id stringlengths 2 115 | author stringlengths 2 42 ⌀ | last_modified timestamp[us, tz=UTC] | downloads int64 0 8.87M | likes int64 0 3.84k | paperswithcode_id stringlengths 2 45 ⌀ | tags list | lastModified timestamp[us, tz=UTC] | createdAt stringlengths 24 24 | key stringclasses 1 value | created timestamp[us] | card stringlengths 1 1.01M | embedding list | library_name stringclasses 21 values | pipeline_tag stringclasses 27 values | mask_token null | card_data null | widget_data null | model_index null | config null | transformers_info null | spaces null | safetensors null | transformersInfo null | modelId stringlengths 5 111 ⌀ | embeddings list |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
squad_it | null | 2023-04-05T13:40:37Z | 437 | 2 | squad-it | [
"task_categories:question-answering",
"task_ids:open-domain-qa",
"task_ids:extractive-qa",
"annotations_creators:machine-generated",
"language_creators:machine-generated",
"multilinguality:monolingual",
"size_categories:unknown",
"source_datasets:extended|squad",
"language:it",
"license:unknown",
"region:us"
] | 2023-04-05T13:40:37Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- machine-generated
language_creators:
- machine-generated
language:
- it
language_bcp47:
- it-IT
license:
- unknown
multilinguality:
- monolingual
size_categories:
- unknown
source_datasets:
- extended|squad
task_categories:
- question-answering
task_ids:
- open-domain-qa
- extractive-qa
paperswithcode_id: squad-it
pretty_name: SQuAD-it
dataset_info:
features:
- name: id
dtype: string
- name: context
dtype: string
- name: question
dtype: string
- name: answers
sequence:
- name: text
dtype: string
- name: answer_start
dtype: int32
splits:
- name: train
num_bytes: 50864824
num_examples: 54159
- name: test
num_bytes: 7858336
num_examples: 7609
download_size: 8776531
dataset_size: 58723160
---
# Dataset Card for "squad_it"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://github.com/crux82/squad-it](https://github.com/crux82/squad-it)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of downloaded dataset files:** 8.78 MB
- **Size of the generated dataset:** 58.79 MB
- **Total amount of disk used:** 67.57 MB
### Dataset Summary
SQuAD-it is derived from the SQuAD dataset and it is obtained through semi-automatic translation of the SQuAD dataset
into Italian. It represents a large-scale dataset for open question answering processes on factoid questions in Italian.
The dataset contains more than 60,000 question/answer pairs derived from the original English dataset. The dataset is
split into training and test sets to support the replicability of the benchmarking of QA systems:
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Dataset Structure
### Data Instances
#### default
- **Size of downloaded dataset files:** 8.78 MB
- **Size of the generated dataset:** 58.79 MB
- **Total amount of disk used:** 67.57 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"answers": "{\"answer_start\": [243, 243, 243, 243, 243], \"text\": [\"evitare di essere presi di mira dal boicottaggio\", \"evitare di essere pres...",
"context": "\"La crisi ha avuto un forte impatto sulle relazioni internazionali e ha creato una frattura all' interno della NATO. Alcune nazi...",
"id": "5725b5a689a1e219009abd28",
"question": "Perchè le nazioni europee e il Giappone si sono separati dagli Stati Uniti durante la crisi?"
}
```
### Data Fields
The data fields are the same among all splits.
#### default
- `id`: a `string` feature.
- `context`: a `string` feature.
- `question`: a `string` feature.
- `answers`: a dictionary feature containing:
- `text`: a `string` feature.
- `answer_start`: a `int32` feature.
### Data Splits
| name | train | test |
| ------- | ----: | ---: |
| default | 54159 | 7609 |
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@InProceedings{10.1007/978-3-030-03840-3_29,
author="Croce, Danilo and Zelenanska, Alexandra and Basili, Roberto",
editor="Ghidini, Chiara and Magnini, Bernardo and Passerini, Andrea and Traverso, Paolo",
title="Neural Learning for Question Answering in Italian",
booktitle="AI*IA 2018 -- Advances in Artificial Intelligence",
year="2018",
publisher="Springer International Publishing",
address="Cham",
pages="389--402",
isbn="978-3-030-03840-3"
}
```
### Contributions
Thanks to [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun), [@albertvillanova](https://github.com/albertvillanova), [@mariamabarham](https://github.com/mariamabarham), [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset. | [
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celikmus/mayo_clinic_symptoms_and_diseases_v1 | celikmus | 2023-07-16T19:37:52Z | 437 | 7 | null | [
"language:en",
"region:us"
] | 2023-07-16T19:37:52Z | 2023-03-21T21:31:15.000Z | 2023-03-21T21:31:15 | ---
language: en
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype: string
splits:
- name: train
num_bytes: 1321926
num_examples: 1058
download_size: 626009
dataset_size: 1321926
---
# Dataset Card for "mayo_clinic_symptoms_and_diseases_v1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
maxolotl/must-c-en-es-02 | maxolotl | 2023-10-22T07:47:19Z | 437 | 0 | null | [
"region:us"
] | 2023-10-22T07:47:19Z | 2023-10-22T07:47:13.000Z | 2023-10-22T07:47:13 | ---
dataset_info:
features:
- name: en
dtype: string
- name: es
dtype: string
splits:
- name: train
num_bytes: 59874575
num_examples: 259892
- name: test
num_bytes: 658214
num_examples: 3035
- name: validation
num_bytes: 310157
num_examples: 1309
download_size: 37502474
dataset_size: 60842946
---
# Dataset Card for "must-c-en-es-02"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
mb23/music_caps_4sec_wave_type | mb23 | 2023-10-25T13:54:12Z | 437 | 0 | null | [
"license:mit",
"region:us"
] | 2023-10-25T13:54:12Z | 2023-10-25T11:36:55.000Z | 2023-10-25T11:36:55 | ---
license: mit
dataset_info:
features:
- name: ytid
dtype: string
- name: number
dtype: int64
- name: audio
dtype:
audio:
sampling_rate: 44100
- name: caption
dtype: string
splits:
- name: train
num_bytes: 8829076858.0
num_examples: 25000
download_size: 0
dataset_size: 8829076858.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
ContextualAI/openwebtext-synthetic-testing | ContextualAI | 2023-11-14T07:33:00Z | 437 | 0 | null | [
"license:apache-2.0",
"region:us"
] | 2023-11-14T07:33:00Z | 2023-11-14T04:57:45.000Z | 2023-11-14T04:57:45 | ---
license: apache-2.0
---
Using 10 records from [openwebtext-10k](https://huggingface.co/datasets/ContextualAI/openwebtext-10k) this dataset is written for very fast testing and can produce a repeat of these 10 records in a form of 1, 2, 3, 4, 5, 10, 100, 300 or 1k records splits, e.g.:
```
$ python -c 'from datasets import load_dataset; \
ds=load_dataset("ContextualAI/openwebtext-synthetic-testing", split="10.repeat"); print(len(ds))'
10
$ python -c 'from datasets import load_dataset; \
ds=load_dataset("ContextualAI/openwebtext-synthetic-testing", split="1k.repeat"); print(len(ds))'
1000
```
Each record is just a single `text` record of several paragraphs long - web articles.
As this is used for very fast functional testing on CI there is no `train` or `validation` splits, you can just repeat the same records.
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
ArmelR/stack-exchange-instruction | ArmelR | 2023-05-26T08:37:42Z | 436 | 49 | null | [
"region:us"
] | 2023-05-26T08:37:42Z | 2023-04-06T16:31:58.000Z | 2023-04-06T16:31:58 | ---
pretty_name : stack exchange instruction
---
# Dataset Card for "stack-exchange-instruction"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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nlu_evaluation_data | null | 2023-01-25T14:41:34Z | 435 | 8 | null | [
"task_categories:text-classification",
"task_ids:intent-classification",
"task_ids:multi-class-classification",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"arxiv:1903.05566",
"region:us"
] | 2023-01-25T14:41:34Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- intent-classification
- multi-class-classification
pretty_name: NLU Evaluation Data
dataset_info:
features:
- name: text
dtype: string
- name: scenario
dtype: string
- name: label
dtype:
class_label:
names:
'0': alarm_query
'1': alarm_remove
'2': alarm_set
'3': audio_volume_down
'4': audio_volume_mute
'5': audio_volume_other
'6': audio_volume_up
'7': calendar_query
'8': calendar_remove
'9': calendar_set
'10': cooking_query
'11': cooking_recipe
'12': datetime_convert
'13': datetime_query
'14': email_addcontact
'15': email_query
'16': email_querycontact
'17': email_sendemail
'18': general_affirm
'19': general_commandstop
'20': general_confirm
'21': general_dontcare
'22': general_explain
'23': general_greet
'24': general_joke
'25': general_negate
'26': general_praise
'27': general_quirky
'28': general_repeat
'29': iot_cleaning
'30': iot_coffee
'31': iot_hue_lightchange
'32': iot_hue_lightdim
'33': iot_hue_lightoff
'34': iot_hue_lighton
'35': iot_hue_lightup
'36': iot_wemo_off
'37': iot_wemo_on
'38': lists_createoradd
'39': lists_query
'40': lists_remove
'41': music_dislikeness
'42': music_likeness
'43': music_query
'44': music_settings
'45': news_query
'46': play_audiobook
'47': play_game
'48': play_music
'49': play_podcasts
'50': play_radio
'51': qa_currency
'52': qa_definition
'53': qa_factoid
'54': qa_maths
'55': qa_stock
'56': recommendation_events
'57': recommendation_locations
'58': recommendation_movies
'59': social_post
'60': social_query
'61': takeaway_order
'62': takeaway_query
'63': transport_query
'64': transport_taxi
'65': transport_ticket
'66': transport_traffic
'67': weather_query
splits:
- name: train
num_bytes: 1447941
num_examples: 25715
download_size: 5867439
dataset_size: 1447941
---
# Dataset Card for NLU Evaluation Data
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [Github](https://github.com/xliuhw/NLU-Evaluation-Data)
- **Repository:** [Github](https://github.com/xliuhw/NLU-Evaluation-Data)
- **Paper:** [ArXiv](https://arxiv.org/abs/1903.05566)
- **Leaderboard:**
- **Point of Contact:** [x.liu@hw.ac.uk](mailto:x.liu@hw.ac.uk)
### Dataset Summary
Dataset with short utterances from conversational domain annotated with their corresponding intents and scenarios.
It has 25 715 non-zero examples (original dataset has 25716 examples) belonging to 18 scenarios and 68 intents.
Originally, the dataset was crowd-sourced and annotated with both intents and named entities
in order to evaluate commercial NLU systems such as RASA, IBM's Watson, Microsoft's LUIS and Google's Dialogflow.
**This version of the dataset only includes intent annotations!**
In contrast to paper claims, released data contains 68 unique intents. This is due to the fact, that NLU systems were
evaluated on more curated part of this dataset which only included 64 most important intents. Read more in [github issue](https://github.com/xliuhw/NLU-Evaluation-Data/issues/5).
### Supported Tasks and Leaderboards
Intent classification, intent detection
### Languages
English
## Dataset Structure
### Data Instances
An example of 'train' looks as follows:
```
{
'label': 2, # integer label corresponding to "alarm_set" intent
'scenario': 'alarm',
'text': 'wake me up at five am this week'
}
```
### Data Fields
- `text`: a string feature.
- `label`: one of classification labels (0-67) corresponding to unique intents.
- `scenario`: a string with one of unique scenarios (18).
Intent names are mapped to `label` in the following way:
| label | intent |
|--------:|:-------------------------|
| 0 | alarm_query |
| 1 | alarm_remove |
| 2 | alarm_set |
| 3 | audio_volume_down |
| 4 | audio_volume_mute |
| 5 | audio_volume_other |
| 6 | audio_volume_up |
| 7 | calendar_query |
| 8 | calendar_remove |
| 9 | calendar_set |
| 10 | cooking_query |
| 11 | cooking_recipe |
| 12 | datetime_convert |
| 13 | datetime_query |
| 14 | email_addcontact |
| 15 | email_query |
| 16 | email_querycontact |
| 17 | email_sendemail |
| 18 | general_affirm |
| 19 | general_commandstop |
| 20 | general_confirm |
| 21 | general_dontcare |
| 22 | general_explain |
| 23 | general_greet |
| 24 | general_joke |
| 25 | general_negate |
| 26 | general_praise |
| 27 | general_quirky |
| 28 | general_repeat |
| 29 | iot_cleaning |
| 30 | iot_coffee |
| 31 | iot_hue_lightchange |
| 32 | iot_hue_lightdim |
| 33 | iot_hue_lightoff |
| 34 | iot_hue_lighton |
| 35 | iot_hue_lightup |
| 36 | iot_wemo_off |
| 37 | iot_wemo_on |
| 38 | lists_createoradd |
| 39 | lists_query |
| 40 | lists_remove |
| 41 | music_dislikeness |
| 42 | music_likeness |
| 43 | music_query |
| 44 | music_settings |
| 45 | news_query |
| 46 | play_audiobook |
| 47 | play_game |
| 48 | play_music |
| 49 | play_podcasts |
| 50 | play_radio |
| 51 | qa_currency |
| 52 | qa_definition |
| 53 | qa_factoid |
| 54 | qa_maths |
| 55 | qa_stock |
| 56 | recommendation_events |
| 57 | recommendation_locations |
| 58 | recommendation_movies |
| 59 | social_post |
| 60 | social_query |
| 61 | takeaway_order |
| 62 | takeaway_query |
| 63 | transport_query |
| 64 | transport_taxi |
| 65 | transport_ticket |
| 66 | transport_traffic |
| 67 | weather_query |
### Data Splits
| Dataset statistics | Train |
| --- | --- |
| Number of examples | 25 715 |
| Average character length | 34.32 |
| Number of intents | 68 |
| Number of scenarios | 18 |
## Dataset Creation
### Curation Rationale
The dataset was prepared for a wide coverage evaluation and comparison of some of the most popular NLU services.
At that time, previous benchmarks were done with few intents and spawning limited number of domains. Here, the dataset
is much larger and contains 68 intents from 18 scenarios, which is much larger that any previous evaluation. For more discussion see the paper.
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
> To build the NLU component we collected real user data via Amazon Mechanical Turk (AMT). We designed tasks where the Turker’s goal was to answer questions about how people would interact with the home robot, in a wide range of scenarios designed in advance, namely: alarm, audio, audiobook, calendar, cooking, datetime, email, game, general, IoT, lists, music, news, podcasts, general Q&A, radio, recommendations, social, food takeaway, transport, and weather.
The questions put to Turkers were designed to capture the different requests within each given scenario.
In the ‘calendar’ scenario, for example, these pre-designed intents were included: ‘set event’, ‘delete event’ and ‘query event’.
An example question for intent ‘set event’ is: “How would you ask your PDA to schedule a meeting with someone?” for which a user’s answer example was “Schedule a chat with Adam on Thursday afternoon”.
The Turkers would then type in their answers to these questions and select possible entities from the pre-designed suggested entities list for each of their answers.The Turkers didn’t always follow the instructions fully, e.g. for the specified ‘delete event’ Intent, an answer was: “PDA what is my next event?”; which clearly belongs to ‘query event’ Intent.
We have manually corrected all such errors either during post-processing or the subsequent annotations.
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
The purpose of this dataset it to help develop better intent detection systems.
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
Creative Commons Attribution 4.0 International License (CC BY 4.0)
### Citation Information
```
@InProceedings{XLiu.etal:IWSDS2019,
author = {Xingkun Liu, Arash Eshghi, Pawel Swietojanski and Verena Rieser},
title = {Benchmarking Natural Language Understanding Services for building Conversational Agents},
booktitle = {Proceedings of the Tenth International Workshop on Spoken Dialogue Systems Technology (IWSDS)},
month = {April},
year = {2019},
address = {Ortigia, Siracusa (SR), Italy},
publisher = {Springer},
pages = {xxx--xxx},
url = {http://www.xx.xx/xx/}
}
```
### Contributions
Thanks to [@dkajtoch](https://github.com/dkajtoch) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
jxu124/refcoco | jxu124 | 2023-05-20T18:58:37Z | 434 | 1 | null | [
"region:us"
] | 2023-05-20T18:58:37Z | 2023-04-22T10:52:18.000Z | 2023-04-22T10:52:18 | ---
dataset_info:
features:
- name: sent_ids
sequence: int64
- name: file_name
dtype: string
- name: ann_id
dtype: int64
- name: ref_id
dtype: int64
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- name: sentences
list:
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- name: tokens
sequence: string
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sequence: float64
- name: captions
sequence: string
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splits:
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num_bytes: 81385755
num_examples: 42404
- name: testB
num_bytes: 3284397
num_examples: 1810
- name: test
num_bytes: 3943834
num_examples: 1975
- name: validation
num_bytes: 7355626
num_examples: 3811
download_size: 38895129
dataset_size: 95969612
---
# Dataset Card for "refcoco"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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TheBritishLibrary/EThOS-PhD-metadata | TheBritishLibrary | 2022-07-23T21:14:57Z | 433 | 1 | null | [
"task_categories:text-classification",
"task_categories:fill-mask",
"task_ids:multi-label-classification",
"task_ids:masked-language-modeling",
"multilinguality:monolingual",
"language:en",
"region:us"
] | 2022-07-23T21:14:57Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators: []
language:
- en
language_creators: []
license: []
multilinguality:
- monolingual
pretty_name: EThOS PhD metadata
size_categories: []
source_datasets: []
tags: []
task_categories:
- text-classification
- fill-mask
task_ids:
- multi-label-classification
- masked-language-modeling
---
# Dataset Card for EThOS PhD metadata
## Table of Contents
- [Dataset Card for blbooksgenre](#dataset-card-for-EThOS PhD metadata)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Supervised tasks](#supervised-tasks)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
- [Who are the source language producers?](#who-are-the-source-language-producers)
- [Annotations](#annotations)
- [Annotation process](#annotation-process)
- [Who are the annotators?](#who-are-the-annotators)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**: https://bl.iro.bl.uk/concern/datasets/c815b271-09be-4123-8156-405094429198?locale=en
- **Repository:** https://doi.org/10.23636/ybpt-nh33
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
The data in this collection comprises the bibliographic metadata for all UK doctoral theses listed in EThOS, the UK's national thesis service. We estimate the data covers around 98% of all PhDs ever awarded by UK Higher Education institutions, dating back to 1787. Thesis metadata from every PhD-awarding university in the UK is included. You can investigate and re-use this unique collection of UK universities' PhD thesis data to analyse trends in postgraduate research, make connections between researchers, apply large data analysis, improve citation of theses and many more applications.
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
#### Supervised tasks
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
[More Information Needed]
### Data Instances
An example data instance:
```python
{'Abstract': ' ',
'Author': 'Loizou, Panos A.',
'Author ISNI': 'https://isni.org/isni/0000000136122593',
'DOI': ' ',
'Date': datetime.datetime(1989, 1, 1, 0, 0),
'EThOS URL': 'https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.232781',
'Funder(s)': ' ',
'IR URL': ' ',
'Institution': 'University of Manchester',
'Institution ISNI': 'https://isni.org/isni/0000000121662407',
'ORCID': ' ',
'Qualification': 'Thesis (Ph.D.)',
'Subject Discipline': 0,
'Supervisor(s)': ' ',
'Title': 'Computation and measurement of turbulent flow through idealized turbine blade passages'}
```
### Data Fields
[More Information Needed]
### Data Splits
This dataset contains a single split `train`.
## Dataset Creation
[More Information Needed]
### Curation Rationale
[More Information Needed]
### Source Data
[More Information Needed]
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
[More Information Needed]
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
[More Information Needed]
### 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
The books are licensed under the [CC BY 4.0 Attribution](https://creativecommons.org/licenses/by/4.0/) license.
### Citation Information
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
diwank/silicone-merged | diwank | 2022-03-06T11:30:57Z | 433 | 1 | null | [
"license:mit",
"region:us"
] | 2022-03-06T11:30:57Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
license: mit
---
# diwank/silicone-merged
> Merged and simplified dialog act datasets from the [silicone collection](https://huggingface.co/datasets/silicone/)
All of the subsets of the original collection have been filtered (for errors and ambiguous classes), merged together and grouped into pairs of dialog turns. It is hypothesized that training dialog act classifier by including the previous utterance can help models pick up additional contextual cues and be better at inference esp if an utterance pair is provided.
## Example training script
```python
from datasets import load_dataset
from simpletransformers.classification import (
ClassificationModel, ClassificationArgs
)
# Get data
silicone_merged = load_dataset("diwank/silicone-merged")
train_df = silicone_merged["train"]
eval_df = silicone_merged["validation"]
model_args = ClassificationArgs(
num_train_epochs=8,
model_type="deberta",
model_name="microsoft/deberta-large",
use_multiprocessing=False,
evaluate_during_training=True,
)
# Create a ClassificationModel
model = ClassificationModel("deberta", "microsoft/deberta-large", args=model_args, num_labels=11) # 11 labels in this dataset
# Train model
model.train_model(train_df, eval_df=eval_df)
```
## Balanced variant of the training set
**Note**: This dataset is highly imbalanced and it is recommended to use a library like [imbalanced-learn](https://imbalanced-learn.org/stable/) before proceeding with training.
Since, balancing can be complicated and resource-intensive, we have shared a balanced variant of the train set that was created via oversampling using the _imbalanced-learn_ library. The balancing used the `SMOTEN` algorithm to deal with categorical data clustering and was resampled on a 16-core, 60GB RAM machine. You can access it using:
```load_dataset("diwank/silicone-merged", "balanced")```
## Feature description
- `text_a`: The utterance prior to the utterance being classified. (Say for dialog with turns 1-2-3, if we are trying to find the dialog act for 2, text_a is 1)
- `text_b`: The utterance to be classified
- `labels`: Dialog act label (as integer between 0-10, as mapped below)
## Labels map
```python
[
(0, 'acknowledge')
(1, 'answer')
(2, 'backchannel')
(3, 'reply_yes')
(4, 'exclaim')
(5, 'say')
(6, 'reply_no')
(7, 'hold')
(8, 'ask')
(9, 'intent')
(10, 'ask_yes_no')
]
```
*****
## Appendix
### How the original datasets were mapped:
```python
mapping = {
"acknowledge": {
"swda": [
"aap_am",
"b",
"bk"
],
"mrda": [],
"oasis": [
"ackn",
"accept",
"complete"
],
"maptask": [
"acknowledge",
"align"
],
"dyda_da": [
"commissive"
]
},
"answer": {
"swda": [
"bf",
],
"mrda": [],
"oasis": [
"answ",
"informCont",
"inform",
"answElab",
"directElab",
"refer"
],
"maptask": [
"reply_w",
"explain"
],
"dyda_da": [
"inform"
]
},
"backchannel": {
"swda": [
"ad",
"bh",
"bd",
"b^m"
],
"mrda": [
"b"
],
"oasis": [
"backch",
"selfTalk",
"init"
],
"maptask": ["ready"],
"dyda_da": []
},
"reply_yes": {
"swda": [
"na",
"aa"
],
"mrda": [],
"oasis": [
"confirm"
],
"maptask": [
"reply_y"
],
"dyda_da": []
},
"exclaim": {
"swda": [
"ft",
"fa",
"fc",
"fp"
],
"mrda": [],
"oasis": [
"appreciate",
"bye",
"exclaim",
"greet",
"thank",
"pardon",
"thank-identitySelf",
"expressRegret"
],
"maptask": [],
"dyda_da": []
},
"say": {
"swda": [
"qh",
"sd"
],
"mrda": ["s"],
"oasis": [
"expressPossibility",
"expressOpinion",
"suggest"
],
"maptask": [],
"dyda_da": []
},
"reply_no": {
"swda": [
"nn",
"ng",
"ar"
],
"mrda": [],
"oasis": [
"refuse",
"negate"
],
"maptask": [
"reply_n"
],
"dyda_da": []
},
"hold": {
"swda": [
"^h",
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],
"mrda": [
"f"
],
"oasis": [
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],
"maptask": [],
"dyda_da": []
},
"ask": {
"swda": [
"qw",
"qo",
"qw^d",
"br",
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],
"mrda": [
"q"
],
"oasis": [
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"offer"
],
"maptask": [
"query_w"
],
"dyda_da": [
"question"
]
},
"intent": {
"swda": [],
"mrda": [],
"oasis": [
"informIntent",
"informIntent-hold",
"expressWish",
"direct",
"raiseIssue",
"correct"
],
"maptask": [
"instruct",
"clarify"
],
"dyda_da": [
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]
},
"ask_yes_no": {
"swda": [
"qy^d",
"^g"
],
"mrda": [],
"oasis": [
"reqModal"
],
"maptask": [
"query_yn",
"check"
],
"dyda_da": []
}
}
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result-kand2-sdxl-wuerst-karlo/6f9ae809 | result-kand2-sdxl-wuerst-karlo | 2023-11-06T22:35:41Z | 433 | 0 | null | [
"region:us"
] | 2023-11-06T22:35:41Z | 2023-11-06T22:35:39.000Z | 2023-11-06T22:35:39 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 170
num_examples: 10
download_size: 1327
dataset_size: 170
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "6f9ae809"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
GEM/xsum | GEM | 2022-10-24T15:31:30Z | 432 | 0 | null | [
"task_categories:summarization",
"annotations_creators:none",
"language_creators:unknown",
"multilinguality:unknown",
"size_categories:unknown",
"source_datasets:original",
"language:en",
"license:cc-by-sa-4.0",
"region:us"
] | 2022-10-24T15:31:30Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- none
language_creators:
- unknown
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- unknown
size_categories:
- unknown
source_datasets:
- original
task_categories:
- summarization
task_ids: []
pretty_name: xsum
---
# Dataset Card for GEM/xsum
## Dataset Description
- **Homepage:** n/a
- **Repository:** https://github.com/EdinburghNLP/XSum
- **Paper:** https://www.aclweb.org/anthology/D18-1206
- **Leaderboard:** N/A
- **Point of Contact:** Shashi Narayan
### Link to Main Data Card
You can find the main data card on the [GEM Website](https://gem-benchmark.com/data_cards/xsum).
