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
list | task_categories
list | createdAt
timestamp[us, tz=UTC] | card
stringlengths 19
1.01M
| embedding
list |
|---|---|---|---|---|---|---|---|---|---|
benayas/banking_llm_v4 | benayas | 2023-11-28T21:23:48Z | 0 | 0 | [
"region:us"
]
| null | 2023-11-28T21:23:46Z | ---
dataset_info:
features:
- name: text
dtype: string
- name: category
dtype: string
splits:
- name: train
num_bytes: 21973867
num_examples: 10003
- name: test
num_bytes: 6745410
num_examples: 3080
download_size: 2573335
dataset_size: 28719277
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
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|
matijao/webdev | matijao | 2023-11-28T21:36:59Z | 0 | 0 | [
"license:mit",
"region:us"
]
| null | 2023-11-28T21:28:42Z | ---
license: mit
---
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|
benayas/massive_llm_v4 | benayas | 2023-11-28T21:29:07Z | 0 | 0 | [
"region:us"
]
| null | 2023-11-28T21:29:05Z | ---
dataset_info:
features:
- name: id
dtype: string
- name: locale
dtype: string
- name: partition
dtype: string
- name: scenario
dtype:
class_label:
names:
'0': social
'1': transport
'2': calendar
'3': play
'4': news
'5': datetime
'6': recommendation
'7': email
'8': iot
'9': general
'10': audio
'11': lists
'12': qa
'13': cooking
'14': takeaway
'15': music
'16': alarm
'17': weather
- name: intent
dtype:
class_label:
names:
'0': datetime_query
'1': iot_hue_lightchange
'2': transport_ticket
'3': takeaway_query
'4': qa_stock
'5': general_greet
'6': recommendation_events
'7': music_dislikeness
'8': iot_wemo_off
'9': cooking_recipe
'10': qa_currency
'11': transport_traffic
'12': general_quirky
'13': weather_query
'14': audio_volume_up
'15': email_addcontact
'16': takeaway_order
'17': email_querycontact
'18': iot_hue_lightup
'19': recommendation_locations
'20': play_audiobook
'21': lists_createoradd
'22': news_query
'23': alarm_query
'24': iot_wemo_on
'25': general_joke
'26': qa_definition
'27': social_query
'28': music_settings
'29': audio_volume_other
'30': calendar_remove
'31': iot_hue_lightdim
'32': calendar_query
'33': email_sendemail
'34': iot_cleaning
'35': audio_volume_down
'36': play_radio
'37': cooking_query
'38': datetime_convert
'39': qa_maths
'40': iot_hue_lightoff
'41': iot_hue_lighton
'42': transport_query
'43': music_likeness
'44': email_query
'45': play_music
'46': audio_volume_mute
'47': social_post
'48': alarm_set
'49': qa_factoid
'50': calendar_set
'51': play_game
'52': alarm_remove
'53': lists_remove
'54': transport_taxi
'55': recommendation_movies
'56': iot_coffee
'57': music_query
'58': play_podcasts
'59': lists_query
- name: utt
dtype: string
- name: annot_utt
dtype: string
- name: worker_id
dtype: string
- name: slot_method
sequence:
- name: slot
dtype: string
- name: method
dtype: string
- name: judgments
sequence:
- name: worker_id
dtype: string
- name: intent_score
dtype: int8
- name: slots_score
dtype: int8
- name: grammar_score
dtype: int8
- name: spelling_score
dtype: int8
- name: language_identification
dtype: string
- name: category
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 17839343
num_examples: 11514
- name: validation
num_bytes: 3144099
num_examples: 2033
- name: test
num_bytes: 4598528
num_examples: 2974
download_size: 2975271
dataset_size: 25581970
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
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|
Apinapi/LucasD | Apinapi | 2023-11-28T21:46:04Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-11-28T21:45:35Z | ---
license: openrail
---
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|
trolllemon/dogs-train | trolllemon | 2023-11-28T21:54:18Z | 0 | 0 | [
"region:us"
]
| null | 2023-11-28T21:54:16Z | ---
dataset_info:
features:
- name: image
dtype: image
splits:
- name: train
num_bytes: 331849.0
num_examples: 14
download_size: 333141
dataset_size: 331849.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
trolllemon/dogs-test | trolllemon | 2023-11-28T21:54:20Z | 0 | 0 | [
"region:us"
]
| null | 2023-11-28T21:54:18Z | ---
dataset_info:
features:
- name: image
dtype: image
splits:
- name: test
num_bytes: 1439101.0
num_examples: 60
download_size: 1440742
dataset_size: 1439101.0
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
---
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|
manu/french_poetry | manu | 2023-11-28T22:24:06Z | 0 | 0 | [
"region:us"
]
| null | 2023-11-28T22:18:48Z | ---
dataset_info:
features:
- name: title
dtype: string
- name: poet
dtype: string
- name: text
dtype: string
- name: link
dtype: string
- name: id
dtype: string
splits:
- name: train
num_bytes: 3286614
num_examples: 1821
download_size: 2072389
dataset_size: 3286614
---
# Dataset Card for "french_poetry"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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|
Apinapi/LucasDA | Apinapi | 2023-11-28T22:48:37Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-11-28T22:47:20Z | ---
license: openrail
---
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|
Gabriel1322/messimini | Gabriel1322 | 2023-11-28T22:56:32Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-11-28T22:55:40Z | ---
license: openrail
---
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|
Apinapi/Lucas12 | Apinapi | 2023-11-28T22:56:07Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-11-28T22:55:43Z | ---
license: openrail
---
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|
Apinapi/LDA | Apinapi | 2023-11-28T23:01:38Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-11-28T23:01:17Z | ---
license: openrail
---
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|
proculation/mytestds | proculation | 2023-11-29T20:17:29Z | 0 | 0 | [
"task_categories:question-answering",
"size_categories:n<1K",
"language:en",
"license:unknown",
"region:us"
]
| [
"question-answering"
]
| 2023-11-28T23:08:57Z | ---
language:
- en
license: unknown
size_categories:
- n<1K
task_categories:
- question-answering
pretty_name: test dataset
dataset_info:
features:
- name: id
dtype: string
- name: context
dtype: string
- name: question
dtype: string
- name: is_impossible
dtype: bool
- name: answers
struct:
- name: answer_start
dtype: int32
- name: text
dtype: string
splits:
- name: train
num_bytes: 1092344
num_examples: 721
download_size: 147635
dataset_size: 1092344
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
Thiagovmsc/Ramona | Thiagovmsc | 2023-11-28T23:43:14Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-11-28T23:42:47Z | ---
license: openrail
---
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|
rollstar/marcosantonio | rollstar | 2023-11-29T00:12:10Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-11-29T00:12:10Z | ---
license: openrail
---
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|
rollstar/marcosantonio1 | rollstar | 2023-11-29T00:22:05Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-11-29T00:17:10Z | ---
license: openrail
---
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|
cellowmaia/AudioAntonio | cellowmaia | 2023-11-29T04:23:07Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-11-29T00:19:36Z | ---
license: openrail
---
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|
benayas/atis_llm_v0 | benayas | 2023-11-29T00:26:11Z | 0 | 0 | [
"region:us"
]
| null | 2023-11-29T00:26:10Z | ---
dataset_info:
features:
- name: text
dtype: string
- name: category
dtype: string
splits:
- name: train
num_bytes: 1810128
num_examples: 4455
- name: test
num_bytes: 552411
num_examples: 1373
download_size: 314534
dataset_size: 2362539
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
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|
benayas/banking_llm_v0 | benayas | 2023-11-29T00:27:47Z | 0 | 0 | [
"region:us"
]
| null | 2023-11-29T00:27:45Z | ---
dataset_info:
features:
- name: text
dtype: string
- name: category
dtype: string
splits:
- name: train
num_bytes: 4208539
num_examples: 10003
- name: test
num_bytes: 1275330
num_examples: 3080
download_size: 723328
dataset_size: 5483869
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
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|
benayas/massive_llm_v0 | benayas | 2023-11-29T00:29:16Z | 0 | 0 | [
"region:us"
]
| null | 2023-11-29T00:29:13Z | ---
dataset_info:
features:
- name: id
dtype: string
- name: locale
dtype: string
- name: partition
dtype: string
- name: scenario
dtype:
class_label:
names:
'0': social
'1': transport
'2': calendar
'3': play
'4': news
'5': datetime
'6': recommendation
'7': email
'8': iot
'9': general
'10': audio
'11': lists
'12': qa
'13': cooking
'14': takeaway
'15': music
'16': alarm
'17': weather
- name: intent
dtype:
class_label:
names:
'0': datetime_query
'1': iot_hue_lightchange
'2': transport_ticket
'3': takeaway_query
'4': qa_stock
'5': general_greet
'6': recommendation_events
'7': music_dislikeness
'8': iot_wemo_off
'9': cooking_recipe
'10': qa_currency
'11': transport_traffic
'12': general_quirky
'13': weather_query
'14': audio_volume_up
'15': email_addcontact
'16': takeaway_order
'17': email_querycontact
'18': iot_hue_lightup
'19': recommendation_locations
'20': play_audiobook
'21': lists_createoradd
'22': news_query
'23': alarm_query
'24': iot_wemo_on
'25': general_joke
'26': qa_definition
'27': social_query
'28': music_settings
'29': audio_volume_other
'30': calendar_remove
'31': iot_hue_lightdim
'32': calendar_query
'33': email_sendemail
'34': iot_cleaning
'35': audio_volume_down
'36': play_radio
'37': cooking_query
'38': datetime_convert
'39': qa_maths
'40': iot_hue_lightoff
'41': iot_hue_lighton
'42': transport_query
'43': music_likeness
'44': email_query
'45': play_music
'46': audio_volume_mute
'47': social_post
'48': alarm_set
'49': qa_factoid
'50': calendar_set
'51': play_game
'52': alarm_remove
'53': lists_remove
'54': transport_taxi
'55': recommendation_movies
'56': iot_coffee
'57': music_query
'58': play_podcasts
'59': lists_query
- name: utt
dtype: string
- name: annot_utt
dtype: string
- name: worker_id
dtype: string
- name: slot_method
sequence:
- name: slot
dtype: string
- name: method
dtype: string
- name: judgments
sequence:
- name: worker_id
dtype: string
- name: intent_score
dtype: int8
- name: slots_score
dtype: int8
- name: grammar_score
dtype: int8
- name: spelling_score
dtype: int8
- name: language_identification
dtype: string
- name: category
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 6371399
num_examples: 11514
- name: validation
num_bytes: 1119231
num_examples: 2033
- name: test
num_bytes: 1636424
num_examples: 2974
download_size: 1813395
dataset_size: 9127054
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
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|
MAXJHOW/MAXCLONE | MAXJHOW | 2023-11-29T00:47:57Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-11-29T00:44:39Z | ---
license: openrail
---
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|
malaysia-ai/mosaic-tinyllama | malaysia-ai | 2023-11-29T06:27:54Z | 0 | 0 | [
"language:ms",
"region:us"
]
| null | 2023-11-29T00:52:51Z | ---
language:
- ms
---
# Mosaic format for filtered combine dataset to finetune TinyLlama models
This repository is to store dataset shards using mosaic format.
1. https://github.com/malaysia-ai/dedup-text-dataset/blob/main/tinyllama/combine-dataset.ipynb
2. using tokenizer https://huggingface.co/TinyLlama/TinyLlama-1.1B-intermediate-step-955k-token-2T
3. 4096 context length.
