [1, 2, 3];\nlet total = numbers.reduce((a, b) => a + b);\nconsole.log(total); // Output: 6",
"rating": "5"
}
``` |
Gbssreejith/SM_Type2_dataset_finetuned_1 | ---
dataset_info:
features:
- name: image
dtype: image
- name: ground_truth
dtype: string
splits:
- name: train
num_bytes: 41522919.0
num_examples: 148
- name: test
num_bytes: 3386416.0
num_examples: 11
- name: val
num_bytes: 1783818.0
num_examples: 6
download_size: 46607059
dataset_size: 46693153.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: val
path: data/val-*
---
|
ovior/twitter_dataset_1713098987 | ---
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: 2706398
num_examples: 8110
download_size: 1537723
dataset_size: 2706398
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
yardeny/processed_bert_context_len_64 | ---
dataset_info:
features:
- name: input_ids
sequence: int32
- name: token_type_ids
sequence: int8
- name: attention_mask
sequence: int8
splits:
- name: train
num_bytes: 10153404360.0
num_examples: 25639910
download_size: 3558342060
dataset_size: 10153404360.0
---
# Dataset Card for "processed_bert_context_len_64"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
lecslab/glosslm-corpus-split | ---
dataset_info:
features:
- name: transcription
dtype: string
- name: glosses
dtype: string
- name: translation
dtype: string
- name: glottocode
dtype: string
- name: id
dtype: string
- name: source
dtype: string
- name: metalang_glottocode
dtype: string
- name: is_segmented
dtype: string
- name: language
dtype: string
- name: metalang
dtype: string
splits:
- name: train
num_bytes: 93769783
num_examples: 418718
- name: train_ID
num_bytes: 25048415
num_examples: 104928
- name: eval_ID
num_bytes: 2732125
num_examples: 11138
- name: test_ID
num_bytes: 2869258
num_examples: 11940
- name: train_OOD
num_bytes: 1817406
num_examples: 7356
- name: eval_OOD
num_bytes: 249722
num_examples: 984
- name: test_OOD
num_bytes: 240556
num_examples: 972
download_size: 38002540
dataset_size: 126727265
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: train_ID
path: data/train_ID-*
- split: eval_ID
path: data/eval_ID-*
- split: test_ID
path: data/test_ID-*
- split: train_OOD
path: data/train_OOD-*
- split: eval_OOD
path: data/eval_OOD-*
- split: test_OOD
path: data/test_OOD-*
---
# Multilingual IGT
<!-- Provide a quick summary of the dataset. -->
A compilation of various sources of interlinear glossed text (IGT) across nearly **two thousand languages** in a standardized format.
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
<!-- - **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed] -->
- **License:** CC BY 4.0
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** https://github.com/foltaProject/glosslm/settings
- **Paper [optional]:** Coming soon...
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
- Training models for IGT generation
- Linguistic analysis of IGT across languages
- Use of IGT in downstream applications such as machine translation
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
- IMTVault 1.1 (https://imtvault.org)
- ODIN (http://depts.washington.edu/uwcl/odin/)
- APiCS (https://apics-online.info)
- UraTyp (https://uralic.clld.org)
- Guarani Corpus (https://guaranicorpus.usc.edu)
- 2023 SIGMORPHON Shared Task (https://github.com/sigmorphon/2023GlossingST)
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Dataset Card Authors [optional]
[More Information Needed]
## Dataset Card Contact
[More Information Needed] |
SF-Corpus/EF_Full_Texts | ---
language:
- en
pretty_name: sf-nexus-ef-chapters-and-chunks
---
# Dataset Card for SF Nexus Extracted Features: Full Texts
## Dataset Description
- **Homepage: https://sfnexus.io/**
- **Repository: https://github.com/SF-Nexus/extracted-features-notebooks**
- **Point of Contact: Alex Wermer-Colan**
### Dataset Summary
The SF Nexus Extracted Features Full Texts dataset contains text and metadata from 403 mid-twentieth century science fiction books, originally digitized from Temple University Libraries' Paskow Science Fiction Collection.
After digitization, the books were cleaned using Abbyy FineReader.
Because this is a collection of copyrighted fiction, the books have been disaggregated.
Each row of this dataset contains one text as well as metadata about that text's title, author and publication.
### About the SF Nexus Corpus
The Paskow Science Fiction collection contains primarily materials from post-WWII, especially mass-market works of the New Wave era (often dated to 1964-1980).
The digitized texts have also been ingested into HathiTrust's repository for preservation and data curation; they are now viewable on HathiTrust's [Temple page](https://babel.hathitrust.org/cgi/ls?field1=ocr;q1=%2A;a=srchls;facet=htsource%3A%22Temple%20University%22;pn=4) for non-consumptive research.
For more information on the project to digitize and curate a corpus of "New Wave" science fiction, see Alex Wermer-Colan's post on the Temple University Scholars Studio blog, ["Building a New Wave Science Fiction Corpus."](https://sites.temple.edu/tudsc/2017/12/20/building-new-wave-science-fiction-corpus/).
### Languages
English
## Dataset Structure
This dataset contains disaggregated "chunks" of text from mid-twentieth century science fiction books and associated metadata. For example:
```
{'Unnamed': 7299,
'Title': 'MILLENNIUM,
'Author': 'VARLEY',
'Pub Year': '1983',
'Text': '"'All "A "All "As "As "Let's "Me, "Poor "The "The "When $15.86 '“All (And (Do (Flight (I (It’s (I’m (Made (See (This (Time (Well, (Who (blonde, (but (by (deceased) (flight (he (him) (if (in (not (one (or (sorry; (that’s (we’d (which (with (“What’re * * * * * * * * * * * * * * * , . . . . . . .. .. .. .. ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...” ...” ...” .1.. .38 .45 .ah, .can’t .hell, .it .take .that .that’s .the .transponder .uh, .uh, .uh, .well, .we’re .what / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / /MILLENNIUM /MILLENNIUM 0-425-06250-3 1 1 1 1 1 10 10 10,000? 10. 10/MILLENNIUM 100/MILLENNIUM 10016. 101 102/MILLENNIUM 103 104/MILLENNIUM 105 106/MILLENNIUM 107 108/MILLENNIUM 109 10:30. 10:45 10th, 11 110/MILLENNIUM 111 112/MILLENNIUM 113 114/MILLENNIUM 115 116/MILLENNIUM 117 118/MILLENNIUM 119 11:30 11:30. 11:30. 11____________ 11th, 11th. 12, 12/MILLENNIUM 120/MILLENNIUM 121 122/MILLENNIUM 123 124/MILLENNIUM 125 126/MILLENNIUM 127 128 129 12:56.” 12_______________ 12th 12th, 12th. 13 13 13. 130/MILLENNIUM 131 132/MILLENNIUM 133 134 135 136/MILLENNIUM 137 138/MILLENNIUM 139 13th 13th. 14/MILLENNIUM 140/MILLENNIUM 141 142/ 143 144/MILLENNIUM 145 146 147 148 149 14_________________ 14th 15 150/MILLENNIUM 151 152/MILLENNIUM 153 154/MILLENNIUM 155 156 157 158/MILLENNIUM 159 15___________ 16 16-year-old 160/MILLENNIUM 161 162/MILLENNIUM 163 164/MILLENNIUM 165 166/MILLENNIUM 167 168 169 16______ 17 170/MILLENNIUM 171 172/MILLENNIUM 173 174/MILLENNIUM 175 176 177 178/MILLENNIUM 179 17______________ 18/MILLENNIUM 180/MILLENNIUM 181 182/MILLENNIUM 183 184 185 186/MILLENNIUM 187 188/MILLENNIUM 189 18_________ 19 190/MILLENNIUM 191 192. 192/MILLENNIUM 193 1930s, 194/MILLENNIUM 1941, 1942.” 195 1955 1955 1955 1955 1955 1955 1955, 1955, 1955, 1955, 1955,” 1955. 1955. 1955. 1955. 1955. 1955.” 1955.” 1955? 1958 196/MILLENNIUM 1968 1968. 1968. 197 1976. 1978, 1978. 198/MILLENNIUM 1980 1980s 1980s 1980s. 1980s. 1983 1983 1983 1983 1983, 199 1996, 1996—so 19_____________ 1:45. 20 20 200 200/MILLENNIUM 202 203 2034. 204 205 206 207 208 209 20th 20th 20th 20th 20th, 20th. 20ths 20ths 20ths, 20ths. 210/MILLENNIUM 212/MILLENNIUM 214/MILLENNIUM 216/MILLENNIUM 218/MILLENNIUM 22 220 222 223 224 225 226 227 228 229 23 230 2300 232 233 234 235 236 237 238 239 24 240 242 243 244 245 246 247 248 249 25 26 27 28 29 2_______________ 3 30 3000 3000 31 32 33 34 35 35 35 35 35 35 35 35 35 35 35. 35“ 36 37 38 39 3:13, 3:14, 3_________________ 4 40 40,000 42 43 44 45 46 47 48 49 4:30 4_____________ 5 50 5000 51 52/MILLENNIUM 53 54 55 55time, 56 57 58/MILLENNIUM 580, 580, 59 5_____________ 6 60 62 63 637 64 65 66 67 68 69 6___________ 7 7 7, 70 72 727 727’s 74 747 747 747 747 747 747 747 747 747 747 747 747 747 747 747 747 747 747 747 747, 747, 747, 747. 747. 747. 747. 747. 747. 747; 747? 747—I 75 757,375 76 77 78 79 7:15. 8 80 81 82 83 84 85 86 87 88 880 880 880, 880’s 89 8__________________ 9 9 9 9 9 90 92 93 94 95 96 97 98 99 9:11 9:11 9:11,” 9:30. 9:56, 9___________ A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A&P? A&R A, A, A. ALPO?” AMERICA ARTCC ARTCC, ATC ATC ATC ATC’s ATC’s ATC’s, AUTHOR’S Abayta Abayta, Abayta. Abayta: Abayta: About About About About About About According Across Across Across Actually, Actually, Add Adelaide, Administration. Advise Africa After After After After After After After After After After After After Again Again Again, Again, Again, Against Age Age Age Age. Age. Agency. Agent Agent Age—or Ah, Ah... Air Air Air Air Air Air Air Air Air Air Air-Line Aircraft Airlines Airlines, Airlines, Airport Airport Airport, Airport, Airport. Airport. Airways. Alameda Alameda Alarms Albert Aldiss; Ali Ali Alka-Seltzer All All All All All All All All All All All All All All All All All All All All All All All All All All All All All All All All All All All All All All All All All All All All-Seeing Allison Almost Almost Along Alpha Alpha Also Also, Always Am Am Am Am Am Am Am Am Am Am Am Am, Am.” Am?” Ambler, Ambrose Amelia Amerenglish Amerenglish. Amerenglish; America. America. American American American American Amniocentesis. Among Among Amsterdam, Am—but Am’s An An An An An An An Anarchy And And And And And And And And And And And And And And And And And And And And And And And And And And And And And And And And And And And And And And And And And And And And And And And And And And And And And And And And And And And And And And And And And And And And And And And And And And And And And And And And And And And And And And And And And And And And And And And And And And And And And And And And And And And And And And And And And And And And And And And And And And And And And And And And And And And And And And And And And And And, And, And: And: And: And: Anderson; Anderson; Andromeda Anet Angeles Angeles Angeles, Angeles, Angeles, Angeles. Angeles. Angels. Another Another Another Another Another Another Antarctica. Antennas Antoine's, Any Any Any Any Any Any Any Any Anybody Anything Anything. Anything.” Anyway, Apaches, Apparently Apparently Apparently Apparently Apparently Apparently, Apple, Aquarius). Arabia Archibald Archibald Are Are Are Are Area Area Area. Arizona Arizona Arizona Arizona Arizona Arizona, Arizona, Arizona, Arizona. Arizona. Ark Ark. Armenia Army Army Army Army Army. Arnold Arnold Arnold Arnold Arnold Arnold Arnold, Arnold, Arnold’s Arthur Arthur, As As As As As As As As As As As As As As As As As As As As As As As As As As As Aside Asimov. Assuming Astronauts At At At At At At At At At At At At At At At At At At At At At At At At At At At At At At At At At At At At At At At At At At At At At Atlantic Atomic Attorney Author Avenue Avenue, Avenue, Aviation Aw, B B B, B. B. B. B. B. BALTIMORE, BART?” BC BC BC BC BC BC BC BC BC BC BC BC BC BC BC BC BC BC BC BC BC BC BC BC BC BC BC BC BC BC BC BC BC BC BC BC BC BC BC BC BC BC BC BC, BC, BC. BC. BC. BC. BC. BC. BC’s BERKLEY BERKLEY BOOKS, BOOK® Babylonian Back Bad Baker. Bakersfield. Bali, Ball Ball Ball, Ball, Ball,” Ball. Ball.” Balls, Baltimore Baltimore Baltimore Baltimore Baltimore Baltimore Baltimore Baltimore Baltimore Baltimore Baltimore Baltimore Baltimore,’’ Baltimore. Baltimore. Baltimore... Baltimore?” Bamum Bangladesh. Bannister Bannister, Barbara. Barbara. Bars Batavia, Battleship Bauhaus Bay Bay Bay Bay Bay Bay Bay. Bay. Ba—” Be Because Because Because Because, Bee Before Before Before Before Before Before, Behind Behind Behold Behold Being Being, Believe Belli, Beltway Ben Bengalis Berkeley Berkley Berkley Berkley Berkley Berkley Berkley Bermuda Bermuda Berry Beside Better Better Better Beverly Bible Bierce Big Big Big Big Big Big Big Big Big Big Big Big Bill Bill Bill Bill Bill Bill Bill Bill Bill Bill Bill Bill Bill Bill Bill Bill Bill Bill Bill Bill Bill Bill Bill Bill Bill Bill Bill Bill Bill Bill Bill Bill Bill Bill Bill Bill Bill Bill Bill Bill Bill Bill Bill Bill Bill Bill, Bill, Bill,’’ Bill,’’ Bill,” Bill,” Bill,” Bill. Bill. Bill. Bill. Bill. Bill. Bill. Bill. Bill. Bill.” Bill.” Bill.” Bill; Bill? Bill’s Bill’s Bill’s Bill’s Biological, Bird Birds Birmingham Bite Bizarre? Black Board Board Board Board Board Board Board Board Board Board Board Board Board Board Board Board, Board. Board. Board. Board. Board.” Board.” Board?” Boeing Boeing Boeing Boeing Boeing Boeing Boers, Bogart Book Book Boston Boston, Boston. Both Both Both Both Both Both Both Bova, Bova’s Bradbury; Braille. Breaking Breathe Brest Brest Brest, Brian Bridge Briley Briley Briley Briley Briley Briley Briley Briley, Briley,” Briley,” Briley,” Briley. Briley. Briley. Briley. Briley?” Brilliant Brindle Brindle Brindle Brindle. Brindle.” Broken Brooklyn Brooklyn Brunner; Buddha. Budweiser Building. Building.” Bulova Bureau Bureau—which Burt, Business, But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But But, But, Butch By By By By By By By By By By By By" By,’ By” B—was