### Dataset Summary
XSum is an English news summarization dataset where the task is to predict the first sentence of an article from the rest of it.
You can load the dataset via:
```
import datasets
data = datasets.load_dataset('GEM/xsum')
```
The data loader can be found [here](https://huggingface.co/datasets/GEM/xsum).
#### website
n/a
#### paper
[ACL Anthology](https://www.aclweb.org/anthology/D18-1206)
#### authors
Shashi Narayan, Shay B. Cohen, Mirella Lapata (all affiliated with University of Edinburgh at the time of dataset creation)
## Dataset Overview
### Where to find the Data and its Documentation
#### Download
<!-- info: What is the link to where the original dataset is hosted? -->
<!-- scope: telescope -->
[Github](https://github.com/EdinburghNLP/XSum)
#### Paper
<!-- info: What is the link to the paper describing the dataset (open access preferred)? -->
<!-- scope: telescope -->
[ACL Anthology](https://www.aclweb.org/anthology/D18-1206)
#### BibTex
<!-- info: Provide the BibTex-formatted reference for the dataset. Please use the correct published version (ACL anthology, etc.) instead of google scholar created Bibtex. -->
<!-- scope: microscope -->
```
@InProceedings{xsum-emnlp,
author = "Shashi Narayan and Shay B. Cohen and Mirella Lapata",
title = "Don't Give Me the Details, Just the Summary! {T}opic-Aware Convolutional Neural Networks for Extreme Summarization",
booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing ",
year = "2018",
address = "Brussels, Belgium",
}
```
#### Contact Name
<!-- quick -->
<!-- info: If known, provide the name of at least one person the reader can contact for questions about the dataset. -->
<!-- scope: periscope -->
Shashi Narayan
#### Contact Email
<!-- info: If known, provide the email of at least one person the reader can contact for questions about the dataset. -->
<!-- scope: periscope -->
shashinarayan@google.com
#### Has a Leaderboard?
<!-- info: Does the dataset have an active leaderboard? -->
<!-- scope: telescope -->
no
### Languages and Intended Use
#### Multilingual?
<!-- quick -->
<!-- info: Is the dataset multilingual? -->
<!-- scope: telescope -->
no
#### Covered Dialects
<!-- info: What dialects are covered? Are there multiple dialects per language? -->
<!-- scope: periscope -->
Since the source of the dataset are BBC articles, the language is in British English of the variation written by journalists.
#### Covered Languages
<!-- quick -->
<!-- info: What languages/dialects are covered in the dataset? -->
<!-- scope: telescope -->
`English`
#### Whose Language?
<!-- info: Whose language is in the dataset? -->
<!-- scope: periscope -->
Professional journalists
#### License
<!-- quick -->
<!-- info: What is the license of the dataset? -->
<!-- scope: telescope -->
cc-by-sa-4.0: Creative Commons Attribution Share Alike 4.0 International
#### Intended Use
<!-- info: What is the intended use of the dataset? -->
<!-- scope: microscope -->
The dataset is for the task of abstractive summarization in its extreme form, its about summarizing a document in a single sentence. The idea is to create a short, one-sentence news summary answering the question "What is the article about?".
#### Primary Task
<!-- info: What primary task does the dataset support? -->
<!-- scope: telescope -->
Summarization
#### Communicative Goal
<!-- quick -->
<!-- info: Provide a short description of the communicative goal of a model trained for this task on this dataset. -->
<!-- scope: periscope -->
Given a news article, produce a single sentence summary of the content of the article.
### Credit
#### Curation Organization Type(s)
<!-- info: In what kind of organization did the dataset curation happen? -->
<!-- scope: telescope -->
`academic`
#### Curation Organization(s)
<!-- info: Name the organization(s). -->
<!-- scope: periscope -->
University of Edinburgh
#### Dataset Creators
<!-- info: Who created the original dataset? List the people involved in collecting the dataset and their affiliation(s). -->
<!-- scope: microscope -->
Shashi Narayan, Shay B. Cohen, Mirella Lapata (all affiliated with University of Edinburgh at the time of dataset creation)
#### Funding
<!-- info: Who funded the data creation? -->
<!-- scope: microscope -->
European Research Council (Lapata; award number 681760), the European Union under the Horizon 2020 SUMMA project (Narayan, Cohen; grant agreement 688139), and Huawei Technologies (Cohen).
#### Who added the Dataset to GEM?
<!-- info: Who contributed to the data card and adding the dataset to GEM? List the people+affiliations involved in creating this data card and who helped integrate this dataset into GEM. -->
<!-- scope: microscope -->
The original data card was written by Laura Perez-Beltrachini and the data loader by Yacine Jernite. Sebastian Gehrmann migrated the data card to the new format and extended it. The v2 data loader was migrated by Abinaya Mahendiran
### Dataset Structure
#### Data Fields
<!-- info: List and describe the fields present in the dataset. -->
<!-- scope: telescope -->
- `Document`: Input news article.
- `Summary`: One sentence summary of the article.
- `Id`: BBC ID of the article.
#### Reason for Structure
<!-- info: How was the dataset structure determined? -->
<!-- scope: microscope -->
The Document/Summary format is standard for summarization datasets.
#### How were labels chosen?
<!-- info: How were the labels chosen? -->
<!-- scope: microscope -->
The labels are the first sentence of the source article.
#### Example Instance
<!-- info: Provide a JSON formatted example of a typical instance in the dataset. -->
<!-- scope: periscope -->
```
{
'document': 'The researchers have sequenced the genome of a strain of bacterium that causes the virulent infection.\nA survey in 2007 showed that bleeding canker had spread rapidly, with almost half of the two million horse chestnuts displaying symptoms of the disease.\nThe findings have been published in the journal PLoS One.\nA visible symptom of the disease is a lesion on the bark, which oozes a resin on to the trunk or sometimes the branches.\nThe bark underneath the canker is killed, and if cankers manage to go all the way around the trunk then the horse chestnut (Aesculus hippocastanum) will die because it cuts off the food supply. [...]',
'target': "A team of UK scientists hopes to shed light on the mysteries of bleeding canker, a disease that is threatening the nation's horse chestnut trees.",
}
```
#### Data Splits
<!-- info: Describe and name the splits in the dataset if there are more than one. -->
<!-- scope: periscope -->
| Section | Number of Documents |
| ------------- |:-------------:|
| Training | 204,045 |
| Validation | 11,332 |
| Testing | 11,334 |
| Total | 226k |
| Section | number of words| number of sentences |
| ------------- |:-------------:| :-------------:|
| Documents | 431.07 | 19.77 |
| Summary | 23.26 | 1.00 |
#### Splitting Criteria
<!-- info: Describe any criteria for splitting the data, if used. If there are differences between the splits (e.g., if the training annotations are machine-generated and the dev and test ones are created by humans, or if different numbers of annotators contributed to each example), describe them here. -->
<!-- scope: microscope -->
The identifiers in the URLs were used to randomly split the dataset into training (90%, 204,045), validation (5%, 11,332), and test (5%, 11,334) sets.
## Dataset Curation
### Original Curation
#### Original Curation Rationale
<!-- info: Original curation rationale -->
<!-- scope: telescope -->
Comparable datasets are often very extractive which is not a strategy that works for one-sentence summaries. The dataset curators thus created this dataset as a way to evaluate truly abstractive models
#### Communicative Goal
<!-- info: What was the communicative goal? -->
<!-- scope: periscope -->
Same as the communicative goal in GEM: A model should summarize a news article in a single sentence
#### Sourced from Different Sources
<!-- info: Is the dataset aggregated from different data sources? -->
<!-- scope: telescope -->
no
### Language Data
#### How was Language Data Obtained?
<!-- info: How was the language data obtained? -->
<!-- scope: telescope -->
`Found`
#### Where was it found?
<!-- info: If found, where from? -->
<!-- scope: telescope -->
`Single website`
#### Language Producers
<!-- info: What further information do we have on the language producers? -->
<!-- scope: microscope -->
The data was collected from articles between 2010 and 2017. No other information
#### Topics Covered
<!-- info: Does the language in the dataset focus on specific topics? How would you describe them? -->
<!-- scope: periscope -->
The collected articles included the following topics: News, Politics, Sports, Weather, Business, Technology, Science, Health, Family, Education, Entertainment and Arts
The dataset curators also used LDA to gain insight into this question and found that the following were the top keywords associated with each topic:
- **T1**: charge, court, murder, police, arrest, guilty, sentence, boy, bail, space, crown, trial
- **T2**: church, abuse, bishop, child, catholic, gay, pope, school, christian, priest, cardinal
- **T3**: council, people, government, local, housing, home, house, property, city, plan, authority
- **T4**: clinton, party, trump, climate, poll, vote, plaid, election, debate, change, candidate, campaign
- **T5**: country, growth, report, business, export, fall, bank, security, economy, rise, global, inflation
- **T6**: hospital, patient, trust, nhs, people, care, health, service, staff, report, review, system, child
#### Data Validation
<!-- info: Was the text validated by a different worker or a data curator? -->
<!-- scope: telescope -->
not validated
#### Data Preprocessing
<!-- info: How was the text data pre-processed? (Enter N/A if the text was not pre-processed) -->
<!-- scope: microscope -->
The text was extracted from the HTML of the webpage. No further processing was done.
#### Was Data Filtered?
<!-- info: Were text instances selected or filtered? -->
<!-- scope: telescope -->
not filtered
### Structured Annotations
#### Additional Annotations?
<!-- quick -->
<!-- info: Does the dataset have additional annotations for each instance? -->
<!-- scope: telescope -->
none
#### Annotation Service?
<!-- info: Was an annotation service used? -->
<!-- scope: telescope -->
no
### Consent
#### Any Consent Policy?
<!-- info: Was there a consent policy involved when gathering the data? -->
<!-- scope: telescope -->
no
#### Justification for Using the Data
<!-- info: If not, what is the justification for reusing the data? -->
<!-- scope: microscope -->
The copyright license of the data allows reusing it for this purpose.
### Private Identifying Information (PII)
#### Contains PII?
<!-- quick -->
<!-- info: Does the source language data likely contain Personal Identifying Information about the data creators or subjects? -->
<!-- scope: telescope -->
yes/very likely
#### Categories of PII
<!-- info: What categories of PII are present or suspected in the data? -->
<!-- scope: periscope -->
`generic PII`
#### Any PII Identification?
<!-- info: Did the curators use any automatic/manual method to identify PII in the dataset? -->
<!-- scope: periscope -->
no identification
### Maintenance
#### Any Maintenance Plan?
<!-- info: Does the original dataset have a maintenance plan? -->
<!-- scope: telescope -->
no
## Broader Social Context
### Previous Work on the Social Impact of the Dataset
#### Usage of Models based on the Data
<!-- info: Are you aware of cases where models trained on the task featured in this dataset ore related tasks have been used in automated systems? -->
<!-- scope: telescope -->
no
### Impact on Under-Served Communities
#### Addresses needs of underserved Communities?
<!-- info: Does this dataset address the needs of communities that are traditionally underserved in language technology, and particularly language generation technology? Communities may be underserved for exemple because their language, language variety, or social or geographical context is underepresented in NLP and NLG resources (datasets and models). -->
<!-- scope: telescope -->
no
### Discussion of Biases
#### Any Documented Social Biases?
<!-- info: Are there documented social biases in the dataset? Biases in this context are variations in the ways members of different social categories are represented that can have harmful downstream consequences for members of the more disadvantaged group. -->
<!-- scope: telescope -->
unsure
#### Are the Language Producers Representative of the Language?
<!-- info: Does the distribution of language producers in the dataset accurately represent the full distribution of speakers of the language world-wide? If not, how does it differ? -->
<!-- scope: periscope -->
The language and content of the data is focused on news and language in the UK and as such not representative of the speakers world-wide. Existing selection biases of the BBC exist in this dataset.
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pasinit/xlwic | pasinit | 2022-10-25T09:54:22Z | 432 | 4 | null | [
"task_categories:text-classification",
"task_ids:semantic-similarity-classification",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"language:bg",
"language:zh",
"language:hr",
"language:da",
"language:nl",
"language:et",
"language:fa",
"language:ja",
"language:ko",
"language:it",
"language:fr",
"language:de",
"license:cc-by-nc-4.0",
"region:us"
] | 2022-10-25T09:54:22Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
extended:
- original
language_creators:
- found
language:
- en
- bg
- zh
- hr
- da
- nl
- et
- fa
- ja
- ko
- it
- fr
- de
license:
- cc-by-nc-4.0
multilinguality:
- multilingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- semantic-similarity-classification
---
# XL-WiC
Huggingface dataset for the XL-WiC paper [https://www.aclweb.org/anthology/2020.emnlp-main.584.pdf](https://www.aclweb.org/anthology/2020.emnlp-main.584.pdf).
Please refer to the official [website](https://pilehvar.github.io/xlwic/) for more information.
## Configurations
When loading one of the XL-WSD datasets one has to specify the training language and the target language (on which dev and test will be performed).
Please refer to [Languages](#languages) section to see in which languages training data is available.
For example, we can load the dataset having English as training language and Italian as target language as follows:
```python
from datasets import load_dataset
dataset = load_dataset('pasinit/xlwic', 'en_it')
```
## Languages
**Training data**
- en (English)
- fr (French)
- de (German)
- it (Italian)
**Dev & Test data**
- fr (French)
- de (German)
- it (Italian)
- bg (Bulgarian)
- zh (Chinese)
- hr (Croatian)
- da (Danish)
- nl (Dutch)
- et (Estonian)
- fa (Farsi)
- ja (Japanesse)
- ko (Korean)
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
range3/cc100-ja | range3 | 2023-02-04T05:43:32Z | 432 | 7 | null | [
"task_categories:text-generation",
"task_categories:fill-mask",
"language:ja",
"license:unknown",
"region:us"
] | 2023-02-04T05:43:32Z | 2023-02-04T05:10:34.000Z | 2023-02-04T05:10:34 | ---
license: unknown
task_categories:
- text-generation
- fill-mask
language:
- ja
---
# range3/cc100-ja
This dataset consists of parquet files from the cc100 dataset with only the Japanese language extracted and sharded.
このデータセットは、cc100データセットの日本語のみを抽出し、シャーディングしたparquetファイルで構成されます。 | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
result-kand2-sdxl-wuerst-karlo/e82d3dfd | result-kand2-sdxl-wuerst-karlo | 2023-11-06T22:35:43Z | 432 | 0 | null | [
"region:us"
] | 2023-11-06T22:35:43Z | 2023-11-06T22:35:42.000Z | 2023-11-06T22:35:42 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 170
num_examples: 10
download_size: 1327
dataset_size: 170
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "e82d3dfd"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
bigbio/ddi_corpus | bigbio | 2022-12-22T15:44:31Z | 430 | 2 | null | [
"multilinguality:monolingual",
"language:en",
"license:cc-by-nc-4.0",
"region:us"
] | 2022-12-22T15:44:31Z | 2022-11-13T22:08:08.000Z | 2022-11-13T22:08:08 |
---
language:
- en
bigbio_language:
- English
license: cc-by-nc-4.0
multilinguality: monolingual
bigbio_license_shortname: CC_BY_NC_4p0
pretty_name: DDI Corpus
homepage: https://github.com/isegura/DDICorpus
bigbio_pubmed: True
bigbio_public: True
bigbio_tasks:
- NAMED_ENTITY_RECOGNITION
- RELATION_EXTRACTION
---
# Dataset Card for DDI Corpus
## Dataset Description
- **Homepage:** https://github.com/isegura/DDICorpus
- **Pubmed:** True
- **Public:** True
- **Tasks:** NER,RE
The DDI corpus has been manually annotated with drugs and pharmacokinetics and pharmacodynamics interactions. It contains 1025 documents from two different sources: DrugBank database and MedLine.
## Citation Information
```
@article{HERREROZAZO2013914,
title = {
The DDI corpus: An annotated corpus with pharmacological substances and
drug-drug interactions
},
author = {
María Herrero-Zazo and Isabel Segura-Bedmar and Paloma Martínez and Thierry
Declerck
},
year = 2013,
journal = {Journal of Biomedical Informatics},
volume = 46,
number = 5,
pages = {914--920},
doi = {https://doi.org/10.1016/j.jbi.2013.07.011},
issn = {1532-0464},
url = {https://www.sciencedirect.com/science/article/pii/S1532046413001123},
keywords = {Biomedical corpora, Drug interaction, Information extraction}
}
```
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texturedesign/td01_natural-ground-textures | texturedesign | 2023-09-02T10:21:04Z | 430 | 3 | null | [
"task_categories:unconditional-image-generation",
"annotations_creators:expert-generated",
"size_categories:n<1K",
"source_datasets:original",
"license:cc-by-nc-4.0",
"texture-synthesis",
"photography",
"non-infringing",
"region:us"
] | 2023-09-02T10:21:04Z | 2022-11-19T17:43:30.000Z | 2022-11-19T17:43:30 | ---
annotations_creators:
- expert-generated
language: []
language_creators: []
license:
- cc-by-nc-4.0
multilinguality: []
pretty_name: 'TD01: Natural Ground Texture Photos'
size_categories:
- n<1K
source_datasets:
- original
tags:
- texture-synthesis
- photography
- non-infringing
task_categories:
- unconditional-image-generation
task_ids: []
viewer: false
---
_The Dataset Teaser is now enabled instead! Isn't this better?_
# TD 01: Natural Ground Textures
This dataset contains multi-photo texture captures in outdoor nature scenes — all focusing on the ground. Each set has different photos that showcase texture variety, making them ideal for training a domain-specific image generator!
Overall information about this dataset:
* **Format** — JPEG-XL, lossless RGB
* **Resolution** — 4032 × 2268
* **Device** — mobile camera
* **Technique** — hand-held
* **Orientation** — portrait or landscape
* **Author**: Alex J. Champandard
* **Configurations**: 4K, 2K (default), 1K
To load the medium- and high-resolution images of the dataset, you'll need to install `jxlpy` from [PyPI](https://pypi.org/project/jxlpy/) with `pip install jxlpy`:
```python
# Recommended use, JXL at high-quality.
from jxlpy import JXLImagePlugin
from datasets import load_dataset
d = load_dataset('texturedesign/td01_natural-ground-textures', 'JXL@4K', num_proc=4)
print(len(d['train']), len(d['test']))
```
The lowest-resolution images are available as PNG with a regular installation of `pillow`:
```python
# Alternative use, PNG at low-quality.
from datasets import load_dataset
d = load_dataset('texturedesign/td01_natural-ground-textures', 'PNG@1K', num_proc=4)
# EXAMPLE: Discard all other sets except Set #1.
dataset = dataset.filter(lambda s: s['set'] == 1)
# EXAMPLE: Only keep images with index 0 and 2.
dataset = dataset.select([0, 2])
```
Use built-in dataset `filter()` and `select()` to narrow down the loaded dataset for training, or to ease with development.
## Set #1: Rock and Gravel
* **Description**:
- surface rocks with gravel and coarse sand
- strong sunlight from the left, sharp shadows
* **Number of Photos**:
- 7 train
- 2 test
* **Edits**:
- rotated photos to align sunlight
- removed infrequent objects
* **Size**: 77.8 Mb
## Set #2: Dry Grass with Pine Needles
* **Description**:
- field of dry grass and pine needles
- sunlight from the top right, some shadows
* **Number of Photos**:
- 6 train
- 1 test
* **Edits**:
- removed dry leaves and large plants
- removed sticks, rocks and sporadic daisies
* **Size**: 95.2 Mb
## Set #3: Chipped Stones, Broken Leaves and Twiglets
* **Description**:
- autumn path with chipped stones and dry broken leaves
- diffuse light on a cloudy day, very soft shadows
* **Number of Photos**:
- 9 train
- 3 test
* **Edits**:
- removed anything that looks green, fresh leaves
- removed long sticks and large/odd stones
* **Size**: 126.9 Mb
## Set #4: Grass Clumps and Cracked Dirt
* **Description**:
- clumps of green grass, clover and patches of cracked dirt
- diffuse light on cloudy day, shadows under large blades of grass
* **Number of Photos**:
- 9 train
- 2 test
* **Edits**:
- removed dry leaves, sporadic dandelions, and large objects
- histogram matching for two of the photos so the colors look similar
* **Size**: 126.8 Mb
## Set #5: Dirt, Stones, Rock, Twigs...
* **Description**:
- intricate micro-scene with grey dirt, surface rock, stones, twigs and organic debris
- diffuse light on cloudy day, soft shadows around the larger objects
* **Number of Photos**:
- 9 train
- 3 test
* **Edits**:
- removed odd objects that felt out-of-distribution
* **Size**: 102.1 Mb
## Set #6: Plants with Flowers on Dry Leaves
* **Description**:
- leafy plants with white flowers on a bed of dry brown leaves
- soft diffuse light, shaded areas under the plants
* **Number of Photos**:
- 9 train
- 2 test
* **Edits**:
- none yet, inpainting doesn't work well enough
- would remove long sticks and pieces of wood
* **Size**: 105.1 Mb
## Set #7: Frozen Footpath with Snow
* **Description**:
- frozen ground on a path with footprints
- areas with snow and dark brown ground beneath
- diffuse lighting on a cloudy day
* **Number of Photos**:
- 11 train
- 3 test
* **Size**: 95.5 Mb
## Set #8: Pine Needles Forest Floor
* **Description**:
- forest floor with a mix of brown soil and grass
- variety of dry white leaves, sticks, pinecones, pine needles
- diffuse lighting on a cloudy day
* **Number of Photos**:
- 15 train
- 4 test
* **Size**: 160.6 Mb
## Set #9: Snow on Grass and Dried Leaves
* **Description**:
- field in a park with short green grass
- large dried brown leaves and fallen snow on top
- diffuse lighting on a cloudy day
* **Number of Photos**:
- 8 train
- 3 test
* **Size**: 99.8 Mb
## Set #10: Brown Leaves on Wet Ground
* **Description**:
- fallew brown leaves on wet ground
- occasional tree root and twiglets
- diffuse lighting on a rainy day
* **Number of Photos**:
- 17 train
- 4 test
* **Size**: 186.2 Mb
## Set #11: Wet Sand Path with Debris
* **Description**:
- hard sandy path in the rain
- decomposing leaves and other organic debris
- diffuse lighting on a rainy day
* **Number of Photos**:
- 17 train
- 4 test
* **Size**: 186.2 Mb
## Set #12: Wood Chips & Sawdust Sprinkled on Forest Path
* **Description**:
- wood chips, sawdust, twigs and roots on forest path
- intermittent sunlight with shadows of trees
* **Number of Photos**:
- 8 train
- 2 test
* **Size**: 110.4 Mb
## Set #13: Young Grass Growing in the Dog Park
* **Description**:
- young grass growing in a dog park after overnight rain
- occasional stones, sticks and twigs, pine needles
- diffuse lighting on a cloudy day
* **Number of Photos**:
- 17 train
- 4 test
* **Size**: 193.4 Mb
## Set #14: Wavy Wet Beach Sand
* **Description**:
- wavy wet sand on the beach after the tide retreated
- some dirt and large pieces algae debris
- diffuse lighting on a cloudy day
* **Number of Photos**:
- 11 train
- 3 test
* **Size**: 86.5 Mb
## Set #15: Dry Dirt Road and Debris from Trees
* **Description**:
- dirt road of dry compacted sand with debris on top
- old pine needles and dry brown leaves
- diffuse lighting on a cloudy day
* **Number of Photos**:
- 8 train
- 2 test
* **Size**: 86.9 Mb
## Set #16: Sandy Beach Path with Grass Clumps
* **Description**:
- path with sand and clumps grass heading towards the beach
- occasional blueish stones, leafy weeds, and yellow flowers
- diffuse lighting on a cloudy day
* **Number of Photos**:
- 10 train
- 3 test
* **Size**: 118.8 Mb
## Set #17: Pine Needles and Brown Leaves on Park Floor
* **Description**:
- park floor with predominantly pine needles
- brown leaves from nearby trees, green grass underneath
- diffuse lighting on a cloudy day
* **Number of Photos**:
- 8 train
- 2 test
* **Size**: 99.9 Mb
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bsd_ja_en | null | 2022-11-18T19:24:36Z | 429 | 4 | business-scene-dialogue | [
"task_categories:translation",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:translation",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"language:ja",
"license:cc-by-nc-sa-4.0",
"business-conversations-translation",
"region:us"
] | 2022-11-18T19:24:36Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
- ja
license:
- cc-by-nc-sa-4.0
multilinguality:
- translation
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- translation
task_ids: []
paperswithcode_id: business-scene-dialogue
pretty_name: Business Scene Dialogue
tags:
- business-conversations-translation
dataset_info:
features:
- name: id
dtype: string
- name: tag
dtype: string
- name: title
dtype: string
- name: original_language
dtype: string
- name: 'no'
dtype: int32
- name: en_speaker
dtype: string
- name: ja_speaker
dtype: string
- name: en_sentence
dtype: string
- name: ja_sentence
dtype: string
splits:
- name: train
num_bytes: 4778409
num_examples: 20000
- name: test
num_bytes: 493038
num_examples: 2120
- name: validation
num_bytes: 477964
num_examples: 2051
download_size: 8135045
dataset_size: 5749411
---
# Dataset Card for Business Scene Dialogue
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [Github](https://raw.githubusercontent.com/tsuruoka-lab/BSD/)
- **Repository:** [Github](https://raw.githubusercontent.com/tsuruoka-lab/BSD/)
- **Paper:** [Rikters et al., 2019](https://www.aclweb.org/anthology/D19-5204)
- **Leaderboard:**
- **Point of Contact:** Matīss Rikters
### Dataset Summary
This is the Business Scene Dialogue (BSD) dataset,
a Japanese-English parallel corpus containing written conversations
in various business scenarios.