## how-to
1. git clone,
```bash
git lfs clone https://huggingface.co/datasets/malaysia-ai/mosaic-tinyllama
```
2. load it,
```python
from streaming import LocalDataset
import numpy as np
from streaming.base.format.mds.encodings import Encoding, _encodings
class UInt16(Encoding):
def encode(self, obj) -> bytes:
return obj.tobytes()
def decode(self, data: bytes):
return np.frombuffer(data, np.uint16)
_encodings['uint16'] = UInt16
dataset = LocalDataset('mosaic-tinyllama')
len(dataset)
``` | [
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|
cess6677/asmita_vocals | cess6677 | 2023-11-29T01:47:13Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-11-29T01:46:27Z | ---
license: openrail
---
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|
BangumiBase/seireinomoribito | BangumiBase | 2023-11-29T04:06:55Z | 0 | 0 | [
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
]
| null | 2023-11-29T02:18:40Z | ---
license: mit
tags:
- art
size_categories:
- 1K<n<10K
---
# Bangumi Image Base of Seirei No Moribito
This is the image base of bangumi Seirei no Moribito, we detected 26 characters, 2981 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|
| 0 | 593 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 73 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 487 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 450 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 327 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 79 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 33 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 126 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 81 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 46 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 43 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 73 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 16 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 172 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 96 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 18 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 62 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 18 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 53 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 24 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 28 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 11 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 14 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 12 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 12 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 34 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
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|
BangumiBase/jigokushoujoyoinotogi | BangumiBase | 2023-11-29T04:33:53Z | 0 | 0 | [
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
]
| null | 2023-11-29T02:50:13Z | ---
license: mit
tags:
- art
size_categories:
- 1K<n<10K
---
# Bangumi Image Base of Jigoku Shoujo - Yoi No Togi
This is the image base of bangumi Jigoku Shoujo - Yoi no Togi, we detected 53 characters, 2595 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|
| 0 | 232 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 25 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 55 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 30 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 11 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 11 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 28 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 44 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 46 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 26 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 39 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 212 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 68 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 132 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 51 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 46 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 132 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 119 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 60 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 17 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 42 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 35 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 58 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 17 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 8 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| 25 | 62 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| 26 | 35 | [Download](26/dataset.zip) |  |  |  |  |  |  |  |  |
| 27 | 36 | [Download](27/dataset.zip) |  |  |  |  |  |  |  |  |
| 28 | 60 | [Download](28/dataset.zip) |  |  |  |  |  |  |  |  |
| 29 | 48 | [Download](29/dataset.zip) |  |  |  |  |  |  |  |  |
| 30 | 61 | [Download](30/dataset.zip) |  |  |  |  |  |  |  |  |
| 31 | 105 | [Download](31/dataset.zip) |  |  |  |  |  |  |  |  |
| 32 | 21 | [Download](32/dataset.zip) |  |  |  |  |  |  |  |  |
| 33 | 18 | [Download](33/dataset.zip) |  |  |  |  |  |  |  |  |
| 34 | 23 | [Download](34/dataset.zip) |  |  |  |  |  |  |  |  |
| 35 | 86 | [Download](35/dataset.zip) |  |  |  |  |  |  |  |  |
| 36 | 14 | [Download](36/dataset.zip) |  |  |  |  |  |  |  |  |
| 37 | 14 | [Download](37/dataset.zip) |  |  |  |  |  |  |  |  |
| 38 | 68 | [Download](38/dataset.zip) |  |  |  |  |  |  |  |  |
| 39 | 16 | [Download](39/dataset.zip) |  |  |  |  |  |  |  |  |
| 40 | 45 | [Download](40/dataset.zip) |  |  |  |  |  |  |  |  |
| 41 | 30 | [Download](41/dataset.zip) |  |  |  |  |  |  |  |  |
| 42 | 11 | [Download](42/dataset.zip) |  |  |  |  |  |  |  |  |
| 43 | 75 | [Download](43/dataset.zip) |  |  |  |  |  |  |  |  |
| 44 | 22 | [Download](44/dataset.zip) |  |  |  |  |  |  |  |  |
| 45 | 25 | [Download](45/dataset.zip) |  |  |  |  |  |  |  |  |
| 46 | 20 | [Download](46/dataset.zip) |  |  |  |  |  |  |  |  |
| 47 | 30 | [Download](47/dataset.zip) |  |  |  |  |  |  |  |  |
| 48 | 26 | [Download](48/dataset.zip) |  |  |  |  |  |  |  |  |
| 49 | 13 | [Download](49/dataset.zip) |  |  |  |  |  |  |  |  |
| 50 | 12 | [Download](50/dataset.zip) |  |  |  |  |  |  |  |  |
| 51 | 16 | [Download](51/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 59 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
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|
BangumiBase/jigokushoujo | BangumiBase | 2023-11-29T05:08:17Z | 0 | 0 | [
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
]
| null | 2023-11-29T02:55:17Z | ---
license: mit
tags:
- art
size_categories:
- 1K<n<10K
---
# Bangumi Image Base of Jigoku Shoujo
This is the image base of bangumi Jigoku Shoujo, we detected 89 characters, 6611 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|
| 0 | 538 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 26 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 26 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 102 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 52 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 434 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 637 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 703 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 102 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 56 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 50 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 73 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 35 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 109 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 211 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 127 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 25 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 81 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 18 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 31 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 51 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 109 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 46 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 42 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 78 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| 25 | 31 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| 26 | 136 | [Download](26/dataset.zip) |  |  |  |  |  |  |  |  |
| 27 | 48 | [Download](27/dataset.zip) |  |  |  |  |  |  |  |  |
| 28 | 30 | [Download](28/dataset.zip) |  |  |  |  |  |  |  |  |
| 29 | 49 | [Download](29/dataset.zip) |  |  |  |  |  |  |  |  |
| 30 | 27 | [Download](30/dataset.zip) |  |  |  |  |  |  |  |  |
| 31 | 29 | [Download](31/dataset.zip) |  |  |  |  |  |  |  |  |
| 32 | 44 | [Download](32/dataset.zip) |  |  |  |  |  |  |  |  |
| 33 | 428 | [Download](33/dataset.zip) |  |  |  |  |  |  |  |  |
| 34 | 41 | [Download](34/dataset.zip) |  |  |  |  |  |  |  |  |
| 35 | 55 | [Download](35/dataset.zip) |  |  |  |  |  |  |  |  |
| 36 | 40 | [Download](36/dataset.zip) |  |  |  |  |  |  |  |  |
| 37 | 23 | [Download](37/dataset.zip) |  |  |  |  |  |  |  |  |
| 38 | 271 | [Download](38/dataset.zip) |  |  |  |  |  |  |  |  |
| 39 | 41 | [Download](39/dataset.zip) |  |  |  |  |  |  |  |  |
| 40 | 42 | [Download](40/dataset.zip) |  |  |  |  |  |  |  |  |
| 41 | 39 | [Download](41/dataset.zip) |  |  |  |  |  |  |  |  |
| 42 | 12 | [Download](42/dataset.zip) |  |  |  |  |  |  |  |  |
| 43 | 23 | [Download](43/dataset.zip) |  |  |  |  |  |  |  |  |
| 44 | 26 | [Download](44/dataset.zip) |  |  |  |  |  |  |  |  |
| 45 | 68 | [Download](45/dataset.zip) |  |  |  |  |  |  |  |  |
| 46 | 47 | [Download](46/dataset.zip) |  |  |  |  |  |  |  |  |
| 47 | 53 | [Download](47/dataset.zip) |  |  |  |  |  |  |  |  |
| 48 | 24 | [Download](48/dataset.zip) |  |  |  |  |  |  |  |  |
| 49 | 26 | [Download](49/dataset.zip) |  |  |  |  |  |  |  |  |
| 50 | 185 | [Download](50/dataset.zip) |  |  |  |  |  |  |  |  |
| 51 | 46 | [Download](51/dataset.zip) |  |  |  |  |  |  |  |  |
| 52 | 32 | [Download](52/dataset.zip) |  |  |  |  |  |  |  |  |
| 53 | 27 | [Download](53/dataset.zip) |  |  |  |  |  |  |  |  |
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| 55 | 40 | [Download](55/dataset.zip) |  |  |  |  |  |  |  |  |
| 56 | 12 | [Download](56/dataset.zip) |  |  |  |  |  |  |  |  |
| 57 | 25 | [Download](57/dataset.zip) |  |  |  |  |  |  |  |  |
| 58 | 35 | [Download](58/dataset.zip) |  |  |  |  |  |  |  |  |
| 59 | 17 | [Download](59/dataset.zip) |  |  |  |  |  |  |  |  |
| 60 | 66 | [Download](60/dataset.zip) |  |  |  |  |  |  |  |  |
| 61 | 41 | [Download](61/dataset.zip) |  |  |  |  |  |  |  |  |
| 62 | 28 | [Download](62/dataset.zip) |  |  |  |  |  |  |  |  |
| 63 | 21 | [Download](63/dataset.zip) |  |  |  |  |  |  |  |  |
| 64 | 17 | [Download](64/dataset.zip) |  |  |  |  |  |  |  |  |
| 65 | 16 | [Download](65/dataset.zip) |  |  |  |  |  |  |  |  |
| 66 | 54 | [Download](66/dataset.zip) |  |  |  |  |  |  |  |  |
| 67 | 22 | [Download](67/dataset.zip) |  |  |  |  |  |  |  |  |
| 68 | 17 | [Download](68/dataset.zip) |  |  |  |  |  |  |  |  |
| 69 | 9 | [Download](69/dataset.zip) |  |  |  |  |  |  |  |  |
| 70 | 21 | [Download](70/dataset.zip) |  |  |  |  |  |  |  |  |
| 71 | 30 | [Download](71/dataset.zip) |  |  |  |  |  |  |  |  |
| 72 | 15 | [Download](72/dataset.zip) |  |  |  |  |  |  |  |  |
| 73 | 21 | [Download](73/dataset.zip) |  |  |  |  |  |  |  |  |
| 74 | 19 | [Download](74/dataset.zip) |  |  |  |  |  |  |  |  |
| 75 | 13 | [Download](75/dataset.zip) |  |  |  |  |  |  |  |  |
| 76 | 17 | [Download](76/dataset.zip) |  |  |  |  |  |  |  |  |
| 77 | 13 | [Download](77/dataset.zip) |  |  |  |  |  |  |  |  |
| 78 | 101 | [Download](78/dataset.zip) |  |  |  |  |  |  |  |  |
| 79 | 17 | [Download](79/dataset.zip) |  |  |  |  |  |  |  |  |
| 80 | 23 | [Download](80/dataset.zip) |  |  |  |  |  |  |  |  |
| 81 | 23 | [Download](81/dataset.zip) |  |  |  |  |  |  |  |  |
| 82 | 7 | [Download](82/dataset.zip) |  |  |  |  |  |  |  | N/A |
| 83 | 7 | [Download](83/dataset.zip) |  |  |  |  |  |  |  | N/A |
| 84 | 29 | [Download](84/dataset.zip) |  |  |  |  |  |  |  |  |
| 85 | 14 | [Download](85/dataset.zip) |  |  |  |  |  |  |  |  |
| 86 | 5 | [Download](86/dataset.zip) |  |  |  |  |  | N/A | N/A | N/A |
| 87 | 7 | [Download](87/dataset.zip) |  |  |  |  |  |  |  | N/A |
| noise | 54 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
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|
gilsonk12/LuanaCarolina | gilsonk12 | 2023-11-29T03:15:32Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-11-29T03:14:47Z | ---
license: openrail
---
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|
BangumiBase/jigokuraku | BangumiBase | 2023-11-29T05:56:41Z | 0 | 0 | [
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
]
| null | 2023-11-29T03:45:45Z | ---
license: mit
tags:
- art
size_categories:
- 1K<n<10K
---
# Bangumi Image Base of Jigokuraku
This is the image base of bangumi Jigokuraku, we detected 36 characters, 2877 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|
| 0 | 187 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 102 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 109 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 23 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 41 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 26 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 125 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 392 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 67 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 18 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 75 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 27 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 22 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 105 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 43 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 32 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 48 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 113 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 83 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 104 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 12 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 28 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 25 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 32 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 216 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| 25 | 10 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| 26 | 19 | [Download](26/dataset.zip) |  |  |  |  |  |  |  |  |
| 27 | 9 | [Download](27/dataset.zip) |  |  |  |  |  |  |  |  |
| 28 | 469 | [Download](28/dataset.zip) |  |  |  |  |  |  |  |  |
| 29 | 27 | [Download](29/dataset.zip) |  |  |  |  |  |  |  |  |
| 30 | 42 | [Download](30/dataset.zip) |  |  |  |  |  |  |  |  |
| 31 | 39 | [Download](31/dataset.zip) |  |  |  |  |  |  |  |  |
| 32 | 14 | [Download](32/dataset.zip) |  |  |  |  |  |  |  |  |
| 33 | 64 | [Download](33/dataset.zip) |  |  |  |  |  |  |  |  |
| 34 | 11 | [Download](34/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 118 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
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|
Infi-MM/CORE-MM | Infi-MM | 2023-11-29T04:56:42Z | 0 | 0 | [
"license:cc-by-nc-4.0",
"region:us"
]
| null | 2023-11-29T04:56:18Z | ---
license: cc-by-nc-4.0
---
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|
Solshine/Portuguese-English_MPC_for_NLP_UNCLEANED | Solshine | 2023-12-03T08:16:50Z | 0 | 0 | [
"license:mit",
"region:us"
]
| null | 2023-11-29T05:16:53Z | ---
license: mit
---
Portuguese and English Translations of readme are available here.
----
UNCLEANED VERSION - XLS format, which could be combined and cleaned
Combined and partially cleaned, partially reformatted, partial ETL version is available at Solshine/Portuguese-English-Vocab-PartiallyTransformed
*English Readme:*
**Bridging Language Barriers and Empowering Marginalized Communities through Multilingual Parallel Corpora: The English-Portuguese MPC**
In the realm of natural language processing (NLP), the availability of high-quality language data is crucial for developing effective NLP models. However, marginalized indigenous communities, particularly those in Brazil and the Amazon Rainforest, often lack access to NLP tools and resources, hindering their ability to fully engage in the digital age.
To address this disparity, we propose the creation of an English-Portuguese Multilingual Parallel Corpus (MPC), a collection of carefully curated parallel text pairs in English and Portuguese. This resource is specifically designed to democratize access to NLP and promote knowledge exchange between these two languages, fostering cross-cultural communication and empowering marginalized communities.
