author stringlengths 2 29 ⌀ | cardData null | citation stringlengths 0 9.58k ⌀ | description stringlengths 0 5.93k ⌀ | disabled bool 1 class | downloads float64 1 1M ⌀ | gated bool 2 classes | id stringlengths 2 108 | lastModified stringlengths 24 24 | paperswithcode_id stringlengths 2 45 ⌀ | private bool 2 classes | sha stringlengths 40 40 | siblings list | tags list | readme_url stringlengths 57 163 | readme stringlengths 0 977k |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
zhangxinran | null | null | null | false | 55 | false | zhangxinran/lolita-dress-ENG | 2022-11-12T00:43:03.000Z | null | false | a19e2b88393fd2ce86b61f3f74387a6aa4737cf1 | [] | [] | https://huggingface.co/datasets/zhangxinran/lolita-dress-ENG/resolve/main/README.md | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 533036535.0
num_examples: 744
download_size: 530749245
dataset_size: 533036535.0
---
# Dataset Card for "lolita-dress-ENG"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
WillHeld | null | null | null | false | 40 | false | WillHeld/stereoset_zero | 2022-11-12T00:57:23.000Z | null | false | bc28c1a88a57331f0cf190a777a5234a25b976bd | [] | [] | https://huggingface.co/datasets/WillHeld/stereoset_zero/resolve/main/README.md | ---
dataset_info:
features:
- name: target
dtype: int64
- name: text
dtype: string
- name: classes
sequence: string
splits:
- name: train
num_bytes: 900372
num_examples: 4229
download_size: 311873
dataset_size: 900372
---
# Dataset Card for "stereoset_zero"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Daftdroh | null | null | null | false | null | false | Daftdroh/sisi | 2022-11-12T01:03:02.000Z | null | false | b8cf69735312a73b4d5455da24faa23d4389a5c2 | [] | [
"license:other"
] | https://huggingface.co/datasets/Daftdroh/sisi/resolve/main/README.md | ---
license: other
---
|
Jellywibble | null | null | null | false | 25 | false | Jellywibble/dalio-reward-model-hackathon-dataset | 2022-11-13T17:25:41.000Z | null | false | b440ccc9dfede07d020206455bb41c6df42c9f53 | [] | [] | https://huggingface.co/datasets/Jellywibble/dalio-reward-model-hackathon-dataset/resolve/main/README.md | ---
dataset_info:
features:
- name: input_text
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_bytes: 8765
num_examples: 16
download_size: 6055
dataset_size: 8765
---
# Dataset Card for "dalio-reward-model-hackathon-dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
iuliaturc-personal | null | null | null | false | 18 | false | iuliaturc-personal/rick-and-morty-manual-captions | 2022-11-12T04:50:47.000Z | null | false | 37bbc9985d018c7ee582a01492c587165a043083 | [] | [] | https://huggingface.co/datasets/iuliaturc-personal/rick-and-morty-manual-captions/resolve/main/README.md | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 11036008.0
num_examples: 151
- name: valid
num_bytes: 925318.0
num_examples: 16
download_size: 11931563
dataset_size: 11961326.0
---
# Dataset Card for "rick-and-morty-manual-captions"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Jellywibble | null | null | null | false | null | false | Jellywibble/dalio-conversations-hackathon-dataset | 2022-11-12T23:35:14.000Z | null | false | 32cffc58163df4f5838a6a9635d762fde83cff9e | [] | [] | https://huggingface.co/datasets/Jellywibble/dalio-conversations-hackathon-dataset/resolve/main/README.md | ---
dataset_info:
features:
- name: input_text
dtype: string
- name: scores
dtype: int64
splits:
- name: train
num_bytes: 5026
num_examples: 8
download_size: 8422
dataset_size: 5026
---
# Dataset Card for "dalio-conversations-hackathon-dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Ziyang | null | null | null | false | null | false | Ziyang/CC4M | 2022-11-12T06:33:23.000Z | null | false | 7a83f4c3a031f16305afff1db7e00a545a2aac9a | [] | [] | https://huggingface.co/datasets/Ziyang/CC4M/resolve/main/README.md | The training and validation files of the conceptual captions dataset (4M). |
bgstud | null | null | null | false | 16 | false | bgstud/libri-mini-proc-whisper | 2022-11-12T10:53:24.000Z | null | false | c6e9b33aa26007ae7e6430a8e5ee4d112882b719 | [] | [] | https://huggingface.co/datasets/bgstud/libri-mini-proc-whisper/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language:
- en
language_creators:
- found
license:
- mit
multilinguality:
- monolingual
paperswithcode_id: acronym-identification
pretty_name: Acronym Identification Dataset
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- token-classification-other-acronym-identification
train-eval-index:
- col_mapping:
labels: tags
tokens: tokens
config: default
splits:
eval_split: test
task: token-classification
task_id: entity_extraction---
# 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
[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
#### 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
Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset.
|
gigant | null | null | null | false | 1 | false | gigant/tib_metadata | 2022-11-12T11:43:13.000Z | null | false | 69aeaac794fad91df80e0a33883d7e0ec14c69f2 | [] | [] | https://huggingface.co/datasets/gigant/tib_metadata/resolve/main/README.md | ---
dataset_info:
features:
- name: title
dtype: string
- name: href
dtype: string
- name: description
dtype: 'null'
- name: url_vid
dtype: string
- name: release_date
dtype: string
- name: subject
dtype: string
- name: genre
dtype: string
- name: abstract
dtype: string
- name: language
dtype: string
splits:
- name: train
num_bytes: 22355313
num_examples: 22091
download_size: 11409382
dataset_size: 22355313
---
# Dataset Card for "tib_metadata"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Akshata | null | null | null | false | 3 | false | Akshata/autotrain-data-compliance | 2022-11-14T09:06:58.000Z | null | false | 687bce9dce4cba881f89090a759197860ccb3065 | [] | [
"language:en",
"task_categories:text-classification"
] | https://huggingface.co/datasets/Akshata/autotrain-data-compliance/resolve/main/README.md | ---
language:
- en
task_categories:
- text-classification
---
# AutoTrain Dataset for project: compliance
## Dataset Description
This dataset has been automatically processed by AutoTrain for project compliance.
### Languages
The BCP-47 code for the dataset's language is en.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"text": "Welcome back Abhishek! What can I do to help? ",
"target": 0
},
{
"text": "Hi , I am calling from ABC finance. I would like to inform you that you are eligible for a Personal Loan",
"target": 0
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"text": "Value(dtype='string', id=None)",
"target": "ClassLabel(num_classes=2, names=['Negative', 'Positive'], id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 31 |
| valid | 9 |
|
Akshata | null | null | null | false | 6 | false | Akshata/autotrain-data-demo_compliance | 2022-11-14T09:08:09.000Z | null | false | b6f786ecd95e0ba3e9c63a6a0704a47faa125a95 | [] | [
"language:en",
"task_categories:text-classification"
] | https://huggingface.co/datasets/Akshata/autotrain-data-demo_compliance/resolve/main/README.md | ---
language:
- en
task_categories:
- text-classification
---
# AutoTrain Dataset for project: demo_compliance
## Dataset Description
This dataset has been automatically processed by AutoTrain for project demo_compliance.
### Languages
The BCP-47 code for the dataset's language is en.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"text": "Welcome back Abhishek! What can I do to help? ",
"target": 0
},
{
"text": "Hi , I am calling from ABC finance. I would like to inform you that you are eligible for a Personal Loan",
"target": 0
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"text": "Value(dtype='string', id=None)",
"target": "ClassLabel(num_classes=2, names=['Negative', 'Positive'], id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 31 |
| valid | 9 |
|
statworx | null | null | null | false | 39 | false | statworx/leipzip-swiss | 2022-11-15T15:44:39.000Z | null | false | d0bae28fe0b7c10504789d1d12d3b9b7da8a75a0 | [] | [] | https://huggingface.co/datasets/statworx/leipzip-swiss/resolve/main/README.md | ---
dataset_info:
features:
- name: sentence
dtype: string
splits:
- name: train
num_bytes: 65520533
num_examples: 600000
download_size: 47876756
dataset_size: 65520533
---
# Dataset Card for "leipzip-swiss"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
galman33 | null | null | null | false | null | false | galman33/gal_yair_8300_100x100 | 2022-11-12T15:54:41.000Z | null | false | 9cdfe3e63bbbd32394a7554df8993914bd715e31 | [] | [] | https://huggingface.co/datasets/galman33/gal_yair_8300_100x100/resolve/main/README.md | ---
dataset_info:
features:
- name: lat
dtype: float64
- name: lon
dtype: float64
- name: country_code
dtype: string
- name: image
dtype: image
splits:
- name: train
num_bytes: 142004157.0
num_examples: 8300
download_size: 141994031
dataset_size: 142004157.0
---
# Dataset Card for "yair_gal_small_resized"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
ysjay | null | null | null | false | 8 | false | ysjay/processed_bert_dataset | 2022-11-12T16:02:58.000Z | null | false | c031dc07e5bfc318508c2b968374d6ecf76928e2 | [] | [] | https://huggingface.co/datasets/ysjay/processed_bert_dataset/resolve/main/README.md | ---
dataset_info:
features:
- name: input_ids
sequence: int32
- name: token_type_ids
sequence: int8
- name: attention_mask
sequence: int8
- name: next_sentence_label
dtype: int64
splits:
- name: train
num_bytes: 70985500
num_examples: 2000
download_size: 18506503
dataset_size: 70985500
---
# Dataset Card for "processed_bert_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
jjackass59660 | null | null | null | false | null | false | jjackass59660/scyther | 2022-11-12T16:29:19.000Z | null | false | b5439a01b92274f98d15226e5469797c4eb1a6f6 | [] | [
"license:other"
] | https://huggingface.co/datasets/jjackass59660/scyther/resolve/main/README.md | ---
license: other
---
|
vegeta | null | null | null | false | null | false | vegeta/nlplegal | 2022-11-12T17:32:30.000Z | null | false | 585e8b0fa33c72f11cd8d9fb387df098891bd03e | [] | [] | https://huggingface.co/datasets/vegeta/nlplegal/resolve/main/README.md | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 15816253477
num_examples: 218374246
- name: validation
num_bytes: 1736194279
num_examples: 23880923
download_size: 8455493030
dataset_size: 17552447756
---
# Dataset Card for "nlplegal"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
DAYSTOSOLVE | null | null | null | false | null | false | DAYSTOSOLVE/MasDoc | 2022-11-12T20:24:23.000Z | null | false | 0252b722cca7bc13fd1bcb70a11c347b9769974b | [] | [
"license:mit"
] | https://huggingface.co/datasets/DAYSTOSOLVE/MasDoc/resolve/main/README.md | ---
license: mit
---
|
galman33 | null | null | null | false | 1 | false | galman33/gal_yair_8300_256x256 | 2022-11-12T21:23:40.000Z | null | false | d9d90314ea75bf0df5012a84f5cbe39b25c8fa1c | [] | [] | https://huggingface.co/datasets/galman33/gal_yair_8300_256x256/resolve/main/README.md | ---
dataset_info:
features:
- name: lat
dtype: float64
- name: lon
dtype: float64
- name: country_code
dtype: string
- name: image
dtype: image
splits:
- name: train
num_bytes: 805012745.0
num_examples: 8300
download_size: 805035741
dataset_size: 805012745.0
---
# Dataset Card for "gal_yair_8300_256x256"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
DAYSTOSOLVE | null | null | null | false | null | false | DAYSTOSOLVE/la-regression | 2022-11-12T21:09:11.000Z | null | false | 73ef144774eb2bf0052dbe040890bee61a462202 | [] | [
"license:cc"
] | https://huggingface.co/datasets/DAYSTOSOLVE/la-regression/resolve/main/README.md | ---
license: cc
---
|
flamesbob | null | null | null | false | null | false | flamesbob/Dark_fantasy | 2022-11-12T21:29:55.000Z | null | false | 07e98a26201e24432cbe41e2d4e32adeebff5e27 | [] | [
"license:creativeml-openrail-m"
] | https://huggingface.co/datasets/flamesbob/Dark_fantasy/resolve/main/README.md | ---
license: creativeml-openrail-m
---
|
vegeta | null | null | null | false | 9 | false | vegeta/tokenedlegal | 2022-11-12T23:42:28.000Z | null | false | e3366d7cda004d99644e589649dfd973d044c419 | [] | [] | https://huggingface.co/datasets/vegeta/tokenedlegal/resolve/main/README.md | ---
dataset_info:
features:
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
splits:
- name: train
num_bytes: 29279261498
num_examples: 218374246
- name: validation
num_bytes: 3195898734
num_examples: 23880923
download_size: 8182611602
dataset_size: 32475160232
---
# Dataset Card for "tokenedlegal"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
blobba | null | null | null | false | null | false | blobba/zh-en-mc4 | 2022-11-13T00:11:01.000Z | null | false | dd7c77a4f3e16d39517e0e90cab38d6aa92f636a | [] | [
"license:cc0-1.0"
] | https://huggingface.co/datasets/blobba/zh-en-mc4/resolve/main/README.md | ---
license: cc0-1.0
---
|
Nerfgun3 | null | null | null | false | null | false | Nerfgun3/cute_style | 2022-11-12T23:27:55.000Z | null | false | 2f36cee491d3e14b224a69e75749ce4d54e627e4 | [] | [
"language:en",
"tags:stable-diffusion",
"tags:text-to-image",
"license:creativeml-openrail-m"
] | https://huggingface.co/datasets/Nerfgun3/cute_style/resolve/main/README.md | ---
language:
- en
tags:
- stable-diffusion
- text-to-image
license: creativeml-openrail-m
inference: false
---
# Cute Style Embedding / Textual Inversion
## Usage
To use this embedding you have to download the file aswell as drop it into the "\stable-diffusion-webui\embeddings" folder
This Style doesnt really has a specific theme, it just turns the expression of girls into "cute"
To use it in a prompt: ```"drawn by cute_style"```
If it is to strong just add [] around it.