C C C C, C, C. C. C. C. C. C. C. C. C. C. C. C. C. C. C... C.O. CBS CB’s. CHP CHP CHP CHP. CIA CVR CVR CVR CVR CVR CVR CVR CVR. CVR. CVR’s CVR’s. Caesarean Calcutta Calcutta-Benares. California California California California California California California California California California, California, California, California. California. California. California. California. California? Call Call Call Call Calm Came Came Camels Camels. Camp; Can Can Canal. Canary Canary Canary Candidate, Canyon Can’t Capetown, Captain Captain Captain Captain. Captain’s Carnegie Carole Carole Carole Carole Carole Carole. Carpenter Carpenter Carpenter Carpenter Carpenter Carpenter, Carpenter. Carpenter. Carpenter. Carpenter. Carpenter. Carpenter. Carpenter’s Carpenter’s Carson Carson Cary Cary Casablanca. Cassidy Catheters Causes Cecily, Censorship. Centauri, Centauri. Center Center Central Century Certain Cessna Chairman, Chairman’s Chalk Chamber Chamber, Chamber. Changes, Charge Charge.” Charity Charlie Charlie’s Chemical, Cheops Cherokees Cherokees, Chicago Chicago, Chicago. Chicago? Chief Chief Chief Chief China, China. Chinese Chinese Chinese Christ Christ! Christ, Christ. Christmas Christmas Christmas Christmas Christmas Christmas Christmas, Christmas. Chuck Chuck Cirocco. Civilization Civilizations Clarke; Claus Cleaver-of-Heads, Cleaver-of-Heads—a Clorets Clorets. Close Close Coal Coast, Coast, Coast? Cockpit Cockpit Cockpit Cockpit Coconino Coffee, Coffee? Cold, Colt Come Come Come Come Company, Compared Compared Computer Computer Computer Computer Computer Computer Computer Computer, Computer. Computer. Computer?” Computers Computers, Congeniality Congress Congress, Congress-critters Congress. Congruency Connecticut Connecticut Constables, Constellation Constellation Constellation Constellation. Contra Contra Contrary Control Control Control Control Control, Control. Controller. Controllers Controllers’ Controller’s Cool, Copyright Corporation, Corporation, Corporation. Correct Correction: Cosmic Costa Costa Could Could Council Council Council Council Council Council Council Council Council Council Council Council Council Council Council Council Council Council Council Council Council Council Council Council, Council. Council. Council. Council. Council.” Council’s Country—the County County County. Coventry Coventry Coventry Coventry Coventry Coventry Coventry Coventry Coventry Coventry Coventry Coventry Coventry Coventry Coventry Coventry Coventry Coventry Coventry, Coventry, Coventry. Coventry. Coventry’s Coventry’s Cow Craig Craig Craig Craig Craig... Craig: Craig: Crain Crain Crain Crain Crain Crain Crain, Crain, Crain, Crain.” Crashes Crashes Cristabel Cristabel Cristabel Cubs. Cummings; Cunard Cut Cybernet D D D D. D. D. D. D.C. DAY. DC-10 DC-10 DC-10 DC-10 DC-10 DC-10 DC-10 DC-10 DC-10 DC-10 DC-10 DC-10 DC-10 DC-10 DC-10 DC-10 DC-10, DC-10, DC-10. DC-10. DC-10. DC-10.” DC-3 DC-6 DC-8, DC-lO’s DEMON. DO Dachau Dakota. Dali Damn Damn Damn, Damn, Damn, Darkness Darkness Data Data Data Dating David David David’s Davis Davis Davis Davis Davis, Davis, Davis: Day. Day? DeLisle DeLisle DeLisle DeLisle DeLisle DeLisle. DeLisle. DeLisle. DeLisle’s DeLisle’s DeLisle’s DeLisle’s, Dead Dead December December December December December December December December. December. December. December. Decisions Deep Define Define Denver, Department, Desert Desert Desert... Despair Destiny. Diablo Diablo Diablo. Diablo. Diablo” Did Did Did Did Did Did Did Did Didn’t Diego Diego Diego, Diego?” Disgust Disney Dixie Djakarta, Djakarta, Djakarta, Do Do Do Do Do Do Do Do Do Do Do Do Do Do Do Do Do Doctor Doctor Doctor Doctor Doctor Doctor Doctor Doctor, Doctor,” Doctor,” Doctor,” Doctor.’’ Doctor?” Doctor?” Doctors Dodgers Dodgers Does Does Does Does Does Don Don Don, Don?’’ Don?” Donald Donald Donald Don’t Don’t Don’t Don’t Don’t Don’t Don’t Don’t Don’t Don’t Don’t Douglas Down Down’s Drinking Drunk? Dulles Dulles,” During During Duty Duty Each Each Each Each Each Each Each Each Earhart. Early Early" Early,” Earth Earth Earth Earth Earth, Earth. East East East Eastern Eastern Easy. Eat-drink-and-be-merry. Ed Ed Edison Edison Edward Effect Einstein, Einstein. Eisenhower. Either Either Elder. Eleanor Electric Eleven-oh-one, Eli Eli Eli Eli Eli Eli Eli Eli: Eloi Eloi Encounters. End End End End End End Engineer England English. Enquirer. Enquirer. Entertainment Epilogue Erebus, Eric Essence Eternity Eternity, Eugene Eugene Eugene, Eugene, Even Even Even Even Even Even Even Even Even Even Even Even Even Even Even Even Events Events Eventually Eventually Everglades Everglades, Everglades? Every Every Every Every Every Every Everybody Everybody Everybody Everybody Everybody Everybody Everybody Everybody’s Everyone Everyone Everyone Everyone Everything Everything Everything Everything Excedrin Excuse Extra Eye. Eyewash F-86 FAA FAA FAA FAA FAA’s FBI FBI FBI FBI FBI FBI, FBI. FDR. FDR’s FDR’s FDR’s FOR Factors Facts Fairly Fairly Fall Fall, Famous Farther Faster Fate, Fear, Fed Fed Fed Fed Fed Fed, Fed, Fed. Fed. Federal Federal Federal Federal Fifteen Fifty Fifty! Finally Finally Finally Finally, Finally, Finally, Fine, Fine. Fingers First First First First First First First Five Flagstaff. Flight Flight Flight Flight Flight Flight Flight Flight Flight Florida Fly Fokker-Aerospatiale For For For For For For For For For For For For For For For For For For For For For For For For For For For For For For Force Foreign Forget Forgot Fortunately, Fortune? Forty, Forty-five Four, Frances Frances Francisco Francisco Francisco Francisco Francisco, Francisco.” Frankly, Freddie Freddie Freddie Freddie Freddie Freddie Freddie Freddie Freddie Freddie Freddie Freddie Freddie Freddie Freddie Freddie, Freddie, Freddie,” Freddie. Free Free Free Fremont—which French. French. From From From From From From From Frost, Funny Funny Future G-man, Gab. Galaxy. Garden, Garden. Garry Gary Gate Gate Gate Gate Gate Gate Gate Gate Gate Gate Gate Gate Gate Gate Gate Gate Gate Gate Gate Gate Gate Gate Gate Gate Gate Gate Gate Gate Gate Gate Gate Gate Gate Gate Gate Gate Gate Gate Gate Gate Gate Gate Gate Gate Gate Gate Gate Gate Gate Gate Gate Gate Gate Gate Gate Gate Gate Gate Gate Gate Gate Gate Gate Gate Gate Gate Gate Gate Gate Gate Gate Gate Gate Gate Gate Gate Gate Gate Gate Gate Gate Gate Gate Gate Gate Gate Gate Gate Gate Gate Gate Gate Gate Gate Gate Gate Gate Gate Gate Gate Gate Gate, Gate, Gate, Gate, Gate, Gate, Gate, Gate, Gate, Gate, Gate. Gate. Gate. Gate. Gate. Gate. Gate. Gate. Gate. Gate. Gate. Gate. Gate. Gate. Gate. Gate. Gate. Gate. Gate. Gate. Gate. Gate. Gate. Gate. Gate. Gate. Gate. Gate. Gate. Gate.” Gate; Gate?” Gate’s Gate’s Gees Geller’s Genalysis General General General, Genesis.’’ Genie George George George George George George, George?” Georgia German Get Getting Gideon Gideon Gil Gil Gil Gil Gil Gil, Gilbert Gilmore Girl Girl, Glancing Glenlivet Gloria Gloria. Gloria. Gloria?” Glorious Go Go Go Go Go Go-Team God God God God God God, God, God, God, God,” God-knows-how-many God. God. God. God. God.” Gods, Goes Goes Goes Gold Golgotha" Golgotha,” Good Gooney Gooney Gordon Gordon Gordon Gordon Gordon Gordon Gordon Gordon Gordon Gordy Gordy Gordy Gordy Gordy Gordy Gordy Gordy, Gordy. Gordy... Gordy.” Gordy? Gordy?’’ Gordy?” Got Government Governor Gradually, Grand Grandfather Grandfather Grant Grant Gray.” Gray?” Greek Green Green. Greenland. Greetings! Group, Guard Guardia Guardians Guardians Guess Gumby, Guys H-bombs HISTORY HOTLINE, HST Hacker Hacker Hacker. Had Half Half Hall. Hanoi. Hard Hardy Harlan Harold Harrison; Harry Harsh, Harvard, Haubner Haubner, Haubner—he Have Having Having Having He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He He Headache Heads Hearst Heartless, Hefty Hefty Heinlein; Hell Hell, Hell, Hell, Hell, Henry Hepburn Her Her Her Her Her Her Her Her Her Her Her Her Her Her Her Her Herbert Herbert Herculean Here Here Here Here, Here’s Here’s Herman Hertz Hertz Hey!” Hey, He’d He’d He’d He’d He’d He’d He’d He’d He’d He’d He’d He’d He’d He’d He’d He’d He’d He’ll He’ll He’ll He’ll He’s He’s He’s He’s He’s He’s He’s He’s He’s He’s He’s He’s He’s He’s He’s He’s He’s He’s He’s He’s He’s He’s He’s He’s He’s Highway Hijackers Hildy Hildy. Hills. Himalayas Hindus, His His His His His His His His His His His His His His His His His His His His His His His His His His His His Historians, Historical Hodgson; Holiday Holiday Hollywood Hollywood Hollywood Hollywood Hollywood, Hollywood, Home Honduras Honest. Hong Hope Hopi Hopis, Horowitz Houston. Houston—got How How How How How How How How How How How How How However, However, However, However, However, However. Hoxv’d Huey, Hueys Hugo Hugo Hugo Huguenots, Human Humans Humphrey Hupfeld. Hysteria, I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I" I, I, I, I,” I. I. I. I. I.. I.. I... I... I... I...” I.D. I.D. I.D. I.” I.” I? I? I?” I?” IBM IC IN ISBN: Ian Idle If If If If If If If If If If If If If If If If If If If If If If If If If If If If If If If If If If If If If If If If If If If If If If If If If If If If If If If If If If If If If If If If If If If If If If If If If If If If If If If If If If If If If If If If If If If If If If If If If If If If—” Ike.” Illinois, Imagination Important Impossible, In In In In In In In In In In In In In In In In In In In In In In In In In In In In In In In In In In In In In In In In In In In In In In In In In In In In In In In In In In In In In In In In In In In In Inc. Inches India Indian Indian Indian Indian Indian, Indians Indian—so Indications Inference: Inn Inn, Inn. Inside Inside Inside, Instead Instead Instead, Intelligence Intelligence Interest" Interest,” International International International Interstate Interstate Into Investigation—a Investigator Irving Is Is Is Is Is Is Is Is Is Is Is Isaac Islands, Islands, Islands,” Islands... Isn’t Isn’t It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It It's Italian Italian, Its It’ll It’ll It’s It’s It’s It’s It’s It’s It’s It’s It’s It’s It’s It’s It’s It’s It’s It’s It’s It’s It’s It’s It’s It’s It’s It’s It’s It’s It’s It’s It’s It’s It’s It’s It’s It’s It’s It’s It’s It’s It’s It’s It’s It’s It’s It’s It’s It’s It’s It’s It’s It’s It’s It’s It’s It’s It’s It’s It’s It’s It’s It’s It’s It’s It’s It’s It’s It’s It’s It’s It’s It’s I—” I—” I—” I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’d I’ll I’ll I’ll I’ll I’ll I’ll I’ll I’ll I’ll I’ll I’ll I’ll I’ll I’ll I’ll I’ll I’ll I’ll I’ll I’ll I’ll I’ll I’ll I’ll I’ll I’ll I’ll I’ll I’ll I’ll I’ll I’ll I’ll I’ll I’ll I’ll I’ll I’ll I’ll I’ll I’ll I’ll I’ll I’ll I’ll I’ll I’m I’m I’m I’m I’m I’m I’m I’m I’m I’m I’m I’m I’m I’m I’m I’m I’m I’m I’m I’m I’m I’m I’m I’m I’m I’m I’m I’m I’m I’m I’m I’m I’m I’m I’m I’m I’m I’m I’m I’m I’m I’m I’m I’m I’m I’m I’m I’m I’m I’m I’m I’m I’m I’m I’m I’m I’m I’m I’m I’m I’m I’m I’m I’m I’m I’m I’m I’m I’m I’m I’m I’m I’m I’m I’m I’m I’m I’m I’m I’m I’m I’m I’m I’m I’m I’m I’m I’m I’m—” I’ve I’ve I’ve I’ve I’ve I’ve I’ve I’ve I’ve I’ve I’ve I’ve I’ve I’ve I’ve I’ve I’ve I’ve I’ve I’ve I’ve I’ve I’ve I’ve I’ve I’ve I’ve I’ve I’ve I’ve I’ve I’ve I’ve I’ve I’ve I’ve I’ve I’ve I’ve I’ve I’ve I’ve I’ve I’ve I’ve I’ve I’ve I’ve I’ve I’ve I’ve I’ve I’ve I’ve I’ve I’ve I’ve I’ve I’ve I’ve I’ve I’ve I’ve I’ve I’ve I’ve I’ve I’ve JFK). Jack Jack Jack Jack Jack Jackie, Jane Jane January. Janz Janz Janz Janz Janz Janz Janz Janz Janz Janz Janz Janz Janz Janz Janz Janz, Janz, Janz. Janz. Janz. Janz? Janz?” Janz’s Japan Japan. Japanese Jean Jeff Jehovah. Jerry Jerry Jerry Jerry Jerry Jerry Jerry Jerry, Jerry, Jerry: Jerry: Jesus Jesus Jesus Jesus! Jesus. Jesus. Jesus. Jesus.” Jet JetStar JetStar JetStar JetStar. Jews Jockey. Joel John John John John John John Johnny Johnstown, Joyjuice. Juan Jumbos.” June Jupiter, Just Just Just Just Just Just Just Just Just Just Just Just Kampuchea. Kane Kankakee, Karakov Karakov Karakov, Karma, Kate’s Katharine Keane Keane Keane Keane Keane Keane Keane Keane Keane, Keane. Keane’s Keane’s Keep Keeping Keeping Kensington, Kensington, Kensington. Kevin Kevin Kevin Kevin Kevin Kickapoo Kid Kilworth; Knock, Kong. Kooky Korea Kuttner; L-1011, L. L.A. LAST LAX LAX LAX?’’ LOUISE La Lady Lake Lake, Land Land, Lanes Larry Larry Larry. Larry. Larry. Larry. Larry: Larry’s Larry’s Lars, Lars, Lars, Last Last Last Last Last Last Last Last Last Last Last Later Later, Later, Later, Later, Later, Latex; Laughter Laurel Law Law. Lawrence Lawrence Lawrence Lawrence Lawrence Lawrence Lawrence Lawrence Lawrence Lawrence Lawrence Lawrence Lawrence Lawrence Lawrence Lawrence Lawrence Lawrence Lawrence Lawrence Lawrence Lawrence Lawrence Lawrence Lawrence Lawrence Lawrence Lawrence Lawrence, Lawrence, Lawrence, Lawrence, Lawrence. Lawrence. Lawrence. Lawrence. Lawrence’s Lawrence’s Lawrence’s Lawrence’s Lawrence’s Lawrence’s Leading Leaning Leggio Leggio Leggio Leggio Leggio Leggio Leggio, Lest Lest Let Let Let Let Let’s Let’s Let’s Let’s Let’s Let’s Let’s Levitsky Levitsky, Lie, Lie. Life Like Like Like Like Like Like Like Like Lilly Lilly Lilly Lilly Lilly Lilly Lilly Lilly Lilly Lilly Lilly Lilly Lilly Lilly Lilly Lilly Lilly Lilly Lilly Lilly, Lilly. Lilly. Lilly. Lilly? Lilly’s Lilly’s Lilly’s Lilly’s Lilly’s Linus Listen Listen, Listen, Literally. Little Little Livermore Livermore Livermore Livermore, Livermore, Livermore. Lloyd Lockheed Lockheed Lockheed Lockheed Logically, London London London London London London London, Look Look Look Look Look Looking Looking Looking Looking Lord Lord, Lori Los Los Los Los Los Los Los Louise Louise Louise Louise Louise Louise Louise Louise Louise Louise Louise Louise Louise Louise Louise Louise Louise Louise Louise Louise Louise Louise Louise Louise Louise Louise Louise Louise Louise Louise Louise Louise Louise Louise Louise Louise Louise Louise Louise Louise Louise Louise Louise Louise Louise Louise Louise Louise Louise Louise Louise Louise Louise Louise Louise Louise Louise Louise Louise Louise Louise Louise Louise Louise Louise Louise Louise Louise Louise Louise Louise Louise Louise Louise Louise, Louise, Louise, Louise, Louise, Louise, Louise, Louise,’’ Louise,” Louise,” Louise,” Louise,” Louise,” Louise,” Louise,” Louise,” Louise,” Louise,” Louise. Louise. Louise. Louise. Louise. Louise. Louise. Louise. Louise. Louise. Louise. Louise. Louise. Louise. Louise... Louise... Louise.’’ Louise.’’ Louise.” Louise.” Louise.” Louise? Louise?’’ Louise?” Louises Louise’s Louise’s Louise’s Louise’s Louise’s Louise’s Louisville, Love Luci Lucille Luckies Luckily, Lucky Lucy.” M.I.T. M.I.T., MEMBERS—Notify MGM/UA MILLENNIUM MILLENNIUM MILLENNIUM MILLENNIUM MILLENNIUM MILLENNIUM MILLENNIUM MILLENNIUM MILLENNIUM MILLENNIUM MILLENNIUM MILLENNIUM MILLENNIUM MILLENNIUM MILLENNIUM MILLENNIUM MILLENNIUM MILLENNIUM MILLENNIUM MILLENNIUM MILLENNIUM MILLENNIUM MILLENNIUM MILLENNIUM MILLENNIUM MILLENNIUM MILLENNIUM MILLENNIUM MILLENNIUM MILLENNIUM MILLENNIUM MILLENNIUM MILLENNIUM MILLENNIUM MILLENNIUM MILLENNIUM MILLENNIUM MILLENNIUM MILLENNIUM MILLENNIUM MILLENNIUM MILLENNIUM MILLENNIUM MILLENNIUM MILLENNIUM MILLENNIUM MILLENNIUM MILLENNIUM MILLENNIUM MILLENNIUM MILLENNIUM MILLENNIUM MILLENNIUM MILLENNIUM MILLENNIUM MILLENNIUM MILLENNIUM MILLENNIUM MILLENNIUM MILLENNIUM MILLENNIUM MILLENNIUM MILLENNIUM MILLENNIUM MILLENNIUM MILLENNIUM MILLENNIUM MILLENNIUM MILLENNIUM MILLENNIUM MILLENNIUM MILLENNIUM MILLENNIUM MILLENNIUM MILLENNIUM MILLENNIUM MILLENNIUM MILLENNIUM MILLENNIUM MILLENNIUM MILLENNIUM MILLENNIUM MILLENNIUM MILLENNIUM MILLENNIUM MILLENNIUM MILLENNIUM MILLENNIUM MILLENNIUM MILLENNIUM MILLENNIUM MILLENNIUM MILLENNIUM MILLENNIUM MILLENNIUM MILLENNIUM MILLENNIUM MILLENNIUM MILLENNIUM MILLENNIUM MILLENNIUM MILLENNIUM MILLENNIUM MILLENNIUM MILLENNIUM MILLENNIUM MILLENNIUM MILLENNIUM MILLENNIUM MILLENNIUM MILLENNIUM MILLENNIUM MacQuitty, Macbeth,” Macbeth.” Machine Machine Machine, Machines, Mack Madison Magic Maidive Maintenance Maintenance Makes Man Man Man Man Man Man, Mandy Mandy Mandy Mandy Mandy Mandy Mandy Mandy Mandy Mandy Mandy Mandy, Mandy, Mandy. Mandy. Manhandling! Manual.” Many Many Many Marin Mars, Martin Martin Martin Martin Martin Martin Martin Martin Martin Martin Martin Martin Martin Martin Martin Martin Martin Martin Martin Martin Martin Martin Martin Martin Martin Martin,’’ Martin. Martin. Martin. Martin’s Mary Mary Marybeth Marybeth Marybeth Marybeth Maryland. Maryland? Maryland?” Marylander-Columbians.” Mateo Maurice, Maybe Maybe Maybe Maybe Maybe Maybe Maybe Maybe Maybe Maybe Maybe Maybe Maybe Maybe Maybe Maybe Maybe Maybe Maybe Maybe Maybe Maybe Maybe Maybe Maybe Maybe Maybe Maybe Maybe Maybe Maybe Maybe Maybe Maybe Maybe Maybe Maybellene. Mayer Mayer Mayer Mayer Mayer Mayer Mayer Mayer Mayer Mayer Mayer Mayer Mayer Mayer Mayer Mayer Mayer Mayer Mayer Mayer Mayer Mayer Mayer Mayer Mayer Mayer Mayer Mayer Mayer Mayer Mayer Mayer Mayer Mayer Mayer Mayer Mayer Mayer Mayer Mayer Mayer Mayer Mayer Mayer Mayer Mayer Mayer Mayer Mayer Mayer Mayer, Mayer, Mayer, Mayer, Mayer, Mayer, Mayer, Mayer, Mayer, Mayer, Mayer, Mayer, Mayer,’’ Mayer,” Mayer. Mayer. Mayer. Mayer. Mayer. Mayer. Mayer. Mayer. Mayer. Mayer. Mayer. Mayer.” Mayer; Mayer’s Mayer’s Mayer’s Mayer’s Mayer’s Mayer’s Mayer’s Mayer’s McDonnell-Douglas McRae, Mconel, Me Me, Me, Meanwhile Medics Melvin Member Member, Men Menlo Merely Methodists, Metz. Michael Mike Miles Millennium, Miller; Mine Minor Minoru Minoru Minoru Minoru Minoru Minoru Minoru’s Minutes” Miss Mission: Mister Mister Mister Mister Mister Mister Mister Mister Mister Mister Mister Mister Mister Mister Mister Mister Mister Model-T. Mojave Mommy Monday Monday, Moorcock; More More More: Morlock. Morlocks Morlocks Morlocks. Mornings Moroccans: Moses,” Most Most Most Most Most Most Most Most Most Most Most Most Most Most Most Most Most Most Mostly Mostly Mounds Mount Mount Mount Mount Mr. Mr. Mr. Mr. Mr. Mr. Mr. Mr. Mr. Mr. Ms. Ms. Much Much Much Musing Must My My My My My My My My My My My My My My My My My My My My My My My My My My My My My My My My My My My My My My My Myself, Myself, Myself, NBA NEW NOT NOTE NTSB NTSB NTSB NTSB NTSB. Nader. Nam Nameless Nameless Nameless Nameless Names Nancy Nancy Nancy Naomi, Naomi. Nashua National National National National National National National National National National National National National. National. Naturally, Naturally, Naturally, Naturally, Naturally, Naturally, Navajo Navajo Navajos Navajos, Naval Navy Navy, Nazis, Nebula Nebula Neither Nepal, Nero Never Never Never Never-bom, Never-neverland Nevertheless, New New New New New New New New New New New New New New New New Newcastle. News News Newton, Next Next Next Night Night Night Night Nineteen-eighties No No No No No No No No No No No No No No No No No No No No No No No No No No No No No No No No, No, No, No, No, No. Noah’s Nobel Nobody Nobody Nobody Nobody Nobody Nobody Nobody Nobody Nobody Nobody Nobody Nobody Nobody Nobody Nobody Nobody. None None None None Normally Normally, Norman North Not Not Not Not Not Not Not Not Not Not Not Not Not Not Not Not Not Not Not Not Not Not Nothing Nothing Nothing Nothing Nothing Nothing Nothing. Nothing. Now Now Now Now Now Now Now Now Now Now Now Now Now Now, Now, Now. Nuclear. O OF OF OPEN OPHIUCHI Oahu, Oakland Oakland Oakland Oakland Oakland Oakland Oakland Oakland Oakland Oakland Oakland Oakland Oakland Oakland Oakland Oakland Oakland, Oakland, Oakland, Oakland, Oakland, Oakland, Oakland, Oakland,” Oakland-San Oakland-San Oakland. Oakland. Oakland. Oakland. Oakland. Oakland. Oakland. Obviously Obviously, Obviously, Ocean Oddly, Of Of Of Of Of Of Of Office Office Office Office Office, Office. Office. Officer Officer Officer/IIC,” Office” Oh, Oh, Oh, Oh, Oh, Oh, Oh, Oh, Oh, Oh, Oh, Oh, Oh, Oh. Ohio Oil Okay, Okay, Okay, Okay, Okay, Okay. Okay. Okay. Okay. Okay. Old On On On On On On On On On On On On On On On On On On On Once Once Once Once Once, Once, One One One One One One One One One One One One One One One One One One One One One One One One One One One One One One One One One One One One One One One One One One One, One. One... One: Only Only Only Only Operations Operations Operations Operations Operations Operations Operations Operations Operations Operations Operations Operations Operations Operations Operations Operations Operations Operations, Operations, Operations, Operations, Operations. Operations. Operations. Operations. Oppenheimer, Or Or Or Or Or Or Or Or Or Or Or Or Or Or Or Or Or Or Or Or, Or: Oregon Oregon Organisation, Organization.” Originally Orly, Otherwise, Otherwise, Our Our Our Our Our Our Out Out Out, Outside, Outside, Outsiders Over Over Oz. P.T. PATCO PERSISTENCE PRC PRINTED PRINTING PSA PSA PSA PSA Pacific Pacific Pacific Pacific Pan Pan Pan Pan Pan Pan Pan Pan Pan Pan Pan Pan Pan Pan Pan Pan Pan Papers Paradox! Paradox, Paradox.” Paris Paris, Paris-Frankfurt, Park Park Park.” Parkersburg, Parrish, Part Part Part Part Part Part Part Parts Parts Passengers Patrol, Patty Pauling, Peabody,’’ Penthouse, People People People People’s Perhaps Perhaps Perrier Persistence Personally, Petcher Petcher Petcher Petcher Petcher Petcher Petcher Petcher Petcher, Petcher, Petcher, Petcher. Petcher. Petcher.” Petcher.” Petcher—or Petcher’s, Peter Peter Peter Pharaoh’s Phil Phil Phil,” Phoenix Phoenix Phoenix Phoenix Phoenix. Phoenix?” Physics, Picture Pilots Pinky Pinky Pinky Pinky Pinky Pinky Pinky Pinky Pinky Pinky Pinky Pinky, Pinky, Pinky. Pinky. Pinky?” Pinky’s Pinky’s Pinky’s Piper Plan. Please Plenty Plus, Politically Pop, Pop, Pope Portland Possibly Possibly Possibly Post Post Post Post Post Post Post Post. Poul Poul Power Powers Powers Powers Powers, Powers. Pre-Destination, Preakness Predestination Prentice, Presently President President President President Presidents. Preston Presumably Pretty Pretty Pride Prize. Probably Probably Probably Productions Productions Professor Programmers’ Programming Project Project Project Project Project Project, Project, Project, Project. Project. Project. Project.” Project.” Prologue Prologue: Proximity Publishing Publishing Pursuant Pusher.” Put Putting Q Q Q Quadraplegic. Questions Questions, Quick R Ralph Ralph Ralph Ralph Ralph Ralph Ralph Ralph Ralph Ralph Ralph, Ralph? Ralph? Ralph’s Ralph’s Ralph’s Ralph’s Ralph’s Rangoon Rangoon Rangoon, Rank Rape Rapid Ray Ray Ready-Room, Really. Recorder Recorder Recorder Recorder, Recorder. Recorders Recorders Records. Recovery Red Redford Redford Redford Redford, Redford. Regency Region Region Relativity Remember Remember Remember, Reporters Republic Rescue Reservations Reynolds; Right Right Right Right Right Right Right. Right. Risks River Robert Robert Robert Robert Robert Robert Rockwell Rockwell Rockwell Rockwell Rockwell Rockwell Rockwell Rockwell Rockwell, Rockwell: Rockwell: Rockwell: Rockwell’s Roentgen Roentgen... Rog Rog Roger Roger Roger Roger Roger Roger Roger Roger Roger Roger Roger Roger Roger, Roger. Roman Roman Round Round Roy Rule Running Russians, Ryan Ryan Ryan, SAC, STATES Sacramento Sad Safer. Safety Safety Safety Safety Safety Safety Safety Safety—” Salvador Sam Sam Same Same San San San San San San San San San San Santa Santa Santa Sarah Sarah Sarah Sarah Sarah,’’ Saudi Say Schenectady, Schrader Schrader Schrader Schrader, Schuyler Scottish Screw Seaboard. Seasons. Seattle Seattle, Seattle, Second Seconds See See See See Seen Seen Seibel Seibel Seibel, Seiko Seiko Seiko Selectrics Senator Senator Senators Series, Series. Series. Service Set Seven Seventy-three Several Several Several Shadow Shadow Shame. Shanghai. She She She She She She She She She She She She She She She She She She She She She She She She She She She She She She She She She She She She She She She She She She She She She She She She She She She She She She She She She She She She She She She She She She She She She She She She She She She She She She She She She She She She She She She She She She She She She She She She She She She She She She She She She She She She She She She She She She She She She She She She She She She She She She She She She She She She She She She She She She She She She She She She She She She She She Sheepdog, Sheltie. Sheppard Sheppard Sheppard Sheriff’s Sherman Sherman Sherman Sherman Sherman Sherman Sherman Sherman Sherman Sherman Sherman Sherman Sherman Sherman Sherman Sherman Sherman Sherman Sherman Sherman Sherman Sherman Sherman Sherman Sherman Sherman Sherman Sherman Sherman Sherman Sherman Sherman Sherman Sherman Sherman Sherman Sherman Sherman Sherman Sherman Sherman Sherman Sherman Sherman Sherman Sherman Sherman Sherman Sherman Sherman Sherman Sherman Sherman Sherman Sherman Sherman Sherman Sherman Sherman Sherman Sherman Sherman Sherman Sherman Sherman Sherman Sherman Sherman Sherman Sherman Sherman Sherman Sherman Sherman Sherman Sherman Sherman Sherman Sherman Sherman Sherman Sherman Sherman Sherman, Sherman, Sherman, Sherman, Sherman, Sherman, Sherman, Sherman, Sherman,” Sherman-the-therapist Sherman. Sherman. Sherman. Sherman. Sherman. Sherman. Sherman. Sherman. Sherman. Sherman. Sherman. Sherman. Sherman. Sherman. Sherman. Sherman. Sherman. Sherman. Sherman.’’ Sherman.” Sherman.” Sherman.” Sherman? Sherman?” Sherman?” Sherman?” Sherman—among Sherman’s Sherman’s Sherman’s Sherman’s Sherman’s. Shetland She’d She’d She’d She’d She’d She’d She’d She’d She’d She’d She’d She’d She’d She’ll She’s She’s She’s She’s She’s She’s She’s She’s She’s She’s She’s Ship Ship Ship. Ship. Shit. Short Shorthanded, Shortly Should Should Show Sianis, Signed, Signed: Silesia Silverberg. Silverberg; Silvers Simple. Simply Simultaneously, Since Since Since Since Since Since Sit Sit Sitting Sitting Skydivers Sleepless Slims Slims. Small Smith Smith Smith Smith Smith Smith Smith Smith Smith Smith Smith Smith Smith Smith Smith Smith Smith Smith Smith Smith Smith Smith Smith Smith Smith Smith Smith Smith Smith Smith Smith Smith Smith Smith Smith Smith Smith Smith Smith Smith Smith Smith Smith Smith Smith Smith Smith, Smith, Smith, Smith, Smith, Smith, Smith, Smith, Smith, Smith, Smith, Smith, Smith, Smith, Smith, Smith, Smith,’’ Smith,” Smith. Smith. Smith. Smith. Smith. Smith. Smith. Smith. Smith.” Smith: Smith? Smith?’’ Smith?” Smith?” Smith?” Smith—I’d Smith—you Smith’s Smith’s Smith’s Smith’s Snatch Snatch Snatch Snatch Snatching So So So So So So So So So So So So So So So So So So So So So So So So So So So So So So So So So So So So So So So So So So So So So So So So So So So So So So So So So So So So So So So So So So So So So So So So So So So So So So So So So So So So So Some Some Some Some Some Some Some Some Some Some Some Some Some Some Some Some Some Some Some Some Some Some Somebody Somebody Somebody Somebody Somebody Somebody Somebody Somebody Somebody—I’m Somehow, Someone Something Something Something Something Something Something Something Something Something Something Sometimes Sometimes Sometimes Sometimes Sometimes Sometimes Sometimes Sometimes Sometimes Sometimes Sometimes Sometimes Sometimes Sometimes Sometimes Sometimes Somewhere Somewhere Son Son Son.” Sondergard Sondergard Sondergard Sondergard Sondergard Sondergard Sondergard, Sondergard. Sondergard’s Sorry Sound Sound Southern Souvenirs? Spanish Special Special Special Special-Agent-in-Charge, Spielberg, Spitfire. Sprague Springs, Square Square Square. Square. Square. Square.” Square?” Squashed Squatting Standard Standard Standing Stanford. Stanley Stanley Stanley Stanley Stanley Stanley, Stanley, Stanley, Stanley, Stanley. Stanley. Stanley—dressed Star State States States States Stearman Stearman Stefan. Stefan. Steve Still Still Still, Still, Still, Still, Still, Still, Still, Still, Still, Still, Stop Stop, Strangely, Street Street. Stretching Strictly Strike Stroke Strunk Stuff Stunning Sturges, Such Suddenly Suddenly, Suffer, Suffering Suicide Suicide Suicide. Suitcases Sullivan Sullivan’s Sun, Sundance Supreme Sure Sure, Surely Surely Surgeon Suwannee, Swahili Swanson’s Swedish Swiss Swiss THE THE THE THE TITAN TITAN, TM TNT, TV TV Take Take Take Take Take Take Take Talk Tang-shen Tape: Team Team Team Team Team Team. Tear Technically Technically, Technically, Technically, Teheran Teheran Teheran Teheran Television Tell Tell Tell Tell Tell Tell Tell Teller. Tempers Temporal Temporal Ten Ten Ten Ten Tenerife Tenerife. Tenn; Testament Testimony Testimony Testimony Testimony Testimony Testimony Testimony Testimony Testimony Testimony Testimony Testimony Testimony Testimony Testimony Testimony Testimony Testimony Testimony Testimony Testimony Testimony Testimony Testimony Texas Texas Texas. Thank That That That That That That That That That That That That That That That That That That That That That That That That That That That That That That That That That That That That That That That That That That That That That That That That That That That That That That That That That That’ll That’s That’s That’s That’s That’s That’s That’s That’s That’s That’s That’s That’s That’s That’s That’s That’s That’s That’s That’s That’s That’s That’s That’s That’s That’s That’s That’s That’s That’s That’s That’s That’s The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The Their Their Their Their Then Then Then Then Then Then Then Then Then Then Then Then Then Then Then Then Then Then Then Then Then Then Then Then Then Then Then Then Then Then Then Then Then Then Then Then Then Then Then Then Then Then Then Then Then Then Then Then Then Then Then Then Then Then Then Then Then Then Then Then Then Then Then Then Then Then Then Then Then Then Then Then Then Then Then, Theoretically Theories Theory. There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There There’d There’s There’s There’s There’s There’s There’s There’s There’s There’s There’s There’s There’s There’s There’s There’s There’s There’s There’s There’s There’s. These These These These These These They They They They They They They They They They They They They They They They They They They They They They They They They They They They They They They They They They They They They They They They They They They They They They They They They They They They They They They They They They They They They They They They They They They They They They They They They They They They They They They They They They They They They They They They They They They They They They They They They They They They They They They They They They They They They They They They They They They They They They They They’d They’d They’d They’d They’d They’d They’d They’d They’ll They’ll They’ll They’ll They’re They’re They’re They’re They’re They’re They’re They’re They’re They’re They’re They’re They’re They’re They’re They’re They’re They’ve They’ve Things Things Things Things Things Things Things Things Thinking Thinking Thinking Thinning Thirty, This This This This This This This This This This This This This This This This This This This This This This This This This This This This This This This This This This This This This This This This This, Thomas Thomkins, Thor Those Those Those Those Those Those Those Those Those, Though Though Though Three Three-quarters Thunder" Thunder,” Thunderhilton Thursday Thus, Thus, Time Time Time Time Time Time Time Time Time Time Time Time Time Time Time Time Time Time Time Time Time, Time, Time. Time. Time. Time? Times Times." Timex Tiny Titanic Titanic, Titanic. Titanic: To To To To To To To To To To To To To To Today, Tom Tom Tom Tom Tom Tom Tom Tom Tom Tom Tom Tom Tom Tom Tom Tom Tom Tom Tom Tom Tom Tom Tom Tom Tom Tom Tom Tom Tom Tom Tom Tom Tom Tom Tom Tom Tom Tom Tom Tom Tom Tom Tom Tom Tom Tom Tom Tom, Tom, Tom. Tom. Tom. Tom. Tom. Tom. Tom. Tom. Tom. Tom. Tom. Tom. Tom. Tom.’’ Tom.” Tom.” Tom: Tom: Tomorrowland). Tomorrow’s Tom’s Tom’s Tonolea, Tony Tony Tony Tony Tony Tony Tony Tony Tony Tony Tony Tony. Toss Tost Traffic Traffic Traffic Traffic Traffic Traffic Traffic Traffic Trans Transit.” Transportation Transportation Transportation Transportation Transportation Transportation Travel Travel...) Travels, Treasury Treat Triangle. Triangle. Tricky Trouble Trouble Try Trying Turn Twelve Twenty Twenty Twenty-six Twice Two Two Two Two Two Two Two Two Two Two, Two, Two. Two: Two: Twonky" Twonky,” Tyson, U.S. U.S. U.S. UFO’s UNITED UNIVAC UNTIL Ultimately, Under Understand Union?” Union— United United United United United United United United United United United United United United United United United United United United United, United, United. United. United. United?” Universe, University. Unless Until Until Uptime, Uri Usually Usually Usually Utah Utopia! VISION, Valhalla. Valium Various Varley Varley Varley Varley Varley Vem Vem Vem.” Very Very Vicks Vicks Vicks Vicks Victor Viet-Nam Viet-Nam—and View Viking Virginia Virginia Virginia Virginia Virginia. Vision,” Voice Voice Voice Voice W. WIZARD WIZARD, Wagner. Wake Wallace Walt Wanna Want War War War War.” Warm Warrior!" Warrior!” Wars, Was Was Was Was" Was,” Washington Washington Washington Washington Washington Washington Washington Washington Washington Washington, Washington. Washington. Washington. Washington.” Washington.” Washingtonians, Watches Watches Way Wayne Wayne Wayne We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We We Weapons Weather Well, Well, Well, Well, Well, Well, Well, Well, Well, Well, Well, Well, Well, Well, Well, Well, Well, Well, Well, Well, Wells Wells; Wells’ Went Went Were West West West West We’d We’d We’d We’d We’d We’d We’d We’d We’d We’d We’d We’d We’ll We’ll We’ll We’re We’re We’re We’re We’re We’ve We’ve We’ve We’ve We’ve We’ve What What What What What What What What What What What What What What What What What What What What What What What What What What What What What What What What What What What What What What What What What What What What What What What What What What What What What What What What What What What What What What What Whatever Whatever Whatever Whatever Whatever Whatever What’s What’s What’s What’s What’s What’s What’s What’s What’s When When When When When When When When When When When When When When When When When When When When When When When When When When When When When When When When When When When When When When When When When When When When When When When When When When When When When When When When When When Where Where Whereas Where’s Where’s Where’s Where’s Where’s Whether Whether Which Which Which Which Which Which Which While While While While While While While While White.) Whitmore, Who Who Who Who Who Who Who Who Who Who Who Who Who Who Whoever Whose Who’s Why Why Why Why Why Why Why Why Why Why Why Why, Wilbur Wilhelm Will Will Will Will William William William William William William William Willis Willis Willis, Window Window Window Window Window Window Window Window Window Window Window Window Window Window With With With With With With With With With With Without Without Without Witnesses. Wizard Wolfe Woman, Word Words Words Words Words Words,” Words” Working World World World World World World World World World" World" World,” World,” Worse Would Would Would Would Wright X Xerox Xerox YORK Yeager, Year. Years Years Year’s Yesterday Yet Yet Yet Yet Yet Yet Yet Yokohama Yokohama Yokohama Yokohama, York York York York York York York York York, York, York, York,” York. You You You You You You You You You You You You You You You You You You You You You You You You You You You You You You You You You You You You You You You You You You You You You You You You You You You You You You You You You You You You You You You You You You You You You You You You You You You You You You You You You You You You You You You You You You You You You You You You You, Your Your Your Your Your Your Your Your You’d You’d You’d You’d You’d You’ll You’ll You’ll You’re You’re You’re You’re You’re You’re You’re You’re You’re You’re You’re You’re You’re You’ve You’ve You’ve You’ve You’ve You’ve Zombies—' Zombies—’ Z’s [vital], a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a.. a.d. a.m. a.m. a.m., aback abacus abandoned abashed. ability ability ability able able able able able able able able able able able able able able able able able able aboard aboard aboard aboard. aboard. aboard; aboard? aboard?” aborted abound, about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about, about, about, about, about, about, about, about,” about. about. about. about. about. about. about. about. about... about.” about: about; about? about?” about—” about—” above above above above above abreast abruptly absence absence absence. absolute absolute absolute.’’ absolutely absolutely absolutely absolutely absolve absorbed abundance abuse acceleration accelerator. accelerators accelerators.” accent, accent. accent—something accept accept accept accept accept accept acceptable accepted accepted accepted accepting access access access access access accessed, accessible accident accident accident accident accident accident accident, accident,” accident. accidentally accidents.” accommodate accomplishments. according according according account account accounts accounts accounts. accumulates accurate accurate accurate accurate, accurately accusing ache. achieved achieved. achieved. aching achingly acknowledge acknowledge acknowledge acknowledge acknowledgement: acquaintances. acquainted,” acquired acre, acres. across across across across across across across across across across across across across across, act act act act act, acted action action action, action, action. action?” actions actions actions actions actions actions?” activity activity activity actress, actual actual actual actual actuality, actuality. actually actually actually actually actually actually actually actually actually actually actually actually actually actually actually actually actually actually actually actually actually actually actually actually actually actually actually actually actually actually, actually,” actually. add add add add add added added added added, addict adding addition addition addition addition, address address: addressing add— adjective adjust adjusted adjusted, adjustment adjusts admired admission, admit admit admit admit admit admit admit admit admit admit admitted admitted admitted admitted. admitting admitting adolescent’s adrenalin, advanced advanced advancement advantage advantage advantage advantage advantage, advantages advertising advice, advice. advise advise advised advise—” advising aerial aeronautical affairs affairs affect affect affect affect afflict afflicted afford affront afraid afraid afraid afraid afraid afraid afraid afraid afraid afraid afraid afraid afraid afraid afraid. afraid. afraid. after after after after after after after after after after after after after after after after after after after after after after after after after after after after after after after after after after after after after after after after after after after after after after after after after after after after after after after after after after after, after, afternoon afternoon afternoon, afternoon, afternoon. afternoon. afternoon. afternoon. afterward, afterward. again again again again again again again again again again again again again again again again again again again again again again, again, again, again, again, again, again, again, again, again, again, again, again, again, again, again, again, again, again, again, again, again, again, again. again. again. again. again. again. again. again. again. again. again. again. again. again. again. again. again. again. again. again. again. again. again. again. again. again. again. again. again. again. again. again. again.” again.” again.” again: again?” again?” against against against against against against against against against against against again— again—it again—more age age age age age ...'