The dataset was constructed in 3 steps:
1) selecting business scenes,
2) writing monolingual conversation scenarios according to the selected scenes, and
3) translating the scenarios into the other language.
Half of the monolingual scenarios were written in Japanese
and the other half were written in English.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
English, Japanese.
## Dataset Structure
### Data Instances
Each instance contains a conversation identifier, a sentence number that indicates its
position within the conversation, speaker name in English and Japanese,
text in English and Japanese, original language, scene of the scenario (tag),
and title of the scenario (title).
```python
{
"id": "190315_E004_13",
"no": 14,
"speaker": "Mr. Sam Lee",
"ja_speaker": "サム リーさん",
"en_sentence": "Would you guys consider a different scheme?",
"ja_sentence": "別の事業案も考慮されますか？",
"original_language": "en",
"tag": "phone call",
"title": "Phone: Review spec and scheme"
}
```
### Data Fields
- id: dialogue identifier
- no: sentence pair number within a dialogue
- en_speaker: speaker name in English
- ja_speaker: speaker name in Japanese
- en_sentence: sentence in English
- ja_sentence: sentence in Japanese
- original_language: language in which monolingual scenario was written
- tag: scenario
- title: scenario title
### Data Splits
- There are a total of 24171 sentences / 808 business scenarios.
- Train: 20000 sentences / 670 scenarios
- Dev: 2051 sentences / 69 scenarios
- Test: 2120 sentences / 69 scenarios
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
Dataset provided for research purposes only. Please check dataset license for additional information.
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
This dataset was released under the Creative Commons Attribution-NonCommercial-ShareAlike (CC BY-NC-SA) license.
### Citation Information
```
@inproceedings{rikters-etal-2019-designing,
title = "Designing the Business Conversation Corpus",
author = "Rikters, Mat{\=\i}ss and
Ri, Ryokan and
Li, Tong and
Nakazawa, Toshiaki",
booktitle = "Proceedings of the 6th Workshop on Asian Translation",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/D19-5204",
doi = "10.18653/v1/D19-5204",
pages = "54--61"
}
```
### Contributions
Thanks to [@j-chim](https://github.com/j-chim) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
imagenet_sketch | null | 2023-04-05T13:45:57Z | 429 | 6 | imagenet-sketch | [
"task_categories:image-classification",
"task_ids:multi-class-image-classification",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|imagenet-1k",
"language:en",
"license:unknown",
"arxiv:1905.13549",
"region:us"
] | 2023-04-05T13:45:57Z | 2022-05-20T14:13:58.000Z | 2022-05-20T14:13:58 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|imagenet-1k
task_categories:
- image-classification
task_ids:
- multi-class-image-classification
paperswithcode_id: imagenet-sketch
pretty_name: ImageNet-Sketch
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': tench, Tinca tinca
'1': goldfish, Carassius auratus
'2': great white shark, white shark, man-eater, man-eating shark, Carcharodon
carcharias
'3': tiger shark, Galeocerdo cuvieri
'4': hammerhead, hammerhead shark
'5': electric ray, crampfish, numbfish, torpedo
'6': stingray
'7': cock
'8': hen
'9': ostrich, Struthio camelus
'10': brambling, Fringilla montifringilla
'11': goldfinch, Carduelis carduelis
'12': house finch, linnet, Carpodacus mexicanus
'13': junco, snowbird
'14': indigo bunting, indigo finch, indigo bird, Passerina cyanea
'15': robin, American robin, Turdus migratorius
'16': bulbul
'17': jay
'18': magpie
'19': chickadee
'20': water ouzel, dipper
'21': kite
'22': bald eagle, American eagle, Haliaeetus leucocephalus
'23': vulture
'24': great grey owl, great gray owl, Strix nebulosa
'25': European fire salamander, Salamandra salamandra
'26': common newt, Triturus vulgaris
'27': eft
'28': spotted salamander, Ambystoma maculatum
'29': axolotl, mud puppy, Ambystoma mexicanum
'30': bullfrog, Rana catesbeiana
'31': tree frog, tree-frog
'32': tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui
'33': loggerhead, loggerhead turtle, Caretta caretta
'34': leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea
'35': mud turtle
'36': terrapin
'37': box turtle, box tortoise
'38': banded gecko
'39': common iguana, iguana, Iguana iguana
'40': American chameleon, anole, Anolis carolinensis
'41': whiptail, whiptail lizard
'42': agama
'43': frilled lizard, Chlamydosaurus kingi
'44': alligator lizard
'45': Gila monster, Heloderma suspectum
'46': green lizard, Lacerta viridis
'47': African chameleon, Chamaeleo chamaeleon
'48': Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus
komodoensis
'49': African crocodile, Nile crocodile, Crocodylus niloticus
'50': American alligator, Alligator mississipiensis
'51': triceratops
'52': thunder snake, worm snake, Carphophis amoenus
'53': ringneck snake, ring-necked snake, ring snake
'54': hognose snake, puff adder, sand viper
'55': green snake, grass snake
'56': king snake, kingsnake
'57': garter snake, grass snake
'58': water snake
'59': vine snake
'60': night snake, Hypsiglena torquata
'61': boa constrictor, Constrictor constrictor
'62': rock python, rock snake, Python sebae
'63': Indian cobra, Naja naja
'64': green mamba
'65': sea snake
'66': horned viper, cerastes, sand viper, horned asp, Cerastes cornutus
'67': diamondback, diamondback rattlesnake, Crotalus adamanteus
'68': sidewinder, horned rattlesnake, Crotalus cerastes
'69': trilobite
'70': harvestman, daddy longlegs, Phalangium opilio
'71': scorpion
'72': black and gold garden spider, Argiope aurantia
'73': barn spider, Araneus cavaticus
'74': garden spider, Aranea diademata
'75': black widow, Latrodectus mactans
'76': tarantula
'77': wolf spider, hunting spider
'78': tick
'79': centipede
'80': black grouse
'81': ptarmigan
'82': ruffed grouse, partridge, Bonasa umbellus
'83': prairie chicken, prairie grouse, prairie fowl
'84': peacock
'85': quail
'86': partridge
'87': African grey, African gray, Psittacus erithacus
'88': macaw
'89': sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita
'90': lorikeet
'91': coucal
'92': bee eater
'93': hornbill
'94': hummingbird
'95': jacamar
'96': toucan
'97': drake
'98': red-breasted merganser, Mergus serrator
'99': goose
'100': black swan, Cygnus atratus
'101': tusker
'102': echidna, spiny anteater, anteater
'103': platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus
anatinus
'104': wallaby, brush kangaroo
'105': koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus
'106': wombat
'107': jellyfish
'108': sea anemone, anemone
'109': brain coral
'110': flatworm, platyhelminth
'111': nematode, nematode worm, roundworm
'112': conch
'113': snail
'114': slug
'115': sea slug, nudibranch
'116': chiton, coat-of-mail shell, sea cradle, polyplacophore
'117': chambered nautilus, pearly nautilus, nautilus
'118': Dungeness crab, Cancer magister
'119': rock crab, Cancer irroratus
'120': fiddler crab
'121': king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes
camtschatica
'122': American lobster, Northern lobster, Maine lobster, Homarus americanus
'123': spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish
'124': crayfish, crawfish, crawdad, crawdaddy
'125': hermit crab
'126': isopod
'127': white stork, Ciconia ciconia
'128': black stork, Ciconia nigra
'129': spoonbill
'130': flamingo
'131': little blue heron, Egretta caerulea
'132': American egret, great white heron, Egretta albus
'133': bittern
'134': crane
'135': limpkin, Aramus pictus
'136': European gallinule, Porphyrio porphyrio
'137': American coot, marsh hen, mud hen, water hen, Fulica americana
'138': bustard
'139': ruddy turnstone, Arenaria interpres
'140': red-backed sandpiper, dunlin, Erolia alpina
'141': redshank, Tringa totanus
'142': dowitcher
'143': oystercatcher, oyster catcher
'144': pelican
'145': king penguin, Aptenodytes patagonica
'146': albatross, mollymawk
'147': grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius
robustus
'148': killer whale, killer, orca, grampus, sea wolf, Orcinus orca
'149': dugong, Dugong dugon
'150': sea lion
'151': Chihuahua
'152': Japanese spaniel
'153': Maltese dog, Maltese terrier, Maltese
'154': Pekinese, Pekingese, Peke
'155': Shih-Tzu
'156': Blenheim spaniel
'157': papillon
'158': toy terrier
'159': Rhodesian ridgeback
'160': Afghan hound, Afghan
'161': basset, basset hound
'162': beagle
'163': bloodhound, sleuthhound
'164': bluetick
'165': black-and-tan coonhound
'166': Walker hound, Walker foxhound
'167': English foxhound
'168': redbone
'169': borzoi, Russian wolfhound
'170': Irish wolfhound
'171': Italian greyhound
'172': whippet
'173': Ibizan hound, Ibizan Podenco
'174': Norwegian elkhound, elkhound
'175': otterhound, otter hound
'176': Saluki, gazelle hound
'177': Scottish deerhound, deerhound
'178': Weimaraner
'179': Staffordshire bullterrier, Staffordshire bull terrier
'180': American Staffordshire terrier, Staffordshire terrier, American pit
bull terrier, pit bull terrier
'181': Bedlington terrier
'182': Border terrier
'183': Kerry blue terrier
'184': Irish terrier
'185': Norfolk terrier
'186': Norwich terrier
'187': Yorkshire terrier
'188': wire-haired fox terrier
'189': Lakeland terrier
'190': Sealyham terrier, Sealyham
'191': Airedale, Airedale terrier
'192': cairn, cairn terrier
'193': Australian terrier
'194': Dandie Dinmont, Dandie Dinmont terrier
'195': Boston bull, Boston terrier
'196': miniature schnauzer
'197': giant schnauzer
'198': standard schnauzer
'199': Scotch terrier, Scottish terrier, Scottie
'200': Tibetan terrier, chrysanthemum dog
'201': silky terrier, Sydney silky
'202': soft-coated wheaten terrier
'203': West Highland white terrier
'204': Lhasa, Lhasa apso
'205': flat-coated retriever
'206': curly-coated retriever
'207': golden retriever
'208': Labrador retriever
'209': Chesapeake Bay retriever
'210': German short-haired pointer
'211': vizsla, Hungarian pointer
'212': English setter
'213': Irish setter, red setter
'214': Gordon setter
'215': Brittany spaniel
'216': clumber, clumber spaniel
'217': English springer, English springer spaniel
'218': Welsh springer spaniel
'219': cocker spaniel, English cocker spaniel, cocker
'220': Sussex spaniel
'221': Irish water spaniel
'222': kuvasz
'223': schipperke
'224': groenendael
'225': malinois
'226': briard
'227': kelpie
'228': komondor
'229': Old English sheepdog, bobtail
'230': Shetland sheepdog, Shetland sheep dog, Shetland
'231': collie
'232': Border collie
'233': Bouvier des Flandres, Bouviers des Flandres
'234': Rottweiler
'235': German shepherd, German shepherd dog, German police dog, alsatian
'236': Doberman, Doberman pinscher
'237': miniature pinscher
'238': Greater Swiss Mountain dog
'239': Bernese mountain dog
'240': Appenzeller
'241': EntleBucher
'242': boxer
'243': bull mastiff
'244': Tibetan mastiff
'245': French bulldog
'246': Great Dane
'247': Saint Bernard, St Bernard
'248': Eskimo dog, husky
'249': malamute, malemute, Alaskan malamute
'250': Siberian husky
'251': dalmatian, coach dog, carriage dog
'252': affenpinscher, monkey pinscher, monkey dog
'253': basenji
'254': pug, pug-dog
'255': Leonberg
'256': Newfoundland, Newfoundland dog
'257': Great Pyrenees
'258': Samoyed, Samoyede
'259': Pomeranian
'260': chow, chow chow
'261': keeshond
'262': Brabancon griffon
'263': Pembroke, Pembroke Welsh corgi
'264': Cardigan, Cardigan Welsh corgi
'265': toy poodle
'266': miniature poodle
'267': standard poodle
'268': Mexican hairless
'269': timber wolf, grey wolf, gray wolf, Canis lupus
'270': white wolf, Arctic wolf, Canis lupus tundrarum
'271': red wolf, maned wolf, Canis rufus, Canis niger
'272': coyote, prairie wolf, brush wolf, Canis latrans
'273': dingo, warrigal, warragal, Canis dingo
'274': dhole, Cuon alpinus
'275': African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus
'276': hyena, hyaena
'277': red fox, Vulpes vulpes
'278': kit fox, Vulpes macrotis
'279': Arctic fox, white fox, Alopex lagopus
'280': grey fox, gray fox, Urocyon cinereoargenteus
'281': tabby, tabby cat
'282': tiger cat
'283': Persian cat
'284': Siamese cat, Siamese
'285': Egyptian cat
'286': cougar, puma, catamount, mountain lion, painter, panther, Felis concolor
'287': lynx, catamount
'288': leopard, Panthera pardus
'289': snow leopard, ounce, Panthera uncia
'290': jaguar, panther, Panthera onca, Felis onca
'291': lion, king of beasts, Panthera leo
'292': tiger, Panthera tigris
'293': cheetah, chetah, Acinonyx jubatus
'294': brown bear, bruin, Ursus arctos
'295': American black bear, black bear, Ursus americanus, Euarctos americanus
'296': ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus
'297': sloth bear, Melursus ursinus, Ursus ursinus
'298': mongoose
'299': meerkat, mierkat
'300': tiger beetle
'301': ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle
'302': ground beetle, carabid beetle
'303': long-horned beetle, longicorn, longicorn beetle
'304': leaf beetle, chrysomelid
'305': dung beetle
'306': rhinoceros beetle
'307': weevil
'308': fly
'309': bee
'310': ant, emmet, pismire
'311': grasshopper, hopper
'312': cricket
'313': walking stick, walkingstick, stick insect
'314': cockroach, roach
'315': mantis, mantid
'316': cicada, cicala
'317': leafhopper
'318': lacewing, lacewing fly
'319': dragonfly, darning needle, devil's darning needle, sewing needle,
snake feeder, snake doctor, mosquito hawk, skeeter hawk
'320': damselfly
'321': admiral
'322': ringlet, ringlet butterfly
'323': monarch, monarch butterfly, milkweed butterfly, Danaus plexippus
'324': cabbage butterfly
'325': sulphur butterfly, sulfur butterfly
'326': lycaenid, lycaenid butterfly
'327': starfish, sea star
'328': sea urchin
'329': sea cucumber, holothurian
'330': wood rabbit, cottontail, cottontail rabbit
'331': hare
'332': Angora, Angora rabbit
'333': hamster
'334': porcupine, hedgehog
'335': fox squirrel, eastern fox squirrel, Sciurus niger
'336': marmot
'337': beaver
'338': guinea pig, Cavia cobaya
'339': sorrel
'340': zebra
'341': hog, pig, grunter, squealer, Sus scrofa
'342': wild boar, boar, Sus scrofa
'343': warthog
'344': hippopotamus, hippo, river horse, Hippopotamus amphibius
'345': ox
'346': water buffalo, water ox, Asiatic buffalo, Bubalus bubalis
'347': bison
'348': ram, tup
'349': bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain
sheep, Ovis canadensis
'350': ibex, Capra ibex
'351': hartebeest
'352': impala, Aepyceros melampus
'353': gazelle
'354': Arabian camel, dromedary, Camelus dromedarius
'355': llama
'356': weasel
'357': mink
'358': polecat, fitch, foulmart, foumart, Mustela putorius
'359': black-footed ferret, ferret, Mustela nigripes
'360': otter
'361': skunk, polecat, wood pussy
'362': badger
'363': armadillo
'364': three-toed sloth, ai, Bradypus tridactylus
'365': orangutan, orang, orangutang, Pongo pygmaeus
'366': gorilla, Gorilla gorilla
'367': chimpanzee, chimp, Pan troglodytes
'368': gibbon, Hylobates lar
'369': siamang, Hylobates syndactylus, Symphalangus syndactylus
'370': guenon, guenon monkey
'371': patas, hussar monkey, Erythrocebus patas
'372': baboon
'373': macaque
'374': langur
'375': colobus, colobus monkey
'376': proboscis monkey, Nasalis larvatus
'377': marmoset
'378': capuchin, ringtail, Cebus capucinus
'379': howler monkey, howler
'380': titi, titi monkey
'381': spider monkey, Ateles geoffroyi
'382': squirrel monkey, Saimiri sciureus
'383': Madagascar cat, ring-tailed lemur, Lemur catta
'384': indri, indris, Indri indri, Indri brevicaudatus
'385': Indian elephant, Elephas maximus
'386': African elephant, Loxodonta africana
'387': lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens
'388': giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca
'389': barracouta, snoek
'390': eel
'391': coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus
kisutch
'392': rock beauty, Holocanthus tricolor
'393': anemone fish
'394': sturgeon
'395': gar, garfish, garpike, billfish, Lepisosteus osseus
'396': lionfish
'397': puffer, pufferfish, blowfish, globefish
'398': abacus
'399': abaya
'400': academic gown, academic robe, judge's robe
'401': accordion, piano accordion, squeeze box
'402': acoustic guitar
'403': aircraft carrier, carrier, flattop, attack aircraft carrier
'404': airliner
'405': airship, dirigible
'406': altar
'407': ambulance
'408': amphibian, amphibious vehicle
'409': analog clock
'410': apiary, bee house
'411': apron
'412': ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin,
dustbin, trash barrel, trash bin
'413': assault rifle, assault gun
'414': backpack, back pack, knapsack, packsack, rucksack, haversack
'415': bakery, bakeshop, bakehouse
'416': balance beam, beam
'417': balloon
'418': ballpoint, ballpoint pen, ballpen, Biro
'419': Band Aid
'420': banjo
'421': bannister, banister, balustrade, balusters, handrail
'422': barbell
'423': barber chair
'424': barbershop
'425': barn
'426': barometer
'427': barrel, cask
'428': barrow, garden cart, lawn cart, wheelbarrow
'429': baseball
'430': basketball
'431': bassinet
'432': bassoon
'433': bathing cap, swimming cap
'434': bath towel
'435': bathtub, bathing tub, bath, tub
'436': beach wagon, station wagon, wagon, estate car, beach waggon, station
waggon, waggon
'437': beacon, lighthouse, beacon light, pharos
'438': beaker
'439': bearskin, busby, shako
'440': beer bottle
'441': beer glass
'442': bell cote, bell cot
'443': bib
'444': bicycle-built-for-two, tandem bicycle, tandem
'445': bikini, two-piece
'446': binder, ring-binder
'447': binoculars, field glasses, opera glasses
'448': birdhouse
'449': boathouse
'450': bobsled, bobsleigh, bob
'451': bolo tie, bolo, bola tie, bola
'452': bonnet, poke bonnet
'453': bookcase
'454': bookshop, bookstore, bookstall
'455': bottlecap
'456': bow
'457': bow tie, bow-tie, bowtie
'458': brass, memorial tablet, plaque
'459': brassiere, bra, bandeau
'460': breakwater, groin, groyne, mole, bulwark, seawall, jetty
'461': breastplate, aegis, egis
'462': broom
'463': bucket, pail
'464': buckle
'465': bulletproof vest
'466': bullet train, bullet
'467': butcher shop, meat market
'468': cab, hack, taxi, taxicab
'469': caldron, cauldron
'470': candle, taper, wax light
'471': cannon
'472': canoe
'473': can opener, tin opener
'474': cardigan
'475': car mirror
'476': carousel, carrousel, merry-go-round, roundabout, whirligig
'477': carpenter's kit, tool kit
'478': carton
'479': car wheel
'480': cash machine, cash dispenser, automated teller machine, automatic
teller machine, automated teller, automatic teller, ATM
'481': cassette
'482': cassette player
'483': castle
'484': catamaran
'485': CD player
'486': cello, violoncello
'487': cellular telephone, cellular phone, cellphone, cell, mobile phone
'488': chain
'489': chainlink fence
'490': chain mail, ring mail, mail, chain armor, chain armour, ring armor,
ring armour
'491': chain saw, chainsaw
'492': chest
'493': chiffonier, commode
'494': chime, bell, gong
'495': china cabinet, china closet
'496': Christmas stocking
'497': church, church building
'498': cinema, movie theater, movie theatre, movie house, picture palace
'499': cleaver, meat cleaver, chopper
'500': cliff dwelling
'501': cloak
'502': clog, geta, patten, sabot
'503': cocktail shaker
'504': coffee mug
'505': coffeepot
'506': coil, spiral, volute, whorl, helix
'507': combination lock
'508': computer keyboard, keypad
'509': confectionery, confectionary, candy store
'510': container ship, containership, container vessel
'511': convertible
'512': corkscrew, bottle screw
'513': cornet, horn, trumpet, trump
'514': cowboy boot
'515': cowboy hat, ten-gallon hat
'516': cradle
'517': crane2
'518': crash helmet
'519': crate
'520': crib, cot
'521': Crock Pot
'522': croquet ball
'523': crutch
'524': cuirass
'525': dam, dike, dyke
'526': desk
'527': desktop computer
'528': dial telephone, dial phone
'529': diaper, nappy, napkin
'530': digital clock
'531': digital watch
'532': dining table, board
'533': dishrag, dishcloth
'534': dishwasher, dish washer, dishwashing machine
'535': disk brake, disc brake
'536': dock, dockage, docking facility
'537': dogsled, dog sled, dog sleigh
'538': dome
'539': doormat, welcome mat
'540': drilling platform, offshore rig
'541': drum, membranophone, tympan
'542': drumstick
'543': dumbbell
'544': Dutch oven
'545': electric fan, blower
'546': electric guitar
'547': electric locomotive
'548': entertainment center
'549': envelope
'550': espresso maker
'551': face powder
'552': feather boa, boa
'553': file, file cabinet, filing cabinet
'554': fireboat
'555': fire engine, fire truck
'556': fire screen, fireguard
'557': flagpole, flagstaff
'558': flute, transverse flute
'559': folding chair
'560': football helmet
'561': forklift
'562': fountain
'563': fountain pen
'564': four-poster
'565': freight car
'566': French horn, horn
'567': frying pan, frypan, skillet
'568': fur coat
'569': garbage truck, dustcart
'570': gasmask, respirator, gas helmet
'571': gas pump, gasoline pump, petrol pump, island dispenser
'572': goblet
'573': go-kart
'574': golf ball
'575': golfcart, golf cart
'576': gondola
'577': gong, tam-tam
'578': gown
'579': grand piano, grand
'580': greenhouse, nursery, glasshouse
'581': grille, radiator grille
'582': grocery store, grocery, food market, market
'583': guillotine
'584': hair slide
'585': hair spray
'586': half track
'587': hammer
'588': hamper
'589': hand blower, blow dryer, blow drier, hair dryer, hair drier
'590': hand-held computer, hand-held microcomputer
'591': handkerchief, hankie, hanky, hankey
'592': hard disc, hard disk, fixed disk
'593': harmonica, mouth organ, harp, mouth harp
'594': harp
'595': harvester, reaper
'596': hatchet
'597': holster
'598': home theater, home theatre
'599': honeycomb
'600': hook, claw
'601': hoopskirt, crinoline
'602': horizontal bar, high bar
'603': horse cart, horse-cart
'604': hourglass
'605': iPod
'606': iron, smoothing iron
'607': jack-o'-lantern
'608': jean, blue jean, denim
'609': jeep, landrover
'610': jersey, T-shirt, tee shirt
'611': jigsaw puzzle
'612': jinrikisha, ricksha, rickshaw
'613': joystick
'614': kimono
'615': knee pad
'616': knot
'617': lab coat, laboratory coat
'618': ladle
'619': lampshade, lamp shade
'620': laptop, laptop computer
'621': lawn mower, mower
'622': lens cap, lens cover
'623': letter opener, paper knife, paperknife
'624': library
'625': lifeboat
'626': lighter, light, igniter, ignitor
'627': limousine, limo
'628': liner, ocean liner
'629': lipstick, lip rouge
'630': Loafer
'631': lotion
'632': loudspeaker, speaker, speaker unit, loudspeaker system, speaker system
'633': loupe, jeweler's loupe
'634': lumbermill, sawmill
'635': magnetic compass
'636': mailbag, postbag
'637': mailbox, letter box
'638': maillot
'639': maillot, tank suit
'640': manhole cover
'641': maraca
'642': marimba, xylophone
'643': mask
'644': matchstick
'645': maypole
'646': maze, labyrinth
'647': measuring cup
'648': medicine chest, medicine cabinet
'649': megalith, megalithic structure
'650': microphone, mike
'651': microwave, microwave oven
'652': military uniform
'653': milk can
'654': minibus
'655': miniskirt, mini
'656': minivan
'657': missile
'658': mitten
'659': mixing bowl
'660': mobile home, manufactured home
'661': Model T
'662': modem
'663': monastery
'664': monitor
'665': moped
'666': mortar
'667': mortarboard
'668': mosque
'669': mosquito net
'670': motor scooter, scooter
'671': mountain bike, all-terrain bike, off-roader
'672': mountain tent
'673': mouse, computer mouse
'674': mousetrap
'675': moving van
'676': muzzle
'677': nail
'678': neck brace
'679': necklace
'680': nipple
'681': notebook, notebook computer
'682': obelisk
'683': oboe, hautboy, hautbois
'684': ocarina, sweet potato
'685': odometer, hodometer, mileometer, milometer
'686': oil filter
'687': organ, pipe organ
'688': oscilloscope, scope, cathode-ray oscilloscope, CRO
'689': overskirt
'690': oxcart
'691': oxygen mask
'692': packet
'693': paddle, boat paddle
'694': paddlewheel, paddle wheel
'695': padlock
'696': paintbrush
'697': pajama, pyjama, pj's, jammies
'698': palace
'699': panpipe, pandean pipe, syrinx
'700': paper towel
'701': parachute, chute
'702': parallel bars, bars
'703': park bench
'704': parking meter
'705': passenger car, coach, carriage
'706': patio, terrace
'707': pay-phone, pay-station
'708': pedestal, plinth, footstall
'709': pencil box, pencil case
'710': pencil sharpener
'711': perfume, essence
'712': Petri dish
'713': photocopier
'714': pick, plectrum, plectron
'715': pickelhaube
'716': picket fence, paling
'717': pickup, pickup truck
'718': pier
'719': piggy bank, penny bank
'720': pill bottle
'721': pillow
'722': ping-pong ball
'723': pinwheel
'724': pirate, pirate ship
'725': pitcher, ewer
'726': plane, carpenter's plane, woodworking plane
'727': planetarium
'728': plastic bag
'729': plate rack
'730': plow, plough
'731': plunger, plumber's helper
'732': Polaroid camera, Polaroid Land camera
'733': pole
'734': police van, police wagon, paddy wagon, patrol wagon, wagon, black
Maria
'735': poncho
'736': pool table, billiard table, snooker table
'737': pop bottle, soda bottle
'738': pot, flowerpot
'739': potter's wheel
'740': power drill
'741': prayer rug, prayer mat
'742': printer
'743': prison, prison house
'744': projectile, missile
'745': projector
'746': puck, hockey puck
'747': punching bag, punch bag, punching ball, punchball
'748': purse
'749': quill, quill pen
'750': quilt, comforter, comfort, puff
'751': racer, race car, racing car
'752': racket, racquet
'753': radiator
'754': radio, wireless
'755': radio telescope, radio reflector
'756': rain barrel
'757': recreational vehicle, RV, R.V.