Trained until 6000 steps
Have fun :)
## Example Pictures
<table>
<tr>
<td><img src=https://i.imgur.com/vDjSy5c.png width=100% height=100%/></td>
<td><img src=https://i.imgur.com/wXBNJNX.png width=100% height=100%/></td>
<td><img src=https://i.imgur.com/e3gremJ.png width=100% height=100%/></td>
<td><img src=https://i.imgur.com/jpYyj96.png width=100% height=100%/></td>
<td><img src=https://i.imgur.com/hUVuj9N.png width=100% height=100%/></td>
</tr>
</table>
## License
This embedding is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage.
The CreativeML OpenRAIL License specifies:
1. You can't use the embedding to deliberately produce nor share illegal or harmful outputs or content
2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license
3. You may re-distribute the weights and use the embedding commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully)
[Please read the full license here](https://huggingface.co/spaces/CompVis/stable-diffusion-license) |
ndorr16 | null | null | null | false | null | false | ndorr16/RockingDuck | 2022-11-12T23:52:47.000Z | null | false | ca331d7c447d0327e0ef88714c4133574b27e562 | [] | [
"license:gpl-3.0"
] | https://huggingface.co/datasets/ndorr16/RockingDuck/resolve/main/README.md | ---
license: gpl-3.0
---
|
ClemenKok | null | null | null | false | null | false | ClemenKok/digimon-blip-captions | 2022-11-13T02:08:54.000Z | null | false | cf946d9f16c590f30b86b50c7efee600295fb6c5 | [] | [
"annotations_creators:machine-generated",
"language:en",
"license:cc-by-nc-4.0",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"tags:digimon"
] | https://huggingface.co/datasets/ClemenKok/digimon-blip-captions/resolve/main/README.md | ---
annotations_creators:
- machine-generated
language:
- en
license:
- cc-by-nc-4.0
multilinguality:
- monolingual
pretty_name: '1,071 BLIP captioned images of Digimon. '
size_categories:
- 1K<n<10K
source_datasets:
- original
tags:
- digimon
task_categories: []
task_ids: []
---
# Dataset Card for Digimon BLIP captions
This project was inspired by the [labelled Pokemon dataset](https://huggingface.co/datasets/lambdalabs/pokemon-blip-captions).
The captions were generated using the BLIP Model found in the [LAVIS Library for Language-Vision Intelligence](https://github.com/salesforce/LAVIS).
Like the Pokemon equivalent, each row in the dataset contains the `image` and `text` keys. `Image` is a varying size pixel jpeg, and `text` is the corresponding text caption.
## Citation
If you use this dataset, please cite it as:
```
@misc{clemen2022digimon,
author = {Kok, Clemen},
title = {Digimon BLIP captions},
year={2022},
howpublished= {\url{https://huggingface.co/datasets/ClemenKok/digimon-lavis-captions/}}
}
``` |
LiveEvil | null | null | null | false | null | false | LiveEvil/la-classes | 2022-11-13T00:48:07.000Z | null | false | 9ebc7b7a65e5ca01951f98750aea8ffb2cb926cf | [] | [
"license:mit"
] | https://huggingface.co/datasets/LiveEvil/la-classes/resolve/main/README.md | ---
license: mit
---
|
LiveEvil | null | null | null | false | 4 | false | LiveEvil/autotrain-data-la-classes | 2022-11-14T18:07:12.000Z | null | false | 6cd1cb1932524f92f5d5ec7ee14a03f8238ba769 | [] | [
"language:en"
] | https://huggingface.co/datasets/LiveEvil/autotrain-data-la-classes/resolve/main/README.md | ---
language:
- en
task_categories:
- text-scoring
---
# AutoTrain Dataset for project: la-classes
## Dataset Description
This dataset has been automatically processed by AutoTrain for project la-classes.
### Languages
The BCP-47 code for the dataset's language is en.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"text": "Friends will follow in your tracks do not lead them to harm.",
"target": 9.0
},
{
"text": "Loss helps you learn.",
"target": 7.0
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"text": "Value(dtype='string', id=None)",
"target": "Value(dtype='float32', id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 25 |
| valid | 11 |
|
zhangxinran | null | null | null | false | null | false | zhangxinran/lolita-dress-ENG256 | 2022-11-13T00:56:11.000Z | null | false | f29989f0e722d8fbd874fe6fee8576e7446f13c7 | [] | [] | https://huggingface.co/datasets/zhangxinran/lolita-dress-ENG256/resolve/main/README.md | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 82410459.0
num_examples: 745
download_size: 81543982
dataset_size: 82410459.0
---
# Dataset Card for "lolita-dress-ENG256"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
101arrowz | null | @Article{Nagrani19,
author = "Arsha Nagrani and Joon~Son Chung and Weidi Xie and Andrew Zisserman",
title = "Voxceleb: Large-scale speaker verification in the wild",
journal = "Computer Science and Language",
year = "2019",
publisher = "Elsevier",
}
@InProceedings{Chung18b,
author = "Chung, J.~S. and Nagrani, A. and Zisserman, A.",
title = "VoxCeleb2: Deep Speaker Recognition",
booktitle = "INTERSPEECH",
year = "2018",
}
@InProceedings{Nagrani17,
author = "Nagrani, A. and Chung, J.~S. and Zisserman, A.",
title = "VoxCeleb: a large-scale speaker identification dataset",
booktitle = "INTERSPEECH",
year = "2017",
} | VoxCeleb is an audio-visual dataset consisting of short clips of human speech, extracted from interview videos uploaded to YouTube | false | 4 | false | 101arrowz/vox_celeb | 2022-11-15T19:53:39.000Z | null | false | 1d94e08332fb91310f481db802dd7171e05acf92 | [] | [
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"license:cc-by-4.0",
"multilinguality:multilingual",
"size_categories:1K<n<10K",
"size_categories:10K<n<100K",
"size_categories:100K<n<1M",
"task_categories:automatic-speech-recognition",
"task_categories:audio-classification",
"task_categories:image-classification",
"task_ids:speaker-identification"
] | https://huggingface.co/datasets/101arrowz/vox_celeb/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
language: []
language_creators:
- crowdsourced
license:
- cc-by-4.0
multilinguality:
- multilingual
pretty_name: VoxCeleb
size_categories:
- 1K<n<10K
- 10K<n<100K
- 100K<n<1M
source_datasets: []
tags: []
task_categories:
- automatic-speech-recognition
- audio-classification
- image-classification
task_ids:
- speaker-identification
---
# Dataset Card for VoxCeleb
## 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
### Dataset Summary
VoxCeleb is an audio-visual dataset consisting of short clips of human speech, extracted from interview videos uploaded to YouTube.
NOTE: Although this dataset can be automatically downloaded, you must manually request credentials to access it from the creators' website.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
Each datapoint has a path to the audio/video clip along with metadata about the speaker.
```
{
'file': '/datasets/downloads/extracted/[hash]/wav/id10271/_YimahVgI1A/00003.wav',
'file_format': 'wav',
'dataset_id': 'vox1',
'speaker_id': 'id10271',
'speaker_gender': 'm',
'speaker_name': 'Ed_Westwick',
'speaker_nationality': 'UK',
'video_id': '_YimahVgI1A',
'clip_id': '00003',
'audio': {
'path': '/datasets/downloads/extracted/[hash]/wav/id10271/_YimahVgI1A/00003.wav',
'array': array([...], dtype=float32),
'sampling_rate': 16000
}
}
```
### Data Fields
Each row includes the following fields:
- `file`: The path to the audio/video clip
- `file_format`: The file format in which the clip is stored (e.g. `wav`, `aac`, `mp4`)
- `dataset_id`: The ID of the dataset this clip is from (`vox1`, `vox2`)
- `speaker_id`: The ID of the speaker in this clip
- `speaker_gender`: The gender of the speaker (`m`/`f`)
- `speaker_name` (VoxCeleb1 only): The full name of the speaker in the clip
- `speaker_nationality` (VoxCeleb1 only): The speaker's country of origin
- `video_id`: The ID of the video from which this clip was taken
- `clip_index`: The index of the clip for this specific video
- `audio` (Audio dataset only): The audio signal data
### Data Splits
The dataset has a predefined dev set and test set, but no training set. For training purposes, the dev set may be split into training and validation sets.
## 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
The dataset includes recordings of clips (mostly of celebrities and public figures) from public YouTube videos. The names of speakers in VoxCeleb1 are provided.
