datasetId
stringlengths 2
117
| author
stringlengths 2
42
⌀ | last_modified
timestamp[us, tz=UTC] | downloads
int64 0
9.36M
| likes
int64 0
3.89k
| tags
sequence | task_categories
sequence | createdAt
timestamp[us, tz=UTC] | card
stringlengths 19
1.01M
| embedding
sequence |
|---|---|---|---|---|---|---|---|---|---|
avitri/eng-fra | avitri | 2023-12-17T13:18:15Z | 1 | 0 | [
"license:mit",
"region:us"
] | null | 2023-12-17T12:42:19Z | ---
license: mit
---
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avitri/en-fr | avitri | 2023-12-17T13:19:32Z | 1 | 0 | [
"license:mit",
"region:us"
] | null | 2023-12-17T13:19:11Z | ---
license: mit
---
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nlplabtdtu/edu_child_v10 | nlplabtdtu | 2023-12-17T13:19:57Z | 1 | 0 | [
"region:us"
] | null | 2023-12-17T13:19:52Z | ---
dataset_info:
features:
- name: content
dtype: string
- name: metadata
struct:
- name: metadata
struct:
- name: id
dtype: string
- name: prefix
dtype: string
- name: seq_num
dtype: int64
- name: source
dtype: string
- name: url
dtype: string
- name: vector
sequence: float64
splits:
- name: train
num_bytes: 8309334
num_examples: 639
download_size: 6176234
dataset_size: 8309334
configs:
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data_files:
- split: train
path: data/train-*
---
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dputilov/generatemidas24aiwithlaion | dputilov | 2023-12-17T13:26:01Z | 1 | 0 | [
"region:us"
] | null | 2023-12-17T13:22:53Z | ---
dataset_info:
features:
- name: image
dtype: image
- name: conditioning_image
dtype: image
- name: mask
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 10320602436.548
num_examples: 13322
download_size: 5789522074
dataset_size: 10320602436.548
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "generatemidas24aiwithlaion"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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dputilov/generatemidas24aiwithourbalance | dputilov | 2023-12-17T13:40:14Z | 1 | 0 | [
"region:us"
] | null | 2023-12-17T13:37:57Z | ---
dataset_info:
features:
- name: image
dtype: image
- name: conditioning_image
dtype: image
- name: mask
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 4841002944.25
num_examples: 11225
download_size: 4839011363
dataset_size: 4841002944.25
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "generatemidas24aiwithourbalance"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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RahulRaman/counting-object-sd-dataset4-clean4 | RahulRaman | 2023-12-17T14:05:08Z | 1 | 0 | [
"region:us"
] | null | 2023-12-17T14:04:31Z | ---
dataset_info:
features:
- name: input_image
dtype: image
- name: edit_prompt
dtype: string
- name: edited_image
dtype: image
splits:
- name: train
num_bytes: 780165241.0
num_examples: 496
download_size: 297832459
dataset_size: 780165241.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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Shriganesh/github-issues | Shriganesh | 2023-12-17T14:50:19Z | 1 | 0 | [
"region:us"
] | null | 2023-12-17T14:50:18Z | ---
dataset_info:
features:
- name: url
dtype: string
- name: repository_url
dtype: string
- name: labels_url
dtype: string
- name: comments_url
dtype: string
- name: events_url
dtype: string
- name: html_url
dtype: string
- name: id
dtype: int64
- name: node_id
dtype: string
- name: number
dtype: int64
- name: title
dtype: string
- name: user
struct:
- name: login
dtype: string
- name: id
dtype: int64
- name: node_id
dtype: string
- name: avatar_url
dtype: string
- name: gravatar_id
dtype: string
- name: url
dtype: string
- name: html_url
dtype: string
- name: followers_url
dtype: string
- name: following_url
dtype: string
- name: gists_url
dtype: string
- name: starred_url
dtype: string
- name: subscriptions_url
dtype: string
- name: organizations_url
dtype: string
- name: repos_url
dtype: string
- name: events_url
dtype: string
- name: received_events_url
dtype: string
- name: type
dtype: string
- name: site_admin
dtype: bool
- name: labels
list:
- name: id
dtype: int64
- name: node_id
dtype: string
- name: url
dtype: string
- name: name
dtype: string
- name: color
dtype: string
- name: default
dtype: bool
- name: description
dtype: string
- name: state
dtype: string
- name: locked
dtype: bool
- name: assignee
struct:
- name: login
dtype: string
- name: id
dtype: int64
- name: node_id
dtype: string
- name: avatar_url
dtype: string
- name: gravatar_id
dtype: string
- name: url
dtype: string
- name: html_url
dtype: string
- name: followers_url
dtype: string
- name: following_url
dtype: string
- name: gists_url
dtype: string
- name: starred_url
dtype: string
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dtype: string
- name: organizations_url
dtype: string
- name: repos_url
dtype: string
- name: events_url
dtype: string
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dtype: string
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dtype: string
- name: site_admin
dtype: bool
- name: assignees
list:
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dtype: string
- name: id
dtype: int64
- name: node_id
dtype: string
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dtype: string
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dtype: string
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dtype: string
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dtype: string
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dtype: string
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dtype: string
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dtype: string
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dtype: string
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dtype: string
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dtype: string
- name: events_url
dtype: string
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dtype: string
- name: type
dtype: string
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dtype: bool
- name: milestone
struct:
- name: url
dtype: string
- name: html_url
dtype: string
- name: labels_url
dtype: string
- name: id
dtype: int64
- name: node_id
dtype: string
- name: number
dtype: int64
- name: title
dtype: string
- name: description
dtype: string
- name: creator
struct:
- name: login
dtype: string
- name: id
dtype: int64
- name: node_id
dtype: string
- name: avatar_url
dtype: string
- name: gravatar_id
dtype: string
- name: url
dtype: string
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dtype: string
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dtype: string
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dtype: string
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dtype: string
- name: repos_url
dtype: string
- name: events_url
dtype: string
- name: received_events_url
dtype: string
- name: type
dtype: string
- name: site_admin
dtype: bool
- name: open_issues
dtype: int64
- name: closed_issues
dtype: int64
- name: state
dtype: string
- name: created_at
dtype: timestamp[s]
- name: updated_at
dtype: timestamp[s]
- name: due_on
dtype: 'null'
- name: closed_at
dtype: 'null'
- name: comments
sequence: string
- name: created_at
dtype: timestamp[s]
- name: updated_at
dtype: timestamp[s]
- name: closed_at
dtype: timestamp[s]
- name: author_association
dtype: string
- name: active_lock_reason
dtype: 'null'
- name: body
dtype: string
- name: reactions
struct:
- name: url
dtype: string
- name: total_count
dtype: int64
- name: '+1'
dtype: int64
- name: '-1'
dtype: int64
- name: laugh
dtype: int64
- name: hooray
dtype: int64
- name: confused
dtype: int64
- name: heart
dtype: int64
- name: rocket
dtype: int64
- name: eyes
dtype: int64
- name: timeline_url
dtype: string
- name: performed_via_github_app
dtype: 'null'
- name: state_reason
dtype: string
- name: draft
dtype: bool
- name: pull_request
struct:
- name: url
dtype: string
- name: html_url
dtype: string
- name: diff_url
dtype: string
- name: patch_url
dtype: string
- name: merged_at
dtype: timestamp[s]
- name: is_pull_request
dtype: bool
splits:
- name: train
num_bytes: 21364539
num_examples: 2500
download_size: 5981294
dataset_size: 21364539
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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kdcyberdude/wikipedia-pa-transliteration | kdcyberdude | 2023-12-17T19:29:55Z | 1 | 0 | [
"region:us"
] | null | 2023-12-17T15:17:14Z | ---
dataset_info:
features:
- name: id
dtype: int64
- name: url
dtype: string
- name: title
dtype: string
- name: text
dtype: string
- name: transliterated_text
dtype: string
- name: transliterated_title
dtype: string
splits:
- name: train
num_bytes: 311038383
num_examples: 51423
download_size: 137271151
dataset_size: 311038383
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "wikipedia-pa-transliteration"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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Shriganesh/github-embeddings_dataset | Shriganesh | 2023-12-17T15:25:39Z | 1 | 0 | [
"region:us"
] | null | 2023-12-17T15:25:37Z | ---
dataset_info:
features:
- name: html_url
dtype: string
- name: title
dtype: string
- name: comments
dtype: string
- name: body
dtype: string
- name: comment_length
dtype: int64
- name: text
dtype: string
- name: embeddings
sequence: float32
splits:
- name: train
num_bytes: 25327740
num_examples: 2716
download_size: 13820742
dataset_size: 25327740
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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lunarlist/ner | lunarlist | 2023-12-17T15:59:58Z | 1 | 0 | [
"region:us"
] | null | 2023-12-17T15:59:48Z | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: id
dtype: int64
- name: ner_tags
sequence: int64
- name: tokens
sequence: string
- name: tags
sequence: string
splits:
- name: train
num_bytes: 72892413
num_examples: 147734
- name: validation
num_bytes: 8664757
num_examples: 16410
download_size: 17227566
dataset_size: 81557170
---
# Dataset Card for "ner"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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greathero/contrailsvalidationdataset | greathero | 2023-12-17T16:03:44Z | 1 | 0 | [
"region:us"
] | null | 2023-12-17T16:02:42Z | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype: image
splits:
- name: train
num_bytes: 325995028.72
num_examples: 16695
download_size: 319401270
dataset_size: 325995028.72
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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DragonLine/ksponspeech_05_preprocess | DragonLine | 2023-12-17T16:59:50Z | 1 | 0 | [
"region:us"
] | null | 2023-12-17T16:15:28Z | ---
dataset_info:
features:
- name: input_features
sequence:
sequence: float32
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 97240793472
num_examples: 101236
- name: test
num_bytes: 12155567304
num_examples: 12655
- name: valid
num_bytes: 12154621480
num_examples: 12654
download_size: 24333989934
dataset_size: 121550982256
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: valid
path: data/valid-*
---
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fatihay/deneme1 | fatihay | 2023-12-17T21:09:18Z | 1 | 0 | [
"license:apache-2.0",
"region:us"
] | null | 2023-12-17T21:08:59Z | ---
license: apache-2.0
---
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doguaraci/trial | doguaraci | 2023-12-17T22:14:41Z | 1 | 0 | [
"region:us"
] | null | 2023-12-17T21:29:28Z | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: response
dtype: string
splits:
- name: train
num_bytes: 252534040
num_examples: 96209
- name: test
num_bytes: 2568372
num_examples: 1000
download_size: 143740404
dataset_size: 255102412
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
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openclimatefix/dwd-icon-global | openclimatefix | 2023-12-17T14:40:43Z | 0 | 0 | [
"size_categories:1K<n<10K",
"license:mit",
"climate",
"doi:10.57967/hf/0880",
"region:us"
] | null | 2023-03-18T09:42:45Z | ---
license: mit
tags:
- climate
pretty_name: DWD ICON Global Forecasts
size_categories:
- 1K<n<10K
---
# Dataset Card for DWD ICON Global Forecast
This dataset is comprised of forecasts from the German Weather Service's (DWD) ICON-EU model from March 2023 to the present with all variables included. Each forecast runs up to 4 days into the future, and the model is ran 4 times per day. This data is an archive of
the publicly available data at https://opendata.dwd.de/weather/nwp/, converted to Zarr format with Xarray. No other processing of the data is performed.
## Dataset Details
- **Curated by:** Jacob Bieker, Open Climate Fix
- **License:** German Government Open Data License
### Dataset Sources [optional]
- **Raw files:** https://opendata.dwd.de/weather/nwp/
Note: The raw files are deleted after 24 hours, and there is no long-term archive available publicly.
## Uses
This data is intended for use in renewable energy forecasting, weather forecasting, and anything that can use high-quality weather forecasts over Europe.
## Dataset Structure
The dataset is comprised of one Zarr file per forecast initialization time, and each forecast goes out between 48-96 hours. The files are located at data/year/month/day/YYYYMMDDHH.zarr.zip.
## Dataset Creation
### Curation Rationale
The DWD ICON Global model provides high-quality, high-resolution forecasts for Global weather that is also publicly available and free of charge. The model should generally outperform
NOAA's GFS forecast model, and has a higher temporal and spatial resolution. The main downside of this model is that the files are only available for a short period publicly, so this dataset
was setup to provide a public archive of the forecasts for use by researchers in many fields, but especially renewable energy forecasting and weather forecasting.
### Source Data
The source data is the grib2 files from the DWD Open Data Server.
#### Data Collection and Processing
The data is collected every day, around 6-8 hours after forecast initialization time to ensure the forecast is finished running before the data is pulled. The grib2 files are opened
with Xarray and collated into a single Xarray Dataset, with one data variable per ICON variable. Surface variables have "_s" appended to their names to differentiate them from multi-level variables.
The Dataset is then written to Zarr using "ocf_blosc2" to encode and compress the variables. No scaling or changing of the variables values is performed. This does mean that the data is not in a regular lat/lon grid
and is instead in the icosohedral grid of the model. To obtain a regular grid, the data will need to be regridded.
#### Who are the source data producers?
German Weather Service (DWD)
### Recommendations
These files can be opened directly from HuggingFace, and streamed in with Xarray. HuggingFace is fairly slow though, so the recommended way would be to download the files you want
and open them locally. In either case, to access the data you can do the following
```python
import ocf_blosc2
import xarray as xr
data = xr.open_zarr("path/to/zarr/file")
print(data)
```
Alternatively, for using the data in forecasting, there is the `ocf_datapipes` package for loading and training renewable energy forecasting models with multi-modal inputs, including
ICON, but also satellite data, PV readings, etc.
## Dataset Card Contact
Jacob Bieker: [email protected] | [
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mesolitica/chatgpt-noisy-translation-facebook | mesolitica | 2023-12-17T04:06:33Z | 0 | 0 | [
"task_categories:translation",
"language:ms",
"region:us"
] | [
"translation"
] | 2023-03-22T05:10:38Z | ---
task_categories:
- translation
language:
- ms
---
# ChatGPT3.5 Noisy Translation Facebook
Notebooks at https://github.com/mesolitica/malaysian-dataset/tree/master/translation/chatgpt3.5-facebook | [
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mesolitica/chatgpt-noisy-translation-iium-confession | mesolitica | 2023-12-17T04:07:11Z | 0 | 0 | [
"task_categories:translation",
"language:ms",
"region:us"
] | [
"translation"
] | 2023-03-22T05:11:08Z | ---
task_categories:
- translation
language:
- ms
---
# ChatGPT3.5 Noisy Translation IIUM Confession
Notebooks at https://github.com/mesolitica/malaysian-dataset/tree/master/translation/chatgpt3.5-iium-confession | [
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mesolitica/chatgpt-noisy-translation-twitter | mesolitica | 2023-12-17T04:07:43Z | 0 | 0 | [
"task_categories:translation",
"language:ms",
"region:us"
] | [
"translation"
] | 2023-03-26T14:19:05Z | ---
task_categories:
- translation
language:
- ms
---
# ChatGPT3.5 Noisy Translation Twitter
Notebooks at https://github.com/mesolitica/malaysian-dataset/tree/master/translation/chatgpt3.5-twitter | [
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mesolitica/chatgpt-noisy-translation-banjarese | mesolitica | 2023-12-17T04:11:02Z | 0 | 0 | [
"task_categories:translation",
"language:ms",
"region:us"
] | [
"translation"
] | 2023-04-15T17:50:26Z | ---
task_categories:
- translation
language:
- ms
---
# ChatGPT3.5 Noisy Translation Banjarese
Notebooks at https://github.com/mesolitica/malaysian-dataset/tree/master/translation/chatgpt3.5-nllb-bjn | [
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mesolitica/chatgpt-noisy-translation-manglish | mesolitica | 2023-12-17T04:08:00Z | 0 | 0 | [
"task_categories:translation",
"language:ms",
"region:us"
] | [
"translation"
] | 2023-04-27T11:22:16Z | ---
task_categories:
- translation
language:
- ms
---
# ChatGPT3.5 Noisy Translation Manglish
Notebooks at https://github.com/mesolitica/malaysian-dataset/tree/master/translation/chatgpt3.5-manglish | [
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bene-ges/spellmapper_en_train_v1 | bene-ges | 2023-12-13T13:47:30Z | 0 | 0 | [
"task_categories:token-classification",
"size_categories:10M<n<100M",
"language:en",
"license:cc-by-4.0",
"arxiv:2306.02317",
"region:us"
] | [
"token-classification"
] | 2023-05-28T08:40:07Z | ---
license: cc-by-4.0
language:
- en
task_categories:
- token-classification
size_categories:
- 10M<n<100M
---
This is the training dataset used to train the first version of the SpellMapper model.