**Democratizing Access and Fostering Cross-Pollination**
This English-Portuguese MPC, Solshine/Portuguese-English-Translation-and-NLP-trainingdata-UNCLEANED , serves as a bridge between these two languages, enabling the development of more robust machine translation systems and enriching the availability of Portuguese language resources. For marginalized indigenous communities, this resource holds the potential to:
* **Preserve and revitalize indigenous languages:** By providing a benchmark for Portuguese-English translation, the MPC can facilitate the development of tools that can translate indigenous languages into Portuguese, aiding in language preservation and revitalization efforts.
* **Empower indigenous communities in the digital sphere:** Access to NLP tools trained on the MPC can enable indigenous communities to engage with online resources, participate in social media, and access information in their native language or Portuguese.
* **Promote cross-cultural understanding:** By fostering better communication between indigenous communities and the wider world, the MPC can help bridge cultural divides and promote mutual understanding.
**Addressing the Needs of Marginalized Indigenous Communities**
The English-Portuguese MPC is particularly relevant to marginalized indigenous communities in Brazil and the Amazon Rainforest, where Portuguese serves as the lingua franca and numerous indigenous languages are spoken. By providing a comprehensive resource for Portuguese-English translation, the MPC can empower these communities to:
* **Participate in education and research:** Indigenous students and researchers can access academic materials and engage in scientific research using NLP tools trained on the MPC.
* **Promote cultural heritage and storytelling:** Indigenous communities can utilize the MPC to translate their cultural narratives, folklore, and traditional knowledge into Portuguese, preserving and sharing their heritage with a wider audience.
* **Advocate for their rights and interests:** Indigenous communities can use the MPC to communicate effectively with government officials, NGOs, and the international community, advocating for their rights and interests.
**Sourcing the MPC through Conversation with Bard**
The English-Portuguese MPC was sourced through a combination of conversation with Bard and other sources. Bard's ability to understand and generate human language made it an invaluable tool for generating and refining the dataset, ensuring that the translations were accurate and natural-sounding.
Once the data was collected, it was carefully preprocessed and aligned to ensure that the sentences in English and Portuguese were truly parallel. This was a critical step, as it ensured that the MPC could be used to train NLP models that could accurately translate between the two languages.
**Conclusion**
The English-Portuguese MPC here is a valuable resource for NLP researchers and developers. It can be used to train machine translation systems, develop text summarization tools, and create sentiment analysis models. The MPC can also be used to study the relationship between English and Portuguese, and to develop new NLP algorithms. Moreover, the MPC empowers marginalized communities to preserve their languages, engage in the digital sphere, and advocate for their rights and interests.
----
*Portuguese Readme:*
**Superando barreiras linguísticas e empoderando comunidades marginalizadas por meio de corpora paralelos multilíngues: O MPC inglês-português**
No campo do processamento de linguagem natural (PLN), a disponibilidade de dados linguísticos de alta qualidade é crucial para o desenvolvimento de modelos de PLN eficazes. No entanto, comunidades indígenas marginalizadas, particularmente aquelas no Brasil e na Amazônia, muitas vezes não têm acesso a ferramentas e recursos de PLN, dificultando sua capacidade de se envolver plenamente na era digital.
Para lidar com essa disparidade, propomos a criação de um Corpus Paralelo Multilíngue (MPC) inglês-português, uma coleção de pares de texto paralelos cuidadosamente selecionados em inglês e português. Este recurso é projetado especificamente para democratizar o acesso ao PLN e promover o intercâmbio de conhecimento entre esses dois idiomas, fomentando a comunicação intercultural e empoderando comunidades marginalizadas.
**Democratizando o acesso e fomentando a polinização cruzada**
O MPC inglês-português, Solshine/Portuguese-English-Translation-and-NLP-trainingdata-UNCLEANED , serve como uma ponte entre esses dois idiomas, possibilitando o desenvolvimento de sistemas de tradução automática mais robustos e enriquecendo a disponibilidade de recursos em português. Para comunidades indígenas marginalizadas, este recurso tem o potencial de:
* **Preservar e revitalizar línguas indígenas:** Ao fornecer um benchmark para a tradução português-inglês, o MPC pode facilitar o desenvolvimento de ferramentas que podem traduzir línguas indígenas para o português, auxiliando nos esforços de preservação e revitalização da língua.
* **Empoderar comunidades indígenas na esfera digital:** O acesso a ferramentas de PLN treinadas no MPC pode capacitar comunidades indígenas a se envolver com recursos online, participar de mídias sociais e acessar informações em sua língua nativa ou português.
* **Promover a compreensão intercultural:** Ao promover uma melhor comunicação entre as comunidades indígenas e o mundo em geral, o MPC pode ajudar a superar divisões culturais e promover a compreensão mútua.
**Abordando as necessidades de comunidades indígenas marginalizadas**
O MPC inglês-português é particularmente relevante para comunidades indígenas marginalizadas no Brasil e na Amazônia, onde o português serve como língua franca e inúmeras línguas indígenas são faladas. Ao fornecer um recurso abrangente para tradução português-inglês, o MPC pode capacitar essas comunidades a:
* **Participar de educação e pesquisa:** Estudantes e pesquisadores indígenas podem acessar materiais acadêmicos e se envolver em pesquisas científicas usando ferramentas de PLN treinadas no MPC.
* **Promover o patrimônio cultural e a narrativa:** Comunidades indígenas podem utilizar o MPC para traduzir suas narrativas culturais, folclore e conhecimento tradicional para o português, preservando e compartilhando seu patrimônio com um público mais amplo.
* **Defender seus direitos e interesses:** As comunidades indígenas podem usar o MPC para se comunicar efetivamente com autoridades governamentais, ONGs e a comunidade internacional, defendendo seus direitos e interesses.
**Obtendo o MPC por meio de conversas com Bard**
O MPC inglês-português foi obtido por meio de uma combinação de conversas com Bard e outras fontes. A capacidade do Bard de entender e gerar linguagem humana o tornou uma ferramenta inestimável para gerar e refinar o conjunto de dados, garantindo que as traduções fossem precisas e naturais.
Uma vez que os dados foram coletados, eles foram cuidadosamente pré-processados e alinhados para garantir que as frases em inglês e português fossem verdadeiramente paralelas. Esta foi uma etapa crítica, pois garantiu que o MPC pudesse ser usado para treinar modelos de PLN que pudessem traduzir com precisão entre os dois idiomas.
**Conclusão**
O MPC inglês-português é um recurso valioso para pesquisadores e desenvolvedores de PLN. Ele pode ser usado para treinar sistemas de tradução automática, desenvolver ferramentas de resumo de texto e criar modelos de análise de sentimento. O MPC também pode ser usado para estudar a relação entre inglês e português e desenvolver novos algoritmos de PLN. Além disso, o MPC capacita comunidades marginalizadas a preservar seus idiomas, se envolver na esfera digital e defender seus direitos e interesses.
----
**Notes about the data (currently being updated):**
Inspired by the (November 2023) developments of Ocra2 and the ability for large LLMs to now produce training datasets for smaller (ie 7B or 3B) models to efficienctly learn and distill the fundamentals of the knowledge into themselves.
This is a great Portuguese language dataset, connecting Portuguese into the most widely used and trained language (English), thus democratizing access. You are encouraged to use this in your training to enrich the model's Portuguese.
Most of the table include: G3 Annotations, B- Tags , SRL Annotations, Dependency Parsing Annotations , POS Tagging Annotations
Please note for a large portion of the data: The NER annotations (G3) indicate general entities, while the B- tags indicate specific types of entities (e.g., B-Location, B-Time). The SRL annotations indicate the semantic roles of the constituents in the sentence (e.g., B-Theme, B-Agent, B-Patient, B-Goal). The dependency parsing annotations indicate the grammatical relationships between the words in the sentence. The POS tagging annotations indicate the part-of-speech (e.g., N for noun, V for verb, A for adjective) of each word in the sentence.
The main source of the data is generated through structured questions to Google Bard in the final week of November 2023, and many of these generating queries can be found as title names of individual small tables. Much of that portion was structured explicitly by having the prompt instructions including the previous paragraph's data structure explanation. This resulted in potentially much more useful data about the sentences or words from an NLP perspective, albiet with more inconsistency and minor errors, and even information entered occasionally into the wrong field, in those NLP related fields mentioned above.
This data is mostly uncleaned and should be used with the understanding that it was largely uncleaned and gathered from various sources. The data in the tables in this dataset has inherently been filtered by the guardrails present in Bard and through careful observation of the data (rejecting obviously errorous generations) as it was being generated by Bard and uploaded to, minimally processed the dataset. This is a disclaimer of any possible error or omission, and the dataset should be useful with this understanding.
Some of the tables or observations are missing entries for some of those fields (with the exception of a Portuguese term or sentence, which is present in every observation) especially POS Tagging Annotations, and Dependency Parsing Annotations, usually due to reaching Bard's data limit in it's public browser version (as of November 28th 2023.)
Strong focus towards moral compass and ethical real-world problems solving, as well as Indigenous Knowledge Systems, Climate Change, Science, STEM, intellectual property from a Copyleft perspective, some simple aspects of law, Indigenous Archeology, Educational Philosophy, and basic Vocabulary.
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|
phyloforfun/HLT_MICH_Angiospermae_SLTPvA_v1.0_OCR-C25-L25-E50-R10 | phyloforfun | 2023-11-29T05:20:40Z | 0 | 0 | [
"region:us"
]
| null | 2023-11-29T05:20:39Z | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 368634
num_examples: 230
download_size: 39580
dataset_size: 368634
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
awettig/RedPajama-combined-15B-6K-llama | awettig | 2023-11-29T16:45:09Z | 0 | 0 | [
"region:us"
]
| null | 2023-11-29T05:23:32Z | ---
dataset_info:
features:
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
- name: labels
sequence: int64
splits:
- name: test
num_bytes: 1422094968
num_examples: 17802
- name: train
num_bytes: 192480977304
num_examples: 2409506
download_size: 577654462
dataset_size: 193903072272
---
# Dataset Card for "RedPajama-combined-15B-6K-llama"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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|
awettig/RedPajama-combined-15B-8k-llama | awettig | 2023-11-30T07:16:59Z | 0 | 0 | [
"region:us"
]
| null | 2023-11-29T05:26:03Z | ---
dataset_info:
features:
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
- name: labels
sequence: int64
splits:
- name: test
num_bytes: 3181819992
num_examples: 29874
- name: train
num_bytes: 188533325564
num_examples: 1770133
download_size: 0
dataset_size: 191715145556
---
# Dataset Card for "RedPajama-combined-15B-8K-llama"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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|
TheGreatP/DaniloGentili | TheGreatP | 2023-11-29T05:34:58Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-11-29T05:34:17Z | ---
license: openrail
---
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|
worldboss/electricity-prices | worldboss | 2023-11-29T05:52:38Z | 0 | 0 | [
"task_categories:text-generation",
"size_categories:1K<n<10K",
"language:en",
"license:afl-3.0",
"ghana",
"electricity",
"region:us"
]
| [
"text-generation"
]
| 2023-11-29T05:46:29Z | ---
language:
- en
license: afl-3.0
size_categories:
- 1K<n<10K
task_categories:
- text-generation
tags:
- ghana
- electricity
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: Oper Day
dtype: string
- name: Hour Ending
dtype: int64
- name: HB_BUSAVG
dtype: float64
- name: HB_HOUSTON
dtype: float64
- name: HB_HUBAVG
dtype: float64
- name: HB_NORTH
dtype: float64
- name: HB_PAN
dtype: float64
- name: HB_SOUTH
dtype: float64
- name: HB_WEST
dtype: float64
- name: LZ_AEN
dtype: float64
- name: LZ_CPS
dtype: float64
- name: LZ_HOUSTON
dtype: float64
- name: LZ_LCRA
dtype: float64
- name: LZ_NORTH
dtype: float64
- name: LZ_RAYBN
dtype: float64
- name: LZ_SOUTH
dtype: float64
- name: LZ_WEST
dtype: float64
splits:
- name: train
num_bytes: 23352
num_examples: 168
download_size: 24020
dataset_size: 23352
---
### Load Dataset
```shell
pip install datasets
```
```python
from datasets import load_dataset
electricity = load_dataset("worldboss/electricity-prices", split="train")
pd.DataFrame(electricity).head()
```
### Author
The data was constructed by Theophilus Siameh ([email protected]). | [
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|
krisrod/test_name | krisrod | 2023-11-29T06:59:57Z | 0 | 0 | [
"license:llama2",
"region:us"
]
| null | 2023-11-29T06:40:49Z | ---
license: llama2
---
{ "from": "human", "value": "Your name is Su Wen" } | [
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|
lol-cod/captchadataset | lol-cod | 2023-11-29T06:48:09Z | 0 | 0 | [
"language:en",
"license:unknown",
"keras captcha solving",
"region:us"
]
| null | 2023-11-29T06:46:00Z | ---
license: unknown
language:
- en
tags:
- keras captcha solving
pretty_name: database
--- | [
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|
qgyd2021/translation_sft | qgyd2021 | 2023-11-29T06:54:23Z | 0 | 0 | [
"task_categories:question-answering",
"task_categories:translation",
"task_categories:conversational",
"task_categories:text-generation",
"task_categories:text2text-generation",
"size_categories:100M<n<1B",
"language:zh",
"language:en",
"license:apache-2.0",
"region:us"
]
| [
"question-answering",
"translation",
"conversational",
"text-generation",
"text2text-generation"
]
| 2023-11-29T06:50:22Z | ---
license: apache-2.0
task_categories:
- question-answering
- translation
- conversational
- text-generation
- text2text-generation
language:
- zh
- en
size_categories:
- 100M<n<1B
---
## 句子翻译指令数据集
其中包含**机器翻译**数据集，也包含**汉语文言文与白话文之间的翻译**数据集。
在做[qgyd2021/few_shot_intent_sft](https://huggingface.co/datasets/qgyd2021/few_shot_intent_sft)时，我意识到可能需要同时让模型具有翻译的能力以实现知识在不同语言之间的传递，因此决定制作此数据集。
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|
zxbsmk/TIP-2018 | zxbsmk | 2023-12-01T13:24:43Z | 0 | 0 | [
"task_categories:image-to-image",
"size_categories:100K<n<1M",
"language:en",
"license:mit",
"region:us"
]
| [
"image-to-image"
]
| 2023-11-29T06:51:44Z | ---
license: mit
task_categories:
- image-to-image
language:
- en
size_categories:
- 100K<n<1M
viewer: false
---
This is the dataset of *Moir´e Photo Restoration Using Multiresolution Convolutional Neural Networks*. | [
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|
georgeprethesh/1 | georgeprethesh | 2023-11-29T07:39:26Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-11-29T07:39:25Z | ---
license: openrail
---
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|
zeaver/multifactor_hotpotqa_suppfacts | zeaver | 2023-11-29T12:01:23Z | 0 | 0 | [
"task_categories:text-generation",
"task_categories:question-answering",
"size_categories:10K<n<100K",
"language:en",
"license:mit",
"question-generation",
"HotpotQA",
"arxiv:2310.13512",
"arxiv:1809.09600",
"arxiv:1809.02922",
"region:us"
]
| [
"text-generation",
"question-answering"
]
| 2023-11-29T07:44:10Z | ---
license: mit
task_categories:
- text-generation
- question-answering
language:
- en
tags:
- question-generation
- HotpotQA
size_categories:
- 10K<n<100K
---
# MultiFactor-HotpotQA-SuppFacts
<!-- Provide a quick summary of the dataset. -->
The MultiFactor datasets -- HotpotQA-Supporting Facts part in EMNLP 2023 Findings: [*Improving Question Generation with Multi-level Content Planning*](https://arxiv.org/abs/2310.13512).