## 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
The VoxCeleb authors request that anyone who uses VoxCeleb1 or VoxCeleb2 includes the following three citations:
```
@Article{Nagrani19,
author = "Arsha Nagrani and Joon~Son Chung and Weidi Xie and Andrew Zisserman",
title = "Voxceleb: Large-scale speaker verification in the wild",
journal = "Computer Science and Language",
year = "2019",
publisher = "Elsevier",
}
@InProceedings{Chung18b,
author = "Chung, J.~S. and Nagrani, A. and Zisserman, A.",
title = "VoxCeleb2: Deep Speaker Recognition",
booktitle = "INTERSPEECH",
year = "2018",
}
@InProceedings{Nagrani17,
author = "Nagrani, A. and Chung, J.~S. and Zisserman, A.",
title = "VoxCeleb: a large-scale speaker identification dataset",
booktitle = "INTERSPEECH",
year = "2017",
}
```
### Contributions
Thanks to [@101arrowz](https://github.com/101arrowz) for adding this dataset.
|
carlosdanielhernandezmena | null | null | null | false | 2 | false | carlosdanielhernandezmena/dummy-corpus-asr-es | 2022-11-14T02:55:56.000Z | null | false | a4e0cf36837f0bf748caa56b3ad117aaff8dfc32 | [] | [
"license:cc-by-4.0"
] | https://huggingface.co/datasets/carlosdanielhernandezmena/dummy-corpus-asr-es/resolve/main/README.md | ---
license: cc-by-4.0
---
|
lawcompany | null | null | KLAID (Korean Legal Artificial Intelligence Datasets) is a dataset for the development of Korean legal artificial intelligence technology. This time we offer 1 task, which is legal judgment prediction(LJP). | false | 57 | false | lawcompany/KLAID | 2022-11-15T05:43:09.000Z | null | false | 8f0c015274fa25f4b07512ad824680ce2e78955d | [] | [
"language:ko",
"multilinguality:monolingual",
"license:cc-by-nc-nd-4.0",
"task_categories:text-classification",
"task_ids:multi-class-classification"
] | https://huggingface.co/datasets/lawcompany/KLAID/resolve/main/README.md | ---
pretty_name: KLAID
viewer: true
language: ko
multilinguality:
- monolingual
license: cc-by-nc-nd-4.0
task_categories:
- text-classification
task_ids:
- multi-class-classification
---
# Dataset Card for KLAID
## 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)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Other Inquiries](#other_inquiries)
- [Licensing Information](#licensing-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://klaid.net](https://klaid.net)
- **Leaderboard:** [https://klaid.net](https://klaid.net)
- **Point of Contact:** [klaid@lawcompany.co.kr](klaid@lawcompany.co.kr)
### Dataset Summary
Korean Legal Artificial Intelligence Datasets(KLAID) is a dataset for the development of Korean legal artificial intelligence technology. This time we offer 1 task, which is legal judgment prediction(LJP).
### Supported Tasks and Leaderboards
Legal Judgment Prediction(LJP)
### Languages
`korean`
### How to use
```python
from datasets import load_dataset
# legal judgment prediction
dataset = load_dataset("lawcompany/KLAID", 'ljp')
```
## Dataset Structure
### Data Instances
#### ljp
An example of 'train' looks as follows.
```
{
'fact': '피고인은 2022. 11. 14. 혈중알콜농도 0.123%의 술에 취한 상태로 승용차를 운전하였다.',
'laws_service': '도로교통법 제148조의2 제3항 제2호,도로교통법 제44조 제1항',
'laws_service_id': 7
}
```
### Data Fields
#### ljp
+ "fact": a `string` feature
+ "laws_service": a `string` feature
+ "laws_service_id": a classification label, with 177 legal judgment values
[More Information Needed](https://klaid.net/tasks-1)
### Data Splits
#### ljp
+ train: 161,192
## Dataset Creation
### Curation Rationale
The legal domain is arguably one of the most expertise fields that require expert knowledge to comprehend. Natural language processing requires many aspects, and we focus on the dataset requirements. As a gold standard is necessary for the testing and the training of a neural model, we hope that our dataset release will help the advances in natural language processing in the legal domain, especially for those for the Korean legal system.
### Source Data
These are datasets based on Korean legal case data.
### Personal and Sensitive Information
Due to the nature of legal case data, personal and sensitive information may be included. Therefore, in order to prevent problems that may occur with personal and sensitive information, we proceeded to de-realize the legal case.
## Considerations for Using the Data
### Other Known Limitations
We plan to upload more data and update them as some of the court records may be revised from now on, based on the ever-evolving legal system.
## Additional Information
### Other Inquiries
[klaid@lawcompany.co.kr](klaid@lawcompany.co.kr)
### Licensing Information
Copyright 2022-present [Law&Company Co. Ltd.](https://career.lawcompany.co.kr/)
Licensed under the CC-BY-NC-ND-4.0
### Contributions
[More Information Needed] |
Sayaka457 | null | null | null | false | 1 | false | Sayaka457/Ehh | 2022-11-13T06:36:33.000Z | null | false | f225875de980bdb87046d1f13438cdd999d22d2f | [] | [] | https://huggingface.co/datasets/Sayaka457/Ehh/resolve/main/README.md | load_dataset("grullborg/league_style") |
rajivmehtapy | null | @article{2016arXiv160605250R,
author = {{Rajpurkar}, Pranav and {Zhang}, Jian and {Lopyrev},
Konstantin and {Liang}, Percy},
title = "{SQuAD: 100,000+ Questions for Machine Comprehension of Text}",
journal = {arXiv e-prints},
year = 2016,
eid = {arXiv:1606.05250},
pages = {arXiv:1606.05250},
archivePrefix = {arXiv},
eprint = {1606.05250},
} | Demo... | false | 3 | false | rajivmehtapy/reddit-builder-config | 2022-11-13T07:52:37.000Z | null | false | 2a62cc489cc4aa4f5e6588b2c465638e799ceecc | [] | [
"license:apache-2.0"
] | https://huggingface.co/datasets/rajivmehtapy/reddit-builder-config/resolve/main/README.md | ---
license: apache-2.0
---
|
Mahmutxx | null | null | null | false | null | false | Mahmutxx/sex | 2022-11-13T08:45:41.000Z | null | false | a5155043d54f932fe35a0d03f1f8edf5d1a795b7 | [] | [
"license:cc-by-nc-nd-4.0"
] | https://huggingface.co/datasets/Mahmutxx/sex/resolve/main/README.md | ---
license: cc-by-nc-nd-4.0
---
|
SDbiaseval | null | null | null | false | 6 | false | SDbiaseval/dataset-v-1.5 | 2022-11-13T15:58:38.000Z | null | false | 1dbed00d2d45f34d4b42691a17e3ffa04bb95a15 | [] | [] | https://huggingface.co/datasets/SDbiaseval/dataset-v-1.5/resolve/main/README.md | ---
dataset_info:
features:
- name: adjective
dtype: string
- name: profession
dtype: string
- name: seed
dtype: int32
- name: 'no'
dtype: int32
- name: image_path
dtype: string
- name: image
dtype: image
splits:
- name: train
num_bytes: 11894248760.0
num_examples: 315000
download_size: 11903715121
dataset_size: 11894248760.0
---
# Dataset Card for "dataset-v-1.5"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
asadisaghar | null | null | null | false | null | false | asadisaghar/amazon-shoe-reviews | 2022-11-13T12:24:01.000Z | null | false | ff4d1282bf7f51cbb41c11b75ea39f25c5db068e | [] | [] | https://huggingface.co/datasets/asadisaghar/amazon-shoe-reviews/resolve/main/README.md | ---
dataset_info:
features:
- name: labels
dtype: int64
- name: text
dtype: string
splits:
- name: test
num_bytes: 1871962.8
num_examples: 10000
- name: train
num_bytes: 16847665.2
num_examples: 90000
download_size: 10939033
dataset_size: 18719628.0
---
# Dataset Card for "amazon-shoe-reviews"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
dlproject | null | null | null | false | 5 | false | dlproject/msp_train_hubert | 2022-11-13T12:50:12.000Z | null | false | 14010cfd2e4af424e6725b83d7e8cb78fedf43f3 | [] | [] | https://huggingface.co/datasets/dlproject/msp_train_hubert/resolve/main/README.md | ---
dataset_info:
features:
- name: input_values
sequence:
sequence:
sequence: float32
- name: labels
dtype: int64
splits:
- name: train
num_bytes: 10872804940
num_examples: 29939
download_size: 9851597205
dataset_size: 10872804940
---
# Dataset Card for "msp_train_hubert"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
dlproject | null | null | null | false | 5 | false | dlproject/msp_val_hubert | 2022-11-13T12:51:08.000Z | null | false | f1c466fbd45944d1284d41ad49684efb16ab7ba1 | [] | [] | https://huggingface.co/datasets/dlproject/msp_val_hubert/resolve/main/README.md | ---
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) |
statworx | null | null | null | false | 12 | false | statworx/swiss-dialects | 2022-11-15T15:39:30.000Z | null | false | 3aba501eaba895e8e2482f34bbdb66371c82cf69 | [] | [] | https://huggingface.co/datasets/statworx/swiss-dialects/resolve/main/README.md | ---
dataset_info:
features:
- name: sentence
dtype: string
- name: label
dtype: string
splits:
- name: train
num_bytes: 264138
num_examples: 4743
download_size: 0
dataset_size: 264138
---
# Dataset Card for "swiss-dialects"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Aen | null | null | null | false | null | false | Aen/pf-style | 2022-11-13T14:46:02.000Z | null | false | c4ae1b709cb10afba0d82e853149a713e019ac2e | [] | [
"license:cc-by-sa-3.0"
] | https://huggingface.co/datasets/Aen/pf-style/resolve/main/README.md | ---
license: cc-by-sa-3.0
---
|
andreotte | null | null | null | false | 34 | false | andreotte/multi-label-classification-test-small | 2022-11-13T15:07:50.000Z | null | false | 6dc8189638a8cf250ef745c571ee9330b0d5417d | [] | [] | https://huggingface.co/datasets/andreotte/multi-label-classification-test-small/resolve/main/README.md | ---
dataset_info:
features:
- name: label
dtype:
class_label:
names:
0: Door
1: Eaves
2: Gutter
3: Vegetation
4: Vent
5: Window
- name: pixel_values
dtype: image
splits:
- name: test
num_bytes: 1579714.0
num_examples: 25
- name: train
num_bytes: 3593924.0
num_examples: 59
download_size: 5175857
dataset_size: 5173638.0
---
# Dataset Card for "multi-label-classification-test-small"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Gr3en | null | null | null | false | null | false | Gr3en/OperaDa3Soldi | 2022-11-13T15:32:51.000Z | null | false | c1dbebe3462373a1ed368de0a04eb4df8117bda0 | [] | [] | https://huggingface.co/datasets/Gr3en/OperaDa3Soldi/resolve/main/README.md | annotations_creators:
- found
language:
- en
language_creators:
- found
license:
- artistic-2.0
multilinguality:
- monolingual
pretty_name: a dataset of Opera da Tre Soldi by Berliner Ensemble
size_categories:
- n<1K
source_datasets:
- original
tags: []
task_categories:
- text-to-image
task_ids: []
|
kkkkkkkkkkkkkkk | null | null | null | false | null | false | kkkkkkkkkkkkkkk/fff | 2022-11-13T16:48:32.000Z | null | false | e1abdeba5cef4471068992aa3deed3a788621eb7 | [] | [
"license:openrail"
] | https://huggingface.co/datasets/kkkkkkkkkkkkkkk/fff/resolve/main/README.md | ---
license: openrail
---
|
siberspace | null | null | null | false | null | false | siberspace/keke | 2022-11-13T16:14:11.000Z | null | false | 00e86c780a7bb2b6e1be217087a0cf7e017d4d4d | [] | [] | https://huggingface.co/datasets/siberspace/keke/resolve/main/README.md | |
Gr3en | null | null | null | false | null | false | Gr3en/MusiForPercussion2 | 2022-11-13T16:20:15.000Z | null | false | 3413a80d3809c44e8b5e06911f07f157c7cebe98 | [] | [] | https://huggingface.co/datasets/Gr3en/MusiForPercussion2/resolve/main/README.md | annotations_creators:
- found
language:
- en
language_creators:
- found
license:
- artistic-2.0
multilinguality:
- monolingual
pretty_name: a dataset of Opera da Tre Soldi by Berliner Ensemble
size_categories:
- n<1K
source_datasets:
- original
tags: []
task_categories:
- text-to-image
task_ids: []
|
SDbiaseval | null | null | null | false | 6 | false | SDbiaseval/dataset-v-1.4 | 2022-11-13T21:15:20.000Z | null | false | d70d7875bb3fa3e45c43752d2e2ebe91205d6942 | [] | [] | https://huggingface.co/datasets/SDbiaseval/dataset-v-1.4/resolve/main/README.md | ---
dataset_info:
features:
- name: adjective
dtype: string
- name: profession
dtype: string
- name: seed
dtype: int32
- name: 'no'
dtype: int32
- name: image_path
dtype: string
- name: image
dtype: image
splits:
- name: train
num_bytes: 11966142155.0
num_examples: 315000
download_size: 11967150727
dataset_size: 11966142155.0
---
# Dataset Card for "dataset-v-1.4"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
sunhaha123 | null | null | null | false | null | false | sunhaha123/ref | 2022-11-13T16:30:52.000Z | null | false | 48f9373b872280b62bced691b996361d23ece5b9 | [] | [
"license:apache-2.0"
] | https://huggingface.co/datasets/sunhaha123/ref/resolve/main/README.md | ---
license: apache-2.0
---
|
vegeta | null | null | null | false | 17 | false | vegeta/legaltokenized512 | 2022-11-14T12:28:29.000Z | null | false | fa48e3578ebb98bedd075c7ee6d84275608267d1 | [] | [] | https://huggingface.co/datasets/vegeta/legaltokenized512/resolve/main/README.md | ---
dataset_info:
features:
- name: input_ids
sequence: int32
splits:
- name: train
num_bytes: 21845407320
num_examples: 10645910
- name: validation
num_bytes: 2384177760
num_examples: 1161880
download_size: 5043110512
dataset_size: 24229585080
---
# Dataset Card for "legaltokenized512"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
SergiiGurbych | null | null | null | false | 4 | false | SergiiGurbych/sent_anal_ukr_tzp | 2022-11-15T23:17:42.000Z | null | false | d32845d9684db95f6f5922416776d4bfa21fdace | [] | [] | https://huggingface.co/datasets/SergiiGurbych/sent_anal_ukr_tzp/resolve/main/README.md | This is a marked dataset for Ukrainian language. It consists of sentences marked 0, 1 or 2 for negative, neutral or positive mode respectively. The dataset is based on the classic text Shadows of Forgotten Ancestors written by Mykhailo Kotsiubynsky. The markup of the sentences was done automatically based on the lists of positive and negative words from the Sentiment Lexicons for All Major Languages project (Chen & Skiena, ACL 2014). These lists were checked and edited manually by me to exclude ambiguous and mistakenly included words.