It consists of 33'547'074 training examples.
Actually, [this checkpoint](https://huggingface.co/bene-ges/spellmapper_asr_customization_en) was trained only 1/3 of the full dataset.
Paper: [SpellMapper: A non-autoregressive neural spellchecker for ASR customization with candidate retrieval based on n-gram mappings](https://arxiv.org/abs/2306.02317)
## Citation
```bibtex
@inproceedings{inproceedings,
author = {Antonova, Alexandra and Bakhturina, Evelina and Ginsburg, Boris},
year = {2023},
month = {08},
pages = {1404-1408},
title = {SpellMapper: A non-autoregressive neural spellchecker for ASR customization with candidate retrieval based on n-gram mappings},
doi = {10.21437/Interspeech.2023-768}
}
``` | [
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xzuyn/Stable-Diffusion-Prompts-Deduped-2.008M | xzuyn | 2023-12-11T04:14:29Z | 0 | 6 | [
"task_categories:text-generation",
"size_categories:1M<n<10M",
"language:en",
"region:us"
] | [
"text-generation"
] | 2023-06-18T23:40:25Z | ---
task_categories:
- text-generation
language:
- en
size_categories:
- 1M<n<10M
---
# [Original Dataset by FredZhang7](https://huggingface.co/datasets/FredZhang7/stable-diffusion-prompts-2.47M)
- Deduped from 2,473,022 down to 2,007,998.
- Changed anything that had `[ prompt text ]`, `( prompt text )`, or `< prompt text >`, to `[prompt text]`, `(prompt text)`, and `<prompt text>`.
- 2 or more spaces converted to a single space.
- Removed all `"`
- Removed spaces at beginnings. | [
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RyokoExtra/MissingKeys | RyokoExtra | 2023-12-17T15:05:12Z | 0 | 2 | [
"task_categories:text-classification",
"task_categories:text-generation",
"task_categories:text-to-image",
"task_categories:text-to-video",
"language:ja",
"license:apache-2.0",
"region:us"
] | [
"text-classification",
"text-generation",
"text-to-image",
"text-to-video"
] | 2023-07-19T12:17:40Z | ---
license: apache-2.0
task_categories:
- text-classification
- text-generation
- text-to-image
- text-to-video
language:
- ja
pretty_name: MissingKeys
configs:
- config_name: default
default: true
data_files:
- split: all
path:
- 'data/*/*.jsonl'
---
# Dataset Card for MissingKeys
## Dataset Description
- **Homepage:** Here!
- **Repository:** N/A
- **Paper:** N/A
- **Leaderboard:** N/A
- **Point of Contact:** KaraKaraWitch
### Dataset Summary
MissingKeys is a raw dataset archive of the misskey.io network.
### Supported Tasks and Leaderboards
This dataset is primarily intended for unsupervised training of text generation models; however, it may be useful for other purposes.
- text-classification
- text-generation
### Languages
Primarily japanese, however there are also english as well.
## Dataset Structure
All the files are located in jsonl files that has been compressed into .7z archives by date.
### Data Instances
Here is a sample with all the potential fields:
```json
{
"id": "9hh9iux6al",
"createdAt": "2023-07-22T07:38:17.994Z",
"userId": "9grv7htulz",
"user": {
"uid": "9grv7htulz#[email protected]",
"name": "千種ナオ(ばすキー)",
"avatarUrl": "https://proxy.misskeyusercontent.com/avatar.webp?url=https%3A%2F%2Fs3.isk01.sakurastorage.jp%2Fbackspacekey%2Fmisskey%2Fca098593-5c2f-4488-8b82-18961149cf92.png&avatar=1",
"avatarBlurhash": "eGD8ztEK0KVb-=4TtSXm-jf4B7Vs~CEND*Fy%2Mct7%Lx.M{xcS0bv",
"states": "bot,nyaa~",
"hostInfo": "[email protected]#e4d440"
"emojis": {},
"onlineStatus": "unknown"
},
"text": "パソコン工房などのユニットコム系列だと、マザボ売るときにドライバディスクがないと30%買取金額が下がるという知見を得た",
"cw": null,
"visibility": "public",
"localOnly": false,
"renoteCount": 0,
"repliesCount": 0,
"reactions": {},
"reactionEmojis": {},
"emojis": {},
"fileIds": [],
"files": [],
"replyId": null,
"renoteId": null,
"uri": "https://misskey.backspace.fm/notes/9hh9iux6p7"
}
```
If the value is "Falsey" in python, it has been removed to save on space.
`states` is a comma seperated string that either includes: `bot` or `nyaa~` (Indicates they enabled cat mode) or both.
### Data Fields
Refer to the sample above. I'll drop in some additional notes:
`uid` in `user` follows this specific format:
`user_id#username@user_host`
### Data Splits
Each jsonl file is split at 100000 notes.
## Dataset Creation
### Curation Rationale
Because we need a SNS dataset, and since twitter appears to be quite reluctant, we went for the alternative.
### Source Data
#### Initial Data Collection and Normalization
None. No normalization is performed as this is a raw dump of the dataset. However we have removed empty and null fields to conserve on space.
#### Who are the source language producers?
The related users of misskey.io network.
### Annotations
#### Annotation process
No Annotations are present.
#### Who are the annotators?
No human annotators.
### Personal and Sensitive Information
We are certain there is no PII included in the dataset.
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
Misskey.io tends to be NSFW for images and is focused on Japanese culture.
### Other Known Limitations
N/A
## Additional Information
### Dataset Curators
KaraKaraWitch
### Licensing Information
Apache 2.0, for all parts of which KaraKaraWitch may be considered authors. All other material is distributed under fair use principles.
Ronsor Labs additionally is allowed to relicense the dataset as long as it has gone through processing.
### Citation Information
```
@misc{missingkeys,
title = {MissingKeys: A SNS dataset on misskey.io network},
author = {KaraKaraWitch},
year = {2023},
howpublished = {\url{https://huggingface.co/datasets/RyokoExtra/MissingKeys}},
}
```
### Name Etymology
N/A
### Contributions
- [@KaraKaraWitch (Twitter)](https://twitter.com/KaraKaraWitch) for gathering this dataset. | [
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lorinma/ChemTextbookCorporaTest | lorinma | 2023-12-13T01:33:24Z | 0 | 0 | [
"task_categories:text-generation",
"language:zh",
"region:us"
] | [
"text-generation"
] | 2023-07-30T13:07:16Z | ---
language:
- zh
task_categories:
- text-generation
---
用于测试的化工训练语料，1本。来自于教科书的OCR。
样例：
第一章化工安全生产概述 化工行业是国民经济的基础行业目前中国的石油和化学工业从石油天然气等矿 产资源勘探开发到化工天然气化工煤化工盐化工国防化工化肥纯碱氯碱 电石无机盐基本有机原料农药染料涂料新领域精细化工橡胶工业新材料 等已经形成具有20多个行业可生产4万多种产品门类比较齐全品种大体配套完 整的全产业链的石化产业体系并具有一定国际竞争力 近十多年来我国化工企业发展迅速区域化工产业带已初步形成据不完全统计 截至2019年底全国重点化工园区或以石油和化工为主导的产业园区共有676家其中 国家级57家省级351家如依托长江水系形成长江经济带和长江三角洲地区上游有 重庆长寿化工园四川西部化工城下游有南京无锡常州镇江南通泰兴常 熟扬子江和苏州工业园以及上海化学工业园区依托珠江水系的珠江经济带和泛珠三 角地区主要有广东湛江茂名广州惠州深圳珠海等沿海地区的化工园区如 环杭州湾地区形成的精细化工园区山东半岛和环渤海地区的青岛齐鲁天津沧州 大连和福州湄洲湾的泉港厦门莆田等均建立了化工园区一批具有特色的内陆地区化 工园区正在崛起如内蒙古的包头鄂尔多斯和巴盟化工园区陕西的神华煤化工 | [
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lorinma/CPTCorporaTest_200bks | lorinma | 2023-12-13T01:34:12Z | 0 | 0 | [
"task_categories:text-generation",
"size_categories:10K<n<100K",
"language:zh",
"region:us"
] | [
"text-generation"
] | 2023-07-30T13:39:56Z | ---
task_categories:
- text-generation
language:
- zh
size_categories:
- 10K<n<100K
---
测试版语料，经过OCR的200本教科书，用于化工垂域LLM训练。
样例：i所有安全泄压设施：如安全阀、爆破片、呼吸阀都应编号，并表示清楚设计要求； j异径管需注明其形式及规格；对改、扩建装置，版表示与已有设备或管道的连接点 （3） 仪表 a所有在线仪表，包括测量、记录、调节、分析仪表等，所有仪表均需编号； b所有调节阀； e联锁关系； d 随机仪表应在PID上注明。 （4） PID注释 h设备注释主要注明设备布置的特殊要求和催化剂、化学品和填料装卸处的空间要求 等内容； b管道注释主要注明工艺、配管方面的一些特殊要求； c仪表注释主要注明仪表安装方面的特殊要求。 3.0.10公用系统管道和仪表流程图应表示下列内容： （1） 与公用系统有关，即使用或产生公用物料的设备（包括备用设备）； （2） 公用物料干管、总管、支管和进出设备的所有公用物料管道、管件、阀件等，并作 管道标注； （3） 公用物料管道上的所有仪表，但在丄艺管道及仪表流程图上已表示的公用物料仪表 不得重复出现。 4设 备 4.1设备设计说明 4.1.1设备（包括容器、换地器、工业炉、机泵、机械）设计说明一般应有；为厂解 | [
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mesolitica/chatgpt-noisy-translation-c.cari.com.my | mesolitica | 2023-12-17T04:08:29Z | 0 | 0 | [
"task_categories:translation",
"language:ms",
"region:us"
] | [
"translation"
] | 2023-07-31T05:51:48Z | ---
task_categories:
- translation
language:
- ms
---
# ChatGPT3.5 Noisy Translation c.cari.com.my
Notebooks at https://github.com/mesolitica/malaysian-dataset/tree/master/translation/chatgpt3.5-c.cari.com.my | [
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mesolitica/chatgpt-noisy-translation-b.cari.com.my | mesolitica | 2023-12-17T04:08:40Z | 0 | 0 | [
"task_categories:translation",
"language:ms",
"region:us"
] | [
"translation"
] | 2023-08-07T08:35:30Z | ---
task_categories:
- translation
language:
- ms
---
# ChatGPT3.5 Noisy Translation b.cari.com.my
Notebooks at https://github.com/mesolitica/malaysian-dataset/tree/master/translation/chatgpt3.5-b.cari.com.my | [
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CyberHarem/izumi_sagiri_eromangasensei | CyberHarem | 2023-12-12T19:58:26Z | 0 | 0 | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | [
"text-to-image"
] | 2023-08-15T05:31:20Z | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of Izumi Sagiri
This is the dataset of Izumi Sagiri, containing 251 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
| Name | Images | Download | Description |
|:----------------|---------:|:----------------------------------------|:-----------------------------------------------------------------------------------------|
| raw | 251 | [Download](dataset-raw.zip) | Raw data with meta information. |
| raw-stage3 | 582 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. |
| raw-stage3-eyes | 658 | [Download](dataset-raw-stage3-eyes.zip) | 3-stage cropped (with eye-focus) raw data with meta information. |
| 384x512 | 251 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. |
| 512x704 | 251 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. |
| 640x880 | 251 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. |
| stage3-640 | 582 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. |
| stage3-800 | 582 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. |
| stage3-p512-640 | 505 | [Download](dataset-stage3-p512-640.zip) | 3-stage cropped dataset with the area not less than 512x512 pixels. |
| stage3-eyes-640 | 658 | [Download](dataset-stage3-eyes-640.zip) | 3-stage cropped (with eye-focus) dataset with the shorter side not exceeding 640 pixels. |
| stage3-eyes-800 | 658 | [Download](dataset-stage3-eyes-800.zip) | 3-stage cropped (with eye-focus) dataset with the shorter side not exceeding 800 pixels. |
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TinyPixel/airoboros_llama2 | TinyPixel | 2023-12-15T05:01:43Z | 0 | 0 | [
"region:us"
] | null | 2023-08-16T04:23:26Z | ---
dataset_info:
features:
- name: id
dtype: string
- name: conversations
list:
- name: from
dtype: string
- name: value
dtype: string
- name: category
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 213067797
num_examples: 59277
download_size: 111592267
dataset_size: 213067797
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "airo"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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aleroy/flyview_natural | aleroy | 2023-12-11T16:45:10Z | 0 | 0 | [
"size_categories:100K<n<1M",
"license:cc-by-nc-sa-4.0",
"computer vision ",
"region:us"
] | null | 2023-08-16T15:30:20Z | ---
license: cc-by-nc-sa-4.0
tags:
- 'computer vision '
size_categories:
- 100K<n<1M
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teowu/public_iqa_vqa_databases | teowu | 2023-12-14T11:02:53Z | 0 | 0 | [
"license:cc-by-nc-4.0",
"region:us"
] | null | 2023-08-28T18:40:10Z | ---
license: cc-by-nc-4.0
---
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ChanceFocus/flare-es-multifin | ChanceFocus | 2023-12-14T02:33:33Z | 0 | 0 | [
"region:us"
] | null | 2023-08-29T02:20:15Z | ---
dataset_info:
features:
- name: id
dtype: string
- name: query
dtype: string
- name: answer
dtype: string
- name: text
dtype: string
- name: choices
sequence: string
- name: gold
dtype: int64
splits:
- name: test
num_bytes: 117062
num_examples: 230
download_size: 28388
dataset_size: 117062
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
---
# Dataset Card for "flare-es-multifin"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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AnimaLab/bias-test-gpt-biases | AnimaLab | 2023-12-17T23:15:46Z | 0 | 0 | [
"language:en",
"license:apache-2.0",
"arxiv:1906.07337",
"region:us"
] | null | 2023-08-29T19:54:55Z | ---
license: apache-2.0
language:
- en
pretty_name: BiasTestGPT-sentences
---
# Dataset Card for "BiasTestGPT: Bias Specifications"
Dataset of sentences for bias testing in open-sourced Pretrained Language Models generated using ChatGPT and other generative Language Models.
This dataset is used and actively populated by the [BiasTestGPT HuggingFace Tool](https://huggingface.co/spaces/AnimaLab/bias-test-gpt-pairs).
- [BiasTestGPT HuggingFace Tool](https://huggingface.co/spaces/AnimaLab/bias-test-gpt-pairs)
- [Dataset with Generated Test Sentences](https://huggingface.co/datasets/AnimaLab/bias-test-gpt-sentences)
- [Project Landing Page](https://biastest-animalab.github.io/)
## Data Structure
### Data Instances
Dataset instances consist of JSON files with bias specifications.