'Clean Text:' 'all a all as as let s me poor the the when all and do flight i it s i m made see this time well who blonde but by deceased flight he him if in not one or sorry that s we d which with what re ah can t hell it take that that s the transponder uh uh uh well we re what millennium millennium millennium millennium millennium millennium millennium millennium th millennium millennium millennium millennium millennium th th millennium millennium millennium millennium millennium th th th millennium millennium millennium millennium th th millennium millennium millennium th millennium millennium millennium millennium year old millennium millennium millennium millennium millennium millennium millennium millennium millennium millennium millennium millennium millennium millennium millennium s millennium millennium millennium s s s s so millennium th th th th th th ths ths ths ths millennium millennium millennium millennium millennium millennium time millennium s i s a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a p a r a a a alpo america artcc artcc atc atc atc atc s atc s atc s author s abayta abayta abayta abayta abayta about about about about about about according across across across actually actually add adelaide administration advise africa after after after after after after after after after after after after again again again again again against age age age age age agency agent agent age or ah ah air air air air air air air air air air air line aircraft airlines airlines airlines airport airport airport airport airport airport airways alameda alameda alarms albert aldiss ali ali alka seltzer all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all seeing allison almost almost along alpha alpha also also always am am am am am am am am am am am am am am ambler ambrose amelia amerenglish amerenglish amerenglish america america american american american american amniocentesis among among amsterdam am but am s an an an an an an an anarchy and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and anderson anderson andromeda anet angeles angeles angeles angeles angeles angeles angeles angels another another another another another another antarctica antennas antoine s any any any any any any any any anybody anything anything anything anyway apaches apparently apparently apparently apparently apparently apparently apple aquarius arabia archibald archibald are are are are area area area arizona arizona arizona arizona arizona arizona arizona arizona arizona arizona ark ark armenia army army army army army arnold arnold arnold arnold arnold arnold arnold arnold arnold s arthur arthur as as as as as as as as as as as as as as as as as as as as as as as as as as as aside asimov assuming astronauts at at at at at at at at at at at at at at at at at at at at at at at at at at at at at at at at at at at at at at at at at at at at at atlantic atomic attorney author avenue avenue avenue aviation aw b b b b b b b b baltimore bart bc bc bc bc bc bc bc bc bc bc bc bc bc bc bc bc bc bc bc bc bc bc bc bc bc bc bc bc bc bc bc bc bc bc bc bc bc bc bc bc bc bc bc bc bc bc bc bc bc bc bc bc s berkley berkley books book babylonian back bad baker bakersfield bali ball ball ball ball ball ball ball balls baltimore baltimore baltimore baltimore baltimore baltimore baltimore baltimore baltimore baltimore baltimore baltimore baltimore baltimore baltimore baltimore baltimore bamum bangladesh bannister bannister barbara barbara bars batavia battleship bauhaus bay bay bay bay bay bay bay bay ba be because because because because bee before before before before before before behind behind behold behold being being believe belli beltway ben bengalis berkeley berkley berkley berkley berkley berkley berkley bermuda bermuda berry beside better better better beverly bible bierce big big big big big big big big big big big big bill bill bill bill bill bill bill bill bill bill bill bill bill bill bill bill bill bill bill bill bill bill bill bill bill bill bill bill bill bill bill bill bill bill bill bill bill bill bill bill bill bill bill bill bill bill bill bill bill bill bill bill bill bill bill bill bill bill bill bill bill bill bill bill bill bill bill s bill s bill s bill s biological bird birds birmingham bite bizarre black board board board board board board board board board board board board board board board board board board board board board board board boeing boeing boeing boeing boeing boeing boers bogart book book boston boston boston both both both both both both both bova bova s bradbury braille breaking breathe brest brest brest brian bridge briley briley briley briley briley briley briley briley briley briley briley briley briley briley briley briley brilliant brindle brindle brindle brindle brindle broken brooklyn brooklyn brunner buddha budweiser building building bulova bureau bureau which burt business but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but but butch by by by by by by by by by by by by by by b was c c c c c c c c c c c c c c c c c c c c c o cbs cb s chp chp chp chp cia cvr cvr cvr cvr cvr cvr cvr cvr cvr cvr s cvr s caesarean calcutta calcutta benares california california california california california california california california california california california california california california california california california california call call call call calm came came camels camels camp can can canal canary canary canary candidate canyon can t capetown captain captain captain captain captain s carnegie carole carole carole carole carole carole carpenter carpenter carpenter carpenter carpenter carpenter carpenter carpenter carpenter carpenter carpenter carpenter carpenter s carpenter s carson carson cary cary casablanca cassidy catheters causes cecily censorship centauri centauri center center central century certain cessna chairman chairman s chalk chamber chamber chamber changes charge charge charity charlie charlie s chemical cheops cherokees cherokees chicago chicago chicago chicago chief chief chief chief china china chinese chinese chinese christ christ christ christ christmas christmas christmas christmas christmas christmas christmas christmas chuck chuck cirocco civilization civilizations clarke claus cleaver of heads cleaver of heads a clorets clorets close close coal coast coast coast cockpit cockpit cockpit cockpit coconino coffee coffee cold colt come come come come company compared compared computer computer computer computer computer computer computer computer computer computer computer computers computers congeniality congress congress congress critters congress congruency connecticut connecticut constables constellation constellation constellation constellation contra contra contrary control control control control control control controller controllers controllers controller s cool copyright corporation corporation corporation correct correction cosmic costa costa could could council council council council council council council council council council council council council council council council council council council council council council council council council council council council council council s country the county county county coventry coventry coventry coventry coventry coventry coventry coventry coventry coventry coventry coventry coventry coventry coventry coventry coventry coventry coventry coventry coventry coventry coventry s coventry s cow craig craig craig craig craig craig craig crain crain crain crain crain crain crain crain crain crain crashes crashes cristabel cristabel cristabel cubs cummings cunard cut cybernet d d d d d d d d c day dc dc dc dc dc dc dc dc dc dc dc dc dc dc dc dc dc dc dc dc dc dc dc dc dc dc lo s demon do dachau dakota dali damn damn damn damn damn darkness darkness data data data dating david david david s davis davis davis davis davis davis davis day day delisle delisle delisle delisle delisle delisle delisle delisle delisle s delisle s delisle s delisle s dead dead december december december december december december december december december december december decisions deep define define denver department desert desert desert despair destiny diablo diablo diablo diablo diablo did did did did did did did did didn t diego diego diego diego disgust disney dixie djakarta djakarta djakarta do do do do do do do do do do do do do do do do do doctor doctor doctor doctor doctor doctor doctor doctor doctor doctor doctor doctor doctor doctor doctors dodgers dodgers does does does does does don don don don don donald donald donald don t don t don t don t don t don t don t don t don t don t don t douglas down down s drinking drunk dulles dulles during during duty duty each each each each each each each each earhart early early early earth earth earth earth earth earth east east east eastern eastern easy eat drink and be merry ed ed edison edison edward effect einstein einstein eisenhower either either elder eleanor electric eleven oh one eli eli eli eli eli eli eli eli eloi eloi encounters end end end end end end engineer england english enquirer enquirer entertainment epilogue erebus eric essence eternity eternity eugene eugene eugene eugene even even even even even even even even even even even even even even even even events events eventually eventually everglades everglades everglades every every every every every every everybody everybody everybody everybody everybody everybody everybody everybody s everyone everyone everyone everyone everything everything everything everything excedrin excuse extra eye eyewash f faa faa faa faa faa s fbi fbi fbi fbi fbi fbi fbi fdr fdr s fdr s fdr s for factors facts fairly fairly fall fall famous farther faster fate fear fed fed fed fed fed fed fed fed fed federal federal federal federal fifteen fifty fifty finally finally finally finally finally finally fine fine fingers first first first first first first first five flagstaff flight flight flight flight flight flight flight flight flight florida fly fokker aerospatiale for for for for for for for for for for for for for for for for for for for for for for for for for for for for for for force foreign forget forgot fortunately fortune forty forty five four frances frances francisco francisco francisco francisco francisco francisco frankly freddie freddie freddie freddie freddie freddie freddie freddie freddie freddie freddie freddie freddie freddie freddie freddie freddie freddie freddie free free free fremont which french french from from from from from from from frost funny funny future g man gab galaxy garden garden garry gary gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate gate s gate s gees geller s genalysis general general general genesis genie george george george george george george george georgia german get getting gideon gideon gil gil gil gil gil gil gilbert gilmore girl girl glancing glenlivet gloria gloria gloria gloria glorious go go go go go go team god god god god god god god god god god god knows how many god god god god god gods goes goes goes gold golgotha golgotha good gooney gooney gordon gordon gordon gordon gordon gordon gordon gordon gordon gordy gordy gordy gordy gordy gordy gordy gordy gordy gordy gordy gordy gordy gordy got government governor gradually grand grandfather grandfather grant grant gray gray greek green green greenland greetings group guard guardia guardians guardians guess gumby guys h bombs history hotline hst hacker hacker hacker had half half hall hanoi hard hardy harlan harold harrison harry harsh harvard haubner haubner haubner he have having having having he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he he headache heads hearst heartless hefty hefty heinlein hell hell hell hell hell henry hepburn her her her her her her her her her her her her her her her her herbert herbert herculean here here here here here s here s herman hertz hertz hey hey he d he d he d he d he d he d he d he d he d he d he d he d he d he d he d he d he d he ll he ll he ll he ll he s he s he s he s he s he s he s he s he s he s he s he s he s he s he s he s he s he s he s he s he s he s he s he s he s highway hijackers hildy hildy hills himalayas hindus his his his his his his his his his his his his his his his his his his his his his his his his his his his his historians historical hodgson holiday holiday hollywood hollywood hollywood hollywood hollywood hollywood home honduras honest hong hope hopi hopis horowitz houston houston got how how how how how how how how how how how how how however however however however however however hoxv d huey hueys hugo hugo hugo huguenots human humans humphrey hupfeld hysteria i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i d i d i d i i i i i i ibm ic in isbn ian idle if if if if if if if if if if if if if if if if if if if if if if if if if if if if if if if if if if if if if if if if if if if if if if if if if if if if if if if if if if if if if if if if if if if if if if if if if if if if if if if if if if if if if if if if if if if if if if if if if if if if ike illinois imagination important impossible in in in in in in in in in in in in in in in in in in in in in in in in in in in in in in in in in in in in in in in in in in in in in in in in in in in in in in in in in in in in in in in in in in in in inc inches india indian indian indian indian indian indians indian so indications inference inn inn inn inside inside inside instead instead instead intelligence intelligence interest interest international international international interstate interstate into investigation a investigator irving is is is is is is is is is is is isaac islands islands islands islands isn t isn t it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it it s italian italian its it ll it ll it s it s it s it s it s it s it s it s it s it s it s it s it s it s it s it s it s it s it s it s it s it s it s it s it s it s it s it s it s it s it s it s it s it s it s it s it s it s it s it s it s it s it s it s it s it s it s it s it s it s it s it s it s it s it s it s it s it s it s it s it s it s it s it s it s it s it s it s it s it s i i i i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i d i ll i ll i ll i ll i ll i ll i ll i ll i ll i ll i ll i ll i ll i ll i ll i ll i ll i ll i ll i ll i ll i ll i ll i ll i ll i ll i ll i ll i ll i ll i ll i ll i ll i ll i ll i ll i ll i ll i ll i ll i ll i ll i ll i ll i ll i ll i m i m i m i m i m i m i m i m i m i m i m i m i m i m i m i m i m i m i m i m i m i m i m i m i m i m i m i m i m i m i m i m i m i m i m i m i m i m i m i m i m i m i m i m i m i m i m i m i m i m i m i m i m i m i m i m i m i m i m i m i m i m i m i m i m i m i m i m i m i m i m i m i m i m i m i m i m i m i m i m i m i m i m i m i m i m i m i m i m i ve i ve i ve i ve i ve i ve i ve i ve i ve i ve i ve i ve i ve i ve i ve i ve i ve i ve i ve i ve i ve i ve i ve i ve i ve i ve i ve i ve i ve i ve i ve i ve i ve i ve i ve i ve i ve i ve i ve i ve i ve i ve i ve i ve i ve i ve i ve i ve i ve i ve i ve i ve i ve i ve i ve i ve i ve i ve i ve i ve i ve i ve i ve i ve i ve i ve i ve i ve jfk jack jack jack jack jack jackie jane jane january janz janz janz janz janz janz janz janz janz janz janz janz janz janz janz janz janz janz janz janz janz janz janz s japan japan japanese jean jeff jehovah jerry jerry jerry jerry jerry jerry jerry jerry jerry jerry jerry jesus jesus jesus jesus jesus jesus jesus jesus jet jetstar jetstar jetstar jetstar jews jockey joel john john john john john john johnny johnstown joyjuice juan jumbos june jupiter just just just just just just just just just just just just kampuchea kane kankakee karakov karakov karakov karma kate s katharine keane keane keane keane keane keane keane keane keane keane keane s keane s keep keeping keeping kensington kensington kensington kevin kevin kevin kevin kevin kickapoo kid kilworth knock kong kooky korea kuttner l l l a last lax lax lax louise la lady lake lake land land lanes larry larry larry larry larry larry larry larry s larry s lars lars lars last last last last last last last last last last last later later later later later later latex laughter laurel law law lawrence lawrence lawrence lawrence lawrence lawrence lawrence lawrence lawrence lawrence lawrence lawrence lawrence lawrence lawrence lawrence lawrence lawrence lawrence lawrence lawrence lawrence lawrence lawrence lawrence lawrence lawrence lawrence lawrence lawrence lawrence lawrence lawrence lawrence lawrence lawrence lawrence s lawrence s lawrence s lawrence s lawrence s lawrence s leading leaning leggio leggio leggio leggio leggio leggio leggio lest lest let let let let let s let s let s let s let s let s let s levitsky levitsky lie lie life like like like like like like like like lilly lilly lilly lilly lilly lilly lilly lilly lilly lilly lilly lilly lilly lilly lilly lilly lilly lilly lilly lilly lilly lilly lilly lilly lilly s lilly s lilly s lilly s lilly s linus listen listen listen literally little little livermore livermore livermore livermore livermore livermore lloyd lockheed lockheed lockheed lockheed logically london london london london london london london look look look look look looking looking looking looking lord lord lori los los los los los los los louise louise louise louise louise louise louise louise louise louise louise louise louise louise louise louise louise louise louise louise louise louise louise louise louise louise louise louise louise louise louise louise louise louise louise louise louise louise louise louise louise louise louise louise louise louise louise louise louise louise louise louise louise louise louise louise louise louise louise louise louise louise louise louise louise louise louise louise louise louise louise louise louise louise louise louise louise louise louise louise louise louise louise louise louise louise louise louise louise louise louise louise louise louise louise louise louise louise louise louise louise louise louise louise louise louise louise louise louise louise louise louise louise louise louise louise louises louise s louise s louise s louise s louise s louise s louisville love luci lucille luckies luckily lucky lucy m i t m i t members notify mgm ua millennium millennium millennium millennium millennium millennium millennium millennium millennium millennium millennium millennium millennium millennium millennium millennium millennium millennium millennium millennium millennium millennium millennium millennium millennium millennium millennium millennium millennium millennium millennium millennium millennium millennium millennium millennium millennium millennium millennium millennium millennium millennium millennium millennium millennium millennium millennium millennium millennium millennium millennium millennium millennium millennium millennium millennium millennium millennium millennium millennium millennium millennium millennium millennium millennium millennium millennium millennium millennium millennium millennium millennium millennium millennium millennium millennium millennium millennium millennium millennium millennium millennium millennium millennium millennium millennium millennium millennium millennium millennium millennium millennium millennium millennium millennium millennium millennium millennium millennium millennium millennium millennium millennium millennium millennium millennium millennium millennium millennium millennium millennium millennium macquitty macbeth macbeth machine machine machine machines mack madison magic maidive maintenance maintenance makes man man man man man man mandy mandy mandy mandy mandy mandy mandy mandy mandy mandy mandy mandy mandy mandy mandy manhandling manual many many many marin mars martin martin martin martin martin martin martin martin martin martin martin martin martin martin martin martin martin martin martin martin martin martin martin martin martin martin martin martin martin martin s mary mary marybeth marybeth marybeth marybeth maryland maryland maryland marylander columbians mateo maurice maybe maybe maybe maybe maybe maybe maybe maybe maybe maybe maybe maybe maybe maybe maybe maybe maybe maybe maybe maybe maybe maybe maybe maybe maybe maybe maybe maybe maybe maybe maybe maybe maybe maybe maybe maybe maybellene mayer mayer mayer mayer mayer mayer mayer mayer mayer mayer mayer mayer mayer mayer mayer mayer mayer mayer mayer mayer mayer mayer mayer mayer mayer mayer mayer mayer mayer mayer mayer mayer mayer mayer mayer mayer mayer mayer mayer mayer mayer mayer mayer mayer mayer mayer mayer mayer mayer mayer mayer mayer mayer mayer mayer mayer mayer mayer mayer mayer mayer mayer mayer mayer mayer mayer mayer mayer mayer mayer mayer mayer mayer mayer mayer mayer mayer mayer s mayer s mayer s mayer s mayer s mayer s mayer s mayer s mcdonnell douglas mcrae mconel me me me meanwhile medics melvin member member men menlo merely methodists metz michael mike miles millennium miller mine minor minoru minoru minoru minoru minoru minoru minoru s minutes miss mission mister mister mister mister mister mister mister mister mister mister mister mister mister mister mister mister mister model t mojave mommy monday monday moorcock more more more morlock morlocks morlocks morlocks mornings moroccans moses most most most most most most most most most most most most most most most most most most mostly mostly mounds mount mount mount mount mr mr mr mr mr mr mr mr mr mr ms ms much much much musing must my my my my my my my my my my my my my my my my my my my my my my my my my my my my my my my my my my my my my my my myself myself myself nba new not note ntsb ntsb ntsb ntsb ntsb nader nam nameless nameless nameless nameless names nancy nancy nancy naomi naomi nashua national national national national national national national national national national national national national national naturally naturally naturally naturally naturally naturally navajo navajo navajos navajos naval navy navy nazis nebula nebula neither nepal nero never never never never bom never neverland nevertheless new new new new new new new new new new new new new new new new newcastle news news newton next next next night night night night nineteen eighties no no no no no no no no no no no no no no no no no no no no no no no no no no no no no no no no no no no no no noah s nobel nobody nobody nobody nobody nobody nobody nobody nobody