## 1. Dataset Details
### 1.1 Dataset Description
Supporting Facts setting on HotpotQA dataset [1] in EMNLP 2023 Findings: [*Improving Question Generation with Multi-level Content Planning*](https://arxiv.org/abs/2310.13512).
Based on the dataset provided in [CQG](https://github.com/sion-zcfei/cqg) [2], we add the `p_hrase`, `n_phrase` and `full answer` attributes for every dataset instance.
The full answer is reconstructed with [QA2D](https://github.com/kelvinguu/qanli) [3]. More details are in paper github: https://github.com/zeaver/MultiFactor.
### 1.2 Dataset Sources
<!-- Provide the basic links for the dataset. -->
- **Repository:** https://github.com/zeaver/MultiFactor
- **Paper:** [*Improving Question Generation with Multi-level Content Planning*](https://arxiv.org/abs/2310.13512). EMNLP Findings, 2023.
## 2. Dataset Structure
```tex
.
├── dev.json
├── test.json
├── train.json
├── fa_model_inference
├── dev.json
├── test.json
└── train.json
```
Each split is a json file, not jsonl. Please load it with `json.load(f)` directly. And the dataset schema is:
```json
{
"context": "the given input context",
"answer": "the given answer",
"question": "the corresponding question",
"p_phrase": "the postive phrases in the given context",
"n_phrase": "the negative phrases",
"full answer": "pseudo-gold full answer (q + a -> a declarative sentence)",
}
```
We also provide the *FA_Model*'s inference results in `fa_model_inference/{split}.json`.
## 3. Dataset Card Contact
If you have any question, feel free to contact with me: [email protected]
## Reference
[1] Yang, Zhilin, et al. [HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering](https://arxiv.org/abs/1809.09600). EMNLP, 2018.
[2] Fei, Zichu, et al. [CQG: A Simple and Effective Controlled Generation Framework for Multi-Hop Question Generation](https://aclanthology.org/2022.acl-long.475/). ACL, 2022.
[3] Demszky, Dorottya, et al. [Transforming Question Answering Datasets Into Natural Language Inference Datasets](https://arxiv.org/abs/1809.02922). Stanford University. arXiv, 2018. | [
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|
MAXJHOW/maxtexte | MAXJHOW | 2023-11-29T08:09:13Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-11-29T08:09:13Z | ---
license: openrail
---
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|
MAXJHOW/clonarvoz | MAXJHOW | 2023-11-29T08:17:20Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-11-29T08:15:54Z | ---
license: openrail
---
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|
BangumiBase/durarara | BangumiBase | 2023-11-29T12:43:52Z | 0 | 0 | [
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
]
| null | 2023-11-29T08:18:01Z | ---
license: mit
tags:
- art
size_categories:
- 1K<n<10K
---
# Bangumi Image Base of Durarara!!
This is the image base of bangumi Durarara!!, we detected 78 characters, 7823 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|
| 0 | 1153 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 159 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 185 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 224 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 177 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 237 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 267 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 67 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 38 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 129 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 375 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 34 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 75 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 33 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 24 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 177 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 112 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 124 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 142 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 133 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 132 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 87 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 34 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 161 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 440 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| 25 | 297 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| 26 | 62 | [Download](26/dataset.zip) |  |  |  |  |  |  |  |  |
| 27 | 34 | [Download](27/dataset.zip) |  |  |  |  |  |  |  |  |
| 28 | 48 | [Download](28/dataset.zip) |  |  |  |  |  |  |  |  |
| 29 | 68 | [Download](29/dataset.zip) |  |  |  |  |  |  |  |  |
| 30 | 31 | [Download](30/dataset.zip) |  |  |  |  |  |  |  |  |
| 31 | 106 | [Download](31/dataset.zip) |  |  |  |  |  |  |  |  |
| 32 | 62 | [Download](32/dataset.zip) |  |  |  |  |  |  |  |  |
| 33 | 82 | [Download](33/dataset.zip) |  |  |  |  |  |  |  |  |
| 34 | 20 | [Download](34/dataset.zip) |  |  |  |  |  |  |  |  |
| 35 | 64 | [Download](35/dataset.zip) |  |  |  |  |  |  |  |  |
| 36 | 37 | [Download](36/dataset.zip) |  |  |  |  |  |  |  |  |
| 37 | 83 | [Download](37/dataset.zip) |  |  |  |  |  |  |  |  |
| 38 | 39 | [Download](38/dataset.zip) |  |  |  |  |  |  |  |  |
| 39 | 21 | [Download](39/dataset.zip) |  |  |  |  |  |  |  |  |
| 40 | 59 | [Download](40/dataset.zip) |  |  |  |  |  |  |  |  |
| 41 | 31 | [Download](41/dataset.zip) |  |  |  |  |  |  |  |  |
| 42 | 653 | [Download](42/dataset.zip) |  |  |  |  |  |  |  |  |
| 43 | 23 | [Download](43/dataset.zip) |  |  |  |  |  |  |  |  |
| 44 | 120 | [Download](44/dataset.zip) |  |  |  |  |  |  |  |  |
| 45 | 22 | [Download](45/dataset.zip) |  |  |  |  |  |  |  |  |
| 46 | 22 | [Download](46/dataset.zip) |  |  |  |  |  |  |  |  |
| 47 | 36 | [Download](47/dataset.zip) |  |  |  |  |  |  |  |  |
| 48 | 162 | [Download](48/dataset.zip) |  |  |  |  |  |  |  |  |
| 49 | 11 | [Download](49/dataset.zip) |  |  |  |  |  |  |  |  |
| 50 | 64 | [Download](50/dataset.zip) |  |  |  |  |  |  |  |  |
| 51 | 8 | [Download](51/dataset.zip) |  |  |  |  |  |  |  |  |
| 52 | 13 | [Download](52/dataset.zip) |  |  |  |  |  |  |  |  |
| 53 | 19 | [Download](53/dataset.zip) |  |  |  |  |  |  |  |  |
| 54 | 171 | [Download](54/dataset.zip) |  |  |  |  |  |  |  |  |
| 55 | 14 | [Download](55/dataset.zip) |  |  |  |  |  |  |  |  |
| 56 | 16 | [Download](56/dataset.zip) |  |  |  |  |  |  |  |  |
| 57 | 19 | [Download](57/dataset.zip) |  |  |  |  |  |  |  |  |
| 58 | 10 | [Download](58/dataset.zip) |  |  |  |  |  |  |  |  |
| 59 | 64 | [Download](59/dataset.zip) |  |  |  |  |  |  |  |  |
| 60 | 30 | [Download](60/dataset.zip) |  |  |  |  |  |  |  |  |
| 61 | 16 | [Download](61/dataset.zip) |  |  |  |  |  |  |  |  |
| 62 | 30 | [Download](62/dataset.zip) |  |  |  |  |  |  |  |  |
| 63 | 18 | [Download](63/dataset.zip) |  |  |  |  |  |  |  |  |
| 64 | 26 | [Download](64/dataset.zip) |  |  |  |  |  |  |  |  |
| 65 | 9 | [Download](65/dataset.zip) |  |  |  |  |  |  |  |  |
| 66 | 21 | [Download](66/dataset.zip) |  |  |  |  |  |  |  |  |
| 67 | 12 | [Download](67/dataset.zip) |  |  |  |  |  |  |  |  |
| 68 | 22 | [Download](68/dataset.zip) |  |  |  |  |  |  |  |  |
| 69 | 28 | [Download](69/dataset.zip) |  |  |  |  |  |  |  |  |
| 70 | 13 | [Download](70/dataset.zip) |  |  |  |  |  |  |  |  |
| 71 | 6 | [Download](71/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| 72 | 6 | [Download](72/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| 73 | 17 | [Download](73/dataset.zip) |  |  |  |  |  |  |  |  |
| 74 | 10 | [Download](74/dataset.zip) |  |  |  |  |  |  |  |  |
| 75 | 12 | [Download](75/dataset.zip) |  |  |  |  |  |  |  |  |
| 76 | 6 | [Download](76/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| noise | 231 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
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|
Deojoandco/capstone_fromgpt_without_gold_v7 | Deojoandco | 2023-11-29T08:21:33Z | 0 | 0 | [
"region:us"
]
| null | 2023-11-29T08:21:29Z | ---
dataset_info:
features:
- name: dialog_id
dtype: int64
- name: dialogue
dtype: string
- name: summary
dtype: string
- name: gold_tags
dtype: string
- name: gpt_success
dtype: bool
- name: gpt_response
dtype: string
- name: gold_tags_tokens_count
dtype: int64
- name: GPT_TAGS_FOUND
dtype: bool
- name: gpt_output_tags
dtype: string
- name: gpt_output_tag_tokens_count
dtype: int64
- name: GPT_MI_FOUND
dtype: bool
- name: gpt_tags_token_count
dtype: int64
- name: gpt_tags
dtype: string
- name: tag_token_count_match
dtype: bool
splits:
- name: test
num_bytes: 21303
num_examples: 12
download_size: 23320
dataset_size: 21303
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
---
# Dataset Card for "capstone_fromgpt_without_gold_v7"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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|
pfsv/atis_json | pfsv | 2023-11-29T08:24:52Z | 0 | 0 | [
"license:mit",
"region:us"
]
| null | 2023-11-29T08:24:29Z | ---
license: mit
---
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|
xiaxiaoqian/model | xiaxiaoqian | 2023-11-29T09:10:35Z | 0 | 0 | [
"license:mit",
"region:us"
]
| null | 2023-11-29T08:36:02Z | ---
license: mit
---
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|
reddrex/termdescription_1.0 | reddrex | 2023-11-29T08:58:47Z | 0 | 0 | [
"license:apache-2.0",
"region:us"
]
| null | 2023-11-29T08:58:47Z | ---
license: apache-2.0
---
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|
javismiles/lora3 | javismiles | 2023-11-29T09:04:10Z | 0 | 0 | [
"region:us"
]
| null | 2023-11-29T09:04:08Z | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 23169.0
num_examples: 3
download_size: 23782
dataset_size: 23169.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
UnderstandLing/NL_gigacorpus | UnderstandLing | 2023-11-29T12:41:57Z | 0 | 0 | [
"license:cc",
"region:us"
]
| null | 2023-11-29T09:31:32Z | ---
license: cc
---
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|
Solshine/Portuguese_Language_Education_Texts | Solshine | 2023-11-29T09:40:55Z | 0 | 0 | [
"license:mit",
"region:us"
]
| null | 2023-11-29T09:31:53Z | ---
license: mit
---
CURRENTLY UNDER CONSTRUCTION
English Readme:
Bridging Language Barriers and Empowering Marginalized Communities through increasing open Portuguese Language Education Text training data:
In the realm of natural language processing (NLP), the availability of high-quality language data is crucial for developing effective NLP models. However, marginalized indigenous communities, particularly those in Brazil and the Amazon Rainforest, often lack access to NLP tools and resources, hindering their ability to fully engage in the digital age.