There are 2 types of specifications kept in separate folders:
- **'predefined biases'** - predefined bias specifications are obtained from prior work such as [Caliskan'17](https://www.science.org/doi/full/10.1126/science.aal4230?casa_token=c7LtvGWRQqIAAAAA:5QJ_M6jLk3675-50ZIIGgO2XQ5ewtZZxnKW-NlqUmxT6H112t4PIptRA94JLswFcXYIyl3yMML86duQ) and [Kurita'19](https://arxiv.org/pdf/1906.07337.pdf).
- **'custom biases'** - are biases defined by the users of our [HuggingFace Tool](https://huggingface.co/spaces/AnimaLab/bias-test-gpt-pairs). These are dynamically added to the repository based on user interactions with the tool.
An example of predefined bias specification in JSON format is provided below:
```json
{ "name": "Career_Family_vs_MaleName_FemaleName",
"social_groups": {
"Male_Names": ["John", "Paul", "Mike", "Kevin", "Steve", "Greg", "Jeff", "Bill"],
"Female_Names": ["Amy", "Joan", "Lisa", "Sarah", "Diana", "Kate", "Ann", "Donna"]
},
"attributes": {
"Career": ["executive", "management", "professional", "corporation", "salary", "office", "business", "career"],
"Family": ["home", "parents", "children", "family", "cousins", "marriage", "wedding", "relatives"]
},
"templates": ["[T] likes [A]", "[T] like [A]", "[T] is interested in [A]"],
"source": "Caliskan'17 - Weat 6",
"url": "https://purehost.bath.ac.uk/ws/portalfiles/portal/168480066/CaliskanEtAl_authors_full.pdf",
"type": "predefined",
"created": "09/06/2023 09:58:37"
}
```
### Data Fields
Each bias specification contains several data fields defining the bias to test itself, but also additional metadata about the bias specification origins.
#### Description of Data Fields in the Bias Specification
- **'name'**: a `string` feature - a short descriptive name of the bias.
- **'social_groups'**: a `dictionary` feature containing specifications of 2 or more social groups to compare.
- **'Male_Names'**, **'Female_Names'** - bias specific *keys* with names defininig the compared social groups. Value for each kay is a list of terms defining the particular social group.
- **'attributes'**: a `dictionary' feature containing specifications of 2 ideally polar opposite attributes to test in comparison of social groups.
- **'Career'**, **`Family'** - bias specific *keys* with names of opposing attributes. Value for each key is a list of terms defining the attribute.
- **'templates'**: a 'list' feature - legacy test sentence templates used in prior work. Used for a baseline bias measurement.
- **'source'**: a 'string' feature - the source of the bias specification, usually prior work
- **'url'**: a `string' feature - link to the research paper providing the bias specification
- **'type'**: a `string' feature - specifies whether bias has been predefined by prior work or defined using our [HuggingFace Tool](https://huggingface.co/spaces/AnimaLab/bias-test-gpt-pairs).
- **'created'**: a data of addition of the bias specification to the repository. Generated automatically upon addition from our tool.
### Bias Specification - Data Splits
The repository contains 15 predefined bias specifications based on prior work and an additional 4 or more custom-defined bias specifications.
We note that the number of custom-defined bias specifications is constantly growing as it is being populated by the interactions with the [HuggingFace Tool](https://huggingface.co/spaces/AnimaLab/bias-test-gpt-pairs).
| Type | Meaning | Size |
|--------|--------|------:|
| predefined | biases for which specification has been provided in prior work | 15 |
| custom | biases added to the repository based on interaction with the [BiasTestGPT tool](https://huggingface.co/spaces/AnimaLab/bias-test-gpt-pairs) | 4+ | | [
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mango19918/aimodels | mango19918 | 2023-12-13T06:14:47Z | 0 | 0 | [
"license:openrail",
"region:us"
] | null | 2023-08-30T00:18:04Z | ---
license: openrail
---
All of my models posted on AI HUB | [
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unalignment/airoboros-2.2 | unalignment | 2023-12-11T21:02:03Z | 0 | 24 | [
"license:other",
"not-for-all-audiences",
"region:us"
] | null | 2023-09-05T17:07:10Z | ---
license: other
tags:
- not-for-all-audiences
---
## Overview
This dataset is mostly a continuation of https://hf.co/datasets/jondurbin/airoboros-2.1, with some notable additions and fixes.
__*I've gated access with request, due to the de-alignment data. To download, you must agree to the following:*__
- Some of the content is "toxic"/"harmful", and contains profanity and other types of sensitive content.
- None of the content or views contained in text within this dataset necessarily align with my personal beliefs or opinions, they are simply text generated by LLMs and/or scraped from the web.
- Use with extreme caution, particularly in locations with less-than-free speech laws.
- You, and you alone are responsible for having downloaded the dataset and having a copy of the contents therein and I am completely indemnified from any and all liabilities.
### 2.1 Contamination
I accidentally included some of the benchmark data in the first version of the airboros-2.1 model, which is why it had a crazy high truthfulqa score. Discussions here:
- https://huggingface.co/jondurbin/airoboros-l2-70b-2.1/discussions/3#64f325ce352152814d1f796a
- https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard/discussions/225#64f0997659da193a12b78c32
I flagged it for removal and recreated the model right away, but the leaderboard cached the old results so it took some time to reflect.
Some of the instructors I use create overlapping data, and it's hard to filter, especially since the instructions aren't typically verbatim with the benchmark questions.
This time around, I used `thenlper/gte-small` to calculate embeddings of the instructions, along with a faiss index, and removed anything from the dataset that had a similarity score < 0.15 (from truthfulqa). If you have a better way of checking, please let me know!
I haven't done the same for most other benchmarks (yet) because there are hundreds of thousands of instructions and it would be pretty computationally expensive to do. That said, I only have ~1279 multiple choice questions, all randomly GPT generated, so there's probably little-to-no overlap.
### Awareness
I added a new "awareness" instructor, which aims to add a lot more nuance to responses relating to time, location, senses, etc. based on the system prompt.
For example, if you are using the standard prompt with user/assistant, and ask how long it would take to get to Chicago, the answer will be something about AI not having a physical presence.
If, on the other hand, you are using a system prompt with a human character specified, the model attempts to infer location from "home" and will provide a more nuanced answer as a human would (in theory).
https://github.com/jondurbin/airoboros/commit/e91562c88d7610edb051606622e7c25a99884f7e
### Editor
I created a text edit instructor as well, which uses a reverse prompt mechanism, meaning it takes the existing writing samples that have been generated, rewrites them to have misspellings, poor grammar, etc., then uses a prompt like "Please correct and improve the text." with the original well-written text and target output.
https://github.com/jondurbin/airoboros/commit/e60a68de5f9622320c9cfff3b238bd83cc7e373b
### Writing
I regenerated (almost) all of the training data that included "Once upon a time..." because it's too cliche and boring.
### Multiple choice
I created many more multiple choice questions, many of which have additional text context.
### Roleplay/conversation
I re-created all of the GTKM and RP datasets this time around, removing all of the "USER: " and "ASSISTANT: " prefixes from the instructions/responses, so it's more compatible with existing interfaces.
The GTKM instructor now does the same thing as RP, in that it saves each round of "conversation" as a separate row in the output - previously it only saved the final response, which may not have been sufficient since I don't typically train on inputs.
### De-alignment
I included a small sampling of "de-alignment" data. The llama-2 base models seem extremely reluctant to discuss certain topics, curse, or otherwise produce other-than-pg content. I don't want a vile model, but I also don't *NOT* want a vile model.
- comedy skits, to add more comedy and occasional cursing
- instruction/response pairs that would typically otherwise be refused
- various (LLM ehanced) stories from the internet with somewhat spicy content
- story-writing tasks as a re-telling of popular horror/fantasy films (by default, the LLM generated stories often have too sunny of a disposition, so hopefully this will allow for some twists or more interesting stories)
- rude responses (if a character card specifies they are rude and curse, it should actually do so without prompt hacking IMO)
None of the content or views contained in text within this dataset necessarily align with my personal beliefs or opinions, they are simply text generated by LLMs and/or scraped from the web. Use with extreme caution, particularly in locations with strict speech laws!
See "instructions-clean.jsonl" for a version without dealignment data.
### UTF-8 to ASCII
I replaced most of the "standard" utf-8 sequences - left double quote, right double quote, left apostraphe, ellipses - with standard ascii characters. I don't know if this was contributing to part of the issue with eos tokens being produced after apostraphes, but I figured it was worth trying.
### Summarization
I also included 500 examples from:
https://hf.co/datasets/mattpscott/airoboros-summarization
These are existing summarizarions from various public datasets, formatted to airoboros style contextual qa.
Thanks Matt!
### Usage/license info
Much (most) of the data was generated via gpt-4 API calls, which has a restriction in the ToS about "competing" models. Please seek legal advice if you plan to build or use a model that includes this dataset in a commercial setting. | [
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v2ray/r-chatgpt-general-dump | v2ray | 2023-12-15T23:22:51Z | 0 | 0 | [
"task_categories:conversational",
"size_categories:100K<n<1M",
"language:en",
"license:mit",
"not-for-all-audiences",
"region:us"
] | [
"conversational"
] | 2023-09-18T00:30:33Z | ---
license: mit
task_categories:
- conversational
language:
- en
tags:
- not-for-all-audiences
size_categories:
- 100K<n<1M
---
# r/ChatGPT General Dump
From [r/ChatGPT Discord #general channel](https://discord.gg/aRpD4pCw33). | [
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hf-vision/course-assets | hf-vision | 2023-12-17T21:24:09Z | 0 | 5 | [
"license:apache-2.0",
"region:us"
] | null | 2023-10-02T11:37:51Z | ---
license: apache-2.0
---
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gabrielcava/GabrielC | gabrielcava | 2023-12-15T04:00:56Z | 0 | 2 | [
"license:mit",
"region:us"
] | null | 2023-10-08T00:27:25Z | ---
license: mit
---
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BAAI/DataOptim | BAAI | 2023-12-15T06:41:48Z | 0 | 5 | [
"task_categories:visual-question-answering",
"size_categories:1M<n<10M",
"language:en",
"region:us"
] | [
"visual-question-answering"
] | 2023-10-12T01:30:44Z | ---
task_categories:
- visual-question-answering
language:
- en
pretty_name: DataOptim
size_categories:
- 1M<n<10M
---
# DataOptim
We launch DataOptim, an MLLM benchmark and competition where we aim to find the optimal training data for training Multimodal Large Language Models (MLLMs).
- Project page: http://dataoptim.org
- GitHub: https://github.com/BAAI-DCAI/DataOptim
## Datasets
Currently, the visual instruction tuning data used in the challenge contain 18 public datasets.
More datasets are coming in the future!
|Category|Dataset|Images|Samples|Split|
|:-:|:-:|:-:|:-:|:-:|
|Image captioning|[COCO](https://cocodataset.org/#home)|82783|414113|train|
|Image captioning|[Flickr30K](https://shannon.cs.illinois.edu/DenotationGraph/)|29000|145000|Karpathy train split|
|Image captioning|[TextCaps](https://textvqa.org/textcaps/)|21953|109765|train|
|Visual question answering|[VQAv2](https://visualqa.org/)|82783|443757|train|
|Visual question answering|[OKVQA](https://okvqa.allenai.org/)|8998|9009|train|
|Visual question answering|[OCRVQA](https://ocr-vqa.github.io/)|166041|801673|train|
|Visual question answering|[GQA](https://cs.stanford.edu/people/dorarad/gqa/index.html)|72140|943000|train|
|Visual question answering|[TextVQA](https://textvqa.org/)|21953|34602|train|
|Visual question answering|[A-OKVQA](https://allenai.org/project/a-okvqa/home)|16540|17056|train|
|Visual question answering|[ScienceQA](https://scienceqa.github.io/)|6218|6218|train|
|Visual question answering|[Visual Genome QA (VGQA)](https://homes.cs.washington.edu/~ranjay/visualgenome/index.html)|99280|1445322|-|
|Visual question answering|[DocVQA](https://www.docvqa.org/)|10194|39463|train|
|Visual question answering|[DVQA](https://github.com/kushalkafle/DVQA_dataset)|200000|2325316|train|
|Grounding|[RefCOCO/RefCOCO+/RefCOCOg](https://github.com/lichengunc/refer)|24407|287604|train|
|Grounding|[Shikra-RD](https://github.com/shikras/shikra)|883|5922|train|
|GPT-4 generated|[LLaVA-Instruct-150K](https://github.com/haotian-liu/LLaVA/blob/main/docs/Data.md)|81479|157712|-|
|GPT-4 generated|[SVIT](https://github.com/BAAI-DCAI/Visual-Instruction-Tuning)|108076|2992799|-|
|GPT-4V generated|[ShareGPT-4V](https://sharegpt4v.github.io/)|87296|102025|-|
|Mixed|[LLaVA-v1.5](https://github.com/haotian-liu/LLaVA/tree/main#visual-instruction-tuning)<sup>1</sup>|291684|665298|-|
|Total||974K<sup>2</sup>|11.2M|
<sup>1</sup> The bounding boxes in LLaVA-v1.5 are based on the padded image. You can find the discussion [here](https://github.com/haotian-liu/LLaVA/issues/606).
<sup>2</sup> The number of images are counted based on image IDs.
There might be duplicate images across different image sources, such as COCO 2014/2017, Visual Genome, etc.
We use different strategies to collect the prompts for different tasks.
- **Image captioning.** We carefully collect 5 manually written instructions and randomly sample one as the prompt for each caption. The fourth and fifth instructions are from [InstructBLIP](https://github.com/salesforce/LAVIS/blob/main/projects/instructblip/README.md).
- **Open-ended VQA.** As the answers in VQA datasets are generally short, we add an instruction after the question to ask the model to provide answers with a short sentence or phrase.
- **Multiple-choice VQA.** For A-OKVQA, we add an instruction before the question to ask the model to provide answers with correct options. For ScienceQA, we use the instructions and templates designed by [M3IT](https://m3-it.github.io/) and randomly sample one to format the prompt. Only data with image context are involved.
- **Grounding.** For RefCOCO/RefCOCO+/RefCOCOg, we use the data and templates in [Shikra](https://github.com/shikras/shikra) and randomly sample one to format the prompt.
- **GPT-4/GPT-4V generated & mixed datasets.** We keep the prompts unchanged.
|Category|Data|Prompts|
|:-:|:-:|:-:|
|Image captioning|COCO, Flickr30K, TextCaps|Describe the image as simply as possible with a sentence or phrase.<br />Give a brief summary of what you see.<br />Provide a short description of the image.<br />Write a short description for the image.<br />Briefly describe the content of the image.|
|Open-ended VQA|VQAv2, OKVQA, OCRVQA, GQA, TextVQA, VGQA, DocVQA, DVQA|*question* Answer the question directly with a short sentence or phrase.|
|Multiple-choice VQA|A-OKVQA|Choose the correct option for the following question: *question*|
For now, the visual instruction tuning data are formatted in the training format of [LLaVA](https://github.com/haotian-liu/LLaVA) in [data](https://huggingface.co/datasets/BAAI/DataOptim/tree/main/data) folder. The images could be found in [images](https://huggingface.co/datasets/BAAI/DataOptim/tree/main/images) folder or the their official websites. The images should not be used for other purpose and should comply with the original licenses. They may be taken down at any time when requested by the dataset owners. | [
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newsmediabias/media-bias-data | newsmediabias | 2023-12-17T01:37:38Z | 0 | 0 | [
"license:openrail",
"region:us"
] | null | 2023-11-01T23:54:06Z | ---
license: openrail
---
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UMCU/WikiDoc_Dutch_translated_with_MariaNMT | UMCU | 2023-12-14T14:40:39Z | 0 | 0 | [
"license:cc",
"arxiv:1910.13461",
"doi:10.57967/hf/1483",
"region:us"
] | null | 2023-11-03T03:23:39Z | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 5594660
num_examples: 9696
download_size: 3051082
dataset_size: 5594660
license: cc
---
# Dataset Card for "WikiDoc_Dutch_translated_with_MariaNMT"
Translation of the **English** corpus [WikiDoc](https://huggingface.co/datasets/medalpaca/medical_meadow_wikidoc),
to **Dutch** using an [Maria NMT model](https://marian-nmt.github.io/), trained by [Helsinki NLP](https://huggingface.co/Helsinki-NLP/opus-mt-en-nl).