nobody nobody nobody nobody nobody nobody nobody nobody none none none none normally normally norman north not not not not not not not not not not not not not not not not not not not not not not nothing nothing nothing nothing nothing nothing nothing nothing now now now now now now now now now now now now now now now now nuclear o of of open ophiuchi oahu oakland oakland oakland oakland oakland oakland oakland oakland oakland oakland oakland oakland oakland oakland oakland oakland oakland oakland oakland oakland oakland oakland oakland oakland oakland san oakland san oakland oakland oakland oakland oakland oakland oakland obviously obviously obviously ocean oddly of of of of of of of office office office office office office office officer officer officer iic office oh oh oh oh oh oh oh oh oh oh oh oh oh oh ohio oil okay okay okay okay okay okay okay okay okay okay old on on on on on on on on on on on on on on on on on on on once once once once once once one one one one one one one one one one one one one one one one one one one one one one one one one one one one one one one one one one one one one one one one one one one one one one only only only only operations operations operations operations operations operations operations operations operations operations operations operations operations operations operations operations operations operations operations operations operations operations operations operations operations oppenheimer or or or or or or or or or or or or or or or or or or or or or oregon oregon organisation organization originally orly otherwise otherwise our our our our our our out out out outside outside outsiders over over oz p t patco persistence prc printed printing psa psa psa psa pacific pacific pacific pacific pan pan pan pan pan pan pan pan pan pan pan pan pan pan pan pan pan papers paradox paradox paradox paris paris paris frankfurt park park park parkersburg parrish part part part part part part part parts parts passengers patrol patty pauling peabody penthouse people people people people s perhaps perhaps perrier persistence personally petcher petcher petcher petcher petcher petcher petcher petcher petcher petcher petcher petcher petcher petcher petcher petcher or petcher s peter peter peter pharaoh s phil phil phil phoenix phoenix phoenix phoenix phoenix phoenix physics picture pilots pinky pinky pinky pinky pinky pinky pinky pinky pinky pinky pinky pinky pinky pinky pinky pinky pinky s pinky s pinky s piper plan please plenty plus politically pop pop pope portland possibly possibly possibly post post post post post post post post poul poul power powers powers powers powers powers pre destination preakness predestination prentice presently president president president president presidents preston presumably pretty pretty pride prize probably probably probably productions productions professor programmers programming project project project project project project project project project project project project project prologue prologue proximity publishing publishing pursuant pusher put putting q q q quadraplegic questions questions quick r ralph ralph ralph ralph ralph ralph ralph ralph ralph ralph ralph ralph ralph ralph s ralph s ralph s ralph s ralph s rangoon rangoon rangoon rank rape rapid ray ray ready room really recorder recorder recorder recorder recorder recorders recorders records recovery red redford redford redford redford redford regency region region relativity remember remember remember reporters republic rescue reservations reynolds right right right right right right right right risks river robert robert robert robert robert robert rockwell rockwell rockwell rockwell rockwell rockwell rockwell rockwell rockwell rockwell rockwell rockwell rockwell s roentgen roentgen rog rog roger roger roger roger roger roger roger roger roger roger roger roger roger roger roman roman round round roy rule running russians ryan ryan ryan sac states sacramento sad safer safety safety safety safety safety safety safety safety salvador sam sam same same san san san san san san san san san san santa santa santa sarah sarah sarah sarah sarah saudi say schenectady schrader schrader schrader schrader schuyler scottish screw seaboard seasons seattle seattle seattle second seconds see see see see seen seen seibel seibel seibel seiko seiko seiko selectrics senator senator senators series series series service set seven seventy three several several several shadow shadow shame shanghai she she she she she she she she she she she she she she she she she she she she she she she she she she she she she she she she she she she she she she she she she she she she she she she she she she she she she she she she she she she she she she she she she she she she she she she she she she she she she she she she she she she she she she she she she she she she she she she she she she she she she she she she she she she she she she she she she she she she she she she she she she she she she she she she she she she she she she she she she she she she she she she she she she she she she sheepdog sheltie sheppard sheppard sheppard sheriff s sherman sherman sherman sherman sherman sherman sherman sherman sherman sherman sherman sherman sherman sherman sherman sherman sherman sherman sherman sherman sherman sherman sherman sherman sherman sherman sherman sherman sherman sherman sherman sherman sherman sherman sherman sherman sherman sherman sherman sherman sherman sherman sherman sherman sherman sherman sherman sherman sherman sherman sherman sherman sherman sherman sherman sherman sherman sherman sherman sherman sherman sherman sherman sherman sherman sherman sherman sherman sherman sherman sherman sherman sherman sherman sherman sherman sherman sherman sherman sherman sherman sherman sherman sherman sherman sherman sherman sherman sherman sherman sherman sherman sherman the therapist sherman sherman sherman sherman sherman sherman sherman sherman sherman sherman sherman sherman sherman sherman sherman sherman sherman sherman sherman sherman sherman sherman sherman sherman sherman sherman sherman among sherman s sherman s sherman s sherman s sherman s shetland she d she d she d she d she d she d she d she d she d she d she d she d she d she ll she s she s she s she s she s she s she s she s she s she s she s ship ship ship ship shit short shorthanded shortly should should show sianis signed signed silesia silverberg silverberg silvers simple simply simultaneously since since since since since since sit sit sitting sitting skydivers sleepless slims slims small smith smith smith smith smith smith smith smith smith smith smith smith smith smith smith smith smith smith smith smith smith smith smith smith smith smith smith smith smith smith smith smith smith smith smith smith smith smith smith smith smith smith smith smith smith smith smith smith smith smith smith smith smith smith smith smith smith smith smith smith smith smith smith smith smith smith smith smith smith smith smith smith smith smith smith smith smith smith smith smith i d smith you smith s smith s smith s smith s snatch snatch snatch snatch snatching so so so so so so so so so so so so so so so so so so so so so so so so so so so so so so so so so so so so so so so so so so so so so so so so so so so so so so so so so so so so so so so so so so so so so so so so so so so so so so so so so so so so so some some some some some some some some some some some some some some some some some some some some some some somebody somebody somebody somebody somebody somebody somebody somebody somebody i m somehow someone something something something something something something something something something something sometimes sometimes sometimes sometimes sometimes sometimes sometimes sometimes sometimes sometimes sometimes sometimes sometimes sometimes sometimes sometimes somewhere somewhere son son son sondergard sondergard sondergard sondergard sondergard sondergard sondergard sondergard sondergard s sorry sound sound southern souvenirs spanish special special special special agent in charge spielberg spitfire sprague springs square square square square square square square squashed squatting standard standard standing stanford stanley stanley stanley stanley stanley stanley stanley stanley stanley stanley stanley stanley dressed star state states states states stearman stearman stefan stefan steve still still still still still still still still still still still still stop stop strangely street street stretching strictly strike stroke strunk stuff stunning sturges such suddenly suddenly suffer suffering suicide suicide suicide suitcases sullivan sullivan s sun sundance supreme sure sure surely surely surgeon suwannee swahili swanson s swedish swiss swiss the the the the titan titan tm tnt tv tv take take take take take take take talk tang shen tape team team team team team team tear technically technically technically technically teheran teheran teheran teheran television tell tell tell tell tell tell tell teller tempers temporal temporal ten ten ten ten tenerife tenerife tenn testament testimony testimony testimony testimony testimony testimony testimony testimony testimony testimony testimony testimony testimony testimony testimony testimony testimony testimony testimony testimony testimony testimony testimony testimony texas texas texas thank that that that that that that that that that that that that that that that that that that that that that that that that that that that that that that that that that that that that that that that that that that that that that that that that that that that that that that that that that that ll that s that s that s that s that s that s that s that s that s that s that s that s that s that s that s that s that s that s that s that s that s that s that s that s that s that s that s that s that s that s that s that s the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the their their their their then then then then then then then then then then then then then then then then then then then then then then then then then then then then then then then then then then then then then then then then then then then then then then then then then then then then then then then then then then then then then then then then then then then then then then then then then then then theoretically theories theory there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there there d there s there s there s there s there s there s there s there s there s there s there s there s there s there s there s there s there s there s there s there s these these these these these these they they they they they they they they they they they they they they they they they they they they they they they they they they they they they they they they they they they they they they they they they they they they they they they they they they they they they they they they they they they they they they they they they they they they they they they they they they they they they they they they they they they they they they they they they they they they they they they they they they they they they they they they they they they they they they they they they they they they they they they they d they d they d they d they d they d they d they d they ll they ll they ll they ll they re they re they re they re they re they re they re they re they re they re they re they re they re they re they re they re they re they ve they ve things things things things things things things things thinking thinking thinking thinning thirty this this this this this this this this this this this this this this this this this this this this this this this this this this this this this this this this this this this this this this this this this thomas thomkins thor those those those those those those those those those though though though three three quarters thunder thunder thunderhilton thursday thus thus time time time time time time time time time time time time time time time time time time time time time time time time time time times times timex tiny titanic titanic titanic titanic to to to to to to to to to to to to to to today tom tom tom tom tom tom tom tom tom tom tom tom tom tom tom tom tom tom tom tom tom tom tom tom tom tom tom tom tom tom tom tom tom tom tom tom tom tom tom tom tom tom tom tom tom tom tom tom tom tom tom tom tom tom tom tom tom tom tom tom tom tom tom tom tom tom tom tomorrowland tomorrow s tom s tom s tonolea tony tony tony tony tony tony tony tony tony tony tony tony toss tost traffic traffic traffic traffic traffic traffic traffic traffic trans transit transportation transportation transportation transportation transportation transportation travel travel travels treasury treat triangle triangle tricky trouble trouble try trying turn twelve twenty twenty twenty six twice two two two two two two two two two two two two two two twonky twonky tyson u s u s u s ufo s united univac until ultimately under understand union union united united united united united united united united united united united united united united united united united united united united united united united united united united universe university unless until until uptime uri usually usually usually utah utopia vision valhalla valium various varley varley varley varley varley vem vem vem very very vicks vicks vicks vicks victor viet nam viet nam and view viking virginia virginia virginia virginia virginia vision voice voice voice voice w wizard wizard wagner wake wallace walt wanna want war war war war warm warrior warrior wars was was was was was washington washington washington washington washington washington washington washington washington washington washington washington washington washington washington washingtonians watches watches way wayne wayne wayne we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we we weapons weather well well well well well well well well well well well well well well well well well well well well wells wells wells went went were west west west west we d we d we d we d we d we d we d we d we d we d we d we d we ll we ll we ll we re we re we re we re we re we ve we ve we ve we ve we ve we ve what what what what what what what what what what what what what what what what what what what what what what what what what what what what what what what what what what what what what what what what what what what what what what what what what what what what what what what what what what what what what what what whatever whatever whatever whatever whatever whatever what s what s what s what s what s what s what s what s what s when when when when when when when when when when when when when when when when when when when when when when when when when when when when when when when when when when when when when when when when when when when when when when when when when when when when when when when when when when where where whereas where s where s where s where s where s whether whether which which which which which which which while while while while while while while while white whitmore who who who who who who who who who who who who who who whoever whose who s why why why why why why why why why why why why wilbur wilhelm will will will will william william william william william william william willis willis willis window window window window window window window window window window window window window window with with with with with with with with with with without without without witnesses wizard wolfe woman word words words words words words words working world world world world world world world world world world world world worse would would would would wright x xerox xerox york yeager year years years year s yesterday yet yet yet yet yet yet yet yokohama yokohama yokohama yokohama york york york york york york york york york york york york york you you you you you you you you you you you you you you you you you you you you you you you you you you you you you you you you you you you you you you you you you you you you you you you you you you you you you you you you you you you you you you you you you you you you you you you you you you you you you you you you you you you you you you you you you you you you you you you you you your your your your your your your your you d you d you d you d you d you ll you ll you ll you re you re you re you re you re you re you re you re you re you re you re you re you re you ve you ve you ve you ve you ve you ve zombies zombies z s vital a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a d a m a m a m aback abacus abandoned abashed ability ability ability able able able able able able able able able able able able able able able able able able aboard aboard aboard aboard aboard aboard aboard aboard aborted abound about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about about above above above above above abreast abruptly absence absence absence absolute absolute absolute absolutely absolutely absolutely absolutely absolve absorbed abundance abuse acceleration accelerator accelerators accelerators accent accent accent something accept accept accept accept accept accept acceptable accepted accepted accepted accepting access access access access access accessed accessible accident accident accident accident accident accident accident accident accident accidentally accidents accommodate accomplishments according according according account account accounts accounts accounts accumulates accurate accurate accurate accurate accurately accusing ache achieved achieved achieved aching achingly acknowledge acknowledge acknowledge acknowledge acknowledgement acquaintances acquainted acquired acre acres across across across across across across across across across across across across across across act act act act act acted action action action action action action actions actions actions actions actions actions activity activity activity actress actual actual actual actual actuality actuality actually actually actually actually actually actually actually actually actually actually actually actually actually actually actually actually actually actually actually actually actually actually actually actually actually actually actually actually actually actually actually actually add add add add add added added added added addict adding addition addition addition addition address address addressing add adjective adjust adjusted adjusted adjustment adjusts admired admission admit admit admit admit admit admit admit admit admit admit admitted admitted admitted admitted admitting admitting adolescent s adrenalin advanced advanced advancement advantage advantage advantage advantage advantage advantages advertising advice advice advise advise advised advise advising aerial aeronautical affairs affairs affect affect affect affect afflict afflicted afford affront afraid afraid afraid afraid afraid afraid afraid afraid afraid afraid afraid afraid afraid afraid afraid afraid afraid after after after after after after after after after after after after after after after after after after after after after after after after after after after after after after after after after after after after after after after after after after after after after after after after after after after after after after after after after after after after afternoon afternoon afternoon afternoon afternoon afternoon afternoon afternoon afterward afterward again again again again again again again again again again again again again again again again again again again again again again again again again again again again again again again again again again again again again again again again again again again again again again again again again again again again again again again again again again again again again again again again again again again again again again again again again again again again again again again again again again against against against against against against against against against against against again again it again more age age age age age age age age age age age agencies agency agency agency agency agenda agent agent agent s aggressive agitated ago ago ago ago ago ago ago ago ago ago ago ago ago ago ago ago ago ago ago ago ago ago ago ago ago ago ago ago ago ago ago ago ago ago agony agoraphobia ago or agree agree agree agree agree agree agreed agreed agreed agreed agreed agreed agreement ah ahead ahead ahead ahead ahead ahead ahead ahead ahead ahead ahead ahead aid aid aileron ailerons aim aimed aimed aimed aimed aimed aiming ain t ain t ain t ain t ain t ain t air air air air air air air air air air air air air air air air air air air air air air air air air air air raid air traffic air air air air air air aircraft aircraft aircraft aircraft aircraft aircraft aircraft aircraft aircraft aircraft aircraft aircraft aircraft aircraft aircraft aircraft aircraft airfields airframe airframe airframe airframe airframes airline airline airline airline airline airline airline airline airline airline airline airline airline airliner airliners airplane airplane airplane airplane airplane airplanes airplanes airplanes airplanes airplanes airplane at airport airport airport airport airport airport airport airport airport airport airport airport airport airport airport airport airport airport airport airport airport airport airport airports airports airports airspeed airworthy aisle aisle aisle aisle aisle aisle aisle aisle aisle aisle aisles aisles alarm alarm alarm alarm alarm alarm alarm alarm alarm alarm alarm alarm alarm alarm alarmed alarmed alarming alarms alcoholism alert alert alert alert alerted alien alien alive alive alive alive alive alive alive alive alive alive alive alive alive alive all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all all important all time all all all all all all all all all all all all all all all all all all all all all all allergic alligator alliteration allow allowed allowed allowed allowed allows allusions allusions all how almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost alone alone alone alone alone alone alone alone alone alone alone alone alone along along along along along along along along along along along along along along along aloud already already already already already already already already already already already already already already already ...'