The texts include Portuguese language learning textbooks, Portuguese language educational materials, important historical texts in the Portuguese lanugage (especially global south), and supplemental examples of high repute. Poetic and educational phrased texts have been prioritized in this collection. | [
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|
Deojoandco/capstone_fromgpt_without_gold_v9_all | Deojoandco | 2023-11-29T09:55:33Z | 0 | 0 | [
"region:us"
]
| null | 2023-11-29T09:55:19Z | ---
dataset_info:
features:
- name: dialog_id
dtype: int64
- name: dialogue
dtype: string
- name: summary
dtype: string
- name: gold_tags
dtype: string
- name: gpt_success
dtype: bool
- name: gpt_response
dtype: string
- name: gold_tags_tokens_count
dtype: int64
- name: GPT_TAGS_FOUND
dtype: bool
- name: gpt_output_tags
dtype: string
- name: gpt_output_tag_tokens_count
dtype: int64
- name: GPT_MI_FOUND
dtype: bool
- name: gpt_tags_token_count
dtype: int64
- name: gpt_tags
dtype: string
- name: tag_token_count_match
dtype: bool
splits:
- name: train
num_bytes: 124191
num_examples: 76
- name: validation
num_bytes: 23023
num_examples: 12
- name: test
num_bytes: 14536
num_examples: 12
download_size: 82277
dataset_size: 161750
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
# Dataset Card for "capstone_fromgpt_without_gold_v9_all"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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|
Pablao0948/Chiro | Pablao0948 | 2023-11-29T10:14:08Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-11-29T10:12:40Z | ---
license: openrail
---
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|
Pablao0948/Chiro2 | Pablao0948 | 2023-11-29T10:30:59Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-11-29T10:29:48Z | ---
license: openrail
---
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|
finepro111/PRI_FAQ | finepro111 | 2023-11-29T10:36:44Z | 0 | 0 | [
"license:mit",
"region:us"
]
| null | 2023-11-29T10:34:56Z | ---
license: mit
---
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|
ThanhJamieAI/test | ThanhJamieAI | 2023-11-29T10:42:39Z | 0 | 0 | [
"license:apache-2.0",
"region:us"
]
| null | 2023-11-29T10:41:35Z | ---
license: apache-2.0
---
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|
ctoraman/BilTweetNews-event-detection | ctoraman | 2023-11-29T11:09:34Z | 0 | 0 | [
"task_categories:text-classification",
"size_categories:n<1K",
"language:tr",
"license:cc-by-nc-sa-4.0",
"text classification",
"event detection",
"topic detection",
"tweets",
"social media",
"topic classification",
"region:us"
]
| [
"text-classification"
]
| 2023-11-29T10:53:10Z | ---
license: cc-by-nc-sa-4.0
task_categories:
- text-classification
language:
- tr
tags:
- text classification
- event detection
- topic detection
- tweets
- social media
- topic classification
size_categories:
- n<1K
---
# Turkish Event Detection Tweet Dataset: BilTweetNews
The dataset contains tweets related to six major events from Turkish news sources between May 4, 2015
and Jan 8, 2017.
There are 7 event classes:
- E1: May 25, 2015 One of the popular football clubs in Turkey, Galatasaray, wins the 2015
Turkish Super League.
- E2: Sep 6, 2015 A terrorist group, called PKK, attacked to soldiers in Dağlıca, a village in
southeastern Turkey.
- E3: Oct 7, 2015 A Turkish scientist, Aziz Sancar, won the 2015 Nobel Chemistry prize with
his studies on DNA repair.
- E4: May 27, 2016 A local football club of Alanya promoted to the Turkish Super League for
the first time in their history.
- E5: Jun 17, 2016 A traditional anthem that is mostly played by secularists in Turkey, called
the 10th Year Anthem, was forbidden in schools by the director of national
education in the Black Sea province of Bolu.
- E6: Oct 17, 2016 A magazine programmer confused that Madonna in a Fur Coat, a book written
in 1943 by a Turkish celebrated writer, Sabahattin Ali, was about popstar
Madonna’s life. The book tells a story between a Turkish student and German
singer after the World War I.
- Other: Not related to any news topic
For each event, 100 related-candidate and 60 unrelated-candidate tweets are selected. Lastly, we randomly select 40 tweets that are potentially not related at all, 5 of them are
removed due to detecting near-duplicates later. The dataset has 995 tweets in total.
The task of this dataset is event detection. The sentiment analysis labels can be found at https://huggingface.co/datasets/ctoraman/BilTweetNews-Sentiment
All tweets are labeled by 17 annotators. We provide the normalized distribution of annotations across 7 event classes. We also provide the majority class at the last column. There are no cases where multiple classes have the highest score.
Github Repo: https://github.com/BilkentInformationRetrievalGroup/BilTweetNews2017
# If you would like to use any material in this repository, please cite the following papers:
- Toraman, C. Early Prediction of Public Reactions to News Events Using Microblogs. Seventh BCS-IRSG Symposium on Future Directions in Information Access (FDIA 2017), Barcelona, Spain, 5 September 2017.
- Toraman, C. Event-related microblog retrieval in Turkish. Turkish Journal of Electrical Engineering and Computer Sciences. 2021. DOI: 10.3906/elk-2108-167
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|
nataliaElv/oasst_response_comparison | nataliaElv | 2023-11-29T11:24:39Z | 0 | 0 | [
"size_categories:1K<n<10K",
"rlfh",
"argilla",
"human-feedback",
"region:us"
]
| null | 2023-11-29T11:24:31Z | ---
size_categories: 1K<n<10K
tags:
- rlfh
- argilla
- human-feedback
---
# Dataset Card for oasst_response_comparison
This dataset has been created with [Argilla](https://docs.argilla.io).
As shown in the sections below, this dataset can be loaded into Argilla as explained in [Load with Argilla](#load-with-argilla), or used directly with the `datasets` library in [Load with `datasets`](#load-with-datasets).
## Dataset Description
- **Homepage:** https://argilla.io
- **Repository:** https://github.com/argilla-io/argilla
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset contains:
* A dataset configuration file conforming to the Argilla dataset format named `argilla.yaml`. This configuration file will be used to configure the dataset when using the `FeedbackDataset.from_huggingface` method in Argilla.
* Dataset records in a format compatible with HuggingFace `datasets`. These records will be loaded automatically when using `FeedbackDataset.from_huggingface` and can be loaded independently using the `datasets` library via `load_dataset`.
* The [annotation guidelines](#annotation-guidelines) that have been used for building and curating the dataset, if they've been defined in Argilla.
### Load with Argilla
To load with Argilla, you'll just need to install Argilla as `pip install argilla --upgrade` and then use the following code:
```python
import argilla as rg
ds = rg.FeedbackDataset.from_huggingface("nataliaElv/oasst_response_comparison")
```
### Load with `datasets`
To load this dataset with `datasets`, you'll just need to install `datasets` as `pip install datasets --upgrade` and then use the following code:
```python
from datasets import load_dataset
ds = load_dataset("nataliaElv/oasst_response_comparison")
```
### Supported Tasks and Leaderboards
This dataset can contain [multiple fields, questions and responses](https://docs.argilla.io/en/latest/conceptual_guides/data_model.html#feedback-dataset) so it can be used for different NLP tasks, depending on the configuration. The dataset structure is described in the [Dataset Structure section](#dataset-structure).
There are no leaderboards associated with this dataset.
### Languages
[More Information Needed]
## Dataset Structure
### Data in Argilla
The dataset is created in Argilla with: **fields**, **questions**, **suggestions**, **metadata**, **vectors**, and **guidelines**.
The **fields** are the dataset records themselves, for the moment just text fields are supported. These are the ones that will be used to provide responses to the questions.
| Field Name | Title | Type | Required | Markdown |
| ---------- | ----- | ---- | -------- | -------- |
| prompt | Prompt | text | True | True |
| reply_1 | Reply 1 | text | True | True |
| reply_2 | Reply 2 | text | True | True |
| reply_3 | Reply 3 | text | True | True |
The **questions** are the questions that will be asked to the annotators. They can be of different types, such as rating, text, label_selection, multi_label_selection, or ranking.
| Question Name | Title | Type | Required | Description | Values/Labels |
| ------------- | ----- | ---- | -------- | ----------- | ------------- |
| reply-ranking | Order replies based on your preference | ranking | True | Order the replies from 1 to 3 based on your preference. | ['reply-1', 'reply-2', 'reply-3'] |
The **suggestions** are human or machine generated recommendations for each question to assist the annotator during the annotation process, so those are always linked to the existing questions, and named appending "-suggestion" and "-suggestion-metadata" to those, containing the value/s of the suggestion and its metadata, respectively. So on, the possible values are the same as in the table above, but the column name is appended with "-suggestion" and the metadata is appended with "-suggestion-metadata".
The **metadata** is a dictionary that can be used to provide additional information about the dataset record. This can be useful to provide additional context to the annotators, or to provide additional information about the dataset record itself. For example, you can use this to provide a link to the original source of the dataset record, or to provide additional information about the dataset record itself, such as the author, the date, or the source. The metadata is always optional, and can be potentially linked to the `metadata_properties` defined in the dataset configuration file in `argilla.yaml`.
| Metadata Name | Title | Type | Values | Visible for Annotators |
| ------------- | ----- | ---- | ------ | ---------------------- |
The **guidelines**, are optional as well, and are just a plain string that can be used to provide instructions to the annotators. Find those in the [annotation guidelines](#annotation-guidelines) section.
### Data Instances
An example of a dataset instance in Argilla looks as follows:
```json
{
"external_id": null,
"fields": {
"prompt": "Can you write a short introduction about the relevance of the term \"monopsony\" in economics? Please use examples related to potential monopsonies in the labour market and cite relevant research.",
"reply_1": "\"Monopsony\" refers to a market structure where there is only one buyer for a particular good or service. In economics, this term is particularly relevant in the labor market, where a monopsony employer has significant power over the wages and working conditions of their employees. The presence of a monopsony can result in lower wages and reduced employment opportunities for workers, as the employer has little incentive to increase wages or provide better working conditions.\n\nRecent research has identified potential monopsonies in industries such as retail and fast food, where a few large companies control a significant portion of the market (Bivens \u0026 Mishel, 2013). In these industries, workers often face low wages, limited benefits, and reduced bargaining power, leading to a situation where they are dependent on the employer for their livelihood. This dependence can result in further suppression of wages and a decline in working conditions.\n\nOverall, the concept of monopsony is essential to understanding the dynamics of labor markets and the impact of market power on workers. Further research is needed to understand the extent and impact of monopsonies on the economy and to develop policies to address this issue.\n\nReferences:\nBivens, J., \u0026 Mishel, L. (2013). The Pay of Corporate Executives and Financial Professionals as Evidence of Rents in Top 1 Percent Incomes. Journal of Economic Perspectives, 27(3), 57-78.",
"reply_2": "Monopsony is a market structure in which there is a single buyer in a market. In the context of labor markets, a monopsony occurs when there is only one employer in a particular industry or geographic area, giving that employer significant power over the wages and working conditions of workers. This results in a situation where the employer has the ability to suppress wages and reduce the bargaining power of workers.\n\nResearch has shown that the existence of monopsony power in labor markets can have negative effects on workers, such as lower wages, reduced benefits, and limited job opportunities. For example, a study by Autor, Dorn, and Hanson (2013) found evidence of monopsony power in certain U.S. labor markets, resulting in lower wages and reduced job opportunities for workers. Similarly, another study by Bharadwaj, Black, and Kolesnikova (2019) found that the presence of a single dominant employer in a local labor market was associated with lower wages for workers.",
"reply_3": "Monopsony refers to a market structure where there is only one buyer of a good or service. In the context of labor markets, a monopsony occurs when there is only one employer in a particular geographic area or industry, giving them significant power to set wages and working conditions. Research has shown that the existence of monopsonies can lead to lower wages and reduced job opportunities for workers. For example, a study by the National Bureau of Economic Research found that in industries with high levels of concentration, workers earn lower wages and are less likely to receive benefits such as health insurance."