Note, for reference: Maria NMT is based on [BART](https://huggingface.co/docs/transformers/model_doc/bart), described [here](https://arxiv.org/abs/1910.13461).
# Attribution
If you use this dataset please use the following to credit the creators of the OPUS-MT models:
```
@InProceedings{TiedemannThottingal:EAMT2020,
author = {J{\"o}rg Tiedemann and Santhosh Thottingal},
title = {{OPUS-MT} — {B}uilding open translation services for the {W}orld},
booktitle = {Proceedings of the 22nd Annual Conferenec of the European Association for Machine Translation (EAMT)},
year = {2020},
address = {Lisbon, Portugal}
}
```
and
```
@misc {van_es_2023,
author = { {Bram van Es} },
title = { WikiDoc_Dutch_translated_with_MariaNMT (Revision 0db6106) },
year = 2023,
url = { https://huggingface.co/datasets/UMCU/WikiDoc_Dutch_translated_with_MariaNMT },
doi = { 10.57967/hf/1483 },
publisher = { Hugging Face }
}
```
# License
For both the Maria NMT model and the original [Helsinki NLP](https://twitter.com/HelsinkiNLP) [Opus MT model](https://huggingface.co/Helsinki-NLP)
we did **not** find a license, if this was in error please let us know and we will add the appropriate licensing promptly.
We adopt the licensing of the WikiDoc corpus: a [Creative Commons License](http://creativecommons.org/licenses/by-sa/4.0/). | [
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UMCU/PubMedCausal_Dutch_translated_with_MariaNMT | UMCU | 2023-12-14T14:33:44Z | 0 | 0 | [
"task_categories:text-classification",
"task_categories:question-answering",
"task_categories:sentence-similarity",
"size_categories:1K<n<10K",
"language:nl",
"license:afl-3.0",
"healthcare",
"arxiv:1910.13461",
"doi:10.57967/hf/1482",
"region:us"
] | [
"text-classification",
"question-answering",
"sentence-similarity"
] | 2023-11-03T11:33:39Z | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 847311
num_examples: 2368
download_size: 224775
dataset_size: 847311
license: afl-3.0
task_categories:
- text-classification
- question-answering
- sentence-similarity
language:
- nl
tags:
- healthcare
pretty_name: PubMedCausal_Dutch
size_categories:
- 1K<n<10K
---
# Dataset Card for "PubMedCausal_Dutch_translated_with_MariaNMT"
Translation of the **English** version of [PubMedCausal](https://huggingface.co/datasets/medalpaca/medical_meadow_pubmed_causal),
to **Dutch** using an [Maria NMT model](https://marian-nmt.github.io/), trained by [Helsinki NLP](https://huggingface.co/Helsinki-NLP/opus-mt-en-nl).
Note, for reference: Maria NMT is based on [BART](https://huggingface.co/docs/transformers/model_doc/bart), described [here](https://arxiv.org/abs/1910.13461).
# Attribution
If you use this dataset please use the following to credit the creators of the Health Advice corpus:
```citation
@inproceedings{yu-etal-2019-detecting,
title = "Detecting Causal Language Use in Science Findings",
author = "Yu, Bei and
Li, Yingya and
Wang, Jun",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-1473",
doi = "10.18653/v1/D19-1473",
pages = "4664--4674",
}
```
The creators of the OPUS-MT models:
```
@InProceedings{TiedemannThottingal:EAMT2020,
author = {J{\"o}rg Tiedemann and Santhosh Thottingal},
title = {{OPUS-MT} — {B}uilding open translation services for the {W}orld},
booktitle = {Proceedings of the 22nd Annual Conferenec of the European Association for Machine Translation (EAMT)},
year = {2020},
address = {Lisbon, Portugal}
}
```
and
```
@misc{van_es_2023,
author = { {Bram van Es} },
title = { PubMedCausal_Dutch_translated_with_MariaNMT (Revision 14bfca1) },
year = 2023,
url = { https://huggingface.co/datasets/UMCU/PubMedCausal_Dutch_translated_with_MariaNMT },
doi = { 10.57967/hf/1482 },
publisher = { Hugging Face }
}
```
# License
For both the Maria NMT model and the original [Helsinki NLP](https://twitter.com/HelsinkiNLP) [Opus MT model](https://huggingface.co/Helsinki-NLP)
we did **not** find a license. We also did not find a license for the HealthAdvice corpus. For these reasons we use an academic free license v3.
license. If this was in error please let us know and we will add the appropriate licensing promptly. | [
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enzostvs/stable-diffusion-tpu-generations | enzostvs | 2023-12-17T23:22:14Z | 0 | 1 | [
"license:mit",
"region:us"
] | null | 2023-11-03T15:57:18Z | ---
license: mit
configs:
- config_name: default
data_files:
- split: train
path: "images/*.png"
---
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UMCU/HealthAdvice_Dutch_translated_with_MariaNMT | UMCU | 2023-12-14T14:34:47Z | 0 | 0 | [
"task_categories:text-classification",
"task_categories:question-answering",
"task_categories:sentence-similarity",
"size_categories:1K<n<10K",
"language:nl",
"license:afl-3.0",
"healthcare",
"arxiv:1910.13461",
"doi:10.57967/hf/1481",
"region:us"
] | [
"text-classification",
"question-answering",
"sentence-similarity"
] | 2023-11-04T13:59:00Z | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 2282530
num_examples: 8384
download_size: 931750
dataset_size: 2282530
license: afl-3.0
task_categories:
- text-classification
- question-answering
- sentence-similarity
language:
- nl
tags:
- healthcare
pretty_name: HealthAdvice_Dutch
size_categories:
- 1K<n<10K
---
# Dataset Card for "HealthAdvice_Dutch_translated_with_MariaNMT"
Translation of the **English** version of [HealthAdvice](https://huggingface.co/datasets/medalpaca/medical_meadow_health_advice),
to **Dutch** using an [Maria NMT model](https://marian-nmt.github.io/), trained by [Helsinki NLP](https://huggingface.co/Helsinki-NLP/opus-mt-en-nl).
Note, for reference: Maria NMT is based on [BART](https://huggingface.co/docs/transformers/model_doc/bart), described [here](https://arxiv.org/abs/1910.13461).
# Attribution
If you use this dataset please use the following to credit the creators of the Health Advice corpus:
```citation
@inproceedings{yu-etal-2019-detecting,
title = "Detecting Causal Language Use in Science Findings",
author = "Yu, Bei and
Li, Yingya and
Wang, Jun",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-1473",
doi = "10.18653/v1/D19-1473",
pages = "4664--4674",
}
```
The creators of the OPUS-MT models:
```
@InProceedings{TiedemannThottingal:EAMT2020,
author = {J{\"o}rg Tiedemann and Santhosh Thottingal},
title = {{OPUS-MT} — {B}uilding open translation services for the {W}orld},
booktitle = {Proceedings of the 22nd Annual Conferenec of the European Association for Machine Translation (EAMT)},
year = {2020},
address = {Lisbon, Portugal}
}
```
and
```
@misc {van_es_2023,
author = { {Bram van Es} },
title = { HealthAdvice_Dutch_translated_with_MariaNMT (Revision ae2436a) },
year = 2023,
url = { https://huggingface.co/datasets/UMCU/HealthAdvice_Dutch_translated_with_MariaNMT },
doi = { 10.57967/hf/1481 },
publisher = { Hugging Face }
}
```
# License
For both the Maria NMT model and the original [Helsinki NLP](https://twitter.com/HelsinkiNLP) [Opus MT model](https://huggingface.co/Helsinki-NLP)
we did **not** find a license. We also did not find a license for the HealthAdvice corpus. For these reasons we use an academic free license v3.
license. If this was in error please let us know and we will add the appropriate licensing promptly. | [
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mikehemberger/planet-earth | mikehemberger | 2023-12-15T20:14:56Z | 0 | 0 | [
"region:us"
] | null | 2023-11-04T15:23:24Z | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': planet-earth-s01-e01-from-pole-to-pole
'1': planet-earth-s01-e02-mountains
'2': planet-earth-s01-e03-freshwater
'3': planet-earth-s01-e04-caves
'4': planet-earth-s01-e05-deserts
'5': planet-earth-s01-e06-ice-worlds
'6': planet-earth-s01-e07-great-plains
'7': planet-earth-s01-e08-jungles
'8': planet-earth-s01-e09-shallow-seas
'9': planet-earth-s01-e10-seasonal-forests
'10': planet-earth-s01-e11-ocean-deep
- name: file_name
dtype: string
- name: show_name
dtype: string
- name: relative_path
dtype: string
splits:
- name: train
num_bytes: 976527400.0
num_examples: 77296
download_size: 968089912
dataset_size: 976527400.0
---
# Dataset Card for "planet-earth"
TODO: upload blip2 captions
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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openskyml/starchat-dialogues | openskyml | 2023-12-17T21:27:22Z | 0 | 4 | [
"license:mit",
"starchat",
"dialogues_data",
"openskyml",
"region:us"
] | null | 2023-11-05T10:39:03Z | ---
license: mit
pretty_name: starchat-dialogues
tags: [starchat, dialogues_data, openskyml]
---
# StarChat Dialogues-Data
In this dataset you will find conversations between users with an AI assistant who have agreed to share their conversations.
## What can you use the dataset for?
You can use this dataset as you wish without breaking the rules outlined in the last section!
Examples use:
1. Train a neural network using OPEN SOURCE CODE
2. Use for your FREE dataset
3. For studying dialogues independently or in educational institutions
4. To compile statistics
## Rules
If you use this dataset with dialogs, you agree to:
1. Do not use dialogues for evil purposes
2. Do not use the dataset in proprietary products, for example, for training PAID neural networks
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yangjinlong/yyds | yangjinlong | 2023-12-15T14:21:54Z | 0 | 0 | [
"license:mit",
"region:us"
] | null | 2023-11-07T16:37:18Z | ---
license: mit
---
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] |
Timbrt/SciOL-CI | Timbrt | 2023-12-14T14:54:27Z | 0 | 0 | [
"size_categories:10M<n<100M",
"language:en",
"license:cc-by-4.0",
"region:us"
] | null | 2023-11-08T16:02:58Z | ---
license: cc-by-4.0
language:
- en
size_categories:
- 10M<n<100M
pretty_name: Scientific Openly-Licensed Publications - Caption Images
---
# Scientific Openly-Licensed Publications
This repository contains companion material for the following publication:
> Tim Tarsi, Heike Adel, Jan Hendrik Metzen, Dan Zhang, Matteo Finco, Annemarie Friedrich. **SciOL and MuLMS-Img: Introducing A Large-Scale Multimodal Scientific Dataset and Models for Image-Text Tasks in the Scientific Domain.** WACV 2024.
Please cite this paper if using the dataset, and direct any questions regarding the dataset
to [Tim Tarsi](mailto:[email protected])
## Summary
Scientific Openly-Licensed Publications (SciOL) is the largest openly-licensed pre-training corpus for multimodal models in the scientific domain, covering multiple sciences including materials science, physics, and computer science. It consists of over 2.7M scientific scientific publications converted into semi-structured data. SciOL contains over 18 Million figure-caption pairs.
**Note: This repository only contains the figures and captions of SciOL. For the textual data see:** [SciOL-text](https://huggingface.co/datasets/Timbrt/SciOL-text)
## Data Format
We provide the data in the webdataset format, e.g., captions in plain text files and group and compress them together with the images. Each tar file contains 1000 images and captions. Corresponding figures and captions have the same filename (excluding extention). We split the data into a train, test and dev set.
## Citation
If you use our dataset in your work, please cite our paper:
```
TBD, please use informal citation above.
```
## License
The SciOL corpus is released under the [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) license. | [
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pluseen/amore_231101 | pluseen | 2023-12-15T02:41:26Z | 0 | 0 | [
"license:apache-2.0",
"region:us"
] | null | 2023-11-09T03:28:10Z | ---
license: apache-2.0
---
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zeio/branch | zeio | 2023-12-17T10:04:57Z | 0 | 0 | [
"license:apache-2.0",
"region:us"
] | null | 2023-11-12T22:18:53Z | ---
license: apache-2.0
---
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orgcatorg/bulgarianmilitary | orgcatorg | 2023-12-17T04:18:00Z | 0 | 0 | [
"region:us"
] | null | 2023-11-13T12:55:38Z | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: content
dtype: string
- name: title
dtype: string
- name: source_link
dtype: string
- name: description
dtype: string
- name: date
dtype: string
- name: image
dtype: string
splits:
- name: train
num_bytes: 568039
num_examples: 95
download_size: 332880
dataset_size: 568039
---
# Dataset Card for "bulgarianmilitary"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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tollefj/sts-concatenated-NOB | tollefj | 2023-12-11T11:38:19Z | 0 | 0 | [
"task_categories:sentence-similarity",
"task_categories:text-classification",
"language:no",
"language:nb",
"region:us"
] | [
"sentence-similarity",
"text-classification"
] | 2023-11-13T17:41:44Z | ---
task_categories:
- sentence-similarity
- text-classification
language:
- 'no'
- nb
---
# Concatenated STS datasets, translated to Norwegian Bokmål
Machine translated using the *No language left behind* model series, specifically the 1.3B variant: https://huggingface.co/facebook/nllb-200-distilled-1.3B
This dataset contains the following data:
```
'tollefj/biosses-sts-NOB',
'tollefj/sickr-sts-NOB',
'tollefj/sts12-sts-NOB',
'tollefj/sts13-sts-NOB',
'tollefj/sts14-sts-NOB',
'tollefj/sts15-sts-NOB',
'tollefj/sts16-sts-NOB'
``` | [
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hubei-hunan/games | hubei-hunan | 2023-12-18T01:21:49Z | 0 | 0 | [
"license:mit",
"region:us"
] | null | 2023-11-15T10:08:48Z | ---
license: mit
---
Game compositions created by users | [
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TanvirOnHF/g_Dorks | TanvirOnHF | 2023-12-13T08:18:11Z | 0 | 0 | [
"size_categories:10K<n<100K",
"language:en",
"license:cc-by-4.0",
"List",
"Google",
"GHDB",
"Dork",
"NSFW",
"region:us"
] | null | 2023-11-15T12:28:15Z | ---
license: cc-by-4.0
pretty_name: Google Dorks
tags:
- List
- Google
- GHDB
- Dork
- NSFW
language:
- en
size_categories:
- 10K<n<100K
---
# g_Dorks [TXT dataset]
A dataset comprising a collection of popular Google dorks acquired from various and numerous origins.
## Data Source
***Secret!***
## Disclaimer
Please note that while I strive to maintain data quality, I cannot guarantee the accuracy or quality of all entries in this dataset. Use it responsibly and exercise caution when relying on the data for any critical applications. Your feedback and contributions are greatly appreciated for improving the dataset's overall quality.