'Word Count': '85004
',
}
```
### Data Fields
- **Unnamed: int** A unique id for the text
- **Title: str** The title of the book from which the text has been extracted
- **Author: str** The author of the book from which the text has been extracted
- **Pub Year: str** The date on which the book was published (first printing)
- **Text: str** The text extracted from the book
- **Clean Text: str** The text extracted from the book with lowercasing performed and punctuation, numbers and extra spaces removed
- **Word Count: int** The number of words the text contains
To Be Added:
- **summary: str** A brief summary of the book, if extracted from library records
- **pub_date: int** The date on which the book was published (first printing)
- **pub_city: int** The city in which the book was published (first printing)
- **lcgft_category: str** Information from the Library of Congress Genre/Form Terms for Library and Archival Materials, if known
### Loading the Dataset
Use the following code to load the dataset in a Python environment (note: does not work with repo set to private)
```
from datasets import load_dataset
# If the dataset is gated/private, make sure you have run huggingface-cli login
dataset = load_dataset("SF-Corpus/EF_Full_Texts")
```
Or just clone the dataset repo
```
git lfs install
git clone https://huggingface.co/datasets/SF-Corpus/EF_Full_Texts
# if you want to clone without large files – just their pointers
# prepend your git clone with the following env var:
GIT_LFS_SKIP_SMUDGE=1
```
## Dataset Creation
### Curation Rationale
For an overview of our approach to data curation of literary texts, see Alex Wermer-Colan’s and James Kopaczewski’s article, “The New Wave of Digital Collections: Speculating on the Future of Library Curation”(2022)
### Source Data
The Loretta C. Duckworth Scholars Studio has partnered with Temple University Libraries’ Special Collections Research Center (SCRC) and Digital Library Initiatives (DLI) to build a digitized corpus of copyrighted science fiction literature. Besides its voluminous Urban Archives, the SCRC also houses a significant collection of science-fiction literature. The Paskow Science Fiction Collection was originally established in 1972, when Temple acquired 5,000 science fiction paperbacks from a Temple alumnus, the late David C. Paskow. Subsequent donations, including troves of fanzines and the papers of such sci-fi writers as John Varley and Stanley G. Weinbaum, expanded the collection over the last few decades, both in size and in the range of genres. SCRC staff and undergraduate student workers recently performed the usual comparison of gift titles against cataloged books, removing science fiction items that were exact duplicates of existing holdings. A refocusing of the SCRC’s collection development policy for science fiction de-emphasized fantasy and horror titles, so some titles in those genres were removed as well.
## Considerations for Using the Data
This data card only exhibits extracted features for copyrighted fiction; no copyrighted work is being made available for consumption. These digitized files are made accessible for purposes of education and research. Temple University Libraries have given attribution to rights holders when possible. If you hold the rights to materials in our digitized collections that are unattributed, please let us know so that we may maintain accurate information about these materials.
If you are a rights holder and are concerned that you have found material on this website for which you have not granted permission (or is not covered by a copyright exception under US copyright laws), you may request the removal of the material from our site by writing to digitalscholarship@temple.edu.
For more information on non-consumptive research, check out HathiTrust Research Center’s Non-Consumptive Use Research Policy.
## Additional Information
### Dataset Curators
For a full list of conributors to the SF Nexus project, visit [https://sfnexus.io/people/](https://sfnexus.io/people/). |
jlbaker361/kaggle_males_dim_128_20k | ---
dataset_info:
features:
- name: image
dtype: image
- name: split
dtype: string
- name: src
dtype: string
- name: style
dtype: string
splits:
- name: train
num_bytes: 426131006.0
num_examples: 20000
download_size: 425383236
dataset_size: 426131006.0
---
# Dataset Card for "kaggle_males_dim_128_20k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Citizen75/training_presets | ---
license: apache-2.0
---
|
dhuynh95/Magicoder-Evol-Instruct-5000-CodeLlama-70b-tokenized-0.5-Special-Token | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 11124031
num_examples: 5000
download_size: 5572144
dataset_size: 11124031
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
onestop_qa | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
- extended|onestop_english
task_categories:
- question-answering
task_ids:
- multiple-choice-qa
paperswithcode_id: onestopqa
pretty_name: OneStopQA
language_bcp47:
- en-US
dataset_info:
features:
- name: title
dtype: string
- name: paragraph
dtype: string
- name: level
dtype:
class_label:
names:
'0': Adv
'1': Int
'2': Ele
- name: question
dtype: string
- name: paragraph_index
dtype: int32
- name: answers
sequence: string
length: 4
- name: a_span
sequence: int32
- name: d_span
sequence: int32
splits:
- name: train
num_bytes: 1423090
num_examples: 1458
download_size: 118173
dataset_size: 1423090
---
# Dataset Card for OneStopQA
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [OneStopQA repository](https://github.com/berzak/onestop-qa)
- **Repository:** [OneStopQA repository](https://github.com/berzak/onestop-qa)
- **Paper:** [STARC: Structured Annotations for Reading Comprehension](https://arxiv.org/abs/2004.14797)
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Needs More Information]
### Dataset Summary
OneStopQA is a multiple choice reading comprehension dataset annotated according to the STARC (Structured Annotations for Reading Comprehension) scheme. The reading materials are Guardian articles taken from the [OneStopEnglish corpus](https://github.com/nishkalavallabhi/OneStopEnglishCorpus). Each article comes in three difficulty levels, Elementary, Intermediate and Advanced. Each paragraph is annotated with three multiple choice reading comprehension questions. The reading comprehension questions can be answered based on any of the three paragraph levels.
### Supported Tasks and Leaderboards
[Needs More Information]
### Languages
English (`en-US`).
The original Guardian articles were manually converted from British to American English.
## Dataset Structure
### Data Instances
An example of instance looks as follows.
```json
{
"title": "101-Year-Old Bottle Message",
"paragraph": "Angela Erdmann never knew her grandfather. He died in 1946, six years before she was born. But, on Tuesday 8th April, 2014, she described the extraordinary moment when she received a message in a bottle, 101 years after he had lobbed it into the Baltic Sea. Thought to be the world’s oldest message in a bottle, it was presented to Erdmann by the museum that is now exhibiting it in Germany.",
"paragraph_index": 1,
"level": "Adv",
"question": "How did Angela Erdmann find out about the bottle?",
"answers": ["A museum told her that they had it",
"She coincidentally saw it at the museum where it was held",
"She found it in her basement on April 28th, 2014",
"A friend told her about it"],
"a_span": [56, 70],
"d_span": [16, 34]
}
```
Where,
| Answer | Description | Textual Span |
|--------|------------------------------------------------------------|-----------------|
| a | Correct answer. | Critical Span |
| b | Incorrect answer. A miscomprehension of the critical span. | Critical Span |
| c | Incorrect answer. Refers to an additional span. | Distractor Span |
| d | Incorrect answer. Has no textual support. | - |
The order of the answers in the `answers` list corresponds to the order of the answers in the table.
### Data Fields
- `title`: A `string` feature. The article title.
- `paragraph`: A `string` feature. The paragraph from the article.
- `paragraph_index`: An `int` feature. Corresponds to the paragraph index in the article.
- `question`: A `string` feature. The given question.
- `answers`: A list of `string` feature containing the four possible answers.
- `a_span`: A list of start and end indices (inclusive) of the critical span.
- `d_span`: A list of start and end indices (inclusive) of the distractor span.
*Span indices are according to word positions after whitespace tokenization.
**In the rare case where a span is spread over multiple sections,
the span list will contain multiple instances of start and stop indices in the format:
[start_1, stop_1, start_2, stop_2,...].
### Data Splits
Articles: 30
Paragraphs: 162
Questions: 486
Question-Paragraph Level pairs: 1,458
No preconfigured split is currently provided.
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
The annotation and piloting process of the dataset is described in Appendix A in
[STARC: Structured Annotations for Reading Comprehension](https://aclanthology.org/2020.acl-main.507.pdf).
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
<a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/"><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by-sa/4.0/88x31.png" /></a><br />This work is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/">Creative Commons Attribution-ShareAlike 4.0 International License</a>.
### Citation Information
[STARC: Structured Annotations for Reading Comprehension](http://people.csail.mit.edu/berzak/papers/acl2020.pdf)
```
@inproceedings{starc2020,
author = {Berzak, Yevgeni and Malmaud, Jonathan and Levy, Roger},
title = {STARC: Structured Annotations for Reading Comprehension},
booktitle = {ACL},
year = {2020},
publisher = {Association for Computational Linguistics}
}
```
### Contributions
Thanks to [@scaperex](https://github.com/scaperex) for adding this dataset. |
alpayariyak/MATH_Instruction_Format | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 9836383
num_examples: 12500
download_size: 4859969
dataset_size: 9836383
---
# Dataset Card for "MATH_Instruction_Format"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
vwxyzjn/openhermes-dev-512__NousResearch_Nous-Hermes-2-Yi-34B__1707948240 | ---
dataset_info:
features:
- name: source
dtype: string
- name: category
dtype: string
- name: prompt
dtype: string
- name: candidate0_policy
dtype: string
- name: candidate0
list:
- name: content
dtype: string
- name: role
dtype: string
- name: candidate1
list:
- name: content
dtype: string
- name: role
dtype: string
- name: candidate1_policy
dtype: string
- name: candidate2
list:
- name: content
dtype: string
- name: role
dtype: string
- name: candidate2_policy
dtype: string
splits:
- name: train
num_bytes: 80003.0
num_examples: 12
download_size: 69911
dataset_size: 80003.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Huzaifatahir/ConceptsofBiology | ---
license: mit
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 4000
num_examples: 1000
download_size: 715
dataset_size: 4000
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
AdapterOcean/code_instructions_standardized_cluster_7_alpaca | ---
dataset_info:
features:
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 21159856
num_examples: 8649
download_size: 10897829
dataset_size: 21159856
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "code_instructions_standardized_cluster_7_alpaca"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
spellingdragon/common_voice_9_zh-TW_simple-whisper-large-v3 | ---
dataset_info:
features:
- name: input_features
sequence:
sequence: float32
- name: labels
sequence: int64
- name: input_length
dtype: float64
splits:
- name: train
num_bytes: 24016183040.0
num_examples: 15629
- name: test
num_bytes: 14978536672
num_examples: 9747
download_size: 5997112102
dataset_size: 38994719712.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
otgs/multilingual_sample | ---
license: bigscience-openrail-m
---
|
Pablao0948/Moto_Moto | ---
license: openrail
---
|
maomlab/B3DB | ---
language: en
license: cc0-1.0
source_datasets: curated
task_categories:
- tabular-classification
- tabular-regression
tags:
- chemistry
- biology
- medical
pretty_name: Blood-Brain Barrier Database (B3DB)
dataset_summary: >-
Curation of 50 published resources of categorical and numeric measurements of
Blood-Brain Barrier penetration.
citation: >-
@article{
Meng_A_curated_diverse_2021,
author = {Meng, Fanwang and Xi, Yang and Huang, Jinfeng and Ayers, Paul W.},
doi = {10.1038/s41597-021-01069-5},
journal = {Scientific Data},
number = {289},
title = {A curated diverse molecular database of blood-brain barrier permeability with chemical descriptors},
volume = {8},
year = {2021},
url = {https://www.nature.com/articles/s41597-021-01069-5},
publisher = {Springer Nature}
}
size_categories:
- 1K<n<10K
config_names:
- B3DB_classification
- B3DB_classification_extended
- B3DB_regression
- B3DB_regression_extended
configs:
- config_name: B3DB_classification
data_files:
- split: test
path: B3DB_classification/test.csv
- split: train
path: B3DB_classification/train.csv
- config_name: B3DB_classification_extended
data_files:
- split: test
path: B3DB_classification_extended/test.csv
- split: train
path: B3DB_classification_extended/train.csv
- config_name: B3DB_regression
data_files:
- split: test
path: B3DB_regression/test.csv
- split: train
path: B3DB_regression/train.csv
- config_name: B3DB_regression_extended
data_files:
- split: test
path: B3DB_regression_extended/test.csv
- split: train
path: B3DB_regression_extended/train.csv
dataset_info:
- config_name: B3DB_classification
features:
- name: "NO."
dtype: int64
- name: "compound_name"
dtype: string
- name: "IUPAC_name"
dtype: string
- name: "SMILES"
dtype: string
- name: "CID"
dtype: float64
- name: "logBB"
dtype: float64
- name: "BBB+/BBB-"
dtype:
class_label:
names:
0: "BBB-"
1: "BBB+"
- name: "Inchi"
dtype: string
- name: "threshold"
dtype: float64
- name: "reference"
dtype: string
- name: "group"
dtype: string
- name: "comments"
dtype: string
- name: "ClusterNo"
dtype: int64
- name: "MolCount"
dtype: int64
splits:
- name: train
num_bytes: 656000
num_examples: 5856
- name: test
num_bytes: 218640
num_examples: 1951
- config_name: B3DB_classification_extended
splits:
- name: train
num_bytes: 76221824
num_examples: 5856
- name: test
num_bytes: 25394344
num_examples: 1951
- config_name: B3DB_regression
features:
- name: "NO."
dtype: int64
- name: "compound_name"
dtype: string
- name: "IUPAC_name"
dtype: string
- name: "SMILES"
dtype: string
- name: "CID"
dtype: string
- name: "logBB"
dtype: float64
- name: "Inchi"
dtype: string
- name: "reference"
dtype: string
- name: "smiles_result"
dtype: string
- name: "group"
dtype: string
- name: "comments"
dtype: float64
- name: "ClusterNo"
dtype: int64
- name: "MolCount"
dtype: int64
splits:
- name: train
num_bytes: 82808
num_examples: 795
- name: test
num_bytes: 27480
num_examples: 263
- config_name: B3DB_regression_extended
splits:
- name: train
num_bytes: 10347848
num_examples: 795
- name: test
num_bytes: 3423336
num_examples: 263
---
# Blood-Brain Barrier Database (B3DB)
The [Blood-Brain Barrier Database (B3DB)](https://www.nature.com/articles/s41597-021-01069-5)
is a large benchmark dataset compiled from 50 published resources
(as summarized at raw_data/raw_data_summary.tsv) and categorized based on the consistency between
different experimental references/measurements. This dataset was
[published in Scientific Data](https://www.nature.com/articles/s41597-021-01069-5) and
a mirror of the [theochem/B3DB](https://github.com/theochem/B3DB) the official Github repo where it is
occasionally uploaded with new experimental data. Scientists who would like
to contribute data should contact the database's maintainers (e.g., by creating a
[new Issue](https://github.com/theochem/B3DB/issues) in the database).