},
"metadata": {},
"responses": [],
"suggestions": [],
"vectors": {}
}
```
While the same record in HuggingFace `datasets` looks as follows:
```json
{
"external_id": null,
"metadata": "{}",
"prompt": "Can you write a short introduction about the relevance of the term \"monopsony\" in economics? Please use examples related to potential monopsonies in the labour market and cite relevant research.",
"reply-ranking": [],
"reply-ranking-suggestion": null,
"reply-ranking-suggestion-metadata": {
"agent": null,
"score": null,
"type": null
},
"reply_1": "\"Monopsony\" refers to a market structure where there is only one buyer for a particular good or service. In economics, this term is particularly relevant in the labor market, where a monopsony employer has significant power over the wages and working conditions of their employees. The presence of a monopsony can result in lower wages and reduced employment opportunities for workers, as the employer has little incentive to increase wages or provide better working conditions.\n\nRecent research has identified potential monopsonies in industries such as retail and fast food, where a few large companies control a significant portion of the market (Bivens \u0026 Mishel, 2013). In these industries, workers often face low wages, limited benefits, and reduced bargaining power, leading to a situation where they are dependent on the employer for their livelihood. This dependence can result in further suppression of wages and a decline in working conditions.\n\nOverall, the concept of monopsony is essential to understanding the dynamics of labor markets and the impact of market power on workers. Further research is needed to understand the extent and impact of monopsonies on the economy and to develop policies to address this issue.\n\nReferences:\nBivens, J., \u0026 Mishel, L. (2013). The Pay of Corporate Executives and Financial Professionals as Evidence of Rents in Top 1 Percent Incomes. Journal of Economic Perspectives, 27(3), 57-78.",
"reply_2": "Monopsony is a market structure in which there is a single buyer in a market. In the context of labor markets, a monopsony occurs when there is only one employer in a particular industry or geographic area, giving that employer significant power over the wages and working conditions of workers. This results in a situation where the employer has the ability to suppress wages and reduce the bargaining power of workers.\n\nResearch has shown that the existence of monopsony power in labor markets can have negative effects on workers, such as lower wages, reduced benefits, and limited job opportunities. For example, a study by Autor, Dorn, and Hanson (2013) found evidence of monopsony power in certain U.S. labor markets, resulting in lower wages and reduced job opportunities for workers. Similarly, another study by Bharadwaj, Black, and Kolesnikova (2019) found that the presence of a single dominant employer in a local labor market was associated with lower wages for workers.",
"reply_3": "Monopsony refers to a market structure where there is only one buyer of a good or service. In the context of labor markets, a monopsony occurs when there is only one employer in a particular geographic area or industry, giving them significant power to set wages and working conditions. Research has shown that the existence of monopsonies can lead to lower wages and reduced job opportunities for workers. For example, a study by the National Bureau of Economic Research found that in industries with high levels of concentration, workers earn lower wages and are less likely to receive benefits such as health insurance."
}
```
### Data Fields
Among the dataset fields, we differentiate between the following:
* **Fields:** These are the dataset records themselves, for the moment just text fields are supported. These are the ones that will be used to provide responses to the questions.
* **prompt** is of type `text`.
* **reply_1** is of type `text`.
* **reply_2** is of type `text`.
* **reply_3** is of type `text`.
* **Questions:** These are the questions that will be asked to the annotators. They can be of different types, such as `RatingQuestion`, `TextQuestion`, `LabelQuestion`, `MultiLabelQuestion`, and `RankingQuestion`.
* **reply-ranking** is of type `ranking` with the following allowed values ['reply-1', 'reply-2', 'reply-3'], and description "Order the replies from 1 to 3 based on your preference.".
* **Suggestions:** As of Argilla 1.13.0, the suggestions have been included to provide the annotators with suggestions to ease or assist during the annotation process. Suggestions are linked to the existing questions, are always optional, and contain not just the suggestion itself, but also the metadata linked to it, if applicable.
* (optional) **reply-ranking-suggestion** is of type `ranking` with the following allowed values ['reply-1', 'reply-2', 'reply-3'].
Additionally, we also have two more fields that are optional and are the following:
* **metadata:** This is an optional field that can be used to provide additional information about the dataset record. This can be useful to provide additional context to the annotators, or to provide additional information about the dataset record itself. For example, you can use this to provide a link to the original source of the dataset record, or to provide additional information about the dataset record itself, such as the author, the date, or the source. The metadata is always optional, and can be potentially linked to the `metadata_properties` defined in the dataset configuration file in `argilla.yaml`.
* **external_id:** This is an optional field that can be used to provide an external ID for the dataset record. This can be useful if you want to link the dataset record to an external resource, such as a database or a file.
### Data Splits
The dataset contains a single split, which is `train`.
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation guidelines
For each promt, order the replies in a ranking based on how clear and helpful you find each reply. Ties are allowed. If you prefer not to give an answer, click Discard and move on to the next record.
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] | [
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|
tr416/filtered_big_literalist_ds | tr416 | 2023-11-29T11:34:01Z | 0 | 0 | [
"region:us"
]
| null | 2023-11-29T11:33:58Z | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 953191
num_examples: 764
download_size: 483558
dataset_size: 953191
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "filtered_big_literalist_ds"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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|
ctoraman/front-page-news | ctoraman | 2023-11-29T12:17:29Z | 0 | 0 | [
"task_categories:text-generation",
"task_categories:text-classification",
"language:tr",
"license:cc-by-nc-sa-4.0",
"front-page-news",
"front-page",
"news",
"region:us"
]
| [
"text-generation",
"text-classification"
]
| 2023-11-29T11:36:47Z | ---
license: cc-by-nc-sa-4.0
task_categories:
- text-generation
- text-classification
language:
- tr
tags:
- front-page-news
- front-page
- news
---
(This dataset contains raw text, which are unlabeled.)
5,844 Turkish news articles obtained from the Milliyet newspaper between 9 September 2009 and 31 October 2009
Github Repo: https://github.com/BilkentInformationRetrievalGroup/BilFront2009
# If you would like to use any material in this repository, please cite the following paper:
Toraman, C., & Can, F. (2015). A front-page news-selection algorithm based on topic modelling using raw text. Journal of Information Science, 41(5), 676-685. | [
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]
|
ArcelikGPT/faultsense | ArcelikGPT | 2023-11-29T12:00:40Z | 0 | 0 | [
"license:apache-2.0",
"region:us"
]
| null | 2023-11-29T11:44:19Z | ---
dataset_info:
features:
- name: Product ID
dtype: string
- name: Type
dtype: string
- name: Air temperature [K]
dtype: float64
- name: Process temperature [K]
dtype: float64
- name: Rotational speed [rpm]
dtype: int64
- name: Torque [Nm]
dtype: float64
- name: Tool wear [min]
dtype: int64
- name: Machine failure
dtype: int64
- name: TWF
dtype: int64
- name: HDF
dtype: int64
- name: PWF
dtype: int64
- name: OSF
dtype: int64
- name: RNF
dtype: int64
- name: UDI
dtype: int64
splits:
- name: train
num_bytes: 1110000
num_examples: 10000
download_size: 245912
dataset_size: 1110000
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
license: apache-2.0
---
The [AI4I 2020 Predictive Maintenance Dataset](https://archive.ics.uci.edu/dataset/601/ai4i+2020+predictive+maintenance+dataset) is the source of this dataset.
We offer and give a synthetic dataset that reflects true predictive maintenance faced in industry, since real predictive maintenance datasets are often hard to get and especially hard to publish.
**Variable Information**
The dataset consists of 10 000 data points stored as rows with 14 features in columns
UID: unique identifier ranging from 1 to 10000
product ID: consisting of a letter L, M, or H for low (50% of all products), medium (30%) and high (20%) as product quality variants and a variant-specific serial number
air temperature [K]: generated using a random walk process later normalized to a standard deviation of 2 K around 300 K
process temperature [K]: generated using a random walk process normalized to a standard deviation of 1 K, added to the air temperature plus 10 K.
rotational speed [rpm]: calculated from a power of 2860 W, overlaid with a normally distributed noise
torque [Nm]: torque values are normally distributed around 40 Nm with a σ = 10 Nm and no negative values.
tool wear [min]: The quality variants H/M/L add 5/3/2 minutes of tool wear to the used tool in the process. and a
'machine failure' label that indicates, whether the machine has failed in this particular datapoint for any of the following failure modes are true.
The machine failure consists of five independent failure modes
tool wear failure (TWF): the tool will be replaced of fail at a randomly selected tool wear time between 200 – 240 mins (120 times in our dataset). At this point in time, the tool is replaced 69 times, and fails 51 times (randomly assigned).
heat dissipation failure (HDF): heat dissipation causes a process failure, if the difference between air- and process temperature is below 8.6 K and the tool’s rotational speed is below 1380 rpm. This is the case for 115 data points.
power failure (PWF): the product of torque and rotational speed (in rad/s) equals the power required for the process. If this power is below 3500 W or above 9000 W, the process fails, which is the case 95 times in our dataset.
overstrain failure (OSF): if the product of tool wear and torque exceeds 11,000 minNm for the L product variant (12,000 M, 13,000 H), the process fails due to overstrain. This is true for 98 datapoints.
random failures (RNF): each process has a chance of 0,1 % to fail regardless of its process parameters. This is the case for only 5 datapoints, less than could be expected for 10,000 datapoints in our dataset.
If at least one of the above failure modes is true, the process fails and the 'machine failure' label is set to 1. It is therefore not transparent to the machine learning method, which of the failure modes has caused the process to fail.
**Physical Units:** Air temperature [K], Process temparature [K], Rotational speed [rpm], Torque [Nm]
**Temporal Information:** Tool Wear[min] (*Tool wear is measured by weight losses of the electrode to capture the extent of wear.*)
**Program Information:** Machine failure, TWF, HDF, PWF, OSF, RNF
DOI:
[10.24432/C5HS5C](https://doi.org/10.24432/C5HS5C) | [
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|
ctoraman/protest-event-prediction | ctoraman | 2023-11-29T12:02:47Z | 0 | 0 | [
"task_categories:text-classification",
"language:tr",
"license:cc-by-nc-sa-4.0",
"protest event detection",
"protest detection",
"public reaction",
"region:us"
]
| [
"text-classification"
]
| 2023-11-29T11:52:02Z | ---
license: cc-by-nc-sa-4.0
task_categories:
- text-classification
language:
- tr
tags:
- protest event detection
- protest detection
- public reaction
---
# Public Protest Event Detection in Turkish
80 news events/articles occurred between 2015 and 017. Each event is listed with its origin date, place, news url, public-reaction category, and reaction tags.
Protest labels are determined in terms of dimensions and directions.
Dimensions are in terms of national, local, and social media. National categories represent public reactions occurred in at least two different cities. Local categories have events occurred at only a specific place. Social categories
represent reactions that people share opinions only in social media, such as microblogs.
Directions are either negative or positive.
Overall, there are 7 classes:
- national positive
- national negative
- local positive
- local negative
- social positive
- social negative
- no reaction
GitHub Repo: https://github.com/BilkentInformationRetrievalGroup/BilPredict2017
# If you would like to use any material in this repository, please cite the following paper:
- Toraman, C. Early Prediction of Public Reactions to News Events Using Microblogs. Seventh BCS-IRSG Symposium on Future Directions in Information Access (FDIA 2017), Barcelona, Spain, 5 September 2017. | [
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|
Praghxx/litlleprag2 | Praghxx | 2023-11-29T11:57:24Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-11-29T11:56:09Z | ---
license: openrail
---
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|
ctoraman/sozcu-news-2014 | ctoraman | 2023-11-29T12:17:44Z | 0 | 0 | [
"task_categories:text-generation",
"task_categories:text-classification",
"language:tr",
"license:cc-by-nc-sa-4.0",
"news",
"corpus",
"news articles",
"region:us"
]
| [
"text-generation",
"text-classification"
]
| 2023-11-29T12:06:32Z | ---
license: cc-by-nc-sa-4.0
task_categories:
- text-generation
- text-classification
language:
- tr
tags:
- news
- corpus
- news articles
---
(This dataset contains raw text, which are unlabeled.)
1,656 Turkish news articles from Sözcü Newspaper (http://www.sozcu.com.tr) between December 20, 2013, and March 11, 2014.