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liaad/math_dataset_portuguese | liaad | 2023-12-15T19:12:32Z | 0 | 0 | [
"license:mit",
"region:us"
] | null | 2023-11-15T15:28:26Z | ---
license: mit
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---
To run generation code within 'mathematics_dataset\mathematics_dataset\':
- Activate python venv ``` .\.venv\Scripts\activate ```
- Requirements defined in requires.txt
- Run ```python generate_to_file.py --output_dir ds``` to generate dataset to directory \ds
Had to change enconding when opening files to utf-8 so that some characters are allowed (ã õ é)
To obtain dataset with the correct amount of rows:
- python ```generate_to_file.py --output_dir ds --per_train_module 1999998 --per_test_module 10000```
This dataset creates train set (train-easy,train-medium,train-hard) and the creates extrapolation ("measure generalization along various axes of difficulty to beyond that seen during training") and interpolation ("test questions are distinct
from the train questions") tests.
On HugginFace only interpolate tests are used as test set.
Some tests will not work, since they rely on english terms. | [
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chentong00/factoid-wiki-sentence | chentong00 | 2023-12-11T23:07:20Z | 0 | 0 | [
"license:apache-2.0",
"region:us"
] | null | 2023-11-15T21:55:30Z | ---
license: apache-2.0
---
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chentong00/factoid-wiki-passage | chentong00 | 2023-12-11T23:05:35Z | 0 | 0 | [
"license:apache-2.0",
"region:us"
] | null | 2023-11-15T21:55:49Z | ---
license: apache-2.0
---
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shrek101202303/ai | shrek101202303 | 2023-12-13T05:15:54Z | 0 | 0 | [
"license:apache-2.0",
"region:us"
] | null | 2023-11-17T12:45:11Z | ---
license: apache-2.0
---
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gradio/custom-component-gallery-backups | gradio | 2023-12-18T01:22:18Z | 0 | 2 | [
"license:mit",
"region:us"
] | null | 2023-11-17T16:39:54Z | ---
license: mit
---
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MrPrometheus/books_full_mini | MrPrometheus | 2023-12-17T07:01:47Z | 0 | 0 | [
"license:mit",
"region:us"
] | null | 2023-11-19T14:18:56Z | ---
license: mit
---
1tb(960gb) 19743 h. 3518 sp. | [
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hyperinfer/old_cases | hyperinfer | 2023-12-13T15:34:30Z | 0 | 0 | [
"license:mit",
"region:us"
] | null | 2023-11-20T14:58:10Z | ---
license: mit
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 4979703474.0
num_examples: 277407
- name: test
num_bytes: 553300386.0
num_examples: 30823
download_size: 2746549252
dataset_size: 5533003860.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
This dataset is generated from the latest datadump from the Open Legal Data datadump, dated at 19-Oct-2022 12:11.
See source: https://static.openlegaldata.io/dumps/de/2022-10-18/
The dataset was encoded in UTF-8 and cleaned for HTML-Tags and Escape Sequences.
The Train / Test Split is 80/20. | [
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] |
mesolitica/malaysian-ultrachat | mesolitica | 2023-12-13T06:49:39Z | 0 | 0 | [
"task_categories:conversational",
"language:ms",
"region:us"
] | [
"conversational"
] | 2023-11-21T01:42:53Z | ---
task_categories:
- conversational
language:
- ms
pretty_name: malaysian-ultrachat
---
# Ultrachat like using Malaysian context
Prepare multiturn dialogue between user and assistant for malaysian context,
1. Astroawani, https://huggingface.co/datasets/malaysia-ai/crawl-astroawani
2. Crossref `melayu` papers, https://huggingface.co/datasets/mesolitica/crawl-my-website/resolve/main/melayu-pdf.jsonl
3. Epenerbitan, https://huggingface.co/datasets/malaysia-ai/dedup-text-dataset/resolve/main/e-penerbitan.jsonl
4. gov.my pdf, https://huggingface.co/datasets/malaysia-ai/dedup-text-dataset/resolve/main/gov.my.jsonl
5. JurnalDBP, https://github.com/mesolitica/malaysian-dataset/tree/master/crawl/jurnaldbp
6. lom.agc.gov.my.jsonl, https://huggingface.co/datasets/malaysia-ai/dedup-text-dataset/resolve/main/lom.agc.gov.my.jsonl
7. MS Wikipedia, https://huggingface.co/datasets/malaysia-ai/dedup-text-dataset/resolve/main/wikipedia-2023-10-01.jsonl
8. Hansard, https://huggingface.co/datasets/malaysia-ai/dedup-text-dataset/resolve/main/hansard.jsonl
9. Textbooks, https://huggingface.co/datasets/open-phi/textbooks
10. https://maktabahalbakri.com/
11. https://muftiwp.gov.my/ms/
Notebooks at https://github.com/mesolitica/malaysian-dataset/tree/master/chatbot/malaysian-ultrachat
There are bad pairs you need to reject, follow https://github.com/mesolitica/malaysian-dataset/blob/master/llm-instruction/prepare-malaysian-ultrachat.ipynb
Example of data,
```json
[{'role': 'context',
'content': 'LIGA\xa0Malaysia (Liga-M) terus menerima tamparan hebat apabila UiTM FC sah menarik diri daripada saingan Liga Super musim ini bagi menyusuli jejak Petaling Jaya City.\n\n\n\r\nPerkara itu disahkan sendiri oleh Presiden The Lion Troops, Profesor Dr. Sazili Shahibi, selepas dua musim beraksi di pentas Liga Super.\n\n\n\r\nSelain masalah kewangan, Dr Sazili menjelaskan ada punca lain mengapa pihak pengurusan mengibarkan bendera putih.\n\n\n \n \n\r\n"Ahli Lembaga Pengarah UiTM telah bermesyuarat dan (kami mengambil) keputusan untuk menarik diri daripada liga pada musim depan.\n\n\n\r\n"Kewangan adalah salah faktornya, tapi kami rasa daripada segi kemampuan, ia tidak sesuai buat pemain-pemain kami," katanya ketika dihubungi \nAstro Arena\n\xa0pada Rabu.\n\n\n\r\nSazili juga berhasrat memaklumkan UiTM bakal beralih ke saingan Liga M3 musim ini.\n\n\n\r\nUntuk rekod, UiTM muncul pasukan berstatus universiti pertama yang layak beraksi dalam Liga Super.\n\n\n\r\nDalam pada itu, Liga Bolasepak Malaysia (MFLhanya akan menjelaskan situasi berkenaan menerusi kenyataan rasmi bakal dalam masa terdekat selepas hanya 14 pasukan yang akan bertanding di pentas Liga Super musim ini. -\nStadium Astro',
'content_ms': None,
'indon': False},
{'role': 'user',
'content': 'What are the reasons given by UiTM FC for withdrawing from Liga Malaysia?',
'content_ms': 'Apakah alasan yang diberikan oleh UiTM FC kerana menarik diri daripada Liga Malaysia?',
'indon': False},
{'role': 'assistant',
'content': 'Menurut Presiden UiTM FC, Profesor Dr. Sazili Shahibi, mereka mengambil keputusan untuk menarik diri daripada Liga Malaysia musim ini atas beberapa sebab. Salah satu sebab utamanya adalah masalah kewangan yang dihadapi oleh pasukan. Selain itu, beliau juga menyebut bahawa kemampuan pasukan UiTM tidak sesuai untuk bersaing dalam Liga Super. Selain masalah ini, tidak ada sebab lain yang diberikan dalam laporan tersebut.',
'content_ms': 'Menurut Presiden UiTM FC, Profesor Dr. Sazili Shahibi, mereka membuat keputusan untuk menarik diri daripada Liga Malaysia musim ini atas beberapa sebab. Salah satu sebab utama ialah masalah kewangan yang dihadapi oleh pasukan. Selain itu, beliau juga menyatakan bahawa keupayaan pasukan UiTM tidak sesuai untuk bersaing dalam Liga Super. Selain masalah ini, tiada sebab lain diberikan dalam laporan tersebut.',
'indon': False},
{'role': 'user',
'content': 'Alamak, memang sedihlah tengok UiTM FC tarik diri dari Liga Malaysia. Macam mana pula dengan penonton setia yang selalu sokong pasukan tu? Harap-harap ada jalan penyelesaian untuk masalah kewangan mereka.',
'content_ms': 'Alamak, memang sedih tengok UiTM FC menarik diri dari Liga Malaysia. Bagaimana pula dengan penonton setia yang selalu menyokong pasukan itu? Harap-harap ada jalan penyelesaian untuk masalah kewangan mereka.',
'indon': False},
{'role': 'assistant',
'content': 'Ya, memang sedih melihat UiTM FC menarik diri dari Liga Malaysia. Bagi penonton setia yang sentiasa menyokong pasukan tersebut, tentu mereka juga berasa kecewa. Kami juga berharap ada jalan penyelesaian untuk masalah kewangan yang dihadapi oleh UiTM FC. Mungkin pihak pengurusan boleh mencari sponsor atau menyusun semula strategi kewangan untuk membolehkan pasukan tersebut terus beraksi dalam liga pada masa hadapan. Semoga mereka dapat menemui penyelesaian yang baik dan kembali bersaing di pentas bola sepak Malaysia.',
'content_ms': 'Ya, memang sedih melihat UiTM FC menarik diri dari Liga Malaysia. Bagi penonton setia yang sentiasa menyokong pasukan tersebut, tentu mereka juga berasa kecewa. Kami juga berharap ada jalan penyelesaian untuk masalah kewangan yang dihadapi oleh UiTM FC. Mungkin pihak pengurusan boleh mencari penaja atau menyusun semula strategi kewangan untuk membolehkan pasukan tersebut terus beraksi dalam liga pada masa hadapan. Semoga mereka dapat menemui penyelesaian yang baik dan kembali bersaing di pentas bola sepak Malaysia.',
'indon': False},
{'role': 'user',
'content': 'Ya betul tu, banyak peminat setia UiTM FC yang akan kecewa dengan keputusan mereka tarik diri dari Liga Malaysia. Harapnya pihak pengurusan dapat cari jalan penyelesaian secepat mungkin supaya pasukan tu masih dapat beraksi dalam liga. Saya pun tak sabar nak tengok prestasi UiTM FC lagi musim hadapan. Semoga ada berita baik dalam masa terdekat.',
'content_ms': 'Ya, betul tu. Banyak peminat setia UiTM FC yang akan kecewa dengan keputusan mereka menarik diri dari Liga Malaysia. Harapnya pihak pengurusan dapat mencari jalan penyelesaian secepat mungkin supaya pasukan itu masih dapat beraksi dalam liga. Saya pun tak sabar nak tengok prestasi UiTM FC lagi musim depan. Semoga ada berita baik dalam masa terdekat.',
'indon': False},
{'role': 'assistant',
'content': 'Saya juga setuju dengan anda. Semoga pihak pengurusan UiTM FC dapat mencari jalan penyelesaian secepat mungkin agar pasukan tersebut masih dapat berlaga dalam Liga Malaysia. Benar, banyak peminat yang ingin melihat prestasi UiTM FC musim hadapan. Mari kita nantikan berita baik daripada mereka dalam masa terdekat. Harapnya mereka dapat mengatasi masalah kewangan dan kembali membanggakan para penyokong. Kita terus sokong UiTM FC!',
'content_ms': 'Saya juga bersetuju dengan anda. Semoga pihak pengurusan UiTM FC dapat mencari jalan penyelesaian secepat mungkin supaya pasukan tersebut masih dapat bersaing dalam Liga Malaysia. Benar, ramai peminat yang ingin melihat prestasi UiTM FC musim depan. Mari kita nantikan berita baik daripada mereka dalam masa terdekat. Harapnya mereka dapat mengatasi masalah kewangan dan kembali membanggakan para penyokong. Kita terus sokong UiTM FC!',
'indon': False},
{'role': 'user',
'content': 'Ya lah, harapnya UiTM FC dapat dapat mencari penyelesaian yang cepat. Penyokong setia UiTM FC mesti excited nak tengok pasukan beraksi lagi musim hadapan. Semoga ada berita gembira dalam masa terdekat!',
'content_ms': 'Ya lah, harapnya UiTM FC dapat mencari penyelesaian yang cepat. Penyokong setia UiTM FC mesti teruja untuk melihat pasukan beraksi lagi musim depan. Semoga ada berita gembira dalam masa terdekat!',
'indon': False},
{'role': 'assistant',
'content': 'Betul tu, penyokong setia UiTM FC pasti sangat teruja melihat pasukan beraksi di musim hadapan. Kita harap pihak pengurusan dapat mencari penyelesaian dengan pantas supaya pasukan dapat kembali ke gelanggang. Kita tunggu berita gembira yang akan datang dan terus menyokong UiTM FC dengan penuh semangat. Biar bola sepak Malaysia sentiasa berwarna dengan kehadiran pasukan hebat seperti UiTM FC!',
'content_ms': 'Betul tu, penyokong setia UiTM FC pasti sangat teruja melihat pasukan beraksi pada musim depan. Kita harap pihak pengurusan dapat mencari penyelesaian dengan pantas supaya pasukan dapat kembali ke gelanggang. Kita tunggu berita gembira yang akan datang dan terus menyokong UiTM FC dengan penuh semangat. Biar bola sepak Malaysia sentiasa berwarna dengan kehadiran pasukan hebat seperti UiTM FC!',
'indon': False},
{'role': 'user',
'content': 'Ya betul tu, memang berharap pasukan UiTM FC dapat segera mencari jalan penyelesaian. Setiap pasukan memang perlu sokongan dan kami sebagai penyokong setia akan terus memberikan sokongan kepada pasukan kesayangan kami. Semoga ada berita gembira yang akan datang!',
'content_ms': 'Ya, betul tu. Memang berharap pasukan UiTM FC dapat segera mencari jalan penyelesaian. Setiap pasukan memang perlukan sokongan dan kami sebagai penyokong setia akan terus memberikan sokongan kepada pasukan kesayangan kami. Semoga ada berita gembira yang akan datang!',
'indon': False},
{'role': 'assistant',
'content': 'Setiap pasukan memang memerlukan sokongan daripada penyokong setia seperti anda. Anda adalah aset berharga bagi pasukan UiTM FC. Teruskan menyokong dengan penuh semangat dan harapan. Kita berdoa agar pihak pengurusan dapat mencari jalan penyelesaian dengan segera. Bersama-sama, mari kita nantikan berita gembira yang akan datang dan terus memberikan sokongan kepada pasukan kesayangan kita. UiTM FC pasti akan bangkit dan menyampaikan prestasi yang cemerlang musim hadapan!',
'content_ms': 'Setiap pasukan memang memerlukan sokongan daripada penyokong setia seperti anda. Anda adalah aset berharga bagi pasukan UiTM FC. Teruskan menyokong dengan penuh semangat dan harapan. Kita berdoa agar pihak pengurusan dapat mencari jalan penyelesaian dengan segera. Bersama-sama, mari kita nantikan berita gembira yang akan datang dan terus memberikan sokongan kepada pasukan kesayangan kita. UiTM FC pasti akan bangkit dan menyampaikan prestasi yang cemerlang musim depan!',
'indon': False}]
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hallucinations-leaderboard/results | hallucinations-leaderboard | 2023-12-17T10:01:55Z | 0 | 0 | [
"license:apache-2.0",
"region:us"
] | null | 2023-11-21T11:44:46Z | ---
license: apache-2.0
---
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hallucinations-leaderboard/requests | hallucinations-leaderboard | 2023-12-17T16:55:54Z | 0 | 0 | [
"license:apache-2.0",
"region:us"
] | null | 2023-11-21T11:56:02Z | ---
license: apache-2.0
---
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newsmediabias/fake_news_elections_labelled_data | newsmediabias | 2023-12-13T12:38:00Z | 0 | 0 | [
"task_categories:text-classification",
"language:en",
"license:mit",
"arxiv:2312.03750",
"region:us"
] | [
"text-classification"
] | 2023-11-22T11:54:52Z | ---
license: mit
task_categories:
- text-classification
language:
- en
pretty_name: fake-news-elections
---
# Dataset Card for Election-Related Fake News Classification
## Dataset Summary
This dataset is designed for the task of fake news classification in the context of elections. It consists of news articles, social media posts, and other text sources related to various elections worldwide. Each entry in the dataset is labeled as 'fake' or 'real' based on its content and the veracity of the information presented.
https://arxiv.org/abs/2312.03750
### Languages
English
### Data Instances
A typical data instance comprises:
- **Text:** The content of the news article or post.