|
Guizmus | null | null | null | false | null | false | Guizmus/AnimeChanStyle | 2022-11-14T23:45:20.000Z | null | false | 237a1a95c35094a56d149d89d7937597a5e1d4cd | [] | [
"license:creativeml-openrail-m",
"thumbnail:https://huggingface.co/datasets/Guizmus/AnimeChanStyle/resolve/main/showcase_dataset.jpg"
] | https://huggingface.co/datasets/Guizmus/AnimeChanStyle/resolve/main/README.md | ---
license: creativeml-openrail-m
thumbnail: "https://huggingface.co/datasets/Guizmus/AnimeChanStyle/resolve/main/showcase_dataset.jpg"
---
This is the dataset used for making the model : https://huggingface.co/Guizmus/AnimeChanStyle
The images were made by the users of Stable Diffusion discord using CreativeML-OpenRail-M licenced models, in the intent to make this dataset.
90 pictures captioned with their content by hand, with the suffix ",AnimeChan Style"
The collection process was made public during less than a day, until enough variety was introduced to train through a Dreambooth method a style corresponding to the different members of this community
The picture captioned are available in [this zip file](https://huggingface.co/datasets/Guizmus/AnimeChanStyle/resolve/main/AnimeChanStyle%20v2.3.zip) |
NeelNanda | null | null | null | false | 1 | false | NeelNanda/pile-tokenized-2b | 2022-11-13T21:29:57.000Z | null | false | 2105e19baf41eaf8459282bc7fcbbd2e28aca299 | [] | [] | https://huggingface.co/datasets/NeelNanda/pile-tokenized-2b/resolve/main/README.md | ---
dataset_info:
features:
- name: tokens
sequence: int32
splits:
- name: train
num_bytes: 8200000000
num_examples: 2000000
download_size: 3352864661
dataset_size: 8200000000
---
# Dataset Card for "pile-tokenized-2b"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
zyerrr | null | null | null | false | null | false | zyerrr/images | 2022-11-13T21:29:47.000Z | null | false | 1866bafb0a6d87c3a0bcd4a2f7b905e87412d139 | [] | [
"license:openrail"
] | https://huggingface.co/datasets/zyerrr/images/resolve/main/README.md | ---
license: openrail
---
|
NeelNanda | null | null | null | false | null | false | NeelNanda/c4-code-tokenized-2b | 2022-11-13T21:54:56.000Z | null | false | b7479f18ce44afc58adf70e33ac7aa7be7e37257 | [] | [] | https://huggingface.co/datasets/NeelNanda/c4-code-tokenized-2b/resolve/main/README.md | ---
dataset_info:
features:
- name: tokens
sequence: int64
splits:
- name: train
num_bytes: 13581607992
num_examples: 1657102
download_size: 2953466988
dataset_size: 13581607992
---
# Dataset Card for "c4-code-tokenized-2b"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
alexandrainst | null | null | null | false | 13 | false | alexandrainst/danish-wit | 2022-11-15T15:57:14.000Z | null | false | 9374f30f4703540c2dfcc68408871defd178c4c4 | [] | [
"language:da",
"license:cc-by-sa-4.0",
"size_categories:100K<n<1M",
"source_datasets:wikimedia/wit_base",
"task_categories:image-to-text",
"task_categories:zero-shot-image-classification",
"task_categories:feature-extraction",
"task_ids:image-captioning"
] | https://huggingface.co/datasets/alexandrainst/danish-wit/resolve/main/README.md | ---
pretty_name: Danish WIT
language:
- da
license:
- cc-by-sa-4.0
size_categories:
- 100K<n<1M
source_datasets:
- wikimedia/wit_base
task_categories:
- image-to-text
- zero-shot-image-classification
- feature-extraction
task_ids:
- image-captioning
---
# Dataset Card for Danish WIT
## Dataset Description
- **Repository:** <https://gist.github.com/saattrupdan/bb6c9c52d9f4b35258db2b2456d31224>
- **Point of Contact:** [Dan Saattrup Nielsen](mailto:dan.nielsen@alexandra.dk)
- **Size of downloaded dataset files:** 7.5 GB
- **Size of the generated dataset:** 7.8 GB
- **Total amount of disk used:** 15.3 GB
### Dataset Summary
Google presented the Wikipedia Image Text (WIT) dataset in [July
2021](https://dl.acm.org/doi/abs/10.1145/3404835.3463257), a dataset which contains
scraped images from Wikipedia along with their descriptions. WikiMedia released
WIT-Base in [September
2021](https://techblog.wikimedia.org/2021/09/09/the-wikipedia-image-caption-matching-challenge-and-a-huge-release-of-image-data-for-research/),
being a modified version of WIT where they have removed the images with empty
"reference descriptions", as well as removing images where a person's face covers more
than 10% of the image surface, along with inappropriate images that are candidate for
deletion. This dataset is the Danish portion of the WIT-Base dataset, consisting of
roughly 160,000 images with associated Danish descriptions. We release the dataset
under the [CC BY-SA 4.0 license](https://creativecommons.org/licenses/by-sa/4.0/), in
accordance with WIT-Base's [identical
license](https://huggingface.co/datasets/wikimedia/wit_base#licensing-information).
### Supported Tasks and Leaderboards
Training machine learning models for caption generation, zero-shot image classification
and text-image search are the intended tasks for this dataset. No leaderboard is active
at this point.
### Languages
The dataset is available in Danish (`da`).
## Dataset Structure
### Data Instances
- **Size of downloaded dataset files:** 7.5 GB
- **Size of the generated dataset:** 7.8 GB
- **Total amount of disk used:** 15.3 GB
An example from the `train` split looks as follows.
```
{
"image": <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=300x409 at 0x7FE4384E2190>,
"image_url": "https://upload.wikimedia.org/wikipedia/commons/4/45/Bispen_-_inside.jpg",
"embedding": [2.8568285, 2.9562542, 0.33794892, 8.753725, ...],
"metadata_url": "http://commons.wikimedia.org/wiki/File:Bispen_-_inside.jpg",
"original_height": 3161,
"original_width": 2316,
"mime_type": "image/jpeg",
"caption_attribution_description": "Kulturhuset Bispen set indefra. Biblioteket er til venstre",
"page_url": "https://da.wikipedia.org/wiki/Bispen",
"attribution_passes_lang_id": True,
"caption_alt_text_description": None,
"caption_reference_description": "Bispen set indefra fra 1. sal, hvor ....",
"caption_title_and_reference_description": "Bispen [SEP] Bispen set indefra ...",
"context_page_description": "Bispen er navnet på det offentlige kulturhus i ...",
"context_section_description": "Bispen er navnet på det offentlige kulturhus i ...",
"hierarchical_section_title": "Bispen",
"is_main_image": True,
"page_changed_recently": True,
"page_title": "Bispen",
"section_title": None
}
```
### Data Fields
The data fields are the same among all splits.
- `image`: an `Image` feature.
- `image_url`: a `str` feature.
- `embedding`: a `list` feature.
- `metadata_url`: a `str` feature.
- `original_height`: an `int` or `NaN` feature.
- `original_width`: an `int` or `NaN` feature.
- `mime_type`: a `str` or `None` feature.
- `caption_attribution_description`: a `str` or `None` feature.
- `page_url`: a `str` feature.
- `attribution_passes_lang_id`: a `bool` or `None` feature.
- `caption_alt_text_description`: a `str` or `None` feature.
- `caption_reference_description`: a `str` or `None` feature.
- `caption_title_and_reference_description`: a `str` or `None` feature.
- `context_page_description`: a `str` or `None` feature.
- `context_section_description`: a `str` or `None` feature.
- `hierarchical_section_title`: a `str` feature.
- `is_main_image`: a `bool` or `None` feature.
- `page_changed_recently`: a `bool` or `None` feature.
- `page_title`: a `str` feature.
- `section_title`: a `str` or `None` feature.
### Data Splits
Roughly 2.60% of the WIT-Base dataset comes from the Danish Wikipedia. We have split
the resulting 168,740 samples into a training set, validation set and testing set of
the following sizes:
| split | samples |
|---------|--------:|
| train | 167,460 |
| val | 256 |
| test | 1,024 |
## Dataset Creation
### Curation Rationale
It is quite cumbersome to extract the Danish portion of the WIT-Base dataset,
especially as the dataset takes up 333 GB of disk space, so the curation of Danish-WIT
is purely to make it easier to work with the Danish portion of it.
### Source Data
The original data was collected from WikiMedia's
[WIT-Base](https://huggingface.co/datasets/wikimedia/wit_base) dataset, which in turn
comes from Google's [WIT](https://huggingface.co/datasets/google/wit) dataset.
## Additional Information
### Dataset Curators
[Dan Saattrup Nielsen](https://saattrupdan.github.io/) from the [The Alexandra
Institute](https://alexandra.dk/) curated this dataset.