'758': reel
'759': reflex camera
'760': refrigerator, icebox
'761': remote control, remote
'762': restaurant, eating house, eating place, eatery
'763': revolver, six-gun, six-shooter
'764': rifle
'765': rocking chair, rocker
'766': rotisserie
'767': rubber eraser, rubber, pencil eraser
'768': rugby ball
'769': rule, ruler
'770': running shoe
'771': safe
'772': safety pin
'773': saltshaker, salt shaker
'774': sandal
'775': sarong
'776': sax, saxophone
'777': scabbard
'778': scale, weighing machine
'779': school bus
'780': schooner
'781': scoreboard
'782': screen, CRT screen
'783': screw
'784': screwdriver
'785': seat belt, seatbelt
'786': sewing machine
'787': shield, buckler
'788': shoe shop, shoe-shop, shoe store
'789': shoji
'790': shopping basket
'791': shopping cart
'792': shovel
'793': shower cap
'794': shower curtain
'795': ski
'796': ski mask
'797': sleeping bag
'798': slide rule, slipstick
'799': sliding door
'800': slot, one-armed bandit
'801': snorkel
'802': snowmobile
'803': snowplow, snowplough
'804': soap dispenser
'805': soccer ball
'806': sock
'807': solar dish, solar collector, solar furnace
'808': sombrero
'809': soup bowl
'810': space bar
'811': space heater
'812': space shuttle
'813': spatula
'814': speedboat
'815': spider web, spider's web
'816': spindle
'817': sports car, sport car
'818': spotlight, spot
'819': stage
'820': steam locomotive
'821': steel arch bridge
'822': steel drum
'823': stethoscope
'824': stole
'825': stone wall
'826': stopwatch, stop watch
'827': stove
'828': strainer
'829': streetcar, tram, tramcar, trolley, trolley car
'830': stretcher
'831': studio couch, day bed
'832': stupa, tope
'833': submarine, pigboat, sub, U-boat
'834': suit, suit of clothes
'835': sundial
'836': sunglass
'837': sunglasses, dark glasses, shades
'838': sunscreen, sunblock, sun blocker
'839': suspension bridge
'840': swab, swob, mop
'841': sweatshirt
'842': swimming trunks, bathing trunks
'843': swing
'844': switch, electric switch, electrical switch
'845': syringe
'846': table lamp
'847': tank, army tank, armored combat vehicle, armoured combat vehicle
'848': tape player
'849': teapot
'850': teddy, teddy bear
'851': television, television system
'852': tennis ball
'853': thatch, thatched roof
'854': theater curtain, theatre curtain
'855': thimble
'856': thresher, thrasher, threshing machine
'857': throne
'858': tile roof
'859': toaster
'860': tobacco shop, tobacconist shop, tobacconist
'861': toilet seat
'862': torch
'863': totem pole
'864': tow truck, tow car, wrecker
'865': toyshop
'866': tractor
'867': trailer truck, tractor trailer, trucking rig, rig, articulated lorry,
semi
'868': tray
'869': trench coat
'870': tricycle, trike, velocipede
'871': trimaran
'872': tripod
'873': triumphal arch
'874': trolleybus, trolley coach, trackless trolley
'875': trombone
'876': tub, vat
'877': turnstile
'878': typewriter keyboard
'879': umbrella
'880': unicycle, monocycle
'881': upright, upright piano
'882': vacuum, vacuum cleaner
'883': vase
'884': vault
'885': velvet
'886': vending machine
'887': vestment
'888': viaduct
'889': violin, fiddle
'890': volleyball
'891': waffle iron
'892': wall clock
'893': wallet, billfold, notecase, pocketbook
'894': wardrobe, closet, press
'895': warplane, military plane
'896': washbasin, handbasin, washbowl, lavabo, wash-hand basin
'897': washer, automatic washer, washing machine
'898': water bottle
'899': water jug
'900': water tower
'901': whiskey jug
'902': whistle
'903': wig
'904': window screen
'905': window shade
'906': Windsor tie
'907': wine bottle
'908': wing
'909': wok
'910': wooden spoon
'911': wool, woolen, woollen
'912': worm fence, snake fence, snake-rail fence, Virginia fence
'913': wreck
'914': yawl
'915': yurt
'916': web site, website, internet site, site
'917': comic book
'918': crossword puzzle, crossword
'919': street sign
'920': traffic light, traffic signal, stoplight
'921': book jacket, dust cover, dust jacket, dust wrapper
'922': menu
'923': plate
'924': guacamole
'925': consomme
'926': hot pot, hotpot
'927': trifle
'928': ice cream, icecream
'929': ice lolly, lolly, lollipop, popsicle
'930': French loaf
'931': bagel, beigel
'932': pretzel
'933': cheeseburger
'934': hotdog, hot dog, red hot
'935': mashed potato
'936': head cabbage
'937': broccoli
'938': cauliflower
'939': zucchini, courgette
'940': spaghetti squash
'941': acorn squash
'942': butternut squash
'943': cucumber, cuke
'944': artichoke, globe artichoke
'945': bell pepper
'946': cardoon
'947': mushroom
'948': Granny Smith
'949': strawberry
'950': orange
'951': lemon
'952': fig
'953': pineapple, ananas
'954': banana
'955': jackfruit, jak, jack
'956': custard apple
'957': pomegranate
'958': hay
'959': carbonara
'960': chocolate sauce, chocolate syrup
'961': dough
'962': meat loaf, meatloaf
'963': pizza, pizza pie
'964': potpie
'965': burrito
'966': red wine
'967': espresso
'968': cup
'969': eggnog
'970': alp
'971': bubble
'972': cliff, drop, drop-off
'973': coral reef
'974': geyser
'975': lakeside, lakeshore
'976': promontory, headland, head, foreland
'977': sandbar, sand bar
'978': seashore, coast, seacoast, sea-coast
'979': valley, vale
'980': volcano
'981': ballplayer, baseball player
'982': groom, bridegroom
'983': scuba diver
'984': rapeseed
'985': daisy
'986': yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus,
Cypripedium parviflorum
'987': corn
'988': acorn
'989': hip, rose hip, rosehip
'990': buckeye, horse chestnut, conker
'991': coral fungus
'992': agaric
'993': gyromitra
'994': stinkhorn, carrion fungus
'995': earthstar
'996': hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola
frondosa
'997': bolete
'998': ear, spike, capitulum
'999': toilet tissue, toilet paper, bathroom tissue
splits:
- name: train
num_bytes: 9919813
num_examples: 50889
download_size: 7593573012
dataset_size: 9919813
---
# Dataset Card for ImageNet-Sketch
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/HaohanWang/ImageNet-Sketch
- **Repository:** https://github.com/HaohanWang/ImageNet-Sketch
- **Paper:** [Learning Robust Global Representations by Penalizing Local Predictive Power](https://arxiv.org/abs/1905.13549v2)
- **Leaderboard:** https://github.com/HaohanWang/ImageNet-Sketch#imagenet-sketch-leaderboard
- **Point of Contact:** [Haohan Wang](mailto:haohanw@andrew.cmu.edu)
- **Size of downloaded dataset files:** 8.15 GB
### Dataset Summary
ImageNet-Sketch data set consists of 50000 images, 50 images for each of the 1000 ImageNet classes. We construct the data set with Google Image queries "sketch of __", where __ is the standard class name. We only search within the "black and white" color scheme. We initially query 100 images for every class, and then manually clean the pulled images by deleting the irrelevant images and images that are for similar but different classes. For some classes, there are less than 50 images after manually cleaning, and then we augment the data set by flipping and rotating the images.
The scripts used to conduct queries and clean images can be found in [the GitHub repository](https://github.com/HaohanWang/ImageNet-Sketch).
### Supported Tasks and Leaderboards
- `image_classification`: The goal of this task is to classify a given image into one of 1000 ImageNet classes. The leaderboard is available [here](https://github.com/HaohanWang/ImageNet-Sketch#imagenet-sketch-leaderboard).
The goal of the leaderboard is to evaluate the out-of-domain classification performance of vision models trained on ImageNet. The evaluation metrics used in the leaderboard are top-1 accuracy and top-5 accuracy.
### Languages
The class labels in the dataset are in English.
## Dataset Structure
### Data Instances
A sample from the training set is provided below:
```
{
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=400x530 at 0x7FB2EF5D4A90>,
'label': 320
}
```
### Data Fields
The data instances have the following fields:
- `image`: A `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`.
- `label`: an `int` classification label.
The labels are indexed based on a sorted list of synset ids such as `n07565083` which we automatically map to original class names. The original dataset is divided into folders based on these synset ids. To get a mapping from original synset names, use the file [LOC_synset_mapping.txt](https://www.kaggle.com/competitions/imagenet-object-localization-challenge/data?select=LOC_synset_mapping.txt) available on Kaggle challenge page. You can also use `dataset_instance.features["label"].int2str` function to get the class for a particular label index.
<details>
<summary>
Click here to see the full list of ImageNet class label mapping:
</summary>
|id|Class|
|--|-----|
|0 | tench, Tinca tinca|
|1 | goldfish, Carassius auratus|
|2 | great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias|
|3 | tiger shark, Galeocerdo cuvieri|
|4 | hammerhead, hammerhead shark|
|5 | electric ray, crampfish, numbfish, torpedo|
|6 | stingray|
|7 | cock|
|8 | hen|
|9 | ostrich, Struthio camelus|
|10 | brambling, Fringilla montifringilla|
|11 | goldfinch, Carduelis carduelis|
|12 | house finch, linnet, Carpodacus mexicanus|
|13 | junco, snowbird|
|14 | indigo bunting, indigo finch, indigo bird, Passerina cyanea|
|15 | robin, American robin, Turdus migratorius|
|16 | bulbul|
|17 | jay|
|18 | magpie|
|19 | chickadee|
|20 | water ouzel, dipper|
|21 | kite|
|22 | bald eagle, American eagle, Haliaeetus leucocephalus|
|23 | vulture|
|24 | great grey owl, great gray owl, Strix nebulosa|
|25 | European fire salamander, Salamandra salamandra|
|26 | common newt, Triturus vulgaris|
|27 | eft|
|28 | spotted salamander, Ambystoma maculatum|
|29 | axolotl, mud puppy, Ambystoma mexicanum|
|30 | bullfrog, Rana catesbeiana|
|31 | tree frog, tree-frog|
|32 | tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui|
|33 | loggerhead, loggerhead turtle, Caretta caretta|
|34 | leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea|
|35 | mud turtle|
|36 | terrapin|
|37 | box turtle, box tortoise|
|38 | banded gecko|
|39 | common iguana, iguana, Iguana iguana|
|40 | American chameleon, anole, Anolis carolinensis|
|41 | whiptail, whiptail lizard|
|42 | agama|
|43 | frilled lizard, Chlamydosaurus kingi|
|44 | alligator lizard|
|45 | Gila monster, Heloderma suspectum|
|46 | green lizard, Lacerta viridis|
|47 | African chameleon, Chamaeleo chamaeleon|
|48 | Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis|
|49 | African crocodile, Nile crocodile, Crocodylus niloticus|
|50 | American alligator, Alligator mississipiensis|
|51 | triceratops|
|52 | thunder snake, worm snake, Carphophis amoenus|
|53 | ringneck snake, ring-necked snake, ring snake|
|54 | hognose snake, puff adder, sand viper|
|55 | green snake, grass snake|
|56 | king snake, kingsnake|
|57 | garter snake, grass snake|
|58 | water snake|
|59 | vine snake|
|60 | night snake, Hypsiglena torquata|
|61 | boa constrictor, Constrictor constrictor|
|62 | rock python, rock snake, Python sebae|
|63 | Indian cobra, Naja naja|
|64 | green mamba|
|65 | sea snake|
|66 | horned viper, cerastes, sand viper, horned asp, Cerastes cornutus|
|67 | diamondback, diamondback rattlesnake, Crotalus adamanteus|
|68 | sidewinder, horned rattlesnake, Crotalus cerastes|
|69 | trilobite|
|70 | harvestman, daddy longlegs, Phalangium opilio|
|71 | scorpion|
|72 | black and gold garden spider, Argiope aurantia|
|73 | barn spider, Araneus cavaticus|
|74 | garden spider, Aranea diademata|
|75 | black widow, Latrodectus mactans|
|76 | tarantula|
|77 | wolf spider, hunting spider|
|78 | tick|
|79 | centipede|
|80 | black grouse|
|81 | ptarmigan|
|82 | ruffed grouse, partridge, Bonasa umbellus|
|83 | prairie chicken, prairie grouse, prairie fowl|
|84 | peacock|
|85 | quail|
|86 | partridge|
|87 | African grey, African gray, Psittacus erithacus|
|88 | macaw|
|89 | sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita|
|90 | lorikeet|
|91 | coucal|
|92 | bee eater|
|93 | hornbill|
|94 | hummingbird|
|95 | jacamar|
|96 | toucan|
|97 | drake|
|98 | red-breasted merganser, Mergus serrator|
|99 | goose|
|100 | black swan, Cygnus atratus|
|101 | tusker|
|102 | echidna, spiny anteater, anteater|
|103 | platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus|
|104 | wallaby, brush kangaroo|
|105 | koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus|
|106 | wombat|
|107 | jellyfish|
|108 | sea anemone, anemone|
|109 | brain coral|
|110 | flatworm, platyhelminth|
|111 | nematode, nematode worm, roundworm|
|112 | conch|
|113 | snail|
|114 | slug|
|115 | sea slug, nudibranch|
|116 | chiton, coat-of-mail shell, sea cradle, polyplacophore|
|117 | chambered nautilus, pearly nautilus, nautilus|
|118 | Dungeness crab, Cancer magister|
|119 | rock crab, Cancer irroratus|
|120 | fiddler crab|
|121 | king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica|
|122 | American lobster, Northern lobster, Maine lobster, Homarus americanus|
|123 | spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish|
|124 | crayfish, crawfish, crawdad, crawdaddy|
|125 | hermit crab|
|126 | isopod|
|127 | white stork, Ciconia ciconia|
|128 | black stork, Ciconia nigra|
|129 | spoonbill|
|130 | flamingo|
|131 | little blue heron, Egretta caerulea|
|132 | American egret, great white heron, Egretta albus|
|133 | bittern|
|134 | crane|
|135 | limpkin, Aramus pictus|
|136 | European gallinule, Porphyrio porphyrio|
|137 | American coot, marsh hen, mud hen, water hen, Fulica americana|
|138 | bustard|
|139 | ruddy turnstone, Arenaria interpres|
|140 | red-backed sandpiper, dunlin, Erolia alpina|
|141 | redshank, Tringa totanus|
|142 | dowitcher|
|143 | oystercatcher, oyster catcher|
|144 | pelican|
|145 | king penguin, Aptenodytes patagonica|
|146 | albatross, mollymawk|
|147 | grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus|
|148 | killer whale, killer, orca, grampus, sea wolf, Orcinus orca|
|149 | dugong, Dugong dugon|
|150 | sea lion|
|151 | Chihuahua|
|152 | Japanese spaniel|
|153 | Maltese dog, Maltese terrier, Maltese|
|154 | Pekinese, Pekingese, Peke|
|155 | Shih-Tzu|
|156 | Blenheim spaniel|
|157 | papillon|
|158 | toy terrier|
|159 | Rhodesian ridgeback|
|160 | Afghan hound, Afghan|
|161 | basset, basset hound|
|162 | beagle|
|163 | bloodhound, sleuthhound|
|164 | bluetick|
|165 | black-and-tan coonhound|
|166 | Walker hound, Walker foxhound|
|167 | English foxhound|
|168 | redbone|
|169 | borzoi, Russian wolfhound|
|170 | Irish wolfhound|
|171 | Italian greyhound|
|172 | whippet|
|173 | Ibizan hound, Ibizan Podenco|
|174 | Norwegian elkhound, elkhound|
|175 | otterhound, otter hound|
|176 | Saluki, gazelle hound|
|177 | Scottish deerhound, deerhound|
|178 | Weimaraner|
|179 | Staffordshire bullterrier, Staffordshire bull terrier|
|180 | American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier|
|181 | Bedlington terrier|
|182 | Border terrier|
|183 | Kerry blue terrier|
|184 | Irish terrier|
|185 | Norfolk terrier|
|186 | Norwich terrier|
|187 | Yorkshire terrier|
|188 | wire-haired fox terrier|
|189 | Lakeland terrier|
|190 | Sealyham terrier, Sealyham|
|191 | Airedale, Airedale terrier|
|192 | cairn, cairn terrier|
|193 | Australian terrier|
|194 | Dandie Dinmont, Dandie Dinmont terrier|
|195 | Boston bull, Boston terrier|
|196 | miniature schnauzer|
|197 | giant schnauzer|
|198 | standard schnauzer|
|199 | Scotch terrier, Scottish terrier, Scottie|
|200 | Tibetan terrier, chrysanthemum dog|
|201 | silky terrier, Sydney silky|
|202 | soft-coated wheaten terrier|
|203 | West Highland white terrier|
|204 | Lhasa, Lhasa apso|
|205 | flat-coated retriever|
|206 | curly-coated retriever|
|207 | golden retriever|
|208 | Labrador retriever|
|209 | Chesapeake Bay retriever|
|210 | German short-haired pointer|
|211 | vizsla, Hungarian pointer|
|212 | English setter|
|213 | Irish setter, red setter|
|214 | Gordon setter|
|215 | Brittany spaniel|
|216 | clumber, clumber spaniel|
|217 | English springer, English springer spaniel|
|218 | Welsh springer spaniel|
|219 | cocker spaniel, English cocker spaniel, cocker|
|220 | Sussex spaniel|
|221 | Irish water spaniel|
|222 | kuvasz|
|223 | schipperke|
|224 | groenendael|
|225 | malinois|
|226 | briard|
|227 | kelpie|
|228 | komondor|
|229 | Old English sheepdog, bobtail|
|230 | Shetland sheepdog, Shetland sheep dog, Shetland|
|231 | collie|
|232 | Border collie|
|233 | Bouvier des Flandres, Bouviers des Flandres|
|234 | Rottweiler|
|235 | German shepherd, German shepherd dog, German police dog, alsatian|
|236 | Doberman, Doberman pinscher|
|237 | miniature pinscher|
|238 | Greater Swiss Mountain dog|
|239 | Bernese mountain dog|
|240 | Appenzeller|
|241 | EntleBucher|
|242 | boxer|
|243 | bull mastiff|
|244 | Tibetan mastiff|
|245 | French bulldog|
|246 | Great Dane|
|247 | Saint Bernard, St Bernard|
|248 | Eskimo dog, husky|
|249 | malamute, malemute, Alaskan malamute|
|250 | Siberian husky|
|251 | dalmatian, coach dog, carriage dog|
|252 | affenpinscher, monkey pinscher, monkey dog|
|253 | basenji|
|254 | pug, pug-dog|
|255 | Leonberg|
|256 | Newfoundland, Newfoundland dog|
|257 | Great Pyrenees|
|258 | Samoyed, Samoyede|
|259 | Pomeranian|
|260 | chow, chow chow|
|261 | keeshond|
|262 | Brabancon griffon|
|263 | Pembroke, Pembroke Welsh corgi|
|264 | Cardigan, Cardigan Welsh corgi|
|265 | toy poodle|
|266 | miniature poodle|
|267 | standard poodle|
|268 | Mexican hairless|
|269 | timber wolf, grey wolf, gray wolf, Canis lupus|
|270 | white wolf, Arctic wolf, Canis lupus tundrarum|
|271 | red wolf, maned wolf, Canis rufus, Canis niger|
|272 | coyote, prairie wolf, brush wolf, Canis latrans|
|273 | dingo, warrigal, warragal, Canis dingo|
|274 | dhole, Cuon alpinus|
|275 | African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus|
|276 | hyena, hyaena|
|277 | red fox, Vulpes vulpes|
|278 | kit fox, Vulpes macrotis|
|279 | Arctic fox, white fox, Alopex lagopus|
|280 | grey fox, gray fox, Urocyon cinereoargenteus|
|281 | tabby, tabby cat|
|282 | tiger cat|
|283 | Persian cat|
|284 | Siamese cat, Siamese|
|285 | Egyptian cat|
|286 | cougar, puma, catamount, mountain lion, painter, panther, Felis concolor|
|287 | lynx, catamount|
|288 | leopard, Panthera pardus|
|289 | snow leopard, ounce, Panthera uncia|
|290 | jaguar, panther, Panthera onca, Felis onca|
|291 | lion, king of beasts, Panthera leo|
|292 | tiger, Panthera tigris|
|293 | cheetah, chetah, Acinonyx jubatus|
|294 | brown bear, bruin, Ursus arctos|
|295 | American black bear, black bear, Ursus americanus, Euarctos americanus|
|296 | ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus|
|297 | sloth bear, Melursus ursinus, Ursus ursinus|
|298 | mongoose|
|299 | meerkat, mierkat|
|300 | tiger beetle|
|301 | ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle|
|302 | ground beetle, carabid beetle|
|303 | long-horned beetle, longicorn, longicorn beetle|
|304 | leaf beetle, chrysomelid|
|305 | dung beetle|
|306 | rhinoceros beetle|
|307 | weevil|
|308 | fly|
|309 | bee|
|310 | ant, emmet, pismire|
|311 | grasshopper, hopper|
|312 | cricket|
|313 | walking stick, walkingstick, stick insect|
|314 | cockroach, roach|
|315 | mantis, mantid|