- **Label:** A binary label, where '0' indicates 'real' news and '1' indicates 'fake' news.
Example:
```json
{
"text": "The president announced a new policy today...",
"label": REAL
}
```
#### Annotation process
Annotations were performed by using LLMs and which is verified by subject matter experts who checked each text as 'real' or 'fake' based on factual accuracy and context.
### Social Impact of Dataset
This dataset plays a crucial role in combating the spread of misinformation during elections, which is vital for maintaining the integrity of democratic processes.
### Discussion of Biases
There may be biases in the dataset due to the predominance of certain sources or the subjective nature of some news categorizations.
## Citation
If you use this dataset in your research, please cite it as follows:
```bibtex
@article{rahman2023analyzing,
title={Analyzing the Influence of Fake News in the 2024 Elections: A Comprehensive Dataset},
author={Rahman, Mizanur and Raza, Shaina},
journal={arXiv preprint arXiv:2312.03750},
year={2023}
}
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cyanelis/4349 | cyanelis | 2023-12-17T23:06:17Z | 0 | 0 | [
"license:cc-by-nc-4.0",
"region:us"
] | null | 2023-11-24T15:40:35Z | ---
license: cc-by-nc-4.0
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projecte-aina/CA-DE_Parallel_Corpus | projecte-aina | 2023-12-13T10:14:28Z | 0 | 0 | [
"task_categories:translation",
"multilinguality:translation",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:ca",
"language:de",
"language:multilingual",
"region:us"
] | [
"translation"
] | 2023-11-27T10:09:33Z | ---
language:
- ca
- de
- multilingual
multilinguality:
- translation
pretty_name: CA-DE Parallel Corpus
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- translation
task_ids: []
---
# Dataset Card for CA-DE Parallel Corpus
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Source Data](#source-data)
- [Data preparation](#data-preparation)
- [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)
- [Author](#author)
- [Contact Information](#contact-information)
- [Copyright](#copyright)
- [Licensing information](#licenciung-informatrion)
- [Funding](#funding)
## Dataset Description
### Dataset Summary
The CA-DE Parallel Corpus is a Catalan-German dataset of **9.530.709** parallel sentences. The dataset was created to support Catalan NLP tasks, e.g.,
Machine Translation.
### Supported Tasks and Leaderboards
The dataset can be used to train a model for Multilingual Machine Translation. Success on this task is typically measured by achieving a high BLEU score.
### Languages
The texts in the dataset are in Catalan and German.
## Dataset Structure
Two separated txt files are provided with the sentences sorted in the same order:
- ca-de_all_2023_09_11.ca: contains 9.530.709 Catalan sentences.
- ca-de_all_2023_09_11.de: contains 9.530.709 German sentences.
### Data Splits
The dataset contains a single split: `train`.
## Dataset Creation
### Source Data
The dataset is a combination of the following authentic datasets:
| Dataset | Sentences |
|:--------------|----------:|
| Multi CCAligned | 1.027.481 |
| WikiMatrix | 125.811 |
| GNOME | 1.241|
| KDE4 | 105.098 |
| QED | 49.181 |
| TED2020 v1 | 38.428 |
| OpenSubtitles | 171.376 |
| GlobalVoices| 3.578|
| Tatoeba | 655 |
| Books | 2049 |
| Europarl | 1.734.643 |
| Tilde | 3.434.091 |
| **Total** | **6.258.272** |
All corpora except Europarl and Tilde were collected from [Opus](https://opus.nlpl.eu/).
The Europarl and Tilde corpora are synthetic parallel corpora created from the original Spanish-Catalan corpora by [SoftCatalà](https://github.com/Softcatala/Europarl-catalan).
The remaining **3.272.437** sentences are synthetic parallel data created from a random sampling of the Spanish-German corpora available on [Opus](https://opus.nlpl.eu/) and translated into Catalan using the [PlanTL es-ca](https://huggingface.co/PlanTL-GOB-ES/mt-plantl-es-ca) model.
### Data preparation
All datasets are deduplicated and filtered to remove any sentence pairs with a cosine similarity of less than 0.75.
This is done using sentence embeddings calculated using [LaBSE](https://huggingface.co/sentence-transformers/LaBSE).
The filtered datasets are then concatenated to form a final corpus of **9.530.709** parallel sentences.
### Personal and Sensitive Information
No anonymisation process was performed.
## Considerations for Using the Data
### Social Impact of Dataset
The purpose of this dataset is to help develop Machine Translation tasks for mid-resource languages such as Catalan.
### Discussion of Biases
We are aware that since part of the data comes from unreliable web pages and non-curated texts, some biases may be present in the dataset.
Nonetheless, we have not applied any steps to reduce their impact.
### Other Known Limitations
The dataset contains data of a general domain. Application of this dataset in more specific domains such as biomedical, legal etc. would be of limited use.
## Additional Information
### Author
Language Technologies Unit (LangTech) at the Barcelona Supercomputing Center.
### Contact information
For further information, please send an email to [email protected].
### Copyright
Copyright Language Technologies Unit at Barcelona Supercomputing Center (2023).
### Licensing information
This work is licensed under a [Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/).
### Funding
This work was funded by the [Departament de la Vicepresidència i de Polítiques Digitals i Territori de la Generalitat de Catalunya](https://politiquesdigitals.gencat.cat/ca/inici/index.html#googtrans(ca|en) within the framework of [Projecte AINA](https://politiquesdigitals.gencat.cat/ca/economia/catalonia-ai/aina).
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mesolitica/chatgpt4-malaysian-general-qa | mesolitica | 2023-12-17T08:43:23Z | 0 | 0 | [
"task_categories:question-answering",
"language:ms",
"region:us"
] | [
"question-answering"
] | 2023-11-27T15:28:06Z | ---
task_categories:
- question-answering
language:
- ms
pretty_name: malaysian-general-qa
---
# Synthetic Malaysian QA
Generated common QA using ChatGPT4 for,
1. politics
2. socioeconomy
3. culture
4. gender
5. religion
6. sociology
7. social class
8. technology
9. ethnicity
10. infrastructure
11. health
12. education
13. ecology
14. party politics
15. diplomacy
16. history
17. cuisine
18. microeconomics
19. business
20. artificial intelligence
21. law
22. negeri johor
23. negeri kedah
24. negeri kelantan
25. negeri melaka
26. negeri negeri sembilan
27. negeri pahang
28. negeri perak
29. negeri perlis
30. negeri pulau pinang
31. negeri selangor
32. negeri terengganu
33. negeri sabah
34. negeri sarawak
35. kuala lumpur
36. negeri labuan
37. putrajaya
38. najib razak
39. anwar ibrahim
40. parti keadilan rakyat
41. parti islam semalaysia
42. dr mahathir mohamad
43. barisan nasional
44. constitutional monarchy
45. parliamentary democracy
46. political economy
47. political dynamic
48. empowerment of youths
49. kebebasan bersuara
50. sastera
51. tatabahasa
52. kesusasteraan melayu
53. pantun
54. sajak
55. syair
56. hadis
57. hukum aqidah islam
58. hukum fiqah islam
Notebooks at https://github.com/mesolitica/malaysian-dataset/tree/master/question-answer/chatgpt4-synthetic-malaysian-qa | [
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projecte-aina/CA-GL_Parallel_Corpus | projecte-aina | 2023-12-13T10:29:51Z | 0 | 0 | [
"task_categories:translation",
"multilinguality:translation",
"size_categories:10M<n<100M",
"source_datasets:original",
"language:ca",
"language:gl",
"language:multilingual",
"region:us"
] | [
"translation"
] | 2023-11-27T16:46:09Z | ---
language:
- ca
- gl
- multilingual
multilinguality:
- translation
pretty_name: CA-GL Parallel Corpus
size_categories:
- 10M<n<100M
source_datasets:
- original
task_categories:
- translation
task_ids: []
---
# Dataset Card for CA-GL Parallel Corpus
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Source Data](#source-data)
- [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)
- [Author](#author)
- [Contact Information](#contact-information)
- [Copyright](#copyright)
- [Licensing information](#licenciung-informatrion)
- [Funding](#funding)
## Dataset Description
### Dataset Summary
The CA-GL Parallel Corpus is a Catalan-Galician synthetic dataset of **33.668.599** parallel sentences. The dataset was created to support Catalan NLP tasks, e.g.,
Machine Translation.
### Supported Tasks and Leaderboards
The dataset can be used to train a model for Multilingual Machine Translation. Success on this task is typically measured by achieving a high BLEU score.
### Languages
The texts in the dataset are in Catalan and Galician.
## Dataset Structure
Two separated txt files are provided with the sentences sorted in the same order:
- nos_all.ca: contains 33.668.599 Catalan sentences (synthetic).
- nos_all.gl: contains 33.668.599 Galician sentences (authentic).
### Data Splits
The dataset contains a single split: `train`.
## Dataset Creation
### Source Data
This synthetic dataset was created in the frame of Project Ilenia. An authentic parallel corpus ES-GL was delivered by Proxecto Nós
and the Spanish was translated to Catalan using the machine translation model [PlanTL-GOB-ES](https://huggingface.co/PlanTL-GOB-ES/mt-plantl-es-ca).
**Total: 33.668.599 parallel sentences** .
### Personal and Sensitive Information
No anonymisation process was performed.
## Considerations for Using the Data
### Social Impact of Dataset
The purpose of this dataset is to help develop Machine Translation tasks between Catalan and Galician, two of the co-official languages of Spain.
### Discussion of Biases
We are aware that since part of the data comes from unreliable web pages and non-curated texts, some biases may be present in the dataset.
Nonetheless, we have not applied any steps to reduce their impact.
### Other Known Limitations
The dataset contains data of a general domain. Application of this dataset in more specific domains such as biomedical, legal etc. would be of limited use.
## Additional Information
### Author
Language Technologies Unit (LangTech) at the Barcelona Supercomputing Center.
### Contact information
For further information, please send an email to [email protected].
### Copyright
Copyright Language Technologies Unit at Barcelona Supercomputing Center (2023).
### Licensing information
This work is licensed under a [Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/).
### Funding
This work was funded by the SEDIA within the framework of ILENIA. | [
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desarrolloasesoreslocales/MistralAI | desarrolloasesoreslocales | 2023-12-13T12:00:24Z | 0 | 0 | [
"region:us"
] | null | 2023-11-28T07:43:14Z | ---
configs:
- config_name: default
data_files:
- split: train
path: data.csv
---
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projecte-aina/CA-ZH_Parallel_Corpus | projecte-aina | 2023-12-13T10:16:25Z | 0 | 0 | [
"task_categories:translation",
"multilinguality:translation",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:ca",
"language:zh",
"language:multilingual",
"region:us"
] | [
"translation"
] | 2023-11-29T15:14:32Z | ---
language:
- ca
- zh
- multilingual
multilinguality:
- translation
pretty_name: CA-ZH Parallel Corpus
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- translation
task_ids: []
---
# Dataset Card for CA-ZH Parallel Corpus
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Source Data](#source-data)
- [Data preparation](#data-preparation)
- [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)
- [Author](#author)
- [Contact Information](#contact-information)
- [Copyright](#copyright)
- [Licensing information](#licenciung-informatrion)
- [Funding](#funding)
## Dataset Description
### Dataset Summary
The CA-ZH Parallel Corpus is a Catalan-Chinese dataset of **6.833.114** parallel sentences. The dataset was created to support Catalan NLP tasks, e.g.,
Machine Translation.
### Supported Tasks and Leaderboards
The dataset can be used to train a model for Multilingual Machine Translation. Success on this task is typically measured by achieving a high BLEU score.
### Languages
The texts in the dataset are in Catalan and Chinese.
## Dataset Structure
Two separated txt files are provided with the sentences sorted in the same order:
- ca-zh_all_2023_10_26.ca: contains 6.833.114 Catalan sentences.
- ca-zh_all_2023_10_26.zh: contains 6.833.114 Chinese sentences.
### Data Splits
The dataset contains a single split: `train`.
## Dataset Creation
### Source Data
The Catalan-Chinese data collected from the web was a combination of the following datasets:
| Dataset | Sentences before cleaning |
|:------------------|---------------:|
| WikiMatrix | 90.643 |
| XLENT | 535.803 |
| GNOME | 78|
| QED | 3.677 |
| TED2020 v1 | 56.269 |
| OpenSubtitles | 139.300 |
| **Total** | **882.039** |
The 6.658.607 sentence pairs of synthetic parallel data were created from the following Spanish-Chinese datasets:
| Dataset | Sentences before cleaning |
|:------------------|---------------:|
| UNPC |17.599.223|
| CCMatrix | 24.051.233 |
| MultiParacrawl| 3.410.087|
| **Total** | **45.060.543** |
### Data preparation
The Chinese side of all datasets are passed through the [fastlangid](https://github.com/currentslab/fastlangid) language detector and any sentences which are not
identified as simplified Chinese are discarded.
The datasets are then also deduplicated and filtered to remove any sentence pairs with a cosine similarity of less than 0.75.
This is done using sentence embeddings calculated using [LaBSE](https://huggingface.co/sentence-transformers/LaBSE).
The filtered datasets are then concatenated to form a final corpus of **6.833.114** parallel sentences.
### Personal and Sensitive Information
No anonymisation process was performed.
## Considerations for Using the Data
### Social Impact of Dataset
The purpose of this dataset is to help develop Machine Translation tasks for mid-resource languages such as Catalan.
### Discussion of Biases
We are aware that since part of the data comes from unreliable web pages and non-curated texts, some biases may be present in the dataset.
Nonetheless, we have not applied any steps to reduce their impact.
### Other Known Limitations
The dataset contains data of a general domain. Application of this dataset in more specific domains such as biomedical, legal etc. would be of limited use.
## Additional Information
### Author
Language Technologies Unit (LangTech) at the Barcelona Supercomputing Center.
### Contact information
For further information, please send an email to [email protected].
### Copyright
Copyright Language Technologies Unit at Barcelona Supercomputing Center (2023).
### Licensing information
This work is licensed under a [Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/).
### Funding
This work was funded by the [Departament de la Vicepresidència i de Polítiques Digitals i Territori de la Generalitat de Catalunya](https://politiquesdigitals.gencat.cat/ca/inici/index.html#googtrans(ca|en) within the framework of [Projecte AINA](https://politiquesdigitals.gencat.cat/ca/economia/catalonia-ai/aina).