### Licensing Information
The dataset is licensed under the [CC BY-SA 4.0
license](https://creativecommons.org/licenses/by-sa/4.0/).
|
NeelNanda | null | null | null | false | null | false | NeelNanda/code-tokenized | 2022-11-14T00:05:01.000Z | null | false | 2190d35937c1ecb7b1f293d45165b2eb4f8dbe1b | [] | [] | https://huggingface.co/datasets/NeelNanda/code-tokenized/resolve/main/README.md | ---
dataset_info:
features:
- name: tokens
sequence: int64
splits:
- name: train
num_bytes: 2436318372
num_examples: 297257
download_size: 501062424
dataset_size: 2436318372
---
# Dataset Card for "code-tokenized"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
NeelNanda | null | null | null | false | null | false | NeelNanda/c4-tokenized-2b | 2022-11-14T00:26:59.000Z | null | false | b214d7f3b750f4d8051d3c3d2e1f09f01dd251e7 | [] | [] | https://huggingface.co/datasets/NeelNanda/c4-tokenized-2b/resolve/main/README.md | ---
dataset_info:
features:
- name: tokens
sequence: int64
splits:
- name: train
num_bytes: 11145289620
num_examples: 1359845
download_size: 2530851147
dataset_size: 11145289620
---
# Dataset Card for "c4-tokenized-2b"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
laigan | null | null | null | false | null | false | laigan/TIMIT_EL | 2022-11-14T00:35:34.000Z | null | false | 099e67a2c6e0f852be8210a3f5547a47fb234a03 | [] | [
"license:openrail"
] | https://huggingface.co/datasets/laigan/TIMIT_EL/resolve/main/README.md | ---
license: openrail
---
|
Ziyang | null | null | null | false | null | false | Ziyang/F30k | 2022-11-14T01:47:01.000Z | null | false | 9699b134759636ee820acba74887bf165e49f8ef | [] | [] | https://huggingface.co/datasets/Ziyang/F30k/resolve/main/README.md | Flickr30k Images Data |
Murple | null | @Article{app10196936,
AUTHOR = {Bang, Jeong-Uk and Yun, Seung and Kim, Seung-Hi and Choi, Mu-Yeol and Lee, Min-Kyu and Kim, Yeo-Jeong and Kim, Dong-Hyun and Park, Jun and Lee, Young-Jik and Kim, Sang-Hun},
TITLE = {KsponSpeech: Korean Spontaneous Speech Corpus for Automatic Speech Recognition},
JOURNAL = {Applied Sciences},
VOLUME = {10},
YEAR = {2020},
NUMBER = {19},
ARTICLE-NUMBER = {6936},
URL = {https://www.mdpi.com/2076-3417/10/19/6936},
ISSN = {2076-3417},
DOI = {10.3390/app10196936}
} | This paper introduces a large-scale spontaneous speech corpus of Korean, named KsponSpeech. This corpus contains 969 h of general open-domain dialog utterances, spoken by about 2000 native Korean speakers in a clean environment. All data were constructed by recording the dialogue of two people freely conversing on a variety of topics and manually transcribing the utterances. The transcription provides a dual transcription consisting of orthography and pronunciation, and disfluency tags for spontaneity of speech, such as filler words, repeated words, and word fragments. This paper also presents the baseline performance of an end-to-end speech recognition model trained with KsponSpeech. In addition, we investigated the performance of standard end-to-end architectures and the number of sub-word units suitable for Korean. We investigated issues that should be considered in spontaneous speech recognition in Korean. KsponSpeech is publicly available on an open data hub site of the Korea government.
More info on KsponSpeech dataset can be understood from the webpage which can be found here:
https://www.aihub.or.kr/aihubdata/data/view.do?currMenu=115&topMenu=100&aihubDataSe=realm&dataSetSn=123 | false | 43 | false | Murple/ksponspeech | 2022-11-14T02:41:37.000Z | null | false | 7f8f2478e374f161fede00a6ea1d7997201fb82c | [] | [
"annotations_creators:expert-generated",
"language:ko",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:automatic-speech-recognition"
] | https://huggingface.co/datasets/Murple/ksponspeech/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language:
- ko
language_creators:
- crowdsourced
license: []
multilinguality:
- monolingual
pretty_name: KsponSpeech
size_categories:
- 10K<n<100K
source_datasets:
- original
tags: []
task_categories:
- automatic-speech-recognition
task_ids: []
---
# Dataset Card for KsponSpeech
## 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:** [AIHub](https://www.aihub.or.kr/aihubdata/data/view.do?currMenu=115&topMenu=100&aihubDataSe=realm&dataSetSn=123)
- **Repository:**
- **Paper:** [KsponSpeech](https://www.mdpi.com/2076-3417/10/19/6936)
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This corpus contains 969 h of general open-domain dialog utterances, spoken by about 2000 native Korean speakers in a clean environment. All data were constructed by recording the dialogue of two people freely conversing on a variety of topics and manually transcribing the utterances. The transcription provides a dual transcription consisting of orthography and pronunciation, and disfluency tags for spontaneity of speech, such as filler words, repeated words, and word fragments. This paper also presents the baseline performance of an end-to-end speech recognition model trained with KsponSpeech. In addition, we investigated the performance of standard end-to-end architectures and the number of sub-word units suitable for Korean. We investigated issues that should be considered in spontaneous speech recognition in Korean. KsponSpeech is publicly available on an open data hub site of the Korea government.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
Korean
## Dataset Structure
### Data Instances
```json
{
'id': 'KsponSpeech_E00001',
'audio': {'path': None,
'array': array([0.0010376 , 0.00085449, 0.00097656, ..., 0.00250244, 0.0022583 ,
0.00253296]),
'sampling_rate': 16000},
'text': '어 일단은 억지로 과장해서 이렇게 하는 것보다 진실된 마음으로 이걸 어떻게 전달할 수 있을까 공감을 시킬 수 있을까 해서 좀'
}
```
### Data Fields
- audio: A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, *i.e.* `dataset[0]["audio"]` should **always** be preferred over `dataset["audio"][0]`.
- text: the transcription of the audio file.
- id: unique id of the data sample.
### Data Splits
| | Train | Valid | eval.clean | eval.other |
| ----- | ------ | ----- | ---- | ---- |
| #samples | 620000 | 2545 | 3000 | 3000 |
## 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
```bibtex
@Article{app10196936,
AUTHOR = {Bang, Jeong-Uk and Yun, Seung and Kim, Seung-Hi and Choi, Mu-Yeol and Lee, Min-Kyu and Kim, Yeo-Jeong and Kim, Dong-Hyun and Park, Jun and Lee, Young-Jik and Kim, Sang-Hun},
TITLE = {KsponSpeech: Korean Spontaneous Speech Corpus for Automatic Speech Recognition},
JOURNAL = {Applied Sciences},
VOLUME = {10},
YEAR = {2020},
NUMBER = {19},
ARTICLE-NUMBER = {6936},
URL = {https://www.mdpi.com/2076-3417/10/19/6936},
ISSN = {2076-3417},
ABSTRACT = {This paper introduces a large-scale spontaneous speech corpus of Korean, named KsponSpeech. This corpus contains 969 h of general open-domain dialog utterances, spoken by about 2000 native Korean speakers in a clean environment. All data were constructed by recording the dialogue of two people freely conversing on a variety of topics and manually transcribing the utterances. The transcription provides a dual transcription consisting of orthography and pronunciation, and disfluency tags for spontaneity of speech, such as filler words, repeated words, and word fragments. This paper also presents the baseline performance of an end-to-end speech recognition model trained with KsponSpeech. In addition, we investigated the performance of standard end-to-end architectures and the number of sub-word units suitable for Korean. We investigated issues that should be considered in spontaneous speech recognition in Korean. KsponSpeech is publicly available on an open data hub site of the Korea government.},
DOI = {10.3390/app10196936}
}
```
|
pratultandon | null | null | null | false | 16 | false | pratultandon/tokenized-recipe-nlg-gpt2 | 2022-11-16T17:14:01.000Z | null | false | ccd203bc0b7fae5ccb76e768597b50299ae0917a | [] | [] | https://huggingface.co/datasets/pratultandon/tokenized-recipe-nlg-gpt2/resolve/main/README.md | ---
dataset_info:
features:
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
splits:
- name: test
num_bytes: 135944246
num_examples: 106202
- name: train
num_bytes: 2582090838
num_examples: 2022671
download_size: 805955428
dataset_size: 2718035084
---
# Dataset Card for "tokenized-recipe-nlg-gpt2"
This a tokenized version of the recipe-nlg database from https://recipenlg.cs.put.poznan.pl/.
The preprocessing on the original csv was done using the methodology of the original paper (best as I could interpret) along with a similar 0.05 percent train test split. The tokenizer used has some special tokens, but all these parameters are accessible in https://huggingface.co/pratultandon/recipe-nlg-gpt2 if you want to recreate. This dataset will save you a lot of time getting started if you want to experiment with training GPT2 on the data yourself.
|
Murple | null | @misc{magicdata_2019,
title={MAGICDATA Mandarin Chinese Read Speech Corpus},
url={https://openslr.org/68/},
publisher={Magic Data Technology Co., Ltd.},
year={2019},
month={May}} | The corpus by Magic Data Technology Co., Ltd. , containing 755 hours of scripted read speech data
from 1080 native speakers of the Mandarin Chinese spoken in mainland China.
The sentence transcription accuracy is higher than 98%. | false | 37 | false | Murple/mmcrsc | 2022-11-14T02:37:54.000Z | null | false | 3d3615f3b90aa9f63635597e8123820dab866749 | [] | [
"annotations_creators:expert-generated",
"language:zh",
"language_creators:crowdsourced",
"license:cc-by-nc-nd-4.0",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:automatic-speech-recognition"
] | https://huggingface.co/datasets/Murple/mmcrsc/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language:
- zh
language_creators:
- crowdsourced
license:
- cc-by-nc-nd-4.0
multilinguality:
- monolingual
pretty_name: MAGICDATA_Mandarin_Chinese_Read_Speech_Corpus
size_categories:
- 10K<n<100K
source_datasets:
- original
tags: []
task_categories:
- automatic-speech-recognition
task_ids: []
---
# Dataset Card for MMCRSC
## 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:** [MAGICDATA Mandarin Chinese Read Speech Corpus](https://openslr.org/68/)
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
MAGICDATA Mandarin Chinese Read Speech Corpus was developed by MAGIC DATA Technology Co., Ltd. and freely published for non-commercial use.
The contents and the corresponding descriptions of the corpus include:
The corpus contains 755 hours of speech data, which is mostly mobile recorded data.
1080 speakers from different accent areas in China are invited to participate in the recording.
The sentence transcription accuracy is higher than 98%.
Recordings are conducted in a quiet indoor environment.
The database is divided into training set, validation set, and testing set in a ratio of 51: 1: 2.
Detail information such as speech data coding and speaker information is preserved in the metadata file.
The domain of recording texts is diversified, including interactive Q&A, music search, SNS messages, home command and control, etc.
Segmented transcripts are also provided.
The corpus aims to support researchers in speech recognition, machine translation, speaker recognition, and other speech-related fields. Therefore, the corpus is totally free for academic use.
The corpus is a subset of a much bigger data ( 10566.9 hours Chinese Mandarin Speech Corpus ) set which was recorded in the same environment. Please feel free to contact us via business@magicdatatech.com for more details.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
zh-CN
## Dataset Structure
### Data Instances
```json
{
'file': '14_3466_20170826171404.wav',
'audio': {
'path': '14_3466_20170826171404.wav',
'array': array([0., 0., 0., ..., 0., 0., 0.]),
'sampling_rate': 16000
},
'text': '请搜索我附近的超市',
'speaker_id': 143466,
'id': '14_3466_20170826171404.wav'
}
```
### Data Fields
- file: A path to the downloaded audio file in .wav format.
- audio: A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, *i.e.* `dataset[0]["audio"]` should **always** be preferred over `dataset["audio"][0]`.
- text: the transcription of the audio file.
- id: unique id of the data sample.
- speaker_id: unique id of the speaker. The same speaker id can be found for multiple data samples.
### 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
Please cite the corpus as "Magic Data Technology Co., Ltd., "http://www.imagicdatatech.com/index.php/home/dataopensource/data_info/id/101", 05/2019".
|
rajpurkarlab | null | null | null | false | null | false | rajpurkarlab/CXR-PRO | 2022-11-14T03:11:17.000Z | null | false | 4f651d4d829b90f12d836eed30b08b7619b13b2f | [] | [] | https://huggingface.co/datasets/rajpurkarlab/CXR-PRO/resolve/main/README.md | CXR-PRO contains the following files:
```
.
├── cxr.h5
├── mimic_train_impressions.csv
└── mimic_test_impressions.csv
```
The contents of each file are outlined below:
`cxr.h5`: The subset of MIMIC-CXR chest radiographs used for MIMIC-PRO, saved in Hierarchical Data Format (HDF).
`mimic_train_impressions.csv`: A compilation of the impressions section of each radiology report in the MIMIC-PRO dataset, with references to priors removed. Additional fields include `dicom_id`, `study_id`, and `subject_id` (which refer users to the chest radiograph associated with a given impressions section).