|316 | cicada, cicala|
|317 | leafhopper|
|318 | lacewing, lacewing fly|
|319 | dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk|
|320 | damselfly|
|321 | admiral|
|322 | ringlet, ringlet butterfly|
|323 | monarch, monarch butterfly, milkweed butterfly, Danaus plexippus|
|324 | cabbage butterfly|
|325 | sulphur butterfly, sulfur butterfly|
|326 | lycaenid, lycaenid butterfly|
|327 | starfish, sea star|
|328 | sea urchin|
|329 | sea cucumber, holothurian|
|330 | wood rabbit, cottontail, cottontail rabbit|
|331 | hare|
|332 | Angora, Angora rabbit|
|333 | hamster|
|334 | porcupine, hedgehog|
|335 | fox squirrel, eastern fox squirrel, Sciurus niger|
|336 | marmot|
|337 | beaver|
|338 | guinea pig, Cavia cobaya|
|339 | sorrel|
|340 | zebra|
|341 | hog, pig, grunter, squealer, Sus scrofa|
|342 | wild boar, boar, Sus scrofa|
|343 | warthog|
|344 | hippopotamus, hippo, river horse, Hippopotamus amphibius|
|345 | ox|
|346 | water buffalo, water ox, Asiatic buffalo, Bubalus bubalis|
|347 | bison|
|348 | ram, tup|
|349 | bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis|
|350 | ibex, Capra ibex|
|351 | hartebeest|
|352 | impala, Aepyceros melampus|
|353 | gazelle|
|354 | Arabian camel, dromedary, Camelus dromedarius|
|355 | llama|
|356 | weasel|
|357 | mink|
|358 | polecat, fitch, foulmart, foumart, Mustela putorius|
|359 | black-footed ferret, ferret, Mustela nigripes|
|360 | otter|
|361 | skunk, polecat, wood pussy|
|362 | badger|
|363 | armadillo|
|364 | three-toed sloth, ai, Bradypus tridactylus|
|365 | orangutan, orang, orangutang, Pongo pygmaeus|
|366 | gorilla, Gorilla gorilla|
|367 | chimpanzee, chimp, Pan troglodytes|
|368 | gibbon, Hylobates lar|
|369 | siamang, Hylobates syndactylus, Symphalangus syndactylus|
|370 | guenon, guenon monkey|
|371 | patas, hussar monkey, Erythrocebus patas|
|372 | baboon|
|373 | macaque|
|374 | langur|
|375 | colobus, colobus monkey|
|376 | proboscis monkey, Nasalis larvatus|
|377 | marmoset|
|378 | capuchin, ringtail, Cebus capucinus|
|379 | howler monkey, howler|
|380 | titi, titi monkey|
|381 | spider monkey, Ateles geoffroyi|
|382 | squirrel monkey, Saimiri sciureus|
|383 | Madagascar cat, ring-tailed lemur, Lemur catta|
|384 | indri, indris, Indri indri, Indri brevicaudatus|
|385 | Indian elephant, Elephas maximus|
|386 | African elephant, Loxodonta africana|
|387 | lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens|
|388 | giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca|
|389 | barracouta, snoek|
|390 | eel|
|391 | coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch|
|392 | rock beauty, Holocanthus tricolor|
|393 | anemone fish|
|394 | sturgeon|
|395 | gar, garfish, garpike, billfish, Lepisosteus osseus|
|396 | lionfish|
|397 | puffer, pufferfish, blowfish, globefish|
|398 | abacus|
|399 | abaya|
|400 | academic gown, academic robe, judge's robe|
|401 | accordion, piano accordion, squeeze box|
|402 | acoustic guitar|
|403 | aircraft carrier, carrier, flattop, attack aircraft carrier|
|404 | airliner|
|405 | airship, dirigible|
|406 | altar|
|407 | ambulance|
|408 | amphibian, amphibious vehicle|
|409 | analog clock|
|410 | apiary, bee house|
|411 | apron|
|412 | ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin|
|413 | assault rifle, assault gun|
|414 | backpack, back pack, knapsack, packsack, rucksack, haversack|
|415 | bakery, bakeshop, bakehouse|
|416 | balance beam, beam|
|417 | balloon|
|418 | ballpoint, ballpoint pen, ballpen, Biro|
|419 | Band Aid|
|420 | banjo|
|421 | bannister, banister, balustrade, balusters, handrail|
|422 | barbell|
|423 | barber chair|
|424 | barbershop|
|425 | barn|
|426 | barometer|
|427 | barrel, cask|
|428 | barrow, garden cart, lawn cart, wheelbarrow|
|429 | baseball|
|430 | basketball|
|431 | bassinet|
|432 | bassoon|
|433 | bathing cap, swimming cap|
|434 | bath towel|
|435 | bathtub, bathing tub, bath, tub|
|436 | beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon|
|437 | beacon, lighthouse, beacon light, pharos|
|438 | beaker|
|439 | bearskin, busby, shako|
|440 | beer bottle|
|441 | beer glass|
|442 | bell cote, bell cot|
|443 | bib|
|444 | bicycle-built-for-two, tandem bicycle, tandem|
|445 | bikini, two-piece|
|446 | binder, ring-binder|
|447 | binoculars, field glasses, opera glasses|
|448 | birdhouse|
|449 | boathouse|
|450 | bobsled, bobsleigh, bob|
|451 | bolo tie, bolo, bola tie, bola|
|452 | bonnet, poke bonnet|
|453 | bookcase|
|454 | bookshop, bookstore, bookstall|
|455 | bottlecap|
|456 | bow|
|457 | bow tie, bow-tie, bowtie|
|458 | brass, memorial tablet, plaque|
|459 | brassiere, bra, bandeau|
|460 | breakwater, groin, groyne, mole, bulwark, seawall, jetty|
|461 | breastplate, aegis, egis|
|462 | broom|
|463 | bucket, pail|
|464 | buckle|
|465 | bulletproof vest|
|466 | bullet train, bullet|
|467 | butcher shop, meat market|
|468 | cab, hack, taxi, taxicab|
|469 | caldron, cauldron|
|470 | candle, taper, wax light|
|471 | cannon|
|472 | canoe|
|473 | can opener, tin opener|
|474 | cardigan|
|475 | car mirror|
|476 | carousel, carrousel, merry-go-round, roundabout, whirligig|
|477 | carpenter's kit, tool kit|
|478 | carton|
|479 | car wheel|
|480 | cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM|
|481 | cassette|
|482 | cassette player|
|483 | castle|
|484 | catamaran|
|485 | CD player|
|486 | cello, violoncello|
|487 | cellular telephone, cellular phone, cellphone, cell, mobile phone|
|488 | chain|
|489 | chainlink fence|
|490 | chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour|
|491 | chain saw, chainsaw|
|492 | chest|
|493 | chiffonier, commode|
|494 | chime, bell, gong|
|495 | china cabinet, china closet|
|496 | Christmas stocking|
|497 | church, church building|
|498 | cinema, movie theater, movie theatre, movie house, picture palace|
|499 | cleaver, meat cleaver, chopper|
|500 | cliff dwelling|
|501 | cloak|
|502 | clog, geta, patten, sabot|
|503 | cocktail shaker|
|504 | coffee mug|
|505 | coffeepot|
|506 | coil, spiral, volute, whorl, helix|
|507 | combination lock|
|508 | computer keyboard, keypad|
|509 | confectionery, confectionary, candy store|
|510 | container ship, containership, container vessel|
|511 | convertible|
|512 | corkscrew, bottle screw|
|513 | cornet, horn, trumpet, trump|
|514 | cowboy boot|
|515 | cowboy hat, ten-gallon hat|
|516 | cradle|
|517 | crane_1|
|518 | crash helmet|
|519 | crate|
|520 | crib, cot|
|521 | Crock Pot|
|522 | croquet ball|
|523 | crutch|
|524 | cuirass|
|525 | dam, dike, dyke|
|526 | desk|
|527 | desktop computer|
|528 | dial telephone, dial phone|
|529 | diaper, nappy, napkin|
|530 | digital clock|
|531 | digital watch|
|532 | dining table, board|
|533 | dishrag, dishcloth|
|534 | dishwasher, dish washer, dishwashing machine|
|535 | disk brake, disc brake|
|536 | dock, dockage, docking facility|
|537 | dogsled, dog sled, dog sleigh|
|538 | dome|
|539 | doormat, welcome mat|
|540 | drilling platform, offshore rig|
|541 | drum, membranophone, tympan|
|542 | drumstick|
|543 | dumbbell|
|544 | Dutch oven|
|545 | electric fan, blower|
|546 | electric guitar|
|547 | electric locomotive|
|548 | entertainment center|
|549 | envelope|
|550 | espresso maker|
|551 | face powder|
|552 | feather boa, boa|
|553 | file, file cabinet, filing cabinet|
|554 | fireboat|
|555 | fire engine, fire truck|
|556 | fire screen, fireguard|
|557 | flagpole, flagstaff|
|558 | flute, transverse flute|
|559 | folding chair|
|560 | football helmet|
|561 | forklift|
|562 | fountain|
|563 | fountain pen|
|564 | four-poster|
|565 | freight car|
|566 | French horn, horn|
|567 | frying pan, frypan, skillet|
|568 | fur coat|
|569 | garbage truck, dustcart|
|570 | gasmask, respirator, gas helmet|
|571 | gas pump, gasoline pump, petrol pump, island dispenser|
|572 | goblet|
|573 | go-kart|
|574 | golf ball|
|575 | golfcart, golf cart|
|576 | gondola|
|577 | gong, tam-tam|
|578 | gown|
|579 | grand piano, grand|
|580 | greenhouse, nursery, glasshouse|
|581 | grille, radiator grille|
|582 | grocery store, grocery, food market, market|
|583 | guillotine|
|584 | hair slide|
|585 | hair spray|
|586 | half track|
|587 | hammer|
|588 | hamper|
|589 | hand blower, blow dryer, blow drier, hair dryer, hair drier|
|590 | hand-held computer, hand-held microcomputer|
|591 | handkerchief, hankie, hanky, hankey|
|592 | hard disc, hard disk, fixed disk|
|593 | harmonica, mouth organ, harp, mouth harp|
|594 | harp|
|595 | harvester, reaper|
|596 | hatchet|
|597 | holster|
|598 | home theater, home theatre|
|599 | honeycomb|
|600 | hook, claw|
|601 | hoopskirt, crinoline|
|602 | horizontal bar, high bar|
|603 | horse cart, horse-cart|
|604 | hourglass|
|605 | iPod|
|606 | iron, smoothing iron|
|607 | jack-o'-lantern|
|608 | jean, blue jean, denim|
|609 | jeep, landrover|
|610 | jersey, T-shirt, tee shirt|
|611 | jigsaw puzzle|
|612 | jinrikisha, ricksha, rickshaw|
|613 | joystick|
|614 | kimono|
|615 | knee pad|
|616 | knot|
|617 | lab coat, laboratory coat|
|618 | ladle|
|619 | lampshade, lamp shade|
|620 | laptop, laptop computer|
|621 | lawn mower, mower|
|622 | lens cap, lens cover|
|623 | letter opener, paper knife, paperknife|
|624 | library|
|625 | lifeboat|
|626 | lighter, light, igniter, ignitor|
|627 | limousine, limo|
|628 | liner, ocean liner|
|629 | lipstick, lip rouge|
|630 | Loafer|
|631 | lotion|
|632 | loudspeaker, speaker, speaker unit, loudspeaker system, speaker system|
|633 | loupe, jeweler's loupe|
|634 | lumbermill, sawmill|
|635 | magnetic compass|
|636 | mailbag, postbag|
|637 | mailbox, letter box|
|638 | maillot|
|639 | maillot, tank suit|
|640 | manhole cover|
|641 | maraca|
|642 | marimba, xylophone|
|643 | mask|
|644 | matchstick|
|645 | maypole|
|646 | maze, labyrinth|
|647 | measuring cup|
|648 | medicine chest, medicine cabinet|
|649 | megalith, megalithic structure|
|650 | microphone, mike|
|651 | microwave, microwave oven|
|652 | military uniform|
|653 | milk can|
|654 | minibus|
|655 | miniskirt, mini|
|656 | minivan|
|657 | missile|
|658 | mitten|
|659 | mixing bowl|
|660 | mobile home, manufactured home|
|661 | Model T|
|662 | modem|
|663 | monastery|
|664 | monitor|
|665 | moped|
|666 | mortar|
|667 | mortarboard|
|668 | mosque|
|669 | mosquito net|
|670 | motor scooter, scooter|
|671 | mountain bike, all-terrain bike, off-roader|
|672 | mountain tent|
|673 | mouse, computer mouse|
|674 | mousetrap|
|675 | moving van|
|676 | muzzle|
|677 | nail|
|678 | neck brace|
|679 | necklace|
|680 | nipple|
|681 | notebook, notebook computer|
|682 | obelisk|
|683 | oboe, hautboy, hautbois|
|684 | ocarina, sweet potato|
|685 | odometer, hodometer, mileometer, milometer|
|686 | oil filter|
|687 | organ, pipe organ|
|688 | oscilloscope, scope, cathode-ray oscilloscope, CRO|
|689 | overskirt|
|690 | oxcart|
|691 | oxygen mask|
|692 | packet|
|693 | paddle, boat paddle|
|694 | paddlewheel, paddle wheel|
|695 | padlock|
|696 | paintbrush|
|697 | pajama, pyjama, pj's, jammies|
|698 | palace|
|699 | panpipe, pandean pipe, syrinx|
|700 | paper towel|
|701 | parachute, chute|
|702 | parallel bars, bars|
|703 | park bench|
|704 | parking meter|
|705 | passenger car, coach, carriage|
|706 | patio, terrace|
|707 | pay-phone, pay-station|
|708 | pedestal, plinth, footstall|
|709 | pencil box, pencil case|
|710 | pencil sharpener|
|711 | perfume, essence|
|712 | Petri dish|
|713 | photocopier|
|714 | pick, plectrum, plectron|
|715 | pickelhaube|
|716 | picket fence, paling|
|717 | pickup, pickup truck|
|718 | pier|
|719 | piggy bank, penny bank|
|720 | pill bottle|
|721 | pillow|
|722 | ping-pong ball|
|723 | pinwheel|
|724 | pirate, pirate ship|
|725 | pitcher, ewer|
|726 | plane, carpenter's plane, woodworking plane|
|727 | planetarium|
|728 | plastic bag|
|729 | plate rack|
|730 | plow, plough|
|731 | plunger, plumber's helper|
|732 | Polaroid camera, Polaroid Land camera|
|733 | pole|
|734 | police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria|
|735 | poncho|
|736 | pool table, billiard table, snooker table|
|737 | pop bottle, soda bottle|
|738 | pot, flowerpot|
|739 | potter's wheel|
|740 | power drill|
|741 | prayer rug, prayer mat|
|742 | printer|
|743 | prison, prison house|
|744 | projectile, missile|
|745 | projector|
|746 | puck, hockey puck|
|747 | punching bag, punch bag, punching ball, punchball|
|748 | purse|
|749 | quill, quill pen|
|750 | quilt, comforter, comfort, puff|
|751 | racer, race car, racing car|
|752 | racket, racquet|
|753 | radiator|
|754 | radio, wireless|
|755 | radio telescope, radio reflector|
|756 | rain barrel|
|757 | recreational vehicle, RV, R.V.|
|758 | reel|
|759 | reflex camera|
|760 | refrigerator, icebox|
|761 | remote control, remote|
|762 | restaurant, eating house, eating place, eatery|
|763 | revolver, six-gun, six-shooter|
|764 | rifle|
|765 | rocking chair, rocker|
|766 | rotisserie|
|767 | rubber eraser, rubber, pencil eraser|
|768 | rugby ball|
|769 | rule, ruler|
|770 | running shoe|
|771 | safe|
|772 | safety pin|
|773 | saltshaker, salt shaker|
|774 | sandal|
|775 | sarong|
|776 | sax, saxophone|
|777 | scabbard|
|778 | scale, weighing machine|
|779 | school bus|
|780 | schooner|
|781 | scoreboard|
|782 | screen, CRT screen|
|783 | screw|
|784 | screwdriver|
|785 | seat belt, seatbelt|
|786 | sewing machine|
|787 | shield, buckler|
|788 | shoe shop, shoe-shop, shoe store|
|789 | shoji|
|790 | shopping basket|
|791 | shopping cart|
|792 | shovel|
|793 | shower cap|
|794 | shower curtain|
|795 | ski|
|796 | ski mask|
|797 | sleeping bag|
|798 | slide rule, slipstick|
|799 | sliding door|
|800 | slot, one-armed bandit|
|801 | snorkel|
|802 | snowmobile|
|803 | snowplow, snowplough|
|804 | soap dispenser|
|805 | soccer ball|
|806 | sock|
|807 | solar dish, solar collector, solar furnace|
|808 | sombrero|
|809 | soup bowl|
|810 | space bar|
|811 | space heater|
|812 | space shuttle|
|813 | spatula|
|814 | speedboat|
|815 | spider web, spider's web|
|816 | spindle|
|817 | sports car, sport car|
|818 | spotlight, spot|
|819 | stage|
|820 | steam locomotive|
|821 | steel arch bridge|
|822 | steel drum|
|823 | stethoscope|
|824 | stole|
|825 | stone wall|
|826 | stopwatch, stop watch|
|827 | stove|
|828 | strainer|
|829 | streetcar, tram, tramcar, trolley, trolley car|
|830 | stretcher|
|831 | studio couch, day bed|
|832 | stupa, tope|
|833 | submarine, pigboat, sub, U-boat|
|834 | suit, suit of clothes|
|835 | sundial|
|836 | sunglass|
|837 | sunglasses, dark glasses, shades|
|838 | sunscreen, sunblock, sun blocker|
|839 | suspension bridge|
|840 | swab, swob, mop|
|841 | sweatshirt|
|842 | swimming trunks, bathing trunks|
|843 | swing|
|844 | switch, electric switch, electrical switch|
|845 | syringe|
|846 | table lamp|
|847 | tank, army tank, armored combat vehicle, armoured combat vehicle|
|848 | tape player|
|849 | teapot|
|850 | teddy, teddy bear|
|851 | television, television system|
|852 | tennis ball|
|853 | thatch, thatched roof|
|854 | theater curtain, theatre curtain|
|855 | thimble|
|856 | thresher, thrasher, threshing machine|
|857 | throne|
|858 | tile roof|
|859 | toaster|
|860 | tobacco shop, tobacconist shop, tobacconist|
|861 | toilet seat|
|862 | torch|
|863 | totem pole|
|864 | tow truck, tow car, wrecker|
|865 | toyshop|
|866 | tractor|
|867 | trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi|
|868 | tray|
|869 | trench coat|
|870 | tricycle, trike, velocipede|
|871 | trimaran|
|872 | tripod|
|873 | triumphal arch|
|874 | trolleybus, trolley coach, trackless trolley|
|875 | trombone|
|876 | tub, vat|
|877 | turnstile|
|878 | typewriter keyboard|
|879 | umbrella|
|880 | unicycle, monocycle|
|881 | upright, upright piano|
|882 | vacuum, vacuum cleaner|
|883 | vase|
|884 | vault|
|885 | velvet|
|886 | vending machine|
|887 | vestment|
|888 | viaduct|
|889 | violin, fiddle|
|890 | volleyball|
|891 | waffle iron|
|892 | wall clock|
|893 | wallet, billfold, notecase, pocketbook|
|894 | wardrobe, closet, press|
|895 | warplane, military plane|
|896 | washbasin, handbasin, washbowl, lavabo, wash-hand basin|
|897 | washer, automatic washer, washing machine|
|898 | water bottle|
|899 | water jug|
|900 | water tower|
|901 | whiskey jug|
|902 | whistle|
|903 | wig|
|904 | window screen|
|905 | window shade|
|906 | Windsor tie|
|907 | wine bottle|
|908 | wing|
|909 | wok|
|910 | wooden spoon|
|911 | wool, woolen, woollen|
|912 | worm fence, snake fence, snake-rail fence, Virginia fence|
|913 | wreck|
|914 | yawl|
|915 | yurt|
|916 | web site, website, internet site, site|
|917 | comic book|
|918 | crossword puzzle, crossword|
|919 | street sign|
|920 | traffic light, traffic signal, stoplight|
|921 | book jacket, dust cover, dust jacket, dust wrapper|
|922 | menu|
|923 | plate|
|924 | guacamole|
|925 | consomme|
|926 | hot pot, hotpot|
|927 | trifle|
|928 | ice cream, icecream|
|929 | ice lolly, lolly, lollipop, popsicle|
|930 | French loaf|
|931 | bagel, beigel|
|932 | pretzel|
|933 | cheeseburger|
|934 | hotdog, hot dog, red hot|
|935 | mashed potato|
|936 | head cabbage|
|937 | broccoli|
|938 | cauliflower|
|939 | zucchini, courgette|
|940 | spaghetti squash|
|941 | acorn squash|
|942 | butternut squash|
|943 | cucumber, cuke|
|944 | artichoke, globe artichoke|
|945 | bell pepper|
|946 | cardoon|
|947 | mushroom|
|948 | Granny Smith|
|949 | strawberry|
|950 | orange|
|951 | lemon|
|952 | fig|
|953 | pineapple, ananas|
|954 | banana|
|955 | jackfruit, jak, jack|
|956 | custard apple|
|957 | pomegranate|
|958 | hay|
|959 | carbonara|
|960 | chocolate sauce, chocolate syrup|
|961 | dough|
|962 | meat loaf, meatloaf|
|963 | pizza, pizza pie|
|964 | potpie|
|965 | burrito|
|966 | red wine|
|967 | espresso|
|968 | cup|
|969 | eggnog|
|970 | alp|
|971 | bubble|
|972 | cliff, drop, drop-off|
|973 | coral reef|
|974 | geyser|
|975 | lakeside, lakeshore|
|976 | promontory, headland, head, foreland|
|977 | sandbar, sand bar|
|978 | seashore, coast, seacoast, sea-coast|
|979 | valley, vale|
|980 | volcano|
|981 | ballplayer, baseball player|
|982 | groom, bridegroom|
|983 | scuba diver|
|984 | rapeseed|
|985 | daisy|
|986 | yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum|
|987 | corn|
|988 | acorn|
|989 | hip, rose hip, rosehip|
|990 | buckeye, horse chestnut, conker|
|991 | coral fungus|
|992 | agaric|
|993 | gyromitra|
|994 | stinkhorn, carrion fungus|
|995 | earthstar|
|996 | hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa|
|997 | bolete|
|998 | ear, spike, capitulum|
|999 | toilet tissue, toilet paper, bathroom tissue|
</details>
### Data Splits
| |train|
|-------------|----:|
|# of examples|50000|
## Dataset Creation
### Curation Rationale
From the paper:
> Inspired by the Sketch data of (Li et al., 2017a) with seven classes, and several other Sketch datasets,
such as the Sketchy dataset (Sangkloy et al., 2016) with 125 classes and the Quick Draw! dataset
(QuickDraw, 2018) with 345 classes, and motivated by absence of a large-scale sketch dataset fitting
the shape and size of popular image classification benchmarks, we construct the ImageNet-Sketch
data set for evaluating the out-of-domain classification performance of vision models trained on
ImageNet.