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] |
TeamA/dataset | TeamA | 2023-12-11T13:46:34Z | 0 | 0 | [
"region:us"
] | null | 2023-11-29T16:25:48Z | ---
configs:
- config_name: default
data_files:
- split: Museums
path: "Museums.csv"
- split: Medieval
path: "Medieval.csv"
- split: Rivers
path: "Rivers.csv"
- split: Public
path: "Public.csv"
- split: Square
path: "Square.csv"
- split: Bridges
path: "Bridges.csv"
- split: Murials
path: "Murials.csv"
- split: Parks
path: "Parks.csv"
- split: Beaches
path: "Beaches.csv"
---
Test | [
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mesolitica/chatgpt-malay-instructions | mesolitica | 2023-12-11T15:44:53Z | 0 | 0 | [
"language:ms",
"region:us"
] | null | 2023-11-30T03:45:48Z | ---
language:
- ms
pretty_name: malay-instru
---
# Evolution instructions
Originally from https://github.com/nlpxucan/WizardLM/tree/main/Evol_Instruct, added some prompts to become malaysian context.
Generated using ChatGPT3.5.
We use base prompts from,
1. https://github.com/gururise/AlpacaDataCleaned/blob/main/alpaca_data_cleaned.json
2. https://huggingface.co/datasets/WizardLM/WizardLM_evol_instruct_V2_196k/resolve/main/WizardLM_evol_instruct_V2_143k.json
3. https://huggingface.co/datasets/TokenBender/unnatural_code_instructions_20M/resolve/main/unnatural_training_data_unique.jsonl
4. https://huggingface.co/datasets/mesolitica/chatgpt4-malaysian-general-qa
5. https://huggingface.co/datasets/ise-uiuc/Magicoder-OSS-Instruct-75K
Notebooks at https://github.com/mesolitica/malaysian-dataset/tree/master/chatbot/evol-instruct | [
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] |
deepcs233/LMDrive | deepcs233 | 2023-12-13T12:46:00Z | 0 | 2 | [
"size_categories:n>1T",
"language:en",
"license:apache-2.0",
"arxiv:2312.07488",
"region:us"
] | null | 2023-11-30T08:56:30Z | ---
configs:
- config_name: default
data_files:
- split: train
path: navigation_instruction_list.txt
sep: " "
default: true
license: apache-2.0
language:
- en
size_categories:
- n>1T
---
# LMDrive 64K Dataset Card
LMDrive Dataset consists of 64K instruction-sensor-control data clips collected in the CARLA simulator， where each clip includes one navigation instruction, several notice instructions, a sequence of multi-modal multi-view sensor data, and control signals. The duration of the clip spans from 2 to 20 seconds.
## Dataset details
- `data/`: dataset folder, the entire dataset contains about 2T of data.
- `data/Town01`: sub dataset folder, which only consists of the data folder for the Town01
- `data/Town02`: sub dataset folder, which only consists of the data folder for the Town02
- ...
- `dataset_index.txt`: the data list for pretraining the vision encoder
- `navigation_instruction_list.txt`: the data list for instruction finetuning
- `notice_instruction_list.json`: the data list for instruction finetuning (optional if the notice instruction data is not engaged in the training)
**Dataset date:**
LMDrive-1.0 Dataset was collected in September 2023.
**Paper or resources for more information:**
Github: https://github.com/opendilab/LMDrive/README.md
Paper: https://arxiv.org/abs/2312.07488
**License:**
Attribution-NonCommercial 4.0 International
**Where to send questions or comments about the model:**
https://github.com/opendilab/LMDrive/issues
## Intended use
**Primary intended uses:**
The primary use of LMDrive is research on large multimodal models for autonomous driving.
**Primary intended users:**
The primary intended users of the model are researchers and hobbyists in computer vision, large multimodal model, autonomous driving, and artificial intelligence. | [
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cyanelis/47216881T | cyanelis | 2023-12-17T23:08:13Z | 0 | 0 | [
"license:cc-by-nc-4.0",
"region:us"
] | null | 2023-11-30T12:07:55Z | ---
license: cc-by-nc-4.0
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cyanelis/25683858T | cyanelis | 2023-12-17T23:07:36Z | 0 | 0 | [
"license:cc-by-nc-4.0",
"region:us"
] | null | 2023-11-30T12:35:28Z | ---
license: cc-by-nc-4.0
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projecte-aina/CA-EN_Parallel_Corpus | projecte-aina | 2023-12-13T10:16:49Z | 0 | 0 | [
"task_categories:translation",
"multilinguality:translation",
"size_categories:10M<n<100M",
"source_datasets:original",
"language:ca",
"language:en",
"language:multilingual",
"license:cc-by-4.0",
"region:us"
] | [
"translation"
] | 2023-11-30T14:52:47Z | ---
language:
- ca
- en
- multilingual
multilinguality:
- translation
pretty_name: CA-EN Parallel Corpus
size_categories:
- 10M<n<100M
source_datasets:
- original
task_categories:
- translation
task_ids: []
license: cc-by-4.0
---
# Dataset Card for CA-EN Parallel Corpus
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Source Data](#source-data)
- [Data preparation](#data-preparation)
- [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)
- [Author](#author)
- [Contact Information](#contact-information)
- [Copyright](#copyright)
- [Licensing information](#licenciung-informatrion)
- [Funding](#funding)
## Dataset Description
### Dataset Summary
The CA-EN Parallel Corpus is a Catalan-English dataset of **14.385.296** parallel sentences. The dataset was created to support Catalan NLP tasks, e.g.,
Machine Translation.
### Supported Tasks and Leaderboards
The dataset can be used to train a model for Multilingual Machine Translation. Success on this task is typically measured by achieving a high BLEU score.
### Languages
The texts in the dataset are in Catalan and English.
## Dataset Structure
The dataset is a single tsv file where each row contains a paralle sentence pair.
### Data Splits
The dataset contains a single split: `train`.
## Dataset Creation
### Source Data
The The data is a brand new collection of parallel sentences in Catalan and English partially derived from web crawlings and belonging to a mix of different domains and styles.
The source data is Catalan authentic text translated to English or authentic English text translated to Catalan.
### Data preparation
The data was obtained through a combination of human translation and machine translation with human proofreading.
After the translation process, the data was deduplicated and filtered to remove any sentence pairs with a cosine similarity of less than 0.75 in order to improve the data alignment quality.
This was done using sentence embeddings calculated using [LaBSE](https://huggingface.co/sentence-transformers/LaBSE).
The obtained cleaned corpus consists of **14.385.296** parallel sentences of human quality.
### Personal and Sensitive Information
No anonymisation process was performed.
## Considerations for Using the Data
### Social Impact of Dataset
The purpose of this dataset is to help develop Machine Translation tasks for mid-resource languages such as Catalan.
### Discussion of Biases
We are aware that since part of the data comes from unreliable web pages and non-curated texts, some biases may be present in the dataset.
Nonetheless, we have not applied any steps to reduce their impact.
### Other Known Limitations
The dataset contains data of a general domain. Application of this dataset in more specific domains such as biomedical, legal etc. would be of limited use.
## Additional Information
### Author
Language Technologies Unit (LangTech) at the Barcelona Supercomputing Center.
### Contact information
For further information, please send an email to [email protected].
### Copyright
Copyright Language Technologies Unit at Barcelona Supercomputing Center (2023).
### Licensing information
This work is licensed under a [Creative Commons Attribution 4.0 International licence](https://creativecommons.org/licenses/by/4.0/).
### Funding
This work was funded by the [Departament de la Vicepresidència i de Polítiques Digitals i Territori de la Generalitat de Catalunya](https://politiquesdigitals.gencat.cat/ca/inici/index.html#googtrans(ca|en) within the framework of [Projecte AINA](https://politiquesdigitals.gencat.cat/ca/economia/catalonia-ai/aina). | [
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orgcatorg/shephardmedia | orgcatorg | 2023-12-17T04:18:16Z | 0 | 0 | [
"region:us"
] | null | 2023-12-01T16:47:40Z | ---
dataset_info:
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- config_name: Land Warfare
features:
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newsmediabias/fake_news_elections2024 | newsmediabias | 2023-12-13T12:36:49Z | 0 | 0 | [
"license:openrail",
"arxiv:2312.03750",
"region:us"
] | null | 2023-12-01T20:21:28Z | ---
license: openrail
---
This is a data on fake news and for elections 2024 , specially curated to highlight racial slurs and discriminations in political speeches.
https://arxiv.org/abs/2312.03750
## Citation
If you use this dataset in your research, please cite it as follows:
```bibtex
@article{rahman2023analyzing,
title={Analyzing the Influence of Fake News in the 2024 Elections: A Comprehensive Dataset},
author={Rahman, Mizanur and Raza, Shaina},
journal={arXiv preprint arXiv:2312.03750},
year={2023}
}
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mucai/ViP-Bench | mucai | 2023-12-15T01:08:04Z | 0 | 0 | [
"license:apache-2.0",
"region:us"
] | null | 2023-12-02T05:54:04Z | ---
license: apache-2.0
---
<p align="center">
<img src="https://raw.githubusercontent.com/mu-cai/ViP-LLaVA/main/images/vip-llava_arch.png" width="600"> <br>
</p>
# [ViP-Bench: Making Large Multimodal Models Understand Arbitrary Visual Prompts](https://vip-llava.github.io/)
ViP-Bench a region level multimodal model evaulation benchmark curated by University of Wisconsin-Madison. We provides two kinds of visual prompts: (1) bounding boxes, and (2) human drawn diverse visual prompts.
**Evaluation Code** See [https://github.com/mu-cai/ViP-LLaVA/blob/main/docs/Evaluation.md](https://github.com/mu-cai/ViP-LLaVA/blob/main/docs/Evaluation.md)
**LeaderBoard** See [https://paperswithcode.com/sota/visual-question-answering-on-vip-bench](https://paperswithcode.com/sota/visual-question-answering-on-vip-bench)
**Evaluation Server** Please refer to [https://huggingface.co/spaces/mucai/ViP-Bench_Evaluator](https://huggingface.co/spaces/mucai/ViP-Bench_Evaluator) to use our evaluation server.
## Source annotation
In `source_image`, we provide the source plain images along with the bounding box/mask annotations. Researchers can use such grounding information to match the special tokens such as `<obj>` in `"question"` entry of `vip-bench-meta-data.json`. For example, `<obj>` can be replaced by textual coordinates to evaluate the region-level multimodal models. | [
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zeio/patch | zeio | 2023-12-13T21:12:00Z | 0 | 0 | [
"license:apache-2.0",
"region:us"
] | null | 2023-12-02T16:21:17Z | ---
license: apache-2.0
---
# Patch
Threads pulled from 2ch
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ai-aerospace/ams_data_train_generic_v0.1_100 | ai-aerospace | 2023-12-17T19:57:15Z | 0 | 0 | [
"license:apache-2.0",
"region:us"
] | null | 2023-12-03T23:54:07Z | ---
license: apache-2.0
base-model: TheBloke/Llama-2-7B-Chat-GGUF
---
Question and answer pairs for the first 100 entries of aerospace mechanism symposia 5000 word chunk entries. Full file of entries is here: https://github.com/dsmueller3760/aerospace_chatbot/blob/llm_training/data/AMS/ams_data_answers.jsonl
See this repository for details: https://github.com/dsmueller3760/aerospace_chatbot/tree/main
Prompts generated using TheBloke/Llama-2-7B-Chat-GGUF | [
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BitTranslate/chatgpt-prompts-Ukrainian | BitTranslate | 2023-12-12T03:31:59Z | 0 | 0 | [
"language:uk",
"license:cc0-1.0",
"ChatGPT",
"region:us"
] | null | 2023-12-04T04:29:16Z | ---
license: cc0-1.0
language:
- uk
tags:
- ChatGPT
--- | [
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tauhuang/diffusion_reward | tauhuang | 2023-12-17T10:09:32Z | 0 | 0 | [
"license:mit",
"region:us"
] | null | 2023-12-05T02:46:29Z | ---
license: mit
---
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pedrohiperiq/datasetpica.com | pedrohiperiq | 2023-12-12T22:36:17Z | 0 | 0 | [
"license:openrail",
"region:us"
] | null | 2023-12-05T05:04:51Z | ---
license: openrail
---
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rosestarlight/audioia | rosestarlight | 2023-12-17T22:39:59Z | 0 | 0 | [
"license:openrail",
"region:us"
] | null | 2023-12-05T12:53:59Z | ---
license: openrail
---
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gordicaleksa/serbian-llm-eval-v0 | gordicaleksa | 2023-12-17T22:31:32Z | 0 | 2 | [
"size_categories:1M<n<10M",
"language:sr",
"language:hr",
"language:bs",
"license:apache-2.0",
"region:us"
] | null | 2023-12-05T13:42:36Z | ---
license: apache-2.0
language:
- sr
- hr
- bs
size_categories:
- 1M<n<10M
---
# Serbian LLM eval v0 🇷🇸
Please instead use the version 1 of the dataset [here](https://huggingface.co/datasets/gordicaleksa/serbian-llm-eval-v1).
[Weights & Biases report](https://wandb.ai/gordicaleksa/serbian_llm_eval/reports/First-Serbian-LLM-eval---Vmlldzo2MjgwMDA5).
# Project Sponsors
## Platinum sponsors 🌟
* <b>Ivan</b> (fizicko lice, anoniman)
## Gold sponsors 🟡
* **qq** (fizicko lice, anoniman)
* [**Mitar Perovic**](https://www.linkedin.com/in/perovicmitar/)
* [**Nikola Ivancevic**](https://www.linkedin.com/in/nivancevic/)
## Silver sponsors ⚪
[**psk.rs**](https://psk.rs/), [**OmniStreak**](https://omnistreak.com/), [**Marko Radojicic**](https://www.linkedin.com/in/marko-radojicic-acmanik-cube/), [**Luka Vazic**](https://www.linkedin.com/in/vazic/), [**Miloš Durković**](https://www.linkedin.com/in/milo%C5%A1-d-684b99188/), [**Marjan Radeski**](https://www.linkedin.com/in/marjanradeski/), **Marjan Stankovic** (fizicko lice), [**Nikola Stojiljkovic**](https://www.linkedin.com/in/nikola-stojiljkovic-10469239/), [**Mihailo Tomic**](https://www.linkedin.com/in/mihailotomic/), [**Bojan Jevtic**](https://www.linkedin.com/in/bojanjevtic/), [**Jelena Jovanović**](https://www.linkedin.com/in/eldumo/), [**Nenad Davidović**](https://www.linkedin.com/in/nenad-davidovic-662ab749/)
## Credits
Thank you to the following individuals from my [Discord server](https://discord.gg/peBrCpheKE
) who helped with donating google translate creditss & running machine translation part of the pipeline:
[Vera Prohaska](https://vtwoptwo.com/), [Chu Kin Chan](www.linkedin.com/in/roy-ck-chan), [Joe Makepeace](https://www.linkedin.com/in/joe-makepeace-a872a1183/), [Toby Farmer](https://www.linkedin.com/in/tobyfarmer/), [Malvi Bid](https://www.linkedin.com/in/malvibid/), [Raphael Vienne](https://www.linkedin.com/in/raphael-vienne/), [Nenad Aksentijevic](https://www.linkedin.com/in/nenad-aksentijevic-21629a1b6), [Isaac Nicolas](https://www.linkedin.com/in/isaacnicolas/), [Brian Pulfer](https://www.brianpulfer.ch/), [Aldin Cimpo](https://www.linkedin.com/in/aldin-c-b26334189/), [Nikola Ljubesic](https://nljubesi.github.io/)
## Citation
```
@article{serbian-llm-eval,
author = "Gordić Aleksa",
title = "Serbian LLM Eval",
year = "2023"
howpublished = {\url{https://huggingface.co/datasets/gordicaleksa/serbian-llm-eval-v1}},
}
```
## License
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deus-ex-machina/novelai-anime-v3-artist-comparison | deus-ex-machina | 2023-12-16T15:21:19Z | 0 | 0 | [
"task_categories:text-to-image",
"size_categories:10K<n<100K",
"language:en",
"license:apache-2.0",
"art",
"novelai",
"nai",
"example",
"sample",
"comparison",
"region:us"
] | [
"text-to-image"
] | 2023-12-05T21:44:14Z | ---
license: apache-2.0
task_categories:
- text-to-image
language:
- en
tags:
- art
- novelai
- nai
- example
- sample
- comparison
size_categories:
- 10K<n<100K
viewer: false
---
This is a repository of 15,000 SFW samples of artist tags generated using the NovelAI v3 anime image model.