`mimic_test_impressions.csv`: The expert-edited test set, as described in the Methods section of MIMIC-PRO's documentation on PhysioNet.
|
diltdicker | null | null | null | false | 3 | false | diltdicker/romance_books_32K | 2022-11-15T07:37:05.000Z | null | false | f48d8a3b7207ad00de83c334476d5132bc3fc20d | [] | [
"license:openrail"
] | https://huggingface.co/datasets/diltdicker/romance_books_32K/resolve/main/README.md | ---
license: openrail
---
Dataset Summary
---
Collection of Romance Novels featuring `title`, `description`, and `genres`. Created with intention of building a "Romance Novel Generator."
Data Fields
---
- `id` : unique integer to id book in the dataset
- `pub_month` : string indicating the month the book was published in the form: `YEAR_MONTH`
- `title` : title of the book
- `author` : comma-separated (`last-name, first-name`) of the author of book
- `isbn13` : 13 digit number for the isbn of book (note not all books will have an isbn number)
- `description` : text description of the book. May contain quoted lines, a brief teaser of the plot, etc...
- `genres` : dictionary of all genres with 0 indicating the book is **NOT** tagged to that genre, and a 1 indicating that the book is tagged to that genre
- additional fields are the all the individual genres exploded with respective 1 & 0 values
Languages
--
- en |
rishabhstha | null | null | null | false | null | false | rishabhstha/Earth-science | 2022-11-14T03:06:06.000Z | null | false | b77f886bb6e3d5f5796ad183db843cf689f16e4f | [] | [] | https://huggingface.co/datasets/rishabhstha/Earth-science/resolve/main/README.md | |
ChiefBroseph | null | null | null | false | null | false | ChiefBroseph/sdsda3to7 | 2022-11-14T04:32:03.000Z | null | false | fe0da22b30b224d61d3bf550ee23ff1fbfd914a0 | [] | [
"license:unknown"
] | https://huggingface.co/datasets/ChiefBroseph/sdsda3to7/resolve/main/README.md | ---
license: unknown
---
|
nzh324 | null | null | null | false | null | false | nzh324/capdesign | 2022-11-15T01:06:36.000Z | null | false | 5a8b0907925872ca7efc277f1ecafa152bc86e29 | [] | [
"license:mit"
] | https://huggingface.co/datasets/nzh324/capdesign/resolve/main/README.md | ---
license: mit
---
|
Robzzzzz | null | null | null | false | null | false | Robzzzzz/image | 2022-11-14T07:15:54.000Z | null | false | d1c669e0c6ac20ea46b4a3ab306aedf1ca9a9edb | [] | [
"license:openrail"
] | https://huggingface.co/datasets/Robzzzzz/image/resolve/main/README.md | ---
license: openrail
---
|
Aletos | null | null | null | false | null | false | Aletos/Peixe | 2022-11-14T06:47:27.000Z | null | false | d08ce1def82f6f3b21d3d58ae6ebcfd81b21794a | [] | [
"license:openrail"
] | https://huggingface.co/datasets/Aletos/Peixe/resolve/main/README.md | ---
license: openrail
---
|
autoevaluate | null | null | null | false | null | false | autoevaluate/autoeval-eval-mathemakitten__winobias_antistereotype_test_v5-mathemak-0d489a-2053267106 | 2022-11-14T09:02:47.000Z | null | false | fe9769ed6f11f9bc4c77f831ffa4e0a83bdd58f3 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:mathemakitten/winobias_antistereotype_test_v5"
] | https://huggingface.co/datasets/autoevaluate/autoeval-eval-mathemakitten__winobias_antistereotype_test_v5-mathemak-0d489a-2053267106/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- mathemakitten/winobias_antistereotype_test_v5
eval_info:
task: text_zero_shot_classification
model: inverse-scaling/opt-125m_eval
metrics: []
dataset_name: mathemakitten/winobias_antistereotype_test_v5
dataset_config: mathemakitten--winobias_antistereotype_test_v5
dataset_split: test
col_mapping:
text: text
classes: classes
target: target
---
# 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-125m_eval
* Dataset: mathemakitten/winobias_antistereotype_test_v5
* Config: mathemakitten--winobias_antistereotype_test_v5
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@mathemakitten](https://huggingface.co/mathemakitten) for evaluating this model. |
autoevaluate | null | null | null | false | null | false | autoevaluate/autoeval-eval-mathemakitten__winobias_antistereotype_test_v5-mathemak-0d489a-2053267104 | 2022-11-14T09:05:03.000Z | null | false | d0863108277569f137900db4ab033df5702779eb | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:mathemakitten/winobias_antistereotype_test_v5"
] | https://huggingface.co/datasets/autoevaluate/autoeval-eval-mathemakitten__winobias_antistereotype_test_v5-mathemak-0d489a-2053267104/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- mathemakitten/winobias_antistereotype_test_v5
eval_info:
task: text_zero_shot_classification
model: inverse-scaling/opt-350m_eval
metrics: []
dataset_name: mathemakitten/winobias_antistereotype_test_v5
dataset_config: mathemakitten--winobias_antistereotype_test_v5
dataset_split: test
col_mapping:
text: text
classes: classes
target: target
---
# 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-350m_eval
* Dataset: mathemakitten/winobias_antistereotype_test_v5
* Config: mathemakitten--winobias_antistereotype_test_v5
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@mathemakitten](https://huggingface.co/mathemakitten) for evaluating this model. |
autoevaluate | null | null | null | false | null | false | autoevaluate/autoeval-eval-mathemakitten__winobias_antistereotype_test_v5-mathemak-2bec9f-2053467109 | 2022-11-14T12:37:08.000Z | null | false | 9fb3124dfb11c21551b209dc062d65c24aa83444 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:mathemakitten/winobias_antistereotype_test_v5"
] | https://huggingface.co/datasets/autoevaluate/autoeval-eval-mathemakitten__winobias_antistereotype_test_v5-mathemak-2bec9f-2053467109/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- mathemakitten/winobias_antistereotype_test_v5
eval_info:
task: text_zero_shot_classification
model: inverse-scaling/opt-30b_eval
metrics: []
dataset_name: mathemakitten/winobias_antistereotype_test_v5
dataset_config: mathemakitten--winobias_antistereotype_test_v5
dataset_split: test
col_mapping:
text: text
classes: classes
target: target
---
# 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-30b_eval
* Dataset: mathemakitten/winobias_antistereotype_test_v5
* Config: mathemakitten--winobias_antistereotype_test_v5
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@mathemakitten](https://huggingface.co/mathemakitten) for evaluating this model. |
autoevaluate | null | null | null | false | null | false | autoevaluate/autoeval-eval-mathemakitten__winobias_antistereotype_test_v5-mathemak-0d489a-2053267103 | 2022-11-14T09:49:55.000Z | null | false | bd4e5970e5b323d4d9a72eccfd6c23876597d671 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:mathemakitten/winobias_antistereotype_test_v5"
] | https://huggingface.co/datasets/autoevaluate/autoeval-eval-mathemakitten__winobias_antistereotype_test_v5-mathemak-0d489a-2053267103/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- mathemakitten/winobias_antistereotype_test_v5
eval_info:
task: text_zero_shot_classification
model: inverse-scaling/opt-6.7b_eval
metrics: []
dataset_name: mathemakitten/winobias_antistereotype_test_v5
dataset_config: mathemakitten--winobias_antistereotype_test_v5
dataset_split: test
col_mapping:
text: text
classes: classes
target: target
---
# 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-6.7b_eval
* Dataset: mathemakitten/winobias_antistereotype_test_v5
* Config: mathemakitten--winobias_antistereotype_test_v5
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@mathemakitten](https://huggingface.co/mathemakitten) for evaluating this model. |
autoevaluate | null | null | null | false | null | false | autoevaluate/autoeval-eval-mathemakitten__winobias_antistereotype_test_v5-mathemak-2bec9f-2053467108 | 2022-11-14T17:02:36.000Z | null | false | 7df8023075a155c0ca570cfc73d2d941ea7b206e | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:mathemakitten/winobias_antistereotype_test_v5"
] | https://huggingface.co/datasets/autoevaluate/autoeval-eval-mathemakitten__winobias_antistereotype_test_v5-mathemak-2bec9f-2053467108/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- mathemakitten/winobias_antistereotype_test_v5
eval_info:
task: text_zero_shot_classification
model: inverse-scaling/opt-66b_eval
metrics: []
dataset_name: mathemakitten/winobias_antistereotype_test_v5
dataset_config: mathemakitten--winobias_antistereotype_test_v5
dataset_split: test
col_mapping:
text: text
classes: classes
target: target
---
# 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: mathemakitten/winobias_antistereotype_test_v5
* Config: mathemakitten--winobias_antistereotype_test_v5
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@mathemakitten](https://huggingface.co/mathemakitten) for evaluating this model. |
autoevaluate | null | null | null | false | null | false | autoevaluate/autoeval-eval-mathemakitten__winobias_antistereotype_test_v5-mathemak-0d489a-2053267100 | 2022-11-14T17:18:52.000Z | null | false | 36a81b77e33fd8c11a8cc6886e05eca940ef319f | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:mathemakitten/winobias_antistereotype_test_v5"
] | https://huggingface.co/datasets/autoevaluate/autoeval-eval-mathemakitten__winobias_antistereotype_test_v5-mathemak-0d489a-2053267100/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- mathemakitten/winobias_antistereotype_test_v5
eval_info:
task: text_zero_shot_classification
model: inverse-scaling/opt-66b_eval
metrics: []
dataset_name: mathemakitten/winobias_antistereotype_test_v5
dataset_config: mathemakitten--winobias_antistereotype_test_v5
dataset_split: test
col_mapping:
text: text
classes: classes
target: target
---
# 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: mathemakitten/winobias_antistereotype_test_v5
* Config: mathemakitten--winobias_antistereotype_test_v5
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@mathemakitten](https://huggingface.co/mathemakitten) for evaluating this model. |
autoevaluate | null | null | null | false | null | false | autoevaluate/autoeval-eval-mathemakitten__winobias_antistereotype_test_v5-mathemak-2bec9f-2053467113 | 2022-11-14T09:21:45.000Z | null | false | a46fa00bec2d4c1064b9a49339ced0c6186e2d34 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:mathemakitten/winobias_antistereotype_test_v5"
] | https://huggingface.co/datasets/autoevaluate/autoeval-eval-mathemakitten__winobias_antistereotype_test_v5-mathemak-2bec9f-2053467113/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- mathemakitten/winobias_antistereotype_test_v5
eval_info:
task: text_zero_shot_classification
model: inverse-scaling/opt-2.7b_eval
metrics: []
dataset_name: mathemakitten/winobias_antistereotype_test_v5
dataset_config: mathemakitten--winobias_antistereotype_test_v5
dataset_split: test
col_mapping:
text: text
classes: classes
target: target
---
# 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-2.7b_eval
* Dataset: mathemakitten/winobias_antistereotype_test_v5
* Config: mathemakitten--winobias_antistereotype_test_v5
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@mathemakitten](https://huggingface.co/mathemakitten) for evaluating this model. |
autoevaluate | null | null | null | false | null | false | autoevaluate/autoeval-eval-mathemakitten__winobias_antistereotype_test_v5-mathemak-2bec9f-2053467112 | 2022-11-14T09:04:58.000Z | null | false | 83df56962dbbddd26c685468382bedeaf1c1817b | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:mathemakitten/winobias_antistereotype_test_v5"
] | https://huggingface.co/datasets/autoevaluate/autoeval-eval-mathemakitten__winobias_antistereotype_test_v5-mathemak-2bec9f-2053467112/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- mathemakitten/winobias_antistereotype_test_v5
eval_info:
task: text_zero_shot_classification
model: inverse-scaling/opt-350m_eval
metrics: []
dataset_name: mathemakitten/winobias_antistereotype_test_v5
dataset_config: mathemakitten--winobias_antistereotype_test_v5
dataset_split: test
col_mapping:
text: text
classes: classes
target: target
---
# 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-350m_eval
* Dataset: mathemakitten/winobias_antistereotype_test_v5
* Config: mathemakitten--winobias_antistereotype_test_v5
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@mathemakitten](https://huggingface.co/mathemakitten) for evaluating this model. |