### Source Data
#### Initial Data Collection and Normalization
The initial data collection and normalization is inherited from ImageNet. More information on it can be found [here](https://huggingface.co/datasets/imagenet-1k#initial-data-collection-and-normalization).
Additional preprocessing from the paper:
> We construct the data set with Google Image queries “sketch of ”, where is the
standard class name. We only search within the “black and white” color scheme. We initially query
100 images for every class, and then manually clean the pulled images by deleting the irrelevant
images and images that are for similar but different classes. For some classes, there are less than 50
images after manually cleaning, and then we augment the data set by flipping and rotating the images.
#### Who are the source language producers?
The source language is inherited from ImageNet. More information on the source language produces can be found [here](https://huggingface.co/datasets/imagenet-1k#who-are-the-source-language-producers).
### Annotations
#### Annotation process
The annotations are inherited from ImageNet. More information about the process can be found [here](https://huggingface.co/datasets/imagenet-1k#annotation-process).
#### Who are the annotators?
The same as in [ImageNet](https://huggingface.co/datasets/imagenet-1k#who-are-the-annotators).
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
The biases are inherited from ImageNet. More information about the process can be found [here](https://huggingface.co/datasets/imagenet-1k#discussion-of-biases).
### Other Known Limitations
1. Since most of the images were collected from internet, keep in mind that some images in ImageNet-Sketch might be subject to copyrights.
## Additional Information
### Dataset Curators
Authors of [Learning Robust Global Representations by Penalizing Local Predictive Power](https://arxiv.org/abs/1905.13549v2):
- Haohan Wang
- Songwei Ge
- Eric P. Xing
- Zachary C. Lipton
The dataset was curated using the scripts found in the [GitHub repository](https://github.com/HaohanWang/ImageNet-Sketch).
### Licensing Information
[More Information Needed]
### Citation Information
```bibtex
@inproceedings{wang2019learning,
title={Learning Robust Global Representations by Penalizing Local Predictive Power},
author={Wang, Haohan and Ge, Songwei and Lipton, Zachary and Xing, Eric P},
booktitle={Advances in Neural Information Processing Systems},
pages={10506--10518},
year={2019}
}
```
### Contributions
Thanks to [@nateraw](https://github.com/nateraw) for adding this dataset. | [
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wyzelabs/RuleRecommendation | wyzelabs | 2023-11-02T14:53:43Z | 429 | 9 | null | [
"license:cc-by-nc-nd-4.0",
"IoT",
"Smart Home",
"Rule Recommendation",
"Recommendation Systems",
"region:us"
] | 2023-11-02T14:53:43Z | 2023-07-12T18:32:35.000Z | 2023-07-12T18:32:35 | ---
license: cc-by-nc-nd-4.0
extra_gated_heading: >-
Wyze Rule Recommendation Challenge Participation and Dataset Access Terms and
Conditions
extra_gated_prompt: >-
Please read the <a href="https://drive.google.com/uc?id=1v-4gjp1EQZcdxYn6uZfft6CVKtWh3S87" target="_blank">Wyze Rule Recommendation Challenge Participation and Dataset Access Terms and Conditions</a>
carefully. In order to gain access to the data and take part in the Wyze Rule
Recommendation challenge, you must first read and consent to these terms and
conditions.
extra_gated_fields:
Name: text
Affiliation: text
Email: text
I have read and agree to the Wyze Rule Recommendation Challenge Participation and Dataset Access Terms and Conditions: checkbox
tags:
- IoT
- Smart Home
- Rule Recommendation
- Recommendation Systems
pretty_name: Wyze Rule Recommendation Dataset
---
# Wyze Rule Recommendation Dataset
<img src="https://drive.google.com/uc?id=17X5SpY8m-IQD35EZ7hy0uBlUqDhZiJ4r" alt="WRR" width="100%"/>
<!---
## Dataset Description
- **Paper:TBA**
- **Leaderboard:TBA**
- **Point of Contact:**
--->
## Dataset Summary
The Wyze Rule dataset is a new large-scale dataset designed specifically for smart home rule recommendation research. It contains over 1 million rules generated by 300,000 users from Wyze Labs, offering an extensive collection of real-world automation rules tailored to users' unique smart home setups.
The goal of the Wyze Rule dataset is to advance research and development of personalized rule recommendation systems for smart home automation. As smart devices proliferate in homes, automating their interactions becomes increasingly complex. Rules recommend how a user's devices could be connected to work together automatically, like a motion sensor triggering a camera to record. But with users having different devices, manually configuring these rules is difficult. This dataset enables creating intelligent algorithms that automatically recommend customized rules tailored to each user's specific smart home setup. By training machine learning models on the diverse real-world data of over 1 million rules from 300,000 Wyze users, researchers can build personalized recommendation systems. These would simplify and enhance automation for end users by suggesting rules that connect their devices in useful ways, while respecting their privacy. The Wyze Rule dataset provides the large-scale and varied data needed to make such personalized, private rule recommendation a reality.
The key features of this dataset are:
- Over 1 million automation rules governing how users' smart devices interact
- Rules are highly personalized based on each user's specific devices and needs
- 16 distinct device types like cameras, sensors, lights etc.
- There are 44 different trigger states and 46 different action by various devices.
- 1,641 unique trigger-action device and state (trigger_device + trigger_state + action + action_device) pairs capturing diverse automation logics
- Non-IID distribution among users makes it suitable for federated learning
- Allows development of personalized rule recommendation systems while preserving user privacy
- Enables benchmarking different algorithms on large-scale real-world data
Overall, the Wyze Rule dataset bridges the gap between rule recommendation research and practical applications, facilitating the creation of intelligent home automation systems. Its scale, diversity, and focus on individual users' needs make it a valuable resource for advancing personalized recommendation techniques.
## Dataset Structure
The Wyze Rule dataset contains two main CSV files - one for the rules and one for the devices owned by each user.
Each rule has attributes like user ID, trigger device, trigger state, action device, and action.
For example, a rule could be: user 123, contact sensor, "open", light bulb, "turn on".
This captures the trigger condition and the action to take. The device file maps user IDs to the specific devices owned by each user.
This is key because automating different device setups requires different valid rules.
With 16 device types and 1641 trigger-action state and device pairs, the rules reflect a user's customized needs.
Each user can have multiple instances of a device type, like several motion sensors.
The non-IID distribution of rules among 300,000 users with varying device combinations makes this dataset uniquely suitable for developing personalized federated learning algorithms for rule recommendation.
By separating rules into triggers and actions, the data structure provides flexibility lacking in user-item matrices that treat rules as single items.
Overall, the real-world granularity enables personalized automation.
### Data Fields
The main two files of this dataset, rules and devices, have the following fields:
1. Rule Dataset: This dataset contains data related to the rules that govern the behavior of Wyze smart home devices. Each row represents a single rule and contains various attributes describing the rule. The attributes of this file are as follows:
+ `user_id` (int): A unique integer identifier for the user associated with the rule. This identifier has been anonymized and does not contain any information related to the Wyze users.
+ `trigger_device` (str): The model of the device that triggers the rule when a specific condition is met. It may be a Wyze smart home device such as a sensor or a camera.
+ `trigger_device_id` (int): A unique integer identifier for the trigger device.
+ `trigger_state` (str): The state or condition that needs to be met on the trigger device for the rule to be activated. It may represent values such as "on," "off," "motion detected," or "sensor open."
+ `trigger_state_id` (int): A unique integer identifier for the trigger state.
+ `action` (str): The action to be executed on the action device when the rule is triggered. It may include values like "power on," "power off," "start recording," or "change brightness."
+ `action_id` (int): A unique integer identifier for the action.
+ `action_device` (str): The model of the device that performs an action when the rule is triggered. It is a Wyze smart home device such as a light or a camera.
+ `action_device_id` (int): A unique integer identifier for the action device.
+ `rule` (str): The combination of 4 ids as follows: `trigger_device_id`\_\_`trigger_state_id`\_\_`action_id`\_\_`action_device_id`
3. Device Dataset: This file contains data related to the devices owned by users. Each row represents a single device and contains information about the device model and its association with a specific user. There are a number of devices in this dataset that are not used in any rules by users, and hence, are not present in the rule dataset. The attributes of this dataset are as follows:
+ `user_id` (int): A unique integer identifier for the user associated with the device.
+ `device_id` (int): A unique integer identifier for the device.
+ `device_model` (str): The model or type of the device owned by the user. It represents various Wyze smart home devices such as a camera, a sensor, or a switch
There are a total of 16 different device types included in this dataset as follows:
1. `Camera`
2. `ClimateSensor`
3. `Cloud`
4. `ContactSensor`
5. `Irrigation`
6. `LeakSensor`
7. `Light`
8. `LightStrip`
9. `Lock`
10. `MeshLight`
11. `MotionSensor`
12. `OutdoorPlug`
13. `Plug`
14. `RobotVacuum`
15. `Switch`
16. `Thermostat`
### Data Splits
We have two public splits, which are `train` and `test`. The `train` split contains all the available rules set by the users in the dataset, as well as their device list. In the `test` dataset, for each user in this dataset, we have omitted one rule at random. The goal of building recommendation system is to recommend that omitted rule with high probability. The ground truth for this dataset will be released after the Wyze Rule Recommendation challenge has finished.
### Personal and Sensitive Information
Protecting user privacy was a top priority when creating the Wyze Rule dataset.
Any personally identifiable information or sensitive data that could reveal users' identities has been meticulously obscured.
The user IDs have been anonymized into random numeric values, removing any links to actual Wyze users.
The rules simply capture abstract triggers and actions for automation using generic device types.
By only retaining high-level functionality while erasing all personal attributes, the Wyze Rule dataset enables developing personalized recommendation algorithms without compromising user privacy.
Researchers can leverage this rich real-world data to advance the field of automation systems significantly while ensuring ethical data practices.
The dataset creators' commitment to protecting users' privacy will help propel innovation responsibly.
## Considerations for Using the Data
This data is mainly released for the [Wyze Rule Recommendation Challenge](https://huggingface.co/spaces/competitions/wyze-rule-recommendation).
### Licensing Information
This dataset is licensed by cc-by-nc-nd-4.0, which prohibits commercial use, distribution, modification, and reproduction of the data without permission from the copyright holder.
### Citation Information
TBA
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
JasiekKaczmarczyk/maestro-v1-sustain-masked | JasiekKaczmarczyk | 2023-10-02T10:34:44Z | 429 | 0 | null | [
"region:us"
] | 2023-10-02T10:34:44Z | 2023-10-02T08:08:58.000Z | 2023-10-02T08:08:58 | ---
dataset_info:
features:
- name: midi_filename
dtype: string
- name: source
dtype: string
- name: pitch
sequence: int16
length: 128
- name: dstart
sequence: float32
length: 128
- name: duration
sequence: float32
length: 128
- name: velocity
sequence: int16
length: 128
- name: masking_spaces
struct:
- name: <Random Mask>
sequence: bool
length: 128
- name: <LH Mask>
sequence: bool
length: 128
- name: <RH Mask>
sequence: bool
length: 128
- name: <Harmonic Root Mask>
sequence: bool
length: 128
- name: <Harmonic Outliers Mask>
sequence: bool
length: 128
splits:
- name: train
num_bytes: 86282539
num_examples: 43738
- name: validation
num_bytes: 9735862
num_examples: 4931
- name: test
num_bytes: 11249478
num_examples: 5695
download_size: 40330447
dataset_size: 107267879
---
# Dataset Card for "maestro-v1-sustain-masked"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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Norod78/cartoon-blip-captions | Norod78 | 2022-11-09T16:27:57Z | 428 | 4 | null | [
"task_categories:text-to-image",
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"size_categories:n<1K",
"language:en",
"license:cc-by-nc-sa-4.0",
"region:us"
] | 2022-11-09T16:27:57Z | 2022-10-31T14:48:15.000Z | 2022-10-31T14:48:15 | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 190959102.953
num_examples: 3141
download_size: 190279356
dataset_size: 190959102.953
pretty_name: 'Cartoon BLIP captions'
size_categories:
- n<1K
tags: []
task_categories:
- text-to-image
license: cc-by-nc-sa-4.0
annotations_creators:
- machine-generated
language:
- en
language_creators:
- other
multilinguality:
- monolingual
---
# Dataset Card for "cartoon-blip-captions"
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
asnq | null | 2023-05-16T08:28:22Z | 427 | 1 | asnq | [
"task_categories:multiple-choice",
"task_ids:multiple-choice-qa",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10M<n<100M",
"source_datasets:extended|natural_questions",
"language:en",
"license:cc-by-nc-sa-3.0",
"arxiv:1911.04118",
"region:us"
] | 2023-05-16T08:28:22Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
license:
- cc-by-nc-sa-3.0
multilinguality:
- monolingual
size_categories:
- 10M<n<100M
source_datasets:
- extended|natural_questions
task_categories:
- multiple-choice
task_ids:
- multiple-choice-qa
paperswithcode_id: asnq
pretty_name: Answer Sentence Natural Questions (ASNQ)
dataset_info:
features:
- name: question
dtype: string
- name: sentence
dtype: string
- name: label
dtype:
class_label:
names:
'0': neg
'1': pos
- name: sentence_in_long_answer
dtype: bool
- name: short_answer_in_sentence
dtype: bool
splits:
- name: train
num_bytes: 3656865072
num_examples: 20377568
- name: validation
num_bytes: 168004403
num_examples: 930062
download_size: 1482064429
dataset_size: 3824869475
---
# Dataset Card for "asnq"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://github.com/alexa/wqa_tanda#answer-sentence-natural-questions-asnq](https://github.com/alexa/wqa_tanda#answer-sentence-natural-questions-asnq)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [TANDA: Transfer and Adapt Pre-Trained Transformer Models for Answer Sentence Selection](https://arxiv.org/abs/1911.04118)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of downloaded dataset files:** 3.56 GB
- **Size of the generated dataset:** 3.82 GB
- **Total amount of disk used:** 7.39 GB
### Dataset Summary
ASNQ is a dataset for answer sentence selection derived from
Google's Natural Questions (NQ) dataset (Kwiatkowski et al. 2019).
Each example contains a question, candidate sentence, label indicating whether or not
the sentence answers the question, and two additional features --
sentence_in_long_answer and short_answer_in_sentence indicating whether ot not the
candidate sentence is contained in the long_answer and if the short_answer is in the candidate sentence.
For more details please see
https://arxiv.org/abs/1911.04118
and
https://research.google/pubs/pub47761/
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Dataset Structure
### Data Instances
#### default
- **Size of downloaded dataset files:** 3.56 GB
- **Size of the generated dataset:** 3.82 GB
- **Total amount of disk used:** 7.39 GB
An example of 'validation' looks as follows.
```
{
"label": 0,
"question": "when did somewhere over the rainbow come out",
"sentence": "In films and TV shows ( edit ) In the film Third Finger , Left Hand ( 1940 ) with Myrna Loy , Melvyn Douglas , and Raymond Walburn , the tune played throughout the film in short sequences .",
"sentence_in_long_answer": false,
"short_answer_in_sentence": false
}
```
### Data Fields
The data fields are the same among all splits.
#### default
- `question`: a `string` feature.
- `sentence`: a `string` feature.
- `label`: a classification label, with possible values including `neg` (0), `pos` (1).
- `sentence_in_long_answer`: a `bool` feature.
- `short_answer_in_sentence`: a `bool` feature.
### Data Splits
| name | train |validation|
|-------|-------:|---------:|
|default|20377568| 930062|
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
The data is made available under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License:
https://github.com/alexa/wqa_tanda/blob/master/LICENSE
### Citation Information
```
@article{Garg_2020,
title={TANDA: Transfer and Adapt Pre-Trained Transformer Models for Answer Sentence Selection},
volume={34},
ISSN={2159-5399},
url={http://dx.doi.org/10.1609/AAAI.V34I05.6282},
DOI={10.1609/aaai.v34i05.6282},
number={05},
journal={Proceedings of the AAAI Conference on Artificial Intelligence},
publisher={Association for the Advancement of Artificial Intelligence (AAAI)},
author={Garg, Siddhant and Vu, Thuy and Moschitti, Alessandro},
year={2020},
month={Apr},
pages={7780–7788}
}
```
### Contributions
Thanks to [@mkserge](https://github.com/mkserge) for adding this dataset. | [
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d0rj/curation-corpus | d0rj | 2023-06-13T13:25:32Z | 427 | 1 | curation-corpus | [
"task_categories:summarization",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"news",
"summarization",
"region:us"
] | 2023-06-13T13:25:32Z | 2023-06-12T19:22:21.000Z | 2023-06-12T19:22:21 | ---
dataset_info:
features:
- name: title
dtype: string
- name: summary
dtype: string
- name: url
dtype: string
- name: date
dtype: string
- name: article_content
dtype: string
splits:
- name: train
num_bytes: 127948910
num_examples: 30455
download_size: 76620775
dataset_size: 127948910
license: cc-by-4.0
task_categories:
- summarization
multilinguality:
- monolingual
language:
- en
source_datasets:
- original
tags:
- news
- summarization
pretty_name: Curation Corpus for Abstractive Text Summarisation
paperswithcode_id: curation-corpus
size_categories:
- 10K<n<100K
---
# curation-corpus
## Dataset Description
- **Homepage:** [https://github.com/CurationCorp/curation-corpus](https://github.com/CurationCorp/curation-corpus)
- **Repository:** [https://github.com/CurationCorp/curation-corpus](https://github.com/CurationCorp/curation-corpus)
## Source
Data from [this official repo](https://github.com/CurationCorp/curation-corpus) with downloaded news articles content.
## Citation
```
@misc{curationcorpusbase:2020,
title={Curation Corpus Base},
author={Curation},
year={2020}
}
``` | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
llm-lens/descriptors-text-davinci-003 | llm-lens | 2023-06-29T02:39:27Z | 427 | 0 | null | [
"region:us"
] | 2023-06-29T02:39:27Z | 2023-06-29T02:38:48.000Z | 2023-06-29T02:38:48 | ---
dataset_info:
features:
- name: vocab
dtype: string
- name: descriptions
sequence: string
- name: prompt_descriptions
sequence: string
splits:
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- name: fgvc
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---
# Dataset Card for "descriptors-text-davinci-003"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
teknium/openhermes | teknium | 2023-09-07T20:41:05Z | 427 | 77 | null | [
"task_categories:text-generation",
"language:eng",
"distillation",
"synthetic data",
"gpt",
"region:us"
] | 2023-09-07T20:41:05Z | 2023-09-04T01:31:26.000Z | 2023-09-04T01:31:26 | ---
language:
- eng
pretty_name: "OpenHermes-v1.0"
tags:
- distillation
- synthetic data
- gpt
task_categories:
- text-generation
---
# OpenHermes Dataset
The OpenHermes dataset is composed of 242,000 entries of primarily GPT-4 generated data, from open datasets across the AI landscape, including:
OpenHermes 13B is the first fine tune of the Hermes dataset that has a fully open source dataset!
OpenHermes was trained on 242,000 entries of primarily GPT-4 generated data, from open datasets across the AI landscape, including:
- GPTeacher - General Instruct, Roleplay v1, Roleplay v2, and Code Instruct Datasets, by Teknium
- WizardLM (v1, evol_instruct 70k), by WizardLM Team/nlpxucan
- Airoboros GPT-4 (v1.0), by JonDurbin
- Camel-AI's domain expert datasets, by the Camel-AI Team
- CodeAlpaca, by Sahil2801
- GPT4-LLM and Unnatural Instructions, by Microsoft
Filtering included removal of OpenAI refusals, disclaimers, and "As an AI" type examples and more
The base dataset mix is identical to the original Nous-Hermes', minus the Nous-Instruct and PDACTL datasets which were private datasets. | [
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ar_sarcasm | null | 2023-03-16T14:13:22Z | 426 | 8 | null | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|other-semeval_2017",
"source_datasets:extended|other-astd",
"language:ar",
"license:mit",
"sarcasm-detection",
"region:us"
] | 2023-03-16T14:13:22Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- ar
license:
- mit
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|other-semeval_2017
- extended|other-astd
task_categories:
- text-classification
task_ids:
- sentiment-classification
pretty_name: ArSarcasm
tags:
- sarcasm-detection
dataset_info:
features:
- name: dialect
dtype:
class_label:
names:
'0': egypt
'1': gulf
'2': levant
'3': magreb
'4': msa
- name: sarcasm
dtype:
class_label:
names:
'0': non-sarcastic
'1': sarcastic
- name: sentiment
dtype:
class_label:
names:
'0': negative
'1': neutral
'2': positive
- name: original_sentiment
dtype:
class_label:
names:
'0': negative
'1': neutral
'2': positive
- name: tweet
dtype: string
- name: source
dtype: string
splits:
- name: train
num_bytes: 1829167
num_examples: 8437
- name: test
num_bytes: 458218
num_examples: 2110
download_size: 750717
dataset_size: 2287385
---
# Dataset Card for ArSarcasm
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Repository:** [GitHub](https://github.com/iabufarha/ArSarcasm)
- **Paper:** https://www.aclweb.org/anthology/2020.osact-1.5/
### Dataset Summary
ArSarcasm is a new Arabic sarcasm detection dataset.
The dataset was created using previously available Arabic sentiment analysis
datasets ([SemEval 2017](https://www.aclweb.org/anthology/S17-2088.pdf)
and [ASTD](https://www.aclweb.org/anthology/D15-1299.pdf)) and adds sarcasm and
dialect labels to them.
The dataset contains 10,547 tweets, 1,682 (16%) of which are sarcastic.
For more details, please check the paper
[From Arabic Sentiment Analysis to Sarcasm Detection: The ArSarcasm Dataset](https://www.aclweb.org/anthology/2020.osact-1.5/)
### Supported Tasks and Leaderboards
You can get more information about an Arabic sarcasm tasks and leaderboard
[here](https://sites.google.com/view/ar-sarcasm-sentiment-detection/).