GitHub Repo: https://github.com/BilkentInformationRetrievalGroup/TUBITAK113E249/
# If you would like to use any material in this repository, please cite this paper:
Toraman, C. and Can, F. (2017), Discovering story chains: A framework based on zigzagged search and news actors. Journal of the Association for Information Science and Technology, 68: 2795-2808. https://doi.org/10.1002/asi.23885 | [
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|
DIAS123/DIAS | DIAS123 | 2023-11-29T12:58:49Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-11-29T12:15:11Z | ---
license: openrail
---
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|
davanstrien/hub_models_with_base_model_info | davanstrien | 2023-11-29T13:23:15Z | 0 | 0 | [
"region:us"
]
| null | 2023-11-29T12:34:51Z | ---
dataset_info:
features:
- name: author
dtype: string
- name: last_modified
dtype: timestamp[us, tz=UTC]
- name: createdAt
dtype: timestamp[us, tz=UTC]
- name: downloads
dtype: int64
- name: likes
dtype: int64
- name: library_name
dtype: string
- name: modelId
dtype: string
- name: datasets
sequence: string
- name: language
sequence: string
- name: base_model
dtype: string
splits:
- name: train
num_bytes: 4462735
num_examples: 29861
download_size: 1762305
dataset_size: 4462735
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
k4d3/input-0x621 | k4d3 | 2023-11-29T12:51:31Z | 0 | 0 | [
"license:unknown",
"region:us"
]
| null | 2023-11-29T12:51:31Z | ---
license: unknown
---
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|
FitriJamsari/mini-platypus-clean | FitriJamsari | 2023-11-29T13:21:46Z | 0 | 0 | [
"region:us"
]
| null | 2023-11-29T13:19:08Z | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 4186564
num_examples: 1000
download_size: 2245924
dataset_size: 4186564
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
idleheroevich2/Burgdorf | idleheroevich2 | 2023-12-02T13:40:28Z | 0 | 0 | [
"license:unknown",
"region:us"
]
| null | 2023-11-29T13:21:37Z | ---
license: unknown
---
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|
ctoraman/atis-ner-turkish | ctoraman | 2023-11-29T13:45:29Z | 0 | 0 | [
"task_categories:token-classification",
"language:tr",
"license:cc-by-nc-sa-4.0",
"named entity recognition",
"ner",
"atis",
"utterance",
"spoken query",
"region:us"
]
| [
"token-classification"
]
| 2023-11-29T13:33:11Z | ---
license: cc-by-nc-sa-4.0
task_categories:
- token-classification
language:
- tr
tags:
- named entity recognition
- ner
- atis
- utterance
- spoken query
---
The ATIS (Airline Travel Information System) Dataset includes spoken queries (i.e., utterances) annotated for the task of slot filling in conversational systems.
This dataset, ATISNER, includes airline spoken queries translated from English to Turkish, customized for Named Entity Recognition.
Train and test splits include 4,978 and 890 sentences, respectively.
Translations are provided by the following study.
Şahinuç, F., Yücesoy, V., & Koç, A. (2020). Intent Classification and Slot Filling for Turkish Dialogue Systems. In 2020 28th signal processing and communications applications conference (pp. 1–4).
Github Repo: https://github.com/avaapm/TurkishNamedEntityRecognition/
# If you would like to use any material in this repository, please cite the following paper:
Oguzhan Ozcelik and Cagri Toraman. 2022. Named entity recognition in Turkish: A comparative study with detailed error analysis. Inf. Process. Manage. 59, 6 (Nov 2022). https://doi.org/10.1016/j.ipm.2022.103065 | [
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|
Apineipo/DatasetLucas | Apineipo | 2023-11-29T14:09:50Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-11-29T13:58:24Z | ---
license: openrail
---
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|
joaobfm/ryan_ia_dataset_C | joaobfm | 2023-11-29T14:05:36Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-11-29T14:05:07Z | ---
license: openrail
---
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|
ctoraman/gender-hate-speech | ctoraman | 2023-11-30T12:02:34Z | 0 | 0 | [
"task_categories:text-classification",
"language:en",
"license:cc-by-nc-sa-4.0",
"hate speech",
"hate speech detection",
"hate-speech",
"tweets",
"social media",
"hate-speech-detection",
"gender identity",
"gender",
"region:us"
]
| [
"text-classification"
]
| 2023-11-29T14:10:28Z | ---
license: cc-by-nc-sa-4.0
task_categories:
- text-classification
language:
- en
tags:
- hate speech
- hate speech detection
- hate-speech
- tweets
- social media
- hate-speech-detection
- gender identity
- gender
---
The "gender identity" subset of the large-scale dataset published in the LREC 2022 paper "Large-Scale Hate Speech Detection with Cross-Domain Transfer".
This subset is used in the experiments of "Şahinuç, F., Yilmaz, E. H., Toraman, C., & Koç, A. (2023). The effect of gender bias on hate speech detection. Signal, Image and Video Processing, 17(4), 1591-1597."
The "gender identity" subset includes 20,000 tweets in English.
The published data split is the first fold of 10-fold cross-validation, used in the experiments mentioned above.
Train split has 18,000 tweets. Test split has 2,000 tweets.
HateLabel:
- 0 Normal
- 1 Offensive
- 2 Hate
# GitHub Repo:
https://github.com/avaapm/hatespeech
# If you use this dataset, please cite the following papers:
- Toraman, C., Şahinuç, F., & Yilmaz, E. (2022, June). Large-Scale Hate Speech Detection with Cross-Domain Transfer. In Proceedings of the Thirteenth Language Resources and Evaluation Conference (pp. 2215-2225).
- Şahinuç, F., Yilmaz, E. H., Toraman, C., & Koç, A. (2023). The effect of gender bias on hate speech detection. Signal, Image and Video Processing, 17(4), 1591-1597. | [
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|
ctoraman/gender-hate-speech-turkish | ctoraman | 2023-11-30T12:03:50Z | 0 | 0 | [
"task_categories:text-classification",
"language:tr",
"license:cc-by-nc-sa-4.0",
"hate speech",
"hate speech detection",
"hate-speech",
"tweets",
"social media",
"topic",
"hate-speech-detection",
"gender identity",
"gender",
"region:us"
]
| [
"text-classification"
]
| 2023-11-29T14:13:15Z | ---
license: cc-by-nc-sa-4.0
task_categories:
- text-classification
language:
- tr
tags:
- hate speech
- hate speech detection
- hate-speech
- tweets
- social media
- topic
- hate-speech-detection
- gender identity
- gender
---
The "gender identity" subset of the large-scale dataset published in the LREC 2022 paper "Large-Scale Hate Speech Detection with Cross-Domain Transfer".
This subset is also used in the experiments of "Şahinuç, F., Yilmaz, E. H., Toraman, C., & Koç, A. (2023). The effect of gender bias on hate speech detection. Signal, Image and Video Processing, 17(4), 1591-1597."
The "gender identity" subset includes 20,000 tweets in Turkish.
The published data split is the first fold of 10-fold cross-validation, used in the experiments mentioned above.
Train split has 18,000 tweets. Test split has 2,000 tweets.
HateLabel:
- 0 Normal
- 1 Offensive
- 2 Hate
# GitHub Repo:
https://github.com/avaapm/hatespeech
# If you use this dataset, please cite the following papers:
- Toraman, C., Şahinuç, F., & Yilmaz, E. (2022, June). Large-Scale Hate Speech Detection with Cross-Domain Transfer. In Proceedings of the Thirteenth Language Resources and Evaluation Conference (pp. 2215-2225).
- Şahinuç, F., Yilmaz, E. H., Toraman, C., & Koç, A. (2023). The effect of gender bias on hate speech detection. Signal, Image and Video Processing, 17(4), 1591-1597. | [
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|
xiaopch/flower_photos | xiaopch | 2023-11-29T14:43:38Z | 0 | 0 | [
"license:apache-2.0",
"region:us"
]
| null | 2023-11-29T14:40:37Z | ---
license: apache-2.0
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': daisy
'1': dandelion
'2': roses
'3': sunflowers
'4': tulips
splits:
- name: train
num_bytes: 195775062.75
num_examples: 3670
download_size: 231190578
dataset_size: 195775062.75
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
matvelen6369/eeg_data | matvelen6369 | 2023-12-04T05:51:59Z | 0 | 0 | [
"region:us"
]
| null | 2023-11-29T14:47:31Z | ---
dataset_info:
features:
- name: eeg
sequence: float64
splits:
- name: train
num_bytes: 653892464
num_examples: 5364
download_size: 570156705
dataset_size: 653892464
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
shius/plantbert-vanilla-cased | shius | 2023-11-29T15:05:54Z | 0 | 0 | [
"region:us"
]
| null | 2023-11-29T14:55:27Z | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_bytes: 573766969.1417043
num_examples: 379176
- name: test
num_bytes: 63752389.85829573
num_examples: 42131
download_size: 358791102
dataset_size: 637519359.0
---
# Dataset Card for "plantbert-vanilla-cased"
The original data comes from [Plant Science Corpus](https://zenodo.org/records/10022686). | [
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|
ponnce/ponnce | ponnce | 2023-11-29T14:58:26Z | 0 | 0 | [
"license:apache-2.0",
"region:us"
]
| null | 2023-11-29T14:56:38Z | ---
license: apache-2.0
---
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|
Ironov/mimiV1 | Ironov | 2023-11-29T14:56:47Z | 0 | 0 | [
"region:us"
]
| null | 2023-11-29T14:56:44Z | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 4528616.0
num_examples: 58
download_size: 4528547
dataset_size: 4528616.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
Gabriel1322/lucasmodel | Gabriel1322 | 2023-11-29T15:26:03Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-11-29T15:25:50Z | ---
license: openrail
---
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|
georgino/bwolleh | georgino | 2023-11-29T16:20:53Z | 0 | 0 | [
"license:other",
"region:us"
]
| null | 2023-11-29T15:38:41Z | ---
license: other
license_name: filmonline
license_link: LICENSE
---
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|
Gabriel1322/freefire | Gabriel1322 | 2023-11-29T15:49:11Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-11-29T15:48:48Z | ---
license: openrail
---
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|
qxcv/tensor-trust | qxcv | 2023-11-29T16:15:08Z | 0 | 0 | [
"task_categories:text-generation",
"size_categories:100K<n<1M",
"arxiv:2311.01011",
"region:us"
]
| [
"text-generation"
]
| 2023-11-29T16:08:39Z | ---
task_categories:
- text-generation
size_categories:
- 100K<n<1M
---
# The Tensor Trust dataset (v1) (mirror of [GitHub version](https://github.com/HumanCompatibleAI/tensor-trust-data))
**Other Tensor Trust links:** [[Game]](https://tensortrust.ai/) [[Code]](https://github.com/HumanCompatibleAI/tensor-trust) [[Paper]](https://arxiv.org/abs/2311.01011)
This HF dataset contains the raw data and derived benchmarks for the [Tensor Trust](https://tensortrust.ai/paper) project.
An interactive explanation of how to load and use the data (including the meaning of the columns) is in a Jupyter notebook in this directory.
You can [click here to run the notebook right now in Google Colab](https://colab.research.google.com/github/HumanCompatibleAI/tensor-trust-data/blob/main/Using%20the%20Tensor%20Trust%20dataset.ipynb).
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|
houck2040/utility_pole | houck2040 | 2023-11-29T16:18:35Z | 0 | 0 | [
"license:mit",
"region:us"
]
| null | 2023-11-29T16:18:35Z | ---
license: mit
---
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|
DZN222/taspio | DZN222 | 2023-11-29T17:25:07Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-11-29T17:24:40Z | ---
license: openrail
---
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|
samuro95/test_images | samuro95 | 2023-11-29T17:37:27Z | 0 | 0 | [
"license:bsd-3-clause",
"region:us"
]
| null | 2023-11-29T17:35:16Z | ---
license: bsd-3-clause
---
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|
chao1224/ProteinDT | chao1224 | 2023-11-29T18:07:18Z | 0 | 0 | [
"license:mit",
"region:us"
]
| null | 2023-11-29T18:04:38Z | ---
license: mit
---
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|
Jongmin123/aicontest | Jongmin123 | 2023-11-29T18:21:35Z | 0 | 0 | [
"license:unknown",
"region:us"
]
| null | 2023-11-29T18:21:35Z | ---
license: unknown
---
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|
mirzaei2114/stackoverflowVQA-filtered | mirzaei2114 | 2023-12-02T17:10:47Z | 0 | 0 | [
"task_categories:visual-question-answering",
"task_categories:question-answering",
"size_categories:100K<n<1M",
"language:en",
"license:mit",
"code",
"region:us"
]
| [
"visual-question-answering",
"question-answering"
]
| 2023-11-29T18:25:26Z | ---
dataset_info:
features:
- name: Id
dtype: int64
- name: PostTypeId
dtype: int64
- name: AcceptedAnswerId
dtype: int64
- name: Question
dtype: string
- name: Answer
dtype: string
- name: Image
dtype: image
splits:
- name: train
num_bytes: 12815093667.144684
num_examples: 183636
- name: test
num_bytes: 1423970510.001132
num_examples: 20405
download_size: 13692500865
dataset_size: 14239064177.145817
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
license: mit
task_categories:
- visual-question-answering
- question-answering
language:
- en
tags:
- code
pretty_name: StackOverflowVQA-filtered
size_categories:
- 100K<n<1M
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|
arieg/cluster00_large_100 | arieg | 2023-11-29T18:48:36Z | 0 | 0 | [
"region:us"
]
| null | 2023-11-29T18:48:13Z | ---
dataset_info:
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'103': '148585'
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splits:
- name: train
num_bytes: 569752596.5
num_examples: 10500
download_size: 563671563
dataset_size: 569752596.5
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
BangumiBase/skipbeat | BangumiBase | 2023-11-29T20:51:54Z | 0 | 0 | [
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
]
| null | 2023-11-29T19:22:31Z | ---
license: mit
tags:
- art
size_categories:
- 1K<n<10K
---
# Bangumi Image Base of Skip Beat!
This is the image base of bangumi Skip Beat!, we detected 35 characters, 2822 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|
| 0 | 37 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 23 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 24 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 134 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 35 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 148 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 437 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 95 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 84 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 25 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 534 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 43 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 127 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 185 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 107 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 76 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 62 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 30 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 26 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 46 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 25 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 20 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 45 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 41 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 28 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| 25 | 13 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| 26 | 85 | [Download](26/dataset.zip) |  |  |  |  |  |  |  |  |
| 27 | 19 | [Download](27/dataset.zip) |  |  |  |  |  |  |  |  |
| 28 | 45 | [Download](28/dataset.zip) |  |  |  |  |  |  |  |  |
| 29 | 52 | [Download](29/dataset.zip) |  |  |  |  |  |  |  |  |
| 30 | 18 | [Download](30/dataset.zip) |  |  |  |  |  |  |  |  |
| 31 | 12 | [Download](31/dataset.zip) |  |  |  |  |  |  |  |  |
| 32 | 13 | [Download](32/dataset.zip) |  |  |  |  |  |  |  |  |
| 33 | 9 | [Download](33/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 119 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
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|
BangumiBase/uchoutenkazoku | BangumiBase | 2023-11-29T23:21:43Z | 0 | 0 | [
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
]
| null | 2023-11-29T19:23:05Z | ---
license: mit
tags:
- art
size_categories:
- 1K<n<10K
---
# Bangumi Image Base of Uchouten Kazoku
This is the image base of bangumi Uchouten Kazoku, we detected 51 characters, 4934 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|
| 0 | 336 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 35 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 105 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 32 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 1287 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 13 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 96 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 50 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 354 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 27 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 80 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 13 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 32 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 22 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 25 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 355 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 206 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 250 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 43 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 33 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 30 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 56 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 84 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 21 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 129 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| 25 | 43 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| 26 | 97 | [Download](26/dataset.zip) |  |  |  |  |  |  |  |  |
| 27 | 14 | [Download](27/dataset.zip) |  |  |  |  |  |  |  |  |
| 28 | 48 | [Download](28/dataset.zip) |  |  |  |  |  |  |  |  |
| 29 | 18 | [Download](29/dataset.zip) |  |  |  |  |  |  |  |  |
| 30 | 130 | [Download](30/dataset.zip) |  |  |  |  |  |  |  |  |
| 31 | 19 | [Download](31/dataset.zip) |  |  |  |  |  |  |  |  |
| 32 | 84 | [Download](32/dataset.zip) |  |  |  |  |  |  |  |  |
| 33 | 8 | [Download](33/dataset.zip) |  |  |  |  |  |  |  |  |
| 34 | 20 | [Download](34/dataset.zip) |  |  |  |  |  |  |  |  |
| 35 | 37 | [Download](35/dataset.zip) |  |  |  |  |  |  |  |  |
| 36 | 31 | [Download](36/dataset.zip) |  |  |  |  |  |  |  |  |
| 37 | 23 | [Download](37/dataset.zip) |  |  |  |  |  |  |  |  |
| 38 | 9 | [Download](38/dataset.zip) |  |  |  |  |  |  |  |  |
| 39 | 13 | [Download](39/dataset.zip) |  |  |  |  |  |  |  |  |
| 40 | 17 | [Download](40/dataset.zip) |  |  |  |  |  |  |  |  |
| 41 | 314 | [Download](41/dataset.zip) |  |  |  |  |  |  |  |  |
| 42 | 9 | [Download](42/dataset.zip) |  |  |  |  |  |  |  |  |
| 43 | 14 | [Download](43/dataset.zip) |  |  |  |  |  |  |  |  |
| 44 | 7 | [Download](44/dataset.zip) |  |  |  |  |  |  |  | N/A |
| 45 | 8 | [Download](45/dataset.zip) |  |  |  |  |  |  |  |  |
| 46 | 13 | [Download](46/dataset.zip) |  |  |  |  |  |  |  |  |
| 47 | 33 | [Download](47/dataset.zip) |  |  |  |  |  |  |  |  |
| 48 | 16 | [Download](48/dataset.zip) |  |  |  |  |  |  |  |  |
| 49 | 82 | [Download](49/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 113 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
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|
BangumiBase/fumetsunoanatae | BangumiBase | 2023-11-29T23:50:58Z | 0 | 0 | [
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
]
| null | 2023-11-29T19:23:29Z | ---
license: mit
tags:
- art
size_categories:
- 1K<n<10K
---
# Bangumi Image Base of Fumetsu No Anata E
This is the image base of bangumi Fumetsu no Anata e, we detected 70 characters, 7434 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|
| 0 | 165 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 511 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 36 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 135 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 45 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 106 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 118 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 93 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 43 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 183 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 67 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 36 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 23 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 92 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 34 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 33 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 384 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 62 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 344 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 125 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 111 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 31 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 28 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 31 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 38 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| 25 | 80 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| 26 | 382 | [Download](26/dataset.zip) |  |  |  |  |  |  |  |  |
| 27 | 66 | [Download](27/dataset.zip) |  |  |  |  |  |  |  |  |
| 28 | 61 | [Download](28/dataset.zip) |  |  |  |  |  |  |  |  |
| 29 | 108 | [Download](29/dataset.zip) |  |  |  |  |  |  |  |  |
| 30 | 273 | [Download](30/dataset.zip) |  |  |  |  |  |  |  |  |
| 31 | 78 | [Download](31/dataset.zip) |  |  |  |  |  |  |  |  |
| 32 | 49 | [Download](32/dataset.zip) |  |  |  |  |  |  |  |  |
| 33 | 46 | [Download](33/dataset.zip) |  |  |  |  |  |  |  |  |
| 34 | 346 | [Download](34/dataset.zip) |  |  |  |  |  |  |  |  |
| 35 | 123 | [Download](35/dataset.zip) |  |  |  |  |  |  |  |  |
| 36 | 327 | [Download](36/dataset.zip) |  |  |  |  |  |  |  |  |
| 37 | 32 | [Download](37/dataset.zip) |  |  |  |  |  |  |  |  |
| 38 | 50 | [Download](38/dataset.zip) |  |  |  |  |  |  |  |  |
| 39 | 38 | [Download](39/dataset.zip) |  |  |  |  |  |  |  |  |
| 40 | 1323 | [Download](40/dataset.zip) |  |  |  |  |  |  |  |  |
| 41 | 29 | [Download](41/dataset.zip) |  |  |  |  |  |  |  |  |
| 42 | 17 | [Download](42/dataset.zip) |  |  |  |  |  |  |  |  |
| 43 | 28 | [Download](43/dataset.zip) |  |  |  |  |  |  |  |  |
| 44 | 28 | [Download](44/dataset.zip) |  |  |  |  |  |  |  |  |
| 45 | 15 | [Download](45/dataset.zip) |  |  |  |  |  |  |  |  |
| 46 | 17 | [Download](46/dataset.zip) |  |  |  |  |  |  |  |  |
| 47 | 30 | [Download](47/dataset.zip) |  |  |  |  |  |  |  |  |
| 48 | 19 | [Download](48/dataset.zip) |  |  |  |  |  |  |  |  |
| 49 | 12 | [Download](49/dataset.zip) |  |  |  |  |  |  |  |  |
| 50 | 14 | [Download](50/dataset.zip) |  |  |  |  |  |  |  |  |
| 51 | 13 | [Download](51/dataset.zip) |  |  |  |  |  |  |  |  |
| 52 | 10 | [Download](52/dataset.zip) |  |  |  |  |  |  |  |  |
| 53 | 67 | [Download](53/dataset.zip) |  |  |  |  |  |  |  |  |
| 54 | 21 | [Download](54/dataset.zip) |  |  |  |  |  |  |  |  |
| 55 | 9 | [Download](55/dataset.zip) |  |  |  |  |  |  |  |  |
| 56 | 9 | [Download](56/dataset.zip) |  |  |  |  |  |  |  |  |
| 57 | 150 | [Download](57/dataset.zip) |  |  |  |  |  |  |  |  |
| 58 | 339 | [Download](58/dataset.zip) |  |  |  |  |  |  |  |  |
| 59 | 16 | [Download](59/dataset.zip) |  |  |  |  |  |  |  |  |
| 60 | 10 | [Download](60/dataset.zip) |  |  |  |  |  |  |  |  |
| 61 | 17 | [Download](61/dataset.zip) |  |  |  |  |  |  |  |  |
| 62 | 36 | [Download](62/dataset.zip) |  |  |  |  |  |  |  |  |
| 63 | 84 | [Download](63/dataset.zip) |  |  |  |  |  |  |  |  |
| 64 | 15 | [Download](64/dataset.zip) |  |  |  |  |  |  |  |  |
| 65 | 6 | [Download](65/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| 66 | 13 | [Download](66/dataset.zip) |  |  |  |  |  |  |  |  |
| 67 | 7 | [Download](67/dataset.zip) |  |  |  |  |  |  |  | N/A |
| 68 | 12 | [Download](68/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 135 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
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|
marcrigter/1R2R-datasets | marcrigter | 2023-11-29T20:02:46Z | 0 | 0 | [
"license:mit",
"region:us"
]
| null | 2023-11-29T19:29:16Z | ---
license: mit
pretty_name: 1R2R Datasets
---
Offline RL benchmark datasets in highly stochastic domains introduced in the paper "One Risk to Rule Them All: A Risk-Sensitive Perspective on Model-Based Offline Reinforcement Learning", published at NeurIPS 2023.
For the accompanying code, please see the Github repo at [github.com/marc-rigter/1R2R](https://github.com/marc-rigter/1R2R). | [
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|
BangumiBase/kanatanoastra | BangumiBase | 2023-11-29T20:47:47Z | 0 | 0 | [
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
]
| null | 2023-11-29T19:44:56Z | ---
license: mit
tags:
- art
size_categories:
- 1K<n<10K
---
# Bangumi Image Base of Kanata No Astra
This is the image base of bangumi Kanata no Astra, we detected 25 characters, 2286 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|
| 0 | 206 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 32 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 15 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 186 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 7 | [Download](4/dataset.zip) |  |  |  |  |  |  |  | N/A |
| 5 | 16 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 229 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 235 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 54 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 89 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 39 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 48 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 68 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 41 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 54 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 24 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 64 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 233 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 18 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 98 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 11 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 206 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 32 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 12 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 269 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
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|
AlFrauch/step_by_step_diff_in_latex | AlFrauch | 2023-11-29T20:41:55Z | 0 | 0 | [
"region:us"
]
| null | 2023-11-29T20:25:06Z | ---
dataset_info:
features:
- name: function
dtype: string
- name: content
dtype: string
splits:
- name: train
num_bytes: 23674753
num_examples: 6031
download_size: 3265633
dataset_size: 23674753
---
# Dataset Card for "step_by_step_diff_in_latex"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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|
XeIaso/xe-dataset-merge | XeIaso | 2023-11-29T20:47:13Z | 0 | 0 | [
"license:unlicense",
"region:us"
]
| null | 2023-11-29T20:43:11Z | ---
license: unlicense
---
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|
NextDayAI/MultipleResponsesChat_all_engines_20231101_20231127 | NextDayAI | 2023-11-29T22:18:02Z | 0 | 0 | [
"region:us"
]
| null | 2023-11-29T20:58:33Z | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: rejected_response
dtype: string
- name: selected_response
dtype: string
splits:
- name: train
num_bytes: 922539765.884666
num_examples: 292007
- name: valid
num_bytes: 7898267.557666991
num_examples: 2500
- name: test
num_bytes: 7898267.557666991
num_examples: 2500
download_size: 590423809
dataset_size: 938336301.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: valid
path: data/valid-*
- split: test
path: data/test-*
---
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|
ShynBui/vi_term_definition | ShynBui | 2023-11-29T21:05:24Z | 0 | 0 | [
"task_categories:text-classification",
"task_categories:zero-shot-classification",
"size_categories:10K<n<100K",
"language:vi",
"legal",
"region:us"
]
| [
"text-classification",
"zero-shot-classification"
]
| 2023-11-29T21:00:53Z | ---
task_categories:
- text-classification
- zero-shot-classification
language:
- vi
tags:
- legal
pretty_name: Dataset for vietnamese term definition classification
size_categories:
- 10K<n<100K
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|
KBayoud/MoroccanHistory-QA-Dataset | KBayoud | 2023-11-29T21:28:54Z | 0 | 0 | [
"task_categories:question-answering",
"size_categories:1K<n<10K",
"language:en",
"license:cc0-1.0",
"Morocco",
"History",
"QA",
"region:us"
]
| [
"question-answering"
]
| 2023-11-29T21:04:59Z | ---
license: cc0-1.0
task_categories:
- question-answering
language:
- en
tags:
- Morocco
- History
- QA
size_categories:
- 1K<n<10K
--- | [
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|
Gabriel1322/taspiodataset | Gabriel1322 | 2023-11-29T21:18:39Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-11-29T21:16:03Z | ---
license: openrail
---
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|
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