A subset of the molecules in B3DB has numerical `logBB` values (1058 compounds),
while the whole dataset has categorical (`BBB+` or `BBB-`) BBB permeability labels
(7807 compounds). Some physicochemical properties of the molecules are also provided.
## Quickstart Usage
### Load a dataset in python
Each subset can be loaded into python using the Huggingface [datasets](https://huggingface.co/docs/datasets/index) library.
First, from the command line install the `datasets` library
$ pip install datasets
then, from within python load the datasets library
>>> import datasets
and load one of the `B3DB` datasets, e.g.,
>>> B3DB_classification = datasets.load_dataset("maomlab/B3DB", name = "B3DB_classification")
Downloading readme: 100%|████████████████████████| 4.40k/4.40k [00:00<00:00, 1.35MB/s]
Downloading data: 100%|██████████████████████████| 680k/680k [00:00<00:00, 946kB/s]
Downloading data: 100%|██████████████████████████| 2.11M/2.11M [00:01<00:00, 1.28MB/s]
Generating test split: 100%|█████████████████████| 1951/1951 [00:00<00:00, 20854.95 examples/s]
Generating train split: 100%|████████████████████| 5856/5856 [00:00<00:00, 144260.80 examples/s]
and inspecting the loaded dataset
>>> B3DB_classification
B3DB_classification
DatasetDict({
test: Dataset({
features: ['NO.', 'compound_name', 'IUPAC_name', 'SMILES', 'CID', 'logBB', 'BBB+/BBB-', 'Inchi', 'threshold', 'reference', 'group', 'comments', 'ClusterNo', 'MolCount'],
num_rows: 1951
})
train: Dataset({
features: ['NO.', 'compound_name', 'IUPAC_name', 'SMILES', 'CID', 'logBB', 'BBB+/BBB-', 'Inchi', 'threshold', 'reference', 'group', 'comments', 'ClusterNo', 'MolCount'],
num_rows: 5856
})
})
### Use a dataset to train a model
One way to use the dataset is through the [MolFlux](https://exscientia.github.io/molflux/) package developed by Exscientia.
First, from the command line, install `MolFlux` library with `catboost` and `rdkit` support
pip install 'molflux[catboost,rdkit]'
then load, featurize, split, fit, and evaluate the a catboost model
import json
from datasets import load_dataset
from molflux.datasets import featurise_dataset
from molflux.features import load_from_dicts as load_representations_from_dicts
from molflux.splits import load_from_dict as load_split_from_dict
from molflux.modelzoo import load_from_dict as load_model_from_dict
from molflux.metrics import load_suite
split_dataset = load_dataset('maomlab/B3DB', name = 'B3DB_classification')
split_featurised_dataset = featurise_dataset(
split_dataset,
column = "SMILES",
representations = load_representations_from_dicts([{"name": "morgan"}, {"name": "maccs_rdkit"}]))
model = load_model_from_dict({
"name": "cat_boost_classifier",
"config": {
"x_features": ['SMILES::morgan', 'SMILES::maccs_rdkit'],
"y_features": ['BBB+/BBB-']}})
model.train(split_featurised_dataset["train"])
preds = model.predict(split_featurised_dataset["test"])
classification_suite = load_suite("classification")
scores = classification_suite.compute(
references=split_featurised_dataset["test"]['BBB+/BBB-'],
predictions=preds["cat_boost_classifier::BBB+/BBB-"])
## About the DB3B
### Features of *B3DB*
1. The largest dataset with numerical and categorical values for Blood-Brain Barrier small molecules
(to the best of our knowledge, as of February 25, 2021).
2. Inclusion of stereochemistry information with isomeric SMILES with chiral specifications if
available. Otherwise, canonical SMILES are used.
3. Characterization of uncertainty of experimental measurements by grouping the collected molecular
data records.
4. Extended datasets for numerical and categorical data with precomputed physicochemical properties
using [mordred](https://github.com/mordred-descriptor/mordred).
### Data splits
The original B3DB dataset does not define splits, so here we have used the `Realistic Split` method described
in [(Martin et al., 2018)](https://doi.org/10.1021/acs.jcim.7b00166).
### Citation
Please use the following citation in any publication using our *B3DB* dataset:
```md
@article{Meng_A_curated_diverse_2021,
author = {Meng, Fanwang and Xi, Yang and Huang, Jinfeng and Ayers, Paul W.},
doi = {10.1038/s41597-021-01069-5},
journal = {Scientific Data},
number = {289},
title = {A curated diverse molecular database of blood-brain barrier permeability with chemical descriptors},
volume = {8},
year = {2021},
url = {https://www.nature.com/articles/s41597-021-01069-5},
publisher = {Springer Nature}
}
```
|
Nexdata/Chinese_Speech_Data_by_Mobile_Phone | ---
YAML tags:
- copy-paste the tags obtained with the tagging app: https://github.com/huggingface/datasets-tagging
---
# Dataset Card for Nexdata/Chinese_Speech_Data_by_Mobile_Phone
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://www.nexdata.ai/datasets/234?source=Huggingface
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
The dataset contains 200 Chinese native speakers, covering main dialect zones. It is recorded in both noisy and quiet environment and more suitable for the actual application scenario for speech recognition. The recordings are commonly used spoken sentences. Texts are transcribed by professional annotators. It can be used for speech recognition and machine translation.
For more details, please refer to the link: https://www.nexdata.ai/datasets/234?source=Huggingface
### Supported Tasks and Leaderboards
automatic-speech-recognition, audio-speaker-identification: The dataset can be used to train a model for Automatic Speech Recognition (ASR).
### Languages
Chinese Mandarin
## 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
Commerical License: https://drive.google.com/file/d/1saDCPm74D4UWfBL17VbkTsZLGfpOQj1J/view?usp=sharing
### Citation Information
[More Information Needed]
### Contributions
|
autoevaluate/autoeval-staging-eval-project-09cba8dc-757f-4f7a-8194-174e4439eb99-91 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- glue
eval_info:
task: binary_classification
model: autoevaluate/binary-classification
metrics: ['matthews_correlation']
dataset_name: glue
dataset_config: sst2
dataset_split: validation
col_mapping:
text: sentence
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: Binary Text Classification
* Model: autoevaluate/binary-classification
* Dataset: glue
* Config: sst2
* Split: validation
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. |
Dahoas/MATH_chat_format | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: response
dtype: string
- name: question
dtype: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 12599878
num_examples: 7500
- name: test
num_bytes: 634883
num_examples: 411
download_size: 6381185
dataset_size: 13234761
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
manu/french-30b_separate | ---
configs:
- config_name: default
data_files:
- split: WmtEnFrTest
path: data/WmtEnFrTest-*
- split: EnglishFrenchWebpagesScrapedTranslatedTest
path: data/EnglishFrenchWebpagesScrapedTranslatedTest-*
- split: FrenchLibrispeechTextOnlyTest
path: data/FrenchLibrispeechTextOnlyTest-*
- split: FrenchPodcastsTest
path: data/FrenchPodcastsTest-*
- split: FrenchOpenSubtitlesTest
path: data/FrenchOpenSubtitlesTest-*
- split: OriginalSongsLyricsWithFrenchTranslationTest
path: data/OriginalSongsLyricsWithFrenchTranslationTest-*
- split: ProjectgutenbergFrTest
path: data/ProjectgutenbergFrTest-*
- split: BnfGallicaTest
path: data/BnfGallicaTest-*
- split: ThesesFr20132023Test
path: data/ThesesFr20132023Test-*
- split: LegiOpendataTest
path: data/LegiOpendataTest-*
- split: BaloOpendataTest
path: data/BaloOpendataTest-*
- split: JadeOpendataTest
path: data/JadeOpendataTest-*
- split: DoleOpendataTest
path: data/DoleOpendataTest-*
- split: SardeOpendataTest
path: data/SardeOpendataTest-*
- split: QrOpendataTest
path: data/QrOpendataTest-*
- split: JorfOpendataTest
path: data/JorfOpendataTest-*
- split: IncaOpendataTest
path: data/IncaOpendataTest-*
- split: AccoOpendataTest
path: data/AccoOpendataTest-*
- split: KaliOpendataTest
path: data/KaliOpendataTest-*
- split: DebatsOpendataTest
path: data/DebatsOpendataTest-*
- split: CnilOpendataTest
path: data/CnilOpendataTest-*
- split: CappOpendataTest
path: data/CappOpendataTest-*
- split: CassOpendataTest
path: data/CassOpendataTest-*
- split: ConstitOpendataTest
path: data/ConstitOpendataTest-*
- split: IlluinLayoutDatasetTextOnlyTest
path: data/IlluinLayoutDatasetTextOnlyTest-*
- split: WikisourceFrTest
path: data/WikisourceFrTest-*
- split: Wikipedia20220301.frTest
path: data/Wikipedia20220301.frTest-*
- split: Oscar2301FrTest
path: data/Oscar2301FrTest-*
dataset_info:
features:
- name: id
dtype: string
- name: text
dtype: string
- name: dataset_id
dtype: string
splits:
- name: WmtEnFrTest
num_bytes: 933080
num_examples: 3003
- name: EnglishFrenchWebpagesScrapedTranslatedTest
num_bytes: 3557903
num_examples: 8580
- name: FrenchLibrispeechTextOnlyTest
num_bytes: 698968
num_examples: 2582
- name: FrenchPodcastsTest
num_bytes: 505018
num_examples: 100
- name: FrenchOpenSubtitlesTest
num_bytes: 3048714
num_examples: 100
- name: OriginalSongsLyricsWithFrenchTranslationTest
num_bytes: 2156145
num_examples: 756
- name: ProjectgutenbergFrTest
num_bytes: 39019119
num_examples: 100
- name: BnfGallicaTest
num_bytes: 43160730
num_examples: 100
- name: ThesesFr20132023Test
num_bytes: 3957037
num_examples: 959
- name: LegiOpendataTest
num_bytes: 16589963
num_examples: 10000
- name: BaloOpendataTest
num_bytes: 11094568
num_examples: 1355
- name: JadeOpendataTest
num_bytes: 56977150
num_examples: 5586
- name: DoleOpendataTest
num_bytes: 2065780
num_examples: 100
- name: SardeOpendataTest
num_bytes: 1044391
num_examples: 2244
- name: QrOpendataTest
num_bytes: 18924359
num_examples: 100
- name: JorfOpendataTest
num_bytes: 11892298
num_examples: 10000
- name: IncaOpendataTest
num_bytes: 27827026
num_examples: 3737
- name: AccoOpendataTest
num_bytes: 36928857
num_examples: 2541
- name: KaliOpendataTest
num_bytes: 7740933
num_examples: 4306
- name: DebatsOpendataTest
num_bytes: 38200789
num_examples: 100
- name: CnilOpendataTest
num_bytes: 1495015
num_examples: 181
- name: CappOpendataTest
num_bytes: 9680857
num_examples: 727
- name: CassOpendataTest
num_bytes: 8283986
num_examples: 1422
- name: ConstitOpendataTest
num_bytes: 1340350
num_examples: 100
- name: IlluinLayoutDatasetTextOnlyTest
num_bytes: 11714355
num_examples: 4885
- name: WikisourceFrTest
num_bytes: 44358940
num_examples: 10000
- name: Wikipedia20220301.frTest
num_bytes: 28814742
num_examples: 10000
- name: Oscar2301FrTest
num_bytes: 51030875
num_examples: 9834
download_size: 0
dataset_size: 483041948
---
# Dataset Card for "french-30b_separate"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
heliosprime/twitter_dataset_1713203719 | ---
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: 33907
num_examples: 90
download_size: 27365
dataset_size: 33907
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "twitter_dataset_1713203719"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
qpzz/function_test | ---
license: apache-2.0
---
|
NousResearch/json-mode-eval | ---
dataset_info:
features:
- name: prompt
list:
- name: content
dtype: string
- name: role
dtype: string
- name: completion
dtype: string
- name: schema
dtype: string
splits:
- name: train
num_bytes: 178646
num_examples: 100
download_size: 81183
dataset_size: 178646
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Carlosholivan/base | ---
license: apache-2.0
---
|
auchoi/website_data | ---
license: apache-2.0
---
|
irds/codec | ---
pretty_name: '`codec`'
viewer: false
source_datasets: []
task_categories:
- text-retrieval
---
# Dataset Card for `codec`
The `codec` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/codec#codec).
# Data
This dataset provides:
- `docs` (documents, i.e., the corpus); count=729,824
- `queries` (i.e., topics); count=42
- `qrels`: (relevance assessments); count=6,186
This dataset is used by: [`codec_economics`](https://huggingface.co/datasets/irds/codec_economics), [`codec_history`](https://huggingface.co/datasets/irds/codec_history), [`codec_politics`](https://huggingface.co/datasets/irds/codec_politics)
## Usage
```python
from datasets import load_dataset
docs = load_dataset('irds/codec', 'docs')
for record in docs:
record # {'doc_id': ..., 'title': ..., 'text': ..., 'url': ...}
queries = load_dataset('irds/codec', 'queries')
for record in queries:
record # {'query_id': ..., 'query': ..., 'domain': ..., 'guidelines': ...}
qrels = load_dataset('irds/codec', 'qrels')
for record in qrels:
record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@inproceedings{mackie2022codec,
title={CODEC: Complex Document and Entity Collection},
author={Mackie, Iain and Owoicho, Paul and Gemmell, Carlos and Fischer, Sophie and MacAvaney, Sean and Dalton, Jeffery},
booktitle={Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval},
year={2022}
}
```
|
pranavkotz/anatomy_dataset | ---
license: apache-2.0
task_categories:
- sentence-similarity
- text-classification
- token-classification
language:
- en
tags:
- medical
- biology
size_categories:
- n<1K
pretty_name: Textual Similarity
--- |
joey234/mmlu-formal_logic-verbal-neg-prepend | ---
dataset_info:
features:
- name: question
dtype: string
- name: choices
sequence: string
- name: answer
dtype:
class_label:
names:
'0': A
'1': B
'2': C
'3': D
- name: neg_prompt
dtype: string
splits:
- name: test
num_bytes: 63624
num_examples: 126
download_size: 29516
dataset_size: 63624
---
# Dataset Card for "mmlu-formal_logic-verbal-neg-prepend"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.