### Languages
Arabic (multiple dialects)
## Dataset Structure
### Data Instances
```javascript
{'dialect': 1, 'original_sentiment': 0, 'sarcasm': 0, 'sentiment': 0, 'source': 'semeval', 'tweet': 'نصيحه ما عمرك اتنزل لعبة سوبر ماريو مش زي ما كنّا متوقعين الله يرحم ايامات السيقا والفاميلي #SuperMarioRun'}
```
### Data Fields
- tweet: the original tweet text
- sarcasm: 0 for non-sarcastic, 1 for sarcastic
- sentiment: 0 for negative, 1 for neutral, 2 for positive
- original_sentiment: 0 for negative, 1 for neutral, 2 for positive
- source: the original source of tweet: SemEval or ASTD
- dialect: 0 for Egypt, 1 for Gulf, 2 for Levant, 3 for Magreb, 4 for Modern Standard Arabic (MSA)
### Data Splits
The training set contains 8,437 tweets, while the test set contains 2,110 tweets.
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
The dataset was created using previously available Arabic sentiment analysis datasets (SemEval 2017 and ASTD) and adds sarcasm and dialect labels to them.
#### Who are the source language producers?
SemEval 2017 and ASTD
### Annotations
#### Annotation process
For the annotation process, we used Figure-Eight
crowdsourcing platform. Our main objective was to annotate the
data for sarcasm detection, but due to the challenges imposed by dialectal variations, we decided to add the annotation for dialects. We also include a new annotation for
sentiment labels in order to have a glimpse of the variability and subjectivity between different annotators. Thus, the
annotators were asked to provide three labels for each tweet
as the following:
- Sarcasm: sarcastic or non-sarcastic.
- Sentiment: positive, negative or neutral.
- Dialect: Egyptian, Gulf, Levantine, Maghrebi or Modern Standard Arabic (MSA).
#### Who are the annotators?
Figure-Eight crowdsourcing platform
### 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
- Ibrahim Abu-Farha
- Walid Magdy
### Licensing Information
MIT
### Citation Information
```
@inproceedings{abu-farha-magdy-2020-arabic,
title = "From {A}rabic Sentiment Analysis to Sarcasm Detection: The {A}r{S}arcasm Dataset",
author = "Abu Farha, Ibrahim and Magdy, Walid",
booktitle = "Proceedings of the 4th Workshop on Open-Source Arabic Corpora and Processing Tools, with a Shared Task on Offensive Language Detection",
month = may,
year = "2020",
address = "Marseille, France",
publisher = "European Language Resource Association",
url = "https://www.aclweb.org/anthology/2020.osact-1.5",
pages = "32--39",
language = "English",
ISBN = "979-10-95546-51-1",
}
```
### Contributions
Thanks to [@mapmeld](https://github.com/mapmeld) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
hate_offensive | null | 2023-01-25T14:31:32Z | 426 | 7 | hate-speech-and-offensive-language | [
"task_categories:text-classification",
"task_ids:multi-class-classification",
"annotations_creators:crowdsourced",
"language_creators:machine-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:mit",
"hate-speech-detection",
"arxiv:1905.12516",
"region:us"
] | 2023-01-25T14:31:32Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- machine-generated
language:
- en
license:
- mit
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-class-classification
paperswithcode_id: hate-speech-and-offensive-language
pretty_name: HateOffensive
tags:
- hate-speech-detection
dataset_info:
features:
- name: total_annotation_count
dtype: int32
- name: hate_speech_annotations
dtype: int32
- name: offensive_language_annotations
dtype: int32
- name: neither_annotations
dtype: int32
- name: label
dtype:
class_label:
names:
'0': hate-speech
'1': offensive-language
'2': neither
- name: tweet
dtype: string
splits:
- name: train
num_bytes: 2811298
num_examples: 24783
download_size: 2546446
dataset_size: 2811298
---
# Dataset Card for HateOffensive
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage** : https://arxiv.org/abs/1905.12516
- **Repository** : https://github.com/t-davidson/hate-speech-and-offensive-language
- **Paper** : https://arxiv.org/abs/1905.12516
- **Leaderboard** :
- **Point of Contact** : trd54 at cornell dot edu
### Dataset Summary
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
English (`en`)
## Dataset Structure
### Data Instances
```
{
"count": 3,
"hate_speech_annotation": 0,
"offensive_language_annotation": 0,
"neither_annotation": 3,
"label": 2, # "neither"
"tweet": "!!! RT @mayasolovely: As a woman you shouldn't complain about cleaning up your house. & as a man you should always take the trash out...")
}
```
### Data Fields
count: (Integer) number of users who coded each tweet (min is 3, sometimes more users coded a tweet when judgments were determined to be unreliable,
hate_speech_annotation: (Integer) number of users who judged the tweet to be hate speech,
offensive_language_annotation: (Integer) number of users who judged the tweet to be offensive,
neither_annotation: (Integer) number of users who judged the tweet to be neither offensive nor non-offensive,
label: (Class Label) integer class label for majority of CF users (0: 'hate-speech', 1: 'offensive-language' or 2: 'neither'),
tweet: (string)
### Data Splits
This dataset is not splitted, only the train split is available.
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
Usernames are not anonymized in the dataset.
## 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
MIT License
### Citation Information
@inproceedings{hateoffensive,
title = {Automated Hate Speech Detection and the Problem of Offensive Language},
author = {Davidson, Thomas and Warmsley, Dana and Macy, Michael and Weber, Ingmar},
booktitle = {Proceedings of the 11th International AAAI Conference on Web and Social Media},
series = {ICWSM '17},
year = {2017},
location = {Montreal, Canada},
pages = {512-515}
}
### Contributions
Thanks to [@MisbahKhan789](https://github.com/MisbahKhan789) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
OpenAssistant/OASST-DE | OpenAssistant | 2023-11-13T10:24:29Z | 426 | 7 | null | [
"size_categories:1K<n<10K",
"language:de",
"license:apache-2.0",
"arxiv:2304.07327",
"region:us"
] | 2023-11-13T10:24:29Z | 2023-08-29T12:04:11.000Z | 2023-08-29T12:04:11 | ---
dataset_info:
features:
- name: conversation
list:
- name: role
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 8022604.792326268
num_examples: 3721
download_size: 4325950
dataset_size: 8022604.792326268
license: apache-2.0
language:
- de
size_categories:
- 1K<n<10K
---
# German OpenAssistant Conversations Dataset (OASST-DE)
With the goal of advancing open-source, german-language LLM research, we present
OASST-DE: a high quality subset of a recent (25.08.23) dump from the [OpenAssistant website](https://www.open-assistant.io/)
translated to German using the GPT-3.5 API. More details on how the dataset was filtered and translated under [dataset creation.](#dataset-creation-process)
For more details on the OpenAssistant Project, look at the [first OASST dataset (OASST1)](https://huggingface.co/datasets/OpenAssistant/oasst1), [the Open-Assistant GitHub repo](https://github.com/LAION-AI/Open-Assistant)
or [our paper](https://arxiv.org/abs/2304.07327).
This dataset was created as part of LAION's LeoLM (Linguistically Enhanced Open Language Model) project led by Björn Plüster.
Check out LeoLM-Chat trained with OASST-DE ([7b](https://huggingface.co/LeoLM/leo-hessianai-7b-chat), [13b](https://huggingface.co/LeoLM/leo-hessianai-13b-chat)) finetuned on OASST-DE and read [their blog post](https://laion.ai/blog/leo-lm/)) for more info on LeoLM.
## Dataset Creation Process
This dataset was created from a recent OASST dump by following these steps:
- Filter for Top1 response trees with assistant response leaves
- Filter first prompt quality >= 0.5
- Filter total conversation length < 1900 tokens to fit in GPT3.5 context length
- Filter for `'lang' == 'de'` -> add to dataset
- Filter for `'lang' == 'en'` (other languages often result in failed translations)
- Translate using GPT-3.5-turbo API (total cost ~15$).
This results in around 3.7k samples of high-quality assistant conversations.
## Dataset Structure
This dataset has only one `'conversation'` field. Each example is a list of an alternating conversation between `'prompter'` and `'assistant'`,
where each entry is a dict with `'text'` and `'role'` fields:
```json
"conversation": [
{"role": "prompter", "text": "Moin, wie geht's dir?"},
{"role": "assistant", "text": "Moin Moin! Mir geht es gut, und dir?"},
...
]
```
## Usage with 🤗Datasets:
```python
from datasets import load_dataset
ds = load_dataset("OpenAssistant/OASST-DE", split="train")
print(ds[0]["conversation"])
``` | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
determined-ai/consumer_complaints_medium | determined-ai | 2023-11-15T00:46:34Z | 426 | 0 | null | [
"region:us"
] | 2023-11-15T00:46:34Z | 2023-11-15T00:46:28.000Z | 2023-11-15T00:46:28 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: Issue
dtype: string
- name: Consumer Complaint
dtype: string
splits:
- name: train
num_bytes: 19961957
num_examples: 64292
- name: test
num_bytes: 6654389
num_examples: 21439
download_size: 13546206
dataset_size: 26616346
---
# Dataset Card for "consumer_complaints_medium"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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Jack0508/demo | Jack0508 | 2021-11-07T16:25:20Z | 425 | 0 | null | [
"region:us"
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
lucadiliello/wikiqa | lucadiliello | 2022-12-05T15:09:31Z | 425 | 0 | null | [
"region:us"
] | 2022-12-05T15:09:31Z | 2022-12-05T15:06:32.000Z | 2022-12-05T15:06:32 | ---
dataset_info:
features:
- name: label
dtype: int64
- name: answer
dtype: string
- name: key
dtype: int64
- name: question
dtype: string
splits:
- name: test_clean
num_bytes: 449691
num_examples: 2341
- name: dev_clean
num_bytes: 214886
num_examples: 1126
- name: train
num_bytes: 4017460
num_examples: 20360
- name: test
num_bytes: 1208042
num_examples: 6165
- name: dev
num_bytes: 530358
num_examples: 2733
download_size: 3111254
dataset_size: 6420437
---
# Dataset Card for "wikiqa"
WikiQA dataset for Answer Sentence Selection. The dataset contains 2 additional splits which are `clean` versions of the original development and test sets. `clean` versions contain only questions which have at least a positive and a negative answer candidate. | [
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Zoe10/ner_dataset | Zoe10 | 2021-12-14T11:13:54Z | 424 | 0 | null | [
"region:us"
] | 2021-12-14T11:13:54Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | Entry not found | [
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hf-internal-testing/fixtures_sintel | hf-internal-testing | 2021-12-07T08:13:09Z | 424 | 0 | null | [
"region:us"
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alisawuffles/WANLI | alisawuffles | 2022-11-21T17:31:56Z | 424 | 6 | null | [
"task_categories:text-classification",
"task_ids:natural-language-inference",
"annotations_creators:crowdsourced",
"language_creators:other",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"arxiv:2201.05955",
"region:us"
] | 2022-11-21T17:31:56Z | 2022-04-21T00:57:25.000Z | 2022-04-21T00:57:25 | ---
annotations_creators:
- crowdsourced
language_creators:
- other
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: WANLI
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- natural-language-inference
---
# Dataset Card for WANLI
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** [WANLI homepage](https://wanli.allenai.org/)
- **Repository:** [Github repo](https://github.com/alisawuffles/wanli)
- **Paper:** [arXiv](https://arxiv.org/abs/2201.05955)
- **Point of Contact:** [Alisa Liu](mailto:alisaliu@cs.washington.edu)
### Dataset Summary
WANLI (**W**orker-**A**I Collaboration for **NLI**) is a collection of 108K English sentence pairs for the task of natural language inference (NLI).
Each example is created by first identifying a "pocket" of examples in [MultiNLI (Williams et al., 2018)](https://cims.nyu.edu/~sbowman/multinli/) that share a challenging reasoning pattern, then instructing GPT-3 to write a new example with the same pattern.
The set of generated examples are automatically filtered to contain those most likely to aid model training, and finally labeled and optionally revised by human annotators.
WANLI presents unique empirical strengths compared to existing NLI datasets. Remarkably, training a model on WANLI instead of MultiNLI (which is 4 times larger) improves performance on seven out-of-domain test sets we consider, including by 11% on HANS and 9% on Adversarial NLI.
### Supported Tasks and Leaderboards
The dataset can be used to train a model for natural language inference, which determines whether a premise entails (i.e., implies the truth of) a hypothesis, both expressed in natural language. Success on this task is typically measured by achieving a high accuracy. A RoBERTa-large model currently achieves 75.40%.
Models trained on NLI are often adapted to other downstream tasks, and NLI data can be mixed with other sources of supervision.
### Languages
The dataset consists of English examples generated by GPT-3 and revised by English-speaking crowdworkers located in the United States.
## Dataset Structure
### Data Instances
Here is an example of an NLI example in `data/wanli/train.jsonl` or `data/wanli/test.jsonl`.
```
{
"id": 225295,
"premise": "It is a tribute to the skill of the coach that the team has been able to compete at the highest level.",
"hypothesis": "The coach is a good coach.",
"gold": "entailment",
"genre": "generated",
"pairID": "171408"
}
```
- `id`: unique identifier for the example
- `premise`: a piece of text
- `hypothesis`: a piece of text that may be true, false, or whose truth conditions may not be knowable when compared to the premise
- `gold`: one of `entailment`, `neutral`, and `contradiction`
- `genre`: one of `generated` and `generated_revised`, depending on whether the example was revised by annotators
- `pairID`: id of seed MNLI example, corresponding to those in `data/mnli/train.jsonl`
We also release the raw annotations for each worker, which can be found in `data/wanli/anonymized_annotations.jsonl`.
```
"WorkerId": "EUJ",
"id": 271560,
"nearest_neighbors": [
309783,
202988,
145310,
98030,
148759
],
"premise": "I don't know what I'd do without my cat. He is my only friend.",
"hypothesis": "I would be alone.",
"label": "neutral",
"revised_premise": "I don't know what I'd do without my cat. He is my only friend.",
"revised_hypothesis": "I would be alone without my cat.",
"gold": "entailment",
"revised": true
```
- `WorkerId`: a unique identification for each crowdworker (NOT the real worker ID from AMT)
- `id`: id of generated example
- `nearest_neighbors`: ordered ids of the group of MNLI nearest neighbors that were used as in-context examples, where the first one is seed ambiguous MNLI example. MNLI ids correspond to those in `mnli/train.jsonl`.
- `premise`: GPT-3 generated premise
- `hypothesis`: GPT-3 generated hypothesis
- `label`: the shared label of the in-context examples, which is the "intended" label for this generation
- `revised_premise`: premise after human review
- `revised_hypothesis`: hypothesis after human review
- `gold`: annotator-assigned gold label for the (potentially revised) example
- `revised`: whether the example was revised
### Data Splits
The dataset is randomly split into a *train* and *test* set.
| | train | test |
|-------------------------|------:|-----:|
| Examples | 102885| 5000|
## Dataset Creation
### Curation Rationale
A recurring challenge of crowdsourcing NLP datasets at scale is that human writers often rely on repetitive patterns when crafting examples, leading to a lack of linguistic diversity. On the other hand, there has been remarkable progress in open-ended text generation based on massive language models. We create WANLI to demonstrate the effectiveness an approach that leverages the best of both worlds: a language model's ability to efficiently generate diverse examples, and a human's ability to revise the examples for quality and assign a gold label.
### Source Data
#### Initial Data Collection and Normalization
Our pipeline starts with an existing dataset, MultiNLI (Williams et al., 2018). We use dataset cartography from [Swayamdipta et al. (2020)](https://aclanthology.org/2020.emnlp-main.746/) to automatically identify pockets of examples that demonstrate challenging reasoning patterns rela081 tive to a trained model. Using each group as a set of in-context examples, we leverage a pretrained language model to *generate new examples* likely to have the same pattern. We then automatically filter generations to keep those that are most likely to aid model learning. Finally, we validate the generated examples by subjecting them to human review, where crowdworkers assign a gold label and (optionally) revise for quality.
#### Who are the source language producers?
The GPT-3 Curie model generated examples which were then revised and labeled by crowdworkers on Amazon Mechanical Turk.
Workers were paid $0.12 for each example that they annotate. At the end of data collection, we aggregate the earning and time spent from each crowdworker, and find that the median hourly rate was $22.72, with 85% of workers being paid over the $15/hour target.
### Annotations
#### Annotation process
Given an unlabeled example, annotators are asked to optionally revise it for quality (while preserving the intended meaning as much as possible through minimal revisions), and then assign a label. Alternatively, if an example would require a great deal of revision to fix *or* if it could be perceived as offensive, they were asked to discard it.
Details about instructions, guidelines, and instructional examples can be found in Appendix D of the paper.
Crowdworkers annotate a total of 118,724 examples, with two distinct workers reviewing each example.
For examples that both annotators labeled without revision, annotators achieved a Cohen Kappa score of 0.60, indicating substantial agreement.
#### Who are the annotators?
Annotators were required to have a HIT approval rate of 98%, a total of 10,000 approved HITs, and be located in the United States.
300 Turkers took our qualification test, of which 69 passed. Turkers who were later found to produce extremely careless annotations were removed from the qualification list (and oftentimes, their annotations were discarded, though they were still paid for their work). The number of workers who contributed to the final dataset is 62.
### Personal and Sensitive Information
The dataset does not contain any personal information about the authors or the crowdworkers.
## Considerations for Using the Data
### Social Impact of Dataset
This dataset was developed to explore the potential of worker-AI collaboration for dataset curation, train more robust NLI models, and provide more challenging evaluation of existing systems.
### Discussion of Biases
Text generated from large pretrained language models is susceptible to perpetuating social harms and containing toxic language.
To partially remedy this, we ask annotators to discard any examples that may be perceived as offensive.
Nonetheless, it is possible that harmful examples (especially if they contain subtle biases) may have been missed by annotators and included in the final dataset.
## Additional Information
### Dataset Curators
WANLI was developed by Alisa Liu, Swabha Swayamdipta, Noah A. Smith, and Yejin Choi from the [University of Washington](https://www.cs.washington.edu/) and [AI2](https://allenai.org/).
### Citation Information
```
@misc{liu-etal-2022-wanli,
title = "WANLI: Worker and AI Collaboration for Natural Language Inference Dataset Creation",
author = "Liu, Alisa and
Swayamdipta, Swabha and
Smith, Noah A. and
Choi, Yejin",
month = jan,
year = "2022",
url = "https://arxiv.org/pdf/2201.05955",
}
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
PygmalionAI/PIPPA | PygmalionAI | 2023-09-07T03:07:55Z | 424 | 109 | null | [
"task_categories:conversational",
"size_categories:10K<n<100K",
"language:en",
"license:apache-2.0",
"not-for-all-audiences",
"conversational",
"roleplay",
"custom-format",
"a.",
"arxiv:2308.05884",
"region:us"
] | 2023-09-07T03:07:55Z | 2023-08-08T01:32:40.000Z | 2023-08-08T01:32:40 | ---
license: apache-2.0
task_categories:
- conversational
language:
- en
tags:
- not-for-all-audiences
- conversational
- roleplay
- custom-format
- a.
pretty_name: PIPPA - Personal Interaction Pairs Between People and AI
size_categories:
- 10K<n<100K
viewer: false
---
# PIPPA - Personal Interaction Pairs between People and AI
It's been a long time coming, but we're proud to finally release the public portion of our conversational dataset to the public. **Personal Interaction Pairs between People and AI** (**PIPPA**) is a partially synthetic, community contributed and open-source conversational and roleplaying dataset generated from a subset of submitted logs to the Pygmalion project.
This dataset is a subset of what we have received - it consists only of the valid conversational logs in which the submitter gave consent to redistribute to the public. Furthermore, we have done our best to redact or modify any personal information that could potentially be found within PIPPA. If you have found something within PIPPA which has not been redacted properly, please contact us via. email at `teargosling@pygmalion.chat` or `alpindale@pygmalion.chat` and we'll take care of it for you. You may contact us for any other purpose as well, including yelling at us for when the next model will be released.
**⚠️ CAUTION: PIPPA contains conversations, themes and scenarios which can be considered "not safe for work" (NSFW) and/or heavily disturbing in nature. Models trained purely with PIPPA may have the tendency to generate X-rated output. You have been warned.**
## Dataset Summary
PIPPA consists of just a little more than 1 million lines of dialogue spread out over 26,000 conversations between users of the popular chatbot website "Character.AI" and its large language model, obtained through a large community effort taking place over the course of several months. Tallying shows that over 1,000 unique personas simulating both real and fictional characters are represented within the dataset, allowing PIPPA and LLMs fine-tuned on it to adapt to many different roleplay domains.
The dataset is represented with a JSONL file, with a singular JSON snippet representing one entire conversation. Every snippet contains the following pieces of data:
- `submission_timestamp`: The Unix timestamp of when this particular conversation was submitted to the project, in milliseconds.
- `categories`: The categories assigned to the character on the Character.AI website, if any were assigned. If no categories were assigned, it will be `null`
- `bot_id`: The unique ID assigned to the specific character which the user was conversing with on the website.
- `bot_name`: The name of the character.
- `bot_greeting`: The introductory line of the character to the user. This is always the first utterance of dialogue in a conversation.
- `bot_definitions`: Contains whatever was typed in the **Definitions** field in the character creator on the website. This usually consists of one or more example conversations between the user and the character designed to steer the model towards emulating the persona correctly. Bot definitions required a separate effort to gather, and thus may not be present for a specific persona - if this is the case, an empty string is provided. Because the defintions were written on Character.AI, this field usually follows Character.AI's unique formatting and should be preprocessed before feeding into any model - please see **Appendix A** of the paper for further details.
- `bot_description`: Contains whatever was typed in the **Description** field in the character creator on the website. It usually consists of a few sentences which gives a brief overview of the character and any important details about them.
- `conversation`: The conversation between the user and the model. This is represented as a list of dictionaries, each dictionary representing a single utterance and containing two key-value pairs: `message`, referring to the utterance itself and `is_human`, which designates whether the dialogue was generated by the user or the LLM.
For further information about PIPPA, please refer to our [published paper](https://arxiv.org/abs/2308.05884) or contact us at the emails listed above.
## Files
We publish PIPPA in multiple variants, each a singular JSONL file:
- **pippa.jsonl**: The original dataset, almost exactly as submitted to us (barring any modifications resulting from the redaction of personally identifiable information).
- **pippa_deduped.jsonl**: The 'cleaned' version of PIPPA, with duplicate conversations as well as any conversation with less than three turns removed from the dataset. **We recommend using this file.**
- **pippa_metharme.jsonl**: A version of deduped PIPPA which is formatted in a similar way to our [Metharme instructional models](https://huggingface.co/PygmalionAI/metharme-13b), useful as an example to demonstrate how to properly format the PIPPA dataset.
If you are using HuggingFace's `datasets` library, you can choose the file you wish to use by specifying the name of it (without extension) as an argument, like so: `dataset = load_dataset("PygmalionAI/PIPPA", 'pippa_deduped')`. The default value is `pippa_deduped`.
Thank you for your patience, everyone!