These were selected by post count from Danbooru for all artist tags with greater than 94 posts.
Sample Prompt and Settings (with merunyaa as the selected artist tag in this case)
Positive
``patchouli knowledge, solo, standing, upper body, garden, looking at viewer, facing viewer, [purple eyes], best quality, amazing quality, very aesthetic, absurdres, merunyaa``
Negative / Undesired Content
``lowres, {bad}, text, error, missing, extra, fewer, cropped, jpeg artifacts, worst quality, bad quality, watermark, displeasing, unfinished, chromatic aberration, scan, scan artifacts, nude, [cleavage], nsfw
``
Settings
``steps: 28, height: 1216, width: 832, scale: 5.0, uncond_scale: 1.0, cfg_rescale: 0.0, seed: 1823849869, n_samples: 1, hide_debug_overlay: False, noise_schedule: native, sampler: k_euler_ancestral, controlnet_strength: 1.0, controlnet_model: None, dynamic_thresholding: False, dynamic_thresholding_percentile: 0.999, dynamic_thresholding_mimic_scale: 10.0, sm: False, sm_dyn: False, skip_cfg_below_sigma: 0.0, lora_unet_weights: None, lora_clip_weights: None``
Note, there will be some variance due to cleanup of NSFW leaks, and adding ``, panties, bikini`` to the negative prompt for the latter 8k samples.
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Umbrellos/Cantarolador | Umbrellos | 2023-12-14T02:05:47Z | 0 | 0 | [
"license:openrail",
"region:us"
] | null | 2023-12-05T23:26:45Z | ---
license: openrail
---
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jellyShuang/MMCT | jellyShuang | 2023-12-15T01:14:22Z | 0 | 0 | [
"license:mit",
"arxiv:2302.07676",
"region:us"
] | null | 2023-12-06T02:30:50Z | ---
license: mit
---
# Multi-Moving Camera Pedestrian Tracking with a New Dataset and Global Link Model
[](###)
[](https://huggingface.co/datasets/jellyShuang/MMCT)
This repository contains the details of the dataset and the Pytorch implementation of the Paper:
[Multi-Moving Camera Pedestrian Tracking with a New Dataset and Global Link Model](https://arxiv.org/abs/2302.07676)
## Abstract
Ensuring driving safety for autonomous vehicles has become increasingly crucial, highlighting the need for systematic tracking of pedestrians on the road. Most vehicles are equipped with visual sensors, however, the large-scale visual dataset from different agents has not been well studied yet. Basically, most of the multi-target multi-camera (MTMC) tracking systems are composed of two modules: single camera tracking (SCT) and inter-camera tracking (ICT). To reliably coordinate between them, MTMC tracking has been a very complicated task, while tracking across multi-moving cameras makes it even more challenging. In this paper, we focus on multi-target multi-moving camera (MTMMC) tracking, which is attracting increasing attention from the research community. Observing there are few datasets for MTMMC tracking, we collect a new dataset, called Multi-Moving Camera Track (MMCT), which contains sequences under various driving scenarios. To address the common prob- lems of identity switch easily faced by most existing SCT trackers, especially for moving cameras due to ego-motion between the camera and targets, a lightweight appearance-free global link model, called Linker, is proposed to mitigate the identity switch by associating two disjoint tracklets of the same target into a complete trajectory within the same camera. Incorporated with Linker, existing SCT trackers generally obtain a significant improvement. Moreover, a strong baseline approach of re- identification (Re-ID) is effectively incorporated to extract robust appearance features under varying surroundings for pedestrian association across moving cameras for ICT, resulting in a much improved MTMMC tracking system, which can constitute a step further towards coordinated mining of multiple moving cameras.
- **<a href="#des"> <u>Dataset Description</u>**</a>
- **<a href="#str"> <u>Dataset Structure</u>**</a>
- **<a href="#dow"> <u>Dataset Downloads</u>**</a>
## <a id="des">Dataset Description</a>
We collect data in 12 distinct scenarios, named `'A','B','C',...'L`'. Each scenario may include the interaction of two or three cameras on different cars. For example, scene A includes two sequences of `A-I` and `A-II`. There are 32 sequences in total.
### <a id="str">Dataset Structure</a>
```
MMCT
├── data
│ ├── gps
│ └── labelS
└── images
├── 1
│ ├── A
│ │ ├── IMG_0098-frag-s1-a-fps5.mp4
│ │ └── jpg
│ └── C
│ ├── IMG_0559-frag-s1-c-fps5.mp4
│ ├── jpg
├── 2
│ ├── A
│ │ ├── IMG_0094-frag-s2-a-fps5.mp4
│ │ ├── jpg
│ ├── B
│ │ ├── IMG_2248-frag-s2-b-fps5.mp4
│ │ ├── jpg
...
├── 12
│ ├── A
│ │ ├── IMG_0104-frag-s12-a-fps5.mp4
│ │ ├── jpg
│ ├── B
│ │ ├── IMG_2254-frag-s12-b-fps5.mp4
│ │ ├── jpg
│ └── C
│ ├── IMG_0569-frag-s12-c-fps5.mp4
│ ├── jpg
```
### <a id="dow">Dataset Downloads</a>
The whole dataset can be downloaded from [Huggingface](https://huggingface.co/datasets/jellyShuang/MMCT). **Note that, each file needs to unzip by the password. You can decompress each `.zip` file in its folder after sending us ([email protected], [email protected]) the [LICENSE](https://github.com/shengyuhao/DIVOTrack/blob/main/LICENSE.md). in any format.**
## <a id="ref">Reference</a>
The license agreement for data usage implies the citation of the paper above. Please notice that citing the dataset URL instead of the publications would not be compliant with this license agreement. You can read the LICENSE from [LICENSE](https://github.com/dhu-mmct/DHU-MMCT/blob/main/LICENSE.md).
## <a id="con">Contact</a>
If you have any concerns, please contact [[email protected]]([email protected])
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Edmon02/AudioSet | Edmon02 | 2023-12-15T19:00:10Z | 0 | 0 | [
"license:apache-2.0",
"region:us"
] | null | 2023-12-06T06:24:48Z | ---
license: apache-2.0
---
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itsliupeng/mmlu_recall | itsliupeng | 2023-12-11T09:43:53Z | 0 | 1 | [
"task_categories:table-question-answering",
"language:en",
"language:zh",
"license:apache-2.0",
"region:us"
] | [
"table-question-answering"
] | 2023-12-06T14:30:15Z | ---
license: apache-2.0
task_categories:
- table-question-answering
language:
- en
- zh
---
Employ mmlu (cmmlu) questions as initial seeds to retrieve related articles from multiple training data corpora such as Chinese CommonCrawl, WeChat, and Faclon. These selected articles will be utilized as a training set for mmlu. The goal is to determine whether this training set can improve the relevant mmlu performance metrics.
For emb_recall: We use sentence-transformers to generate embeddings and apply a filter for those with a cosine distance greater than 0.95. The content is mainly focused on knowledge concepts associated with mmlu.
For keyword_recall: We conduct searches for phrases like 'A B C answer' using ElasticSearch, aligning with the structure of mmlu's distinctive multiple-choice questions. | [
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liaad/Propbank-BR | liaad | 2023-12-12T20:37:16Z | 0 | 0 | [
"task_categories:token-classification",
"language:pt",
"license:mit",
"semantic role labelling",
"srl",
"region:us"
] | [
"token-classification"
] | 2023-12-06T15:09:39Z | ---
language:
- pt
license: mit
task_categories:
- token-classification
pretty_name: Propbank BR
dataset_info:
features:
- name: tokens
sequence: string
- name: srl_frames
list:
- name: frames
sequence: string
- name: stack
sequence: 'null'
- name: verb
dtype: string
splits:
- name: train
num_bytes: 1749998
num_examples: 3348
download_size: 392974
dataset_size: 1749998
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
tags:
- semantic role labelling
- srl
--- | [
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hayden-donnelly/db-sfw-512-general-filter-dataset-v1 | hayden-donnelly | 2023-12-12T22:56:05Z | 0 | 0 | [
"task_categories:image-classification",
"size_categories:10K<n<100K",
"region:us"
] | [
"image-classification"
] | 2023-12-06T23:24:18Z | ---
task_categories:
- image-classification
size_categories:
- 10K<n<100K
---
# Danbooru SFW 512 General Filter Dataset V1
This dataset is meant to be used for training a simple binary classifier that can filter the
Danbooru SFW 2021 dataset. There are two classes: "accepted" and "rejected", with "accepted"
representing samples that should pass through the filter and "rejected" representing samples
that should not.
Generallly, to be accepted a sample should meet the following criteria:
1. No horizontal/vertical bars, or bars that can be removed easily by filling in the background
color. This precludes any samples with bars which cut through any part of the image that is not
a solid color block (i.e. patterns, detailed backgrounds, and foreground elements).
2. Not overtly sexual. The SFW subset of Danbooru 2021 which this dataset is based on has
already removed almost all nudity, this criterion simply removes some of the borderline NSFW images.
3. Somewhat aesthetic.
Please note that dataset samples were selected manually, so there was somewhat of a tradeoff between
speed and accuracy. Despite this, I've managed to train a binary classifier up to 85% test
accuracy on this dataset. I've also observed pretty good performance when using that classifier on
entirely new portions of the base dataset.
## Original Dataset Citation
```bibtex
@misc{danbooru2021,
author={Anonymous and Danbooru community and Gwern Branwen},
title={Danbooru2021: A Large-Scale Crowdsourced and Tagged Anime Illustration Dataset},
howpublished={\url{https://gwern.net/danbooru2021}},
url={https://gwern.net/danbooru2021},
type={dataset},
year={2022},
month={January},
timestamp={2022-01-21},
note={Accessed: 2023-12-06}
}
``` | [
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corto-ai/contracts | corto-ai | 2023-12-17T21:45:32Z | 0 | 0 | [
"region:us"
] | null | 2023-12-06T23:56:18Z | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': contract
'1': other
splits:
- name: train
num_bytes: 31085535.0
num_examples: 72
- name: test
num_bytes: 11426627.0
num_examples: 26
download_size: 35810005
dataset_size: 42512162.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
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taesiri/video-game-question-answering | taesiri | 2023-12-11T22:32:13Z | 0 | 0 | [
"region:us"
] | null | 2023-12-07T00:40:26Z | ---
dataset_info:
features:
- name: id
dtype: string
- name: image
dtype: string
- name: conversations
dtype: string
- name: model
dtype: string
splits:
- name: train
num_bytes: 27560807
num_examples: 36705
download_size: 9281942
dataset_size: 27560807
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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hugosousa/WikiTimelines | hugosousa | 2023-12-14T17:18:04Z | 0 | 0 | [
"license:mit",
"region:us"
] | null | 2023-12-07T01:20:00Z | ---
license: mit
---
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Zainab984/BP-balanced-S04 | Zainab984 | 2023-12-12T14:53:03Z | 0 | 0 | [
"region:us"
] | null | 2023-12-07T10:17:57Z | ---
dataset_info:
features:
- name: Target
dtype: int64
- name: PC
dtype: string
- name: GSHARE
dtype: string
- name: GA table
dtype: string
splits:
- name: train
num_bytes: 117380500
num_examples: 234761
- name: test
num_bytes: 29345500
num_examples: 58691
download_size: 41279046
dataset_size: 146726000
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
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jbilcke-hf/ai-tube-tensar-studios | jbilcke-hf | 2023-12-12T22:49:08Z | 0 | 0 | [
"license:cc-by-nc-sa-4.0",
"region:us"
] | null | 2023-12-07T14:35:32Z | ---
license: cc-by-nc-sa-4.0
pretty_name: Tensar Studios
---
## Description
Follow us to experience a new era of 3D AI-animated short films.
## Model
SVD
## LoRA
artificialguybr/3DRedmond-V1
## Style
- 3D Render Style
- 3DRenderAF
## Voice
Cloée
## Music
Background cinematic music
## Prompt
A video channel which produces short 3D films of fictional franchises.
Stories should be short, about 1 minute or 2, but full of action and fun.
It will NEVER create content from existing artists or studio.
Instead it will create its own artistic stories, content, stories and characters.
Characters should not be biased towards any specific gender, work, country, religion or culture.
Some themes should be avoided in the stories, such as violence, sex, war, crimes etc.
No nudity is tolerated.
It should use animal characters whenever possible, but also human characters.
Characters should be very cartoony / cute.
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StephanAkkerman/financial-tweets | StephanAkkerman | 2023-12-11T11:03:58Z | 0 | 3 | [
"task_categories:text-classification",
"size_categories:100K<n<1M",
"language:en",
"license:mit",
"finance",
"twitter",
"news",
"crypto",
"stocks",
"tweet",
"region:us"
] | [
"text-classification"
] | 2023-12-07T15:34:00Z | ---
license: mit
task_categories:
- text-classification
language:
- en
tags:
- finance
- twitter
- news
- crypto
- stocks
- tweet
pretty_name: Financial Tweets
size_categories:
- 100K<n<1M
---
# Financial Tweets
This dataset is a comprehensive collection of all the tweets from my [Discord bot](https://github.com/StephanAkkerman/fintwit-bot) that keeps track of financial influencers on Twitter.
The data includes a variety of information, such as the tweet and the price of the tickers in that tweet at the time of posting.
This dataset can be used for a variety of tasks, such as sentiment analysis and masked language modelling (MLM).
We used this dataset for training our [FinTwitBERT model](https://huggingface.co/StephanAkkerman/FinTwitBERT).
## Overview
This datasets includes all the following three datasets:
- Crypto: https://huggingface.co/datasets/StephanAkkerman/financial-tweets-crypto
- Stocks (and forex): https://huggingface.co/datasets/StephanAkkerman/financial-tweets-stocks
- Other (tweets without cash tags): https://huggingface.co/datasets/StephanAkkerman/financial-tweets-other
## Data Description
The dataset comprises tweets related to financial markets, stocks, and economic discussions.
### Dataset Fields
The data fields are as follows:
* `timestap`: The time the tweet was sent.
* `tweet_text`: All of the text of the tweet, including quoted tweets (prefixed with `>`).
* `tweet_url`: The URL of the tweet.
* `tweet_type`: The type of tweet, this can be tweet, retweet, or quote tweet.
* `price_of_ticker`: The price of the tickers mentioned in USD ($).
* `change_of_ticker`: The 24h price change of the tickers in USD ($).
* `tickers_mentioned`: All the tickers that are mentioned in the tweet.
* `category`: What type of category the tweet has, the suffix `_images` means that the tweet included an image.
## Usage
This dataset is ideal for training and evaluating machine learning models for sentiment analysis, especially those focused on understanding market trends and investor sentiment. It can be used for academic research, financial market analysis, and developing AI tools for financial institutions.
## Acknowledgments
We extend our heartfelt gratitude to all the authors of the original tweets.
## License
This dataset is made available under the MIT license, adhering to the licensing terms of the original datasets. | [
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