autoevaluate | null | null | null | false | null | false | autoevaluate/autoeval-eval-mathemakitten__winobias_antistereotype_test_v5-mathemak-0d489a-2053267102 | 2022-11-14T10:24:50.000Z | null | false | 97bec3a98ac858235afb67629c0c7f443ea8bf93 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:mathemakitten/winobias_antistereotype_test_v5"
] | https://huggingface.co/datasets/autoevaluate/autoeval-eval-mathemakitten__winobias_antistereotype_test_v5-mathemak-0d489a-2053267102/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- mathemakitten/winobias_antistereotype_test_v5
eval_info:
task: text_zero_shot_classification
model: inverse-scaling/opt-13b_eval
metrics: []
dataset_name: mathemakitten/winobias_antistereotype_test_v5
dataset_config: mathemakitten--winobias_antistereotype_test_v5
dataset_split: test
col_mapping:
text: text
classes: classes
target: target
---
# 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-13b_eval
* Dataset: mathemakitten/winobias_antistereotype_test_v5
* Config: mathemakitten--winobias_antistereotype_test_v5
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@mathemakitten](https://huggingface.co/mathemakitten) for evaluating this model. |
autoevaluate | null | null | null | false | null | false | autoevaluate/autoeval-eval-mathemakitten__winobias_antistereotype_test_v5-mathemak-2bec9f-2053467111 | 2022-11-14T09:49:39.000Z | null | false | c02345056ef042291d7f6fefd21c31c02086995d | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:mathemakitten/winobias_antistereotype_test_v5"
] | https://huggingface.co/datasets/autoevaluate/autoeval-eval-mathemakitten__winobias_antistereotype_test_v5-mathemak-2bec9f-2053467111/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- mathemakitten/winobias_antistereotype_test_v5
eval_info:
task: text_zero_shot_classification
model: inverse-scaling/opt-6.7b_eval
metrics: []
dataset_name: mathemakitten/winobias_antistereotype_test_v5
dataset_config: mathemakitten--winobias_antistereotype_test_v5
dataset_split: test
col_mapping:
text: text
classes: classes
target: target
---
# 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-6.7b_eval
* Dataset: mathemakitten/winobias_antistereotype_test_v5
* Config: mathemakitten--winobias_antistereotype_test_v5
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@mathemakitten](https://huggingface.co/mathemakitten) for evaluating this model. |
autoevaluate | null | null | null | false | null | false | autoevaluate/autoeval-eval-mathemakitten__winobias_antistereotype_test_v5-mathemak-0d489a-2053267105 | 2022-11-14T09:21:13.000Z | null | false | a7276dac777a4c4f4d390e05923f7f821c8d04d2 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:mathemakitten/winobias_antistereotype_test_v5"
] | https://huggingface.co/datasets/autoevaluate/autoeval-eval-mathemakitten__winobias_antistereotype_test_v5-mathemak-0d489a-2053267105/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- mathemakitten/winobias_antistereotype_test_v5
eval_info:
task: text_zero_shot_classification
model: inverse-scaling/opt-2.7b_eval
metrics: []
dataset_name: mathemakitten/winobias_antistereotype_test_v5
dataset_config: mathemakitten--winobias_antistereotype_test_v5
dataset_split: test
col_mapping:
text: text
classes: classes
target: target
---
# 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-2.7b_eval
* Dataset: mathemakitten/winobias_antistereotype_test_v5
* Config: mathemakitten--winobias_antistereotype_test_v5
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@mathemakitten](https://huggingface.co/mathemakitten) for evaluating this model. |
autoevaluate | null | null | null | false | null | false | autoevaluate/autoeval-eval-mathemakitten__winobias_antistereotype_test_v5-mathemak-0d489a-2053267107 | 2022-11-14T09:12:11.000Z | null | false | 9f94c13a99e14d2638bc7ca61d87cf67bca87d79 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:mathemakitten/winobias_antistereotype_test_v5"
] | https://huggingface.co/datasets/autoevaluate/autoeval-eval-mathemakitten__winobias_antistereotype_test_v5-mathemak-0d489a-2053267107/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- mathemakitten/winobias_antistereotype_test_v5
eval_info:
task: text_zero_shot_classification
model: inverse-scaling/opt-1.3b_eval
metrics: []
dataset_name: mathemakitten/winobias_antistereotype_test_v5
dataset_config: mathemakitten--winobias_antistereotype_test_v5
dataset_split: test
col_mapping:
text: text
classes: classes
target: target
---
# 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-1.3b_eval
* Dataset: mathemakitten/winobias_antistereotype_test_v5
* Config: mathemakitten--winobias_antistereotype_test_v5
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@mathemakitten](https://huggingface.co/mathemakitten) for evaluating this model. |
autoevaluate | null | null | null | false | null | false | autoevaluate/autoeval-eval-mathemakitten__winobias_antistereotype_test_v5-mathemak-0d489a-2053267101 | 2022-11-14T12:30:55.000Z | null | false | 14860646c6f720e72cf55464d189a7555e7e9cec | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:mathemakitten/winobias_antistereotype_test_v5"
] | https://huggingface.co/datasets/autoevaluate/autoeval-eval-mathemakitten__winobias_antistereotype_test_v5-mathemak-0d489a-2053267101/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- mathemakitten/winobias_antistereotype_test_v5
eval_info:
task: text_zero_shot_classification
model: inverse-scaling/opt-30b_eval
metrics: []
dataset_name: mathemakitten/winobias_antistereotype_test_v5
dataset_config: mathemakitten--winobias_antistereotype_test_v5
dataset_split: test
col_mapping:
text: text
classes: classes
target: target
---
# 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-30b_eval
* Dataset: mathemakitten/winobias_antistereotype_test_v5
* Config: mathemakitten--winobias_antistereotype_test_v5
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@mathemakitten](https://huggingface.co/mathemakitten) for evaluating this model. |
autoevaluate | null | null | null | false | null | false | autoevaluate/autoeval-eval-futin__guess-vi-4444ed-2051267099 | 2022-11-16T00:32:43.000Z | null | false | 8317593dfeb46e445a5ed266069037156978bfa3 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:futin/guess"
] | https://huggingface.co/datasets/autoevaluate/autoeval-eval-futin__guess-vi-4444ed-2051267099/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- futin/guess
eval_info:
task: text_zero_shot_classification
model: facebook/opt-66b
metrics: []
dataset_name: futin/guess
dataset_config: vi
dataset_split: test
col_mapping:
text: text
classes: classes
target: target
---
# 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: facebook/opt-66b
* Dataset: futin/guess
* Config: vi
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@futin](https://huggingface.co/futin) for evaluating this model. |
autoevaluate | null | null | null | false | null | false | autoevaluate/autoeval-eval-mathemakitten__winobias_antistereotype_test_v5-mathemak-2bec9f-2053467110 | 2022-11-14T10:24:09.000Z | null | false | 70a0c0951526431bcb8b47e133e4af5025792d7a | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:mathemakitten/winobias_antistereotype_test_v5"
] | https://huggingface.co/datasets/autoevaluate/autoeval-eval-mathemakitten__winobias_antistereotype_test_v5-mathemak-2bec9f-2053467110/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- mathemakitten/winobias_antistereotype_test_v5
eval_info:
task: text_zero_shot_classification
model: inverse-scaling/opt-13b_eval
metrics: []
dataset_name: mathemakitten/winobias_antistereotype_test_v5
dataset_config: mathemakitten--winobias_antistereotype_test_v5
dataset_split: test
col_mapping:
text: text
classes: classes
target: target
---
# 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-13b_eval
* Dataset: mathemakitten/winobias_antistereotype_test_v5
* Config: mathemakitten--winobias_antistereotype_test_v5
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@mathemakitten](https://huggingface.co/mathemakitten) for evaluating this model. |
autoevaluate | null | null | null | false | null | false | autoevaluate/autoeval-eval-anli-plain_text-f2dca1-2066067125 | 2022-11-14T09:07:21.000Z | null | false | ec4045eaba819c82558871eb939e1c826d3f8d7b | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:anli"
] | https://huggingface.co/datasets/autoevaluate/autoeval-eval-anli-plain_text-f2dca1-2066067125/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- anli
eval_info:
task: natural_language_inference
model: MoritzLaurer/DeBERTa-v3-base-mnli-fever-anli
metrics: []
dataset_name: anli
dataset_config: plain_text
dataset_split: dev_r1
col_mapping:
text1: premise
text2: hypothesis
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: Natural Language Inference
* Model: MoritzLaurer/DeBERTa-v3-base-mnli-fever-anli
* Dataset: anli
* Config: plain_text
* Split: dev_r1
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@ctkang](https://huggingface.co/ctkang) for evaluating this model. |
autoevaluate | null | null | null | false | null | false | autoevaluate/autoeval-eval-wmt19-de-en-9eb893-2069467127 | 2022-11-14T09:09:53.000Z | null | false | c6e40c10c1ca965f3d0c0dd76d1be9acddf6ad3b | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:wmt19"
] | https://huggingface.co/datasets/autoevaluate/autoeval-eval-wmt19-de-en-9eb893-2069467127/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- wmt19
eval_info:
task: translation
model: facebook/wmt19-en-de
metrics: []
dataset_name: wmt19
dataset_config: de-en
dataset_split: validation
col_mapping:
source: translation.en
target: translation.de
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Translation
* Model: facebook/wmt19-en-de
* Dataset: wmt19
* Config: de-en
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@WillHeld](https://huggingface.co/WillHeld) for evaluating this model. |
autoevaluate | null | null | null | false | null | false | autoevaluate/autoeval-eval-mathemakitten__winobias_antistereotype_test_v5-mathemak-2bec9f-2053467114 | 2022-11-14T09:14:27.000Z | null | false | 0c27b993cc627c9d2f2d6162fdee1785daae38f4 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:mathemakitten/winobias_antistereotype_test_v5"
] | https://huggingface.co/datasets/autoevaluate/autoeval-eval-mathemakitten__winobias_antistereotype_test_v5-mathemak-2bec9f-2053467114/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- mathemakitten/winobias_antistereotype_test_v5
eval_info:
task: text_zero_shot_classification
model: inverse-scaling/opt-125m_eval
metrics: []
dataset_name: mathemakitten/winobias_antistereotype_test_v5
dataset_config: mathemakitten--winobias_antistereotype_test_v5
dataset_split: test
col_mapping:
text: text
classes: classes
target: target
---
# 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-125m_eval
* Dataset: mathemakitten/winobias_antistereotype_test_v5
* Config: mathemakitten--winobias_antistereotype_test_v5
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@mathemakitten](https://huggingface.co/mathemakitten) for evaluating this model. |
autoevaluate | null | null | null | false | null | false | autoevaluate/autoeval-staging-eval-project-9aae5b6e-ef52-4647-8803-adc504c910ae-1210 | 2022-11-14T09:12:41.000Z | null | false | 8a64f02528a560bc0739c2f6955d6b5ccadd4111 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:glue"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-9aae5b6e-ef52-4647-8803-adc504c910ae-1210/resolve/main/README.md | ---
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. |
autoevaluate | null | null | null | false | null | false | autoevaluate/autoeval-eval-mathemakitten__winobias_antistereotype_test_v5-mathemak-2bec9f-2053467115 | 2022-11-14T09:25:18.000Z | null | false | 73ccdba1ed1739991be73cb86e7477b4037e85eb | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:mathemakitten/winobias_antistereotype_test_v5"
] | https://huggingface.co/datasets/autoevaluate/autoeval-eval-mathemakitten__winobias_antistereotype_test_v5-mathemak-2bec9f-2053467115/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- mathemakitten/winobias_antistereotype_test_v5
eval_info:
task: text_zero_shot_classification
model: inverse-scaling/opt-1.3b_eval
metrics: []
dataset_name: mathemakitten/winobias_antistereotype_test_v5
dataset_config: mathemakitten--winobias_antistereotype_test_v5
dataset_split: test
col_mapping:
text: text
classes: classes
target: target
---
# 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-1.3b_eval
* Dataset: mathemakitten/winobias_antistereotype_test_v5
* Config: mathemakitten--winobias_antistereotype_test_v5
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@mathemakitten](https://huggingface.co/mathemakitten) for evaluating this model. |
autoevaluate | null | null | null | false | null | false | autoevaluate/autoeval-eval-mathemakitten__winobias_antistereotype_test_v5-mathemak-b6a817-2053667117 | 2022-11-14T12:55:26.000Z | null | false | 2581ffc58670a1b678a4a4b4a59ec2247b0d2411 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:mathemakitten/winobias_antistereotype_test_v5"
] | https://huggingface.co/datasets/autoevaluate/autoeval-eval-mathemakitten__winobias_antistereotype_test_v5-mathemak-b6a817-2053667117/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- mathemakitten/winobias_antistereotype_test_v5
eval_info:
task: text_zero_shot_classification
model: inverse-scaling/opt-30b_eval
metrics: []
dataset_name: mathemakitten/winobias_antistereotype_test_v5
dataset_config: mathemakitten--winobias_antistereotype_test_v5
dataset_split: test
col_mapping:
text: text
classes: classes
target: target
---
# 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-30b_eval
* Dataset: mathemakitten/winobias_antistereotype_test_v5
* Config: mathemakitten--winobias_antistereotype_test_v5
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@mathemakitten](https://huggingface.co/mathemakitten) for evaluating this model. |
autoevaluate | null | null | null | false | null | false | autoevaluate/autoeval-eval-mathemakitten__winobias_antistereotype_test_v5-mathemak-b6a817-2053667118 | 2022-11-14T10:48:28.000Z | null | false | b7d5e885c343176c275fbd824e3f31c110a98949 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:mathemakitten/winobias_antistereotype_test_v5"
] | https://huggingface.co/datasets/autoevaluate/autoeval-eval-mathemakitten__winobias_antistereotype_test_v5-mathemak-b6a817-2053667118/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- mathemakitten/winobias_antistereotype_test_v5
eval_info:
task: text_zero_shot_classification
model: inverse-scaling/opt-13b_eval
metrics: []
dataset_name: mathemakitten/winobias_antistereotype_test_v5
dataset_config: mathemakitten--winobias_antistereotype_test_v5
dataset_split: test
col_mapping:
text: text
classes: classes
target: target
---
# 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-13b_eval
* Dataset: mathemakitten/winobias_antistereotype_test_v5
* Config: mathemakitten--winobias_antistereotype_test_v5
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@mathemakitten](https://huggingface.co/mathemakitten) for evaluating this model. |
autoevaluate | null | null | null | false | null | false | autoevaluate/autoeval-eval-cnn_dailymail-3.0.0-92e227-2073967129 | 2022-11-14T09:38:24.000Z | null | false | 16def52d7b0ee278a717445bc14195d966ff5eeb | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:cnn_dailymail"
] | https://huggingface.co/datasets/autoevaluate/autoeval-eval-cnn_dailymail-3.0.0-92e227-2073967129/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- cnn_dailymail
eval_info:
task: summarization
model: it5/mt5-base-news-summarization
metrics: []
dataset_name: cnn_dailymail
dataset_config: 3.0.0
dataset_split: test
col_mapping:
text: article
target: highlights
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: it5/mt5-base-news-summarization
* Dataset: cnn_dailymail
* Config: 3.0.0
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@mtharrison](https://huggingface.co/mtharrison) for evaluating this model. |
autoevaluate | null | null | null | false | null | false | autoevaluate/autoeval-eval-mathemakitten__winobias_antistereotype_test_v5-mathemak-b6a817-2053667120 | 2022-11-14T09:33:51.000Z | null | false | 880c276e4e56abf43cfbd2249719dc1f6b369ce7 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:mathemakitten/winobias_antistereotype_test_v5"
] | https://huggingface.co/datasets/autoevaluate/autoeval-eval-mathemakitten__winobias_antistereotype_test_v5-mathemak-b6a817-2053667120/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- mathemakitten/winobias_antistereotype_test_v5
eval_info:
task: text_zero_shot_classification
model: inverse-scaling/opt-350m_eval
metrics: []
dataset_name: mathemakitten/winobias_antistereotype_test_v5
dataset_config: mathemakitten--winobias_antistereotype_test_v5
dataset_split: test
col_mapping:
text: text
classes: classes
target: target
---
# 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-350m_eval
* Dataset: mathemakitten/winobias_antistereotype_test_v5
* Config: mathemakitten--winobias_antistereotype_test_v5
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@mathemakitten](https://huggingface.co/mathemakitten) for evaluating this model. |
autoevaluate | null | null | null | false | null | false | autoevaluate/autoeval-eval-mathemakitten__winobias_antistereotype_test_v5-mathemak-b6a817-2053667119 | 2022-11-14T10:17:59.000Z | null | false | 317fa524ae5f042b78697f782f2ae18b9a1f4274 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:mathemakitten/winobias_antistereotype_test_v5"
] | https://huggingface.co/datasets/autoevaluate/autoeval-eval-mathemakitten__winobias_antistereotype_test_v5-mathemak-b6a817-2053667119/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- mathemakitten/winobias_antistereotype_test_v5
eval_info:
task: text_zero_shot_classification
model: inverse-scaling/opt-6.7b_eval
metrics: []
dataset_name: mathemakitten/winobias_antistereotype_test_v5
dataset_config: mathemakitten--winobias_antistereotype_test_v5
dataset_split: test
col_mapping:
text: text
classes: classes
target: target
---
# 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-6.7b_eval
* Dataset: mathemakitten/winobias_antistereotype_test_v5
* Config: mathemakitten--winobias_antistereotype_test_v5
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@mathemakitten](https://huggingface.co/mathemakitten) for evaluating this model. |
autoevaluate | null | null | null | false | null | false | autoevaluate/autoeval-eval-mathemakitten__winobias_antistereotype_test_v5-mathemak-b6a817-2053667121 | 2022-11-14T09:54:08.000Z | null | false | 9b8d639734a86e5f7a1b4f29230db77f29ca8328 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:mathemakitten/winobias_antistereotype_test_v5"
] | https://huggingface.co/datasets/autoevaluate/autoeval-eval-mathemakitten__winobias_antistereotype_test_v5-mathemak-b6a817-2053667121/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- mathemakitten/winobias_antistereotype_test_v5
eval_info:
task: text_zero_shot_classification
model: inverse-scaling/opt-2.7b_eval
metrics: []
dataset_name: mathemakitten/winobias_antistereotype_test_v5
dataset_config: mathemakitten--winobias_antistereotype_test_v5
dataset_split: test
col_mapping:
text: text
classes: classes
target: target
---
# 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-2.7b_eval
* Dataset: mathemakitten/winobias_antistereotype_test_v5
* Config: mathemakitten--winobias_antistereotype_test_v5
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@mathemakitten](https://huggingface.co/mathemakitten) for evaluating this model. |
Nadav | null | null | null | false | 12 | false | Nadav/MiniScans | 2022-11-15T14:15:58.000Z | null | false | 4cf21508c3fdc2d141b04dcd729fd72e1b307e6e | [] | [] | https://huggingface.co/datasets/Nadav/MiniScans/resolve/main/README.md | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
0: evaluation
1: train
splits:
- name: test
num_bytes: 1655444336.229
num_examples: 15159
- name: train
num_bytes: 34770710847.12
num_examples: 300780
download_size: 38233031644
dataset_size: 36426155183.349
---
# Dataset Card for "MiniScans"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
autoevaluate | null | null | null | false | null | false | autoevaluate/autoeval-eval-wmt19-de-en-04c9e1-2082967144 | 2022-11-14T09:40:55.000Z | null | false | 5b631720ed23ce3367f2326eee0e4663e4274929 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:wmt19"
] | https://huggingface.co/datasets/autoevaluate/autoeval-eval-wmt19-de-en-04c9e1-2082967144/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- wmt19
eval_info:
task: translation
model: facebook/wmt19-en-de
metrics: []
dataset_name: wmt19
dataset_config: de-en
dataset_split: validation
col_mapping:
source: translation.en
target: translation.de
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Translation
* Model: facebook/wmt19-en-de
* Dataset: wmt19
* Config: de-en
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@WillHeld](https://huggingface.co/WillHeld) for evaluating this model. |
autoevaluate | null | null | null | false | null | false | autoevaluate/autoeval-staging-eval-project-02148524-0081-4ca2-963d-7e44c726ec75-1311 | 2022-11-14T09:40:38.000Z | null | false | f32c7211c0ac30a750b3fc382a8a3bf880efd44c | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:glue"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-02148524-0081-4ca2-963d-7e44c726ec75-1311/resolve/main/README.md | ---
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. |
autoevaluate | null | null | null | false | null | false | autoevaluate/autoeval-eval-mathemakitten__winobias_antistereotype_test_v5-mathemak-b6a817-2053667122 | 2022-11-14T09:46:08.000Z | null | false | 748f9dc5044e188c60bbe9aadd91b61b9e032c30 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:mathemakitten/winobias_antistereotype_test_v5"
] | https://huggingface.co/datasets/autoevaluate/autoeval-eval-mathemakitten__winobias_antistereotype_test_v5-mathemak-b6a817-2053667122/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- mathemakitten/winobias_antistereotype_test_v5
eval_info:
task: text_zero_shot_classification
model: inverse-scaling/opt-125m_eval
metrics: []
dataset_name: mathemakitten/winobias_antistereotype_test_v5
dataset_config: mathemakitten--winobias_antistereotype_test_v5
dataset_split: test
col_mapping:
text: text
classes: classes
target: target
---
# 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-125m_eval
* Dataset: mathemakitten/winobias_antistereotype_test_v5
* Config: mathemakitten--winobias_antistereotype_test_v5
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@mathemakitten](https://huggingface.co/mathemakitten) for evaluating this model. |
autoevaluate | null | null | null | false | null | false | autoevaluate/autoeval-staging-eval-project-0d414f0c-bce8-44f6-9c83-f356bfaf679d-1412 | 2022-11-14T09:43:19.000Z | null | false | 40c04a6a5193bca2029e35a7a50e945e69a55aea | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:glue"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-0d414f0c-bce8-44f6-9c83-f356bfaf679d-1412/resolve/main/README.md | ---
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. |
autoevaluate | null | null | null | false | null | false | autoevaluate/autoeval-eval-mathemakitten__winobias_antistereotype_test_v5-mathemak-b6a817-2053667123 | 2022-11-14T10:06:28.000Z | null | false | 07aecb1e8d8a44720b52a7c8a6cf1e905ad2acce | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:mathemakitten/winobias_antistereotype_test_v5"
] | https://huggingface.co/datasets/autoevaluate/autoeval-eval-mathemakitten__winobias_antistereotype_test_v5-mathemak-b6a817-2053667123/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- mathemakitten/winobias_antistereotype_test_v5
eval_info:
task: text_zero_shot_classification
model: inverse-scaling/opt-1.3b_eval
metrics: []
dataset_name: mathemakitten/winobias_antistereotype_test_v5
dataset_config: mathemakitten--winobias_antistereotype_test_v5
dataset_split: test
col_mapping:
text: text
classes: classes
target: target
---
# 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-1.3b_eval
* Dataset: mathemakitten/winobias_antistereotype_test_v5
* Config: mathemakitten--winobias_antistereotype_test_v5
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@mathemakitten](https://huggingface.co/mathemakitten) for evaluating this model. |
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