## Citation
If you're using our dataset, please consider citing our work:
```bibtex
@misc{gosling2023pippa,
title={PIPPA: A Partially Synthetic Conversational Dataset},
author={Tear Gosling and Alpin Dale and Yinhe Zheng},
year={2023},
eprint={2308.05884},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
___
Any relationship between the name of this dataset and any public personas is entirely and totally coincidential. | [
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conv_ai | null | 2022-11-03T16:30:55Z | 423 | 2 | null | [
"task_categories:conversational",
"task_categories:text-classification",
"task_ids:text-scoring",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:unknown",
"evaluating-dialogue-systems",
"region:us"
] | 2022-11-03T16:30:55Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- conversational
- text-classification
task_ids:
- text-scoring
paperswithcode_id: null
pretty_name: ConvAi
tags:
- evaluating-dialogue-systems
dataset_info:
features:
- name: id
dtype: int32
- name: dialogId
dtype: int32
- name: context
dtype: string
- name: users
list:
- name: userType
dtype: string
- name: id
dtype: string
- name: evaluation
list:
- name: breadth
dtype: int32
- name: userId
dtype: string
- name: quality
dtype: int32
- name: engagement
dtype: int32
- name: thread
list:
- name: evaluation
dtype: int32
- name: text
dtype: string
- name: userId
dtype: string
- name: time
dtype: int32
config_name: conv_ai
splits:
- name: train
num_bytes: 3924265
num_examples: 2778
download_size: 5804611
dataset_size: 3924265
---
# Dataset Card for ConvAi
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [Add homepage URL here if available (unless it's a GitHub repository)]()
- **Repository:** [If the dataset is hosted on github or has a github homepage, add URL here]()
- **Paper:** [If the dataset was introduced by a paper or there was a paper written describing the dataset, add URL here (landing page for Arxiv paper preferred)]()
- **Leaderboard:** [If the dataset supports an active leaderboard, add link here]()
- **Point of Contact:** [If known, name and email of at least one person the reader can contact for questions about the dataset.]()
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
[More Information Needed]
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
[More Information Needed]
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@patil-suraj](https://github.com/patil-suraj) for adding this dataset. | [
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eurlex | null | 2022-11-18T20:01:34Z | 423 | 4 | eurlex57k | [
"task_categories:text-classification",
"task_ids:multi-label-classification",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-sa-4.0",
"legal-topic-classification",
"region:us"
] | 2022-11-18T20:01:34Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-label-classification
paperswithcode_id: eurlex57k
pretty_name: the EUR-Lex dataset
tags:
- legal-topic-classification
dataset_info:
features:
- name: celex_id
dtype: string
- name: title
dtype: string
- name: text
dtype: string
- name: eurovoc_concepts
sequence: string
config_name: eurlex57k
splits:
- name: train
num_bytes: 167603718
num_examples: 45000
- name: test
num_bytes: 22046706
num_examples: 6000
- name: validation
num_bytes: 21942574
num_examples: 6000
download_size: 50289403
dataset_size: 211592998
---
# Dataset Card for the EUR-Lex dataset
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** http://nlp.cs.aueb.gr/software_and_datasets/EURLEX57K/
- **Repository:** http://nlp.cs.aueb.gr/software_and_datasets/EURLEX57K/
- **Paper:** https://www.aclweb.org/anthology/P19-1636/
- **Leaderboard:** N/A
- **Point of Contact:** [Ilias Chalkidis](mailto:ihalk@aueb.gr)
### Dataset Summary
EURLEX57K can be viewed as an improved version of the dataset released by Mencia and Furnkranzand (2007), which has been widely used in Large-scale Multi-label Text Classification (LMTC) research, but is less than half the size of EURLEX57K (19.6k documents, 4k EUROVOC labels) and more than ten years old.
EURLEX57K contains 57k legislative documents in English from EUR-Lex (https://eur-lex.europa.eu) with an average length of 727 words. Each document contains four major zones:
- the header, which includes the title and name of the legal body enforcing the legal act;
- the recitals, which are legal background references; and
- the main body, usually organized in articles.
**Labeling / Annotation**
All the documents of the dataset have been annotated by the Publications Office of EU (https://publications.europa.eu/en) with multiple concepts from EUROVOC (http://eurovoc.europa.eu/).
While EUROVOC includes approx. 7k concepts (labels), only 4,271 (59.31%) are present in EURLEX57K, from which only 2,049 (47.97%) have been assigned to more than 10 documents. The 4,271 labels are also divided into frequent (746 labels), few-shot (3,362), and zero- shot (163), depending on whether they were assigned to more than 50, fewer than 50 but at least one, or no training documents, respectively.
### Supported Tasks and Leaderboards
The dataset supports:
**Multi-label Text Classification:** Given the text of a document, a model predicts the relevant EUROVOC concepts.
**Few-shot and Zero-shot learning:** As already noted, the labels can be divided into three groups: frequent (746 labels), few-shot (3,362), and zero- shot (163), depending on whether they were assigned to more than 50, fewer than 50 but at least one, or no training documents, respectively.
### Languages
All documents are written in English.
## Dataset Structure
### Data Instances
```json
{
"celex_id": "31979D0509",
"title": "79/509/EEC: Council Decision of 24 May 1979 on financial aid from the Community for the eradication of African swine fever in Spain",
"text": "COUNCIL DECISION of 24 May 1979 on financial aid from the Community for the eradication of African swine fever in Spain (79/509/EEC)\nTHE COUNCIL OF THE EUROPEAN COMMUNITIES\nHaving regard to the Treaty establishing the European Economic Community, and in particular Article 43 thereof,\nHaving regard to the proposal from the Commission (1),\nHaving regard to the opinion of the European Parliament (2),\nWhereas the Community should take all appropriate measures to protect itself against the appearance of African swine fever on its territory;\nWhereas to this end the Community has undertaken, and continues to undertake, action designed to contain outbreaks of this type of disease far from its frontiers by helping countries affected to reinforce their preventive measures ; whereas for this purpose Community subsidies have already been granted to Spain;\nWhereas these measures have unquestionably made an effective contribution to the protection of Community livestock, especially through the creation and maintenance of a buffer zone north of the river Ebro;\nWhereas, however, in the opinion of the Spanish authorities themselves, the measures so far implemented must be reinforced if the fundamental objective of eradicating the disease from the entire country is to be achieved;\nWhereas the Spanish authorities have asked the Community to contribute to the expenses necessary for the efficient implementation of a total eradication programme;\nWhereas a favourable response should be given to this request by granting aid to Spain, having regard to the undertaking given by that country to protect the Community against African swine fever and to eliminate completely this disease by the end of a five-year eradication plan;\nWhereas this eradication plan must include certain measures which guarantee the effectiveness of the action taken, and it must be possible to adapt these measures to developments in the situation by means of a procedure establishing close cooperation between the Member States and the Commission;\nWhereas it is necessary to keep the Member States regularly informed as to the progress of the action undertaken,",
"eurovoc_concepts": ["192", "2356", "2560", "862", "863"]
}
```
### Data Fields
The following data fields are provided for documents (`train`, `dev`, `test`):
`celex_id`: (**str**) The official ID of the document. The CELEX number is the unique identifier for all publications in both Eur-Lex and CELLAR.\
`title`: (**str**) The title of the document.\
`text`: (**str**) The full content of each document, which is represented by its `header`, `recitals` and `main_body`.\
`eurovoc_concepts`: (**List[str]**) The relevant EUROVOC concepts (labels).
If you want to use the descriptors of EUROVOC concepts, similar to Chalkidis et al. (2020), please load: https://archive.org/download/EURLEX57K/eurovoc_concepts.jsonl
```python
import json
with open('./eurovoc_concepts.jsonl') as jsonl_file:
eurovoc_concepts = {json.loads(concept) for concept in jsonl_file.readlines()}
```
### Data Splits
| Split | No of Documents | Avg. words | Avg. labels |
| ------------------- | ------------------------------------ | --- | --- |
| Train | 45,000 | 729 | 5 |
|Development | 6,000 | 714 | 5 |
|Test | 6,000 | 725 | 5 |
## Dataset Creation
### Curation Rationale
The dataset was curated by Chalkidis et al. (2019).\
The documents have been annotated by the Publications Office of EU (https://publications.europa.eu/en).
### Source Data
#### Initial Data Collection and Normalization
The original data are available at EUR-Lex portal (https://eur-lex.europa.eu) in an unprocessed format.
The documents were downloaded from EUR-Lex portal in HTML format.
The relevant metadata and EUROVOC concepts were downloaded from the SPARQL endpoint of the Publications Office of EU (http://publications.europa.eu/webapi/rdf/sparql).
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
* The original documents are available at EUR-Lex portal (https://eur-lex.europa.eu) in an unprocessed HTML format. The HTML code was striped and the documents split into sections.
* The documents have been annotated by the Publications Office of EU (https://publications.europa.eu/en).
#### Who are the annotators?
Publications Office of EU (https://publications.europa.eu/en)
### Personal and Sensitive Information
The dataset does not include personal or sensitive information.
## 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
Chalkidis et al. (2019)
### Licensing Information
© European Union, 1998-2021
The Commission’s document reuse policy is based on Decision 2011/833/EU. Unless otherwise specified, you can re-use the legal documents published in EUR-Lex for commercial or non-commercial purposes.
The copyright for the editorial content of this website, the summaries of EU legislation and the consolidated texts, which is owned by the EU, is licensed under the Creative Commons Attribution 4.0 International licence. This means that you can re-use the content provided you acknowledge the source and indicate any changes you have made.
Source: https://eur-lex.europa.eu/content/legal-notice/legal-notice.html \
Read more: https://eur-lex.europa.eu/content/help/faq/reuse-contents-eurlex.html
### Citation Information
*Ilias Chalkidis, Manos Fergadiotis, Prodromos Malakasiotis and Ion Androutsopoulos.*
*Large-Scale Multi-Label Text Classification on EU Legislation.*
*Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL 2019). Florence, Italy. 2019*
```
@inproceedings{chalkidis-etal-2019-large,
title = "Large-Scale Multi-Label Text Classification on {EU} Legislation",
author = "Chalkidis, Ilias and Fergadiotis, Manos and Malakasiotis, Prodromos and Androutsopoulos, Ion",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/P19-1636",
doi = "10.18653/v1/P19-1636",
pages = "6314--6322"
}
```
### Contributions
Thanks to [@iliaschalkidis](https://github.com/iliaschalkidis) for adding this dataset. | [
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labr | null | 2023-01-25T14:34:10Z | 423 | 0 | labr | [
"task_categories:text-classification",
"task_ids:multi-class-classification",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:ar",
"license:unknown",
"region:us"
] | 2023-01-25T14:34:10Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- found
language:
- ar
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-class-classification
paperswithcode_id: labr
pretty_name: LABR
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': '1'
'1': '2'
'2': '3'
'3': '4'
'4': '5'
config_name: plain_text
splits:
- name: train
num_bytes: 7051103
num_examples: 11760
- name: test
num_bytes: 1703399
num_examples: 2935
download_size: 39953712
dataset_size: 8754502
---
# Dataset Card for LABR
## Table of Contents
- [Dataset Card for LABR](#dataset-card-for-labr)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [|split|num examples|](#splitnum-examples)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
- [Who are the source language producers?](#who-are-the-source-language-producers)
- [Annotations](#annotations)
- [Annotation process](#annotation-process)
- [Who are the annotators?](#who-are-the-annotators)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Repository:** [LABR](https://github.com/mohamedadaly/LABR)
- **Paper:** [LABR: Large-scale Arabic Book Reviews Dataset](https://aclanthology.org/P13-2088/)
- **Point of Contact:** [Mohammed Aly](mailto:mohamed@mohamedaly.info)
### Dataset Summary
This dataset contains over 63,000 book reviews in Arabic. It is the largest sentiment analysis dataset for Arabic to-date. The book reviews were harvested from the website Goodreads during the month or March 2013. Each book review comes with the goodreads review id, the user id, the book id, the rating (1 to 5) and the text of the review.
### Supported Tasks and Leaderboards
The dataset was published on this [paper](https://www.aclweb.org/anthology/P13-2088.pdf).
### Languages
The dataset is based on Arabic.
## Dataset Structure
### Data Instances
A typical data point comprises a rating from 1 to 5 where the higher the rating the better the review.
### Data Fields
- `text` (str): Review text.
- `label` (int): Review rating.
### Data Splits
The data is split into a training and testing. The split is organized as the following
| | train | test |
|---------- |-------:|------:|
|data split | 11,760 | 2,935 |
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
[More Information Needed]
#### Initial Data Collection and Normalization
downloaded over 220,000 reviews from the
book readers social network www.goodreads.com
during the month of March 2013
#### Who are the source language producers?
Reviews.
### Annotations
The dataset does not contain any additional annotations.
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
```
@inproceedings{aly2013labr,
title={Labr: A large scale arabic book reviews dataset},
author={Aly, Mohamed and Atiya, Amir},
booktitle={Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)},
pages={494--498},
year={2013}
}
```
### Contributions
Thanks to [@zaidalyafeai](https://github.com/zaidalyafeai) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
colbertv2/lotte | colbertv2 | 2022-08-04T17:55:59Z | 423 | 1 | null | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:apache-2.0",
"arxiv:2112.01488",
"region:us"
] | 2022-08-04T17:55:59Z | 2022-07-14T22:11:39.000Z | 2022-07-14T22:11:39 | ---
annotations_creators:
- no-annotation
language:
- en
language_creators:
- found
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: 'Lotte queries from ColBERTv2: Effective and Efficient Retrieval via
Lightweight Late Interaction'
size_categories:
- 10K<n<100K
source_datasets:
- original
tags: []
task_categories:
- question-answering
task_ids:
- extractive-qa
---
Queries for Lotte dataset from [ColBERTv2: Effective and Efficient Retrieval via
Lightweight Late Interaction](https://arxiv.org/abs/2112.01488) | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
wangrui6/Zhihu-KOL | wangrui6 | 2023-04-23T13:26:03Z | 423 | 98 | null | [
"task_categories:question-answering",
"language:zh",
"region:us"
] | 2023-04-23T13:26:03Z | 2023-02-25T00:21:29.000Z | 2023-02-25T00:21:29 | ---
dataset_info:
features:
- name: INSTRUCTION
dtype: string
- name: RESPONSE
dtype: string
- name: SOURCE
dtype: string
- name: METADATA
dtype: string
splits:
- name: train
num_bytes: 2295601241
num_examples: 1006218
download_size: 1501204472
dataset_size: 2295601241
task_categories:
- question-answering
language:
- zh
---
# Dataset Card for "Zhihu-KOL"
Zhihu data for training Open Assitant
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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GEM/schema_guided_dialog | GEM | 2022-10-24T15:30:26Z | 422 | 3 | null | [
"task_categories:conversational",
"annotations_creators:crowd-sourced",
"language_creators:unknown",
"multilinguality:unknown",
"size_categories:unknown",
"source_datasets:original",
"language:en",
"license:cc-by-sa-4.0",
"dialog-response-generation",
"arxiv:1909.05855",
"arxiv:2004.15006",
"arxiv:2002.01359",
"region:us"
] | 2022-10-24T15:30:26Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowd-sourced
language_creators:
- unknown
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- unknown
size_categories:
- unknown
source_datasets:
- original
task_categories:
- conversational
task_ids: []
pretty_name: schema_guided_dialog
tags:
- dialog-response-generation
---
# Dataset Card for GEM/schema_guided_dialog
## Dataset Description
- **Homepage:** n/a
- **Repository:** [Github[(https://github.com/google-research-datasets/dstc8-schema-guided-dialogue)
- **Paper:** https://arxiv.org/abs/1909.05855
- **Leaderboard:** N/A
- **Point of Contact:** Abhinav Rastogi
### Link to Main Data Card
You can find the main data card on the [GEM Website](https://gem-benchmark.com/data_cards/schema_guided_dialog).
### Dataset Summary
The GEM version of this dataset functions as a response generation dataset. The input specifies dialog acts that a model needs to verbalize. The Schema-Guided Dialog dataset is challenging since it comprises multiple domains from hotel and travel to restaurants, and a wide range of dialog acts. The context of each conversation is provided as well.
You can load the dataset via:
```
import datasets
data = datasets.load_dataset('GEM/schema_guided_dialog')
```
The data loader can be found [here](https://huggingface.co/datasets/GEM/schema_guided_dialog).
#### website
n/a
#### paper
[Arxiv](https://arxiv.org/abs/1909.05855)
#### authors
Abhinav Rastogi, Xiaoxue Zang, Srinivas Sunkara, Raghav Gupta, Pranav Khaitan, Amir Fayazi, Maria Wang, and Guan-Lin Chao
## Dataset Overview
### Where to find the Data and its Documentation
#### Download
<!-- info: What is the link to where the original dataset is hosted? -->
<!-- scope: telescope -->
[Github[(https://github.com/google-research-datasets/dstc8-schema-guided-dialogue)
#### Paper
<!-- info: What is the link to the paper describing the dataset (open access preferred)? -->
<!-- scope: telescope -->
[Arxiv](https://arxiv.org/abs/1909.05855)
#### BibTex
<!-- info: Provide the BibTex-formatted reference for the dataset. Please use the correct published version (ACL anthology, etc.) instead of google scholar created Bibtex. -->
<!-- scope: microscope -->
```
{
@inproceedings{rastogi2020towards,
title={Towards scalable multi-domain conversational agents: The schema-guided dialogue dataset},
author={Rastogi, Abhinav and Zang, Xiaoxue and Sunkara, Srinivas and Gupta, Raghav and Khaitan, Pranav},
booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
volume={34},
number={05},
pages={8689--8696},
year={2020}
}
```
#### Contact Name
<!-- quick -->
<!-- info: If known, provide the name of at least one person the reader can contact for questions about the dataset. -->
<!-- scope: periscope -->
Abhinav Rastogi
#### Contact Email
<!-- info: If known, provide the email of at least one person the reader can contact for questions about the dataset. -->
<!-- scope: periscope -->
schema-guided-dst@google.com
#### Has a Leaderboard?
<!-- info: Does the dataset have an active leaderboard? -->
<!-- scope: telescope -->
no
### Languages and Intended Use
#### Multilingual?
<!-- quick -->
<!-- info: Is the dataset multilingual? -->
<!-- scope: telescope -->
no
#### Covered Languages
<!-- quick -->
<!-- info: What languages/dialects are covered in the dataset? -->
<!-- scope: telescope -->
`English`
#### Whose Language?
<!-- info: Whose language is in the dataset? -->
<!-- scope: periscope -->
The language structure is machine-generated, and the language realizations are produced by crowd workers.
The dataset paper does not provide demographic information for the crowd workers.
#### License
<!-- quick -->
<!-- info: What is the license of the dataset? -->
<!-- scope: telescope -->
cc-by-sa-4.0: Creative Commons Attribution Share Alike 4.0 International
#### Intended Use
<!-- info: What is the intended use of the dataset? -->
<!-- scope: microscope -->
The Schema-Guided Dialogue (SGD) dataset contains 18K multi-domain task-oriented dialogues between a human and a virtual assistant, which covers 17 domains ranging from banks and events to media, calendar, travel, and weather.
The language presents in the datset is only English.
The SGD dataset provides a challenging testbed for a number of tasks in task-oriented dialogue, including language understanding, slot filling, dialogue state tracking and response generation.
For the creation of the SGD dataset, they developed a multi-domain dialogue simulator that generates dialogue outlines over an arbitrary combination of APIs, dialogue states and system actions. Then, they used a crowd-sourcing procedure to paraphrase these outlines to natural language utterances.
This novel crowd-sourcing procedure preserves all annotations obtained from the simulator and does not require any extra annotations after dialogue collection.
#### Primary Task
<!-- info: What primary task does the dataset support? -->
<!-- scope: telescope -->
Dialog Response Generation
#### Communicative Goal
<!-- quick -->
<!-- info: Provide a short description of the communicative goal of a model trained for this task on this dataset. -->
<!-- scope: periscope -->
The goal of a speaker who generates the target utterance is to help users accomplish tasks including but not limited to finding flights, booking restaurants, searching for nearby events and movies.
### Credit
#### Curation Organization Type(s)
<!-- info: In what kind of organization did the dataset curation happen? -->
<!-- scope: telescope -->
`industry`
#### Curation Organization(s)
<!-- info: Name the organization(s). -->
<!-- scope: periscope -->
Google
#### Dataset Creators
<!-- info: Who created the original dataset? List the people involved in collecting the dataset and their affiliation(s). -->
<!-- scope: microscope -->
Abhinav Rastogi, Xiaoxue Zang, Srinivas Sunkara, Raghav Gupta, Pranav Khaitan, Amir Fayazi, Maria Wang, and Guan-Lin Chao
#### Funding
<!-- info: Who funded the data creation? -->
<!-- scope: microscope -->
Google
#### Who added the Dataset to GEM?
<!-- info: Who contributed to the data card and adding the dataset to GEM? List the people+affiliations involved in creating this data card and who helped integrate this dataset into GEM. -->
<!-- scope: microscope -->
Wanyu Du wrote the initial data card and Yacine Jernite the data loader. Simon Mille updated the data card with the additional splits. Sebastian Gehrmann migrated the data card and loader to the v2 version and extended the missing information.
### Dataset Structure
#### Data Fields
<!-- info: List and describe the fields present in the dataset. -->
<!-- scope: telescope -->
Each dialog instance has the following fields:
* `dialogue_id`: A unique identifier for a dialogue.
* `services`: A list of services present in the dialogue.
* `turns`: A list of annotated system or user utterances. Each turn consists of the following fields:
* `speaker`: The speaker for the turn, either `USER` or `SYSTEM`.
* `utterance`: A string containing the natural language utterance.
* `frames`: A list of frames, each frame containing annotations for a single service and consists of the following fields:
* `service`: The name of the service corresponding to the frame. The slots and intents used in the following fields are taken from the schema of this service.
* `slots`: A list of slot spans in the utterance, only provided for non-categorical slots. Each slot span contains the following fields:
* `slot`: The name of the slot.
* `start`: The index of the starting character in the utterance corresponding to the slot value.
* `exclusive_end`: The index of the character just after the last character corresponding to the slot value in the utterance.
* `actions`: A list of actions corresponding to the system. Each action has the following fields:
* `act`: The type of action.
* `slot`: (optional) A slot argument for some of the actions.
* `values`: (optional) A list of values assigned to the slot. If the values list is non-empty, then the slot must be present.
* `canonical_values`: (optional) The values in their canonicalized form as used by the service. It is a list of strings of the same length as values.
* `service_call`: (system turns only, optional) The request sent to the service. It consists of the following fields:
* `method`: The name of the intent or function of the service or API being executed.
* `parameters`: A pair of lists of the same lengths: `parameter_slot_name` contains slot names and `parameter_canonical_value` contains the corresponding values in their canonicalized form.
* `service_results`: (system turns only, optional) A list of entities containing the results obtained from the service. It is only available for turns in which a service call is made. Each entity is represented as a pair of lists of the same length: `service_slot_name` contains slot names and `service_canonical_value` contains the corresponding canonical values.
* `state`: (user turns only) The dialogue state corresponding to the service. It consists of the following fields:
* `active_intent`: The intent corresponding to the service of the frame which is currently being fulfilled by the system. It takes the value "NONE" if none of the intents are active.
* `requested_slots`: A list of slots requested by the user in the current turn.
* `slot_values`: A pair of lists of the same lengths: `slot_name` contains slot names and `slot_value_list` contains the corresponding lists of strings. For categorical slots, this list contains a single value assigned to the slot. For non-categorical slots, all the values in this list are spoken variations of each other and are equivalent (e.g, "6 pm", "six in the evening", "evening at 6" etc.).
#### Example Instance
<!-- info: Provide a JSON formatted example of a typical instance in the dataset. -->
<!-- scope: periscope -->
```
{'dialogue_id': '1_00000',
'services': ['Restaurants_1'],
'turns':
{'frames':
[{'actions': [{'act': [6],
'canonical_values': [['FindRestaurants']],
'slot': ['intent'],
'values': [['FindRestaurants']]}],
'service': ['Restaurants_1'],
'service_call': [{'method': '',
'parameters': {'parameter_canonical_value': [],
'parameter_slot_name': []}}],
'service_results': [{'service_results_list': []}],
'slots': [{'exclusive_end': [], 'slot': [], 'start': []}],
'state': [{'active_intent': 'FindRestaurants',
'requested_slots': [],
'slot_values': {'slot_name': [], 'slot_value_list': []}}]},
{'actions': [{'act': [13],
'canonical_values': [[]],
'slot': ['city'],
'values': [[]]}],
'service': ['Restaurants_1'],
'service_call': [{'method': '',
'parameters': {'parameter_canonical_value': [],
'parameter_slot_name': []}}],
'service_results': [{'service_results_list': []}],
'slots': [{'exclusive_end': [], 'slot': [], 'start': []}],
'state': [{'active_intent': '',
'requested_slots': [],
'slot_values': {'slot_name': [], 'slot_value_list': []}}]},
...,]}
'speaker': [0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1],
'utterance': [
'I am feeling hungry so I would like to find a place to eat.',
'Do you have a specific which you want the eating place to be located at?',
'I would like for it to be in San Jose.',
'Is there a specific cuisine type you enjoy, such as Mexican, Italian or something else?',
'I usually like eating the American type of food.',
'I see that at 71 Saint Peter there is a good restaurant which is in San Jose.',
'Can you give me the address of this restaurant.',
'If you want to go to this restaurant you can find it at 71 North San Pedro Street.',
'Can you give me the phone number that I can contact them with?',
'If you want to phone them you can at 408-971-8523.',
'Is there some other restaurant which you can suggest?',
'How would you like Bazille restaurant which is situated in San Jose.',
'Do you have another restaurant matching my needs? For example a restaurant which is economical and is located in Palo Alto.',
'I see that 7 restaurants suit to what you requested. Bird Dog seems as a good restaurant and is located in Palo Alto.',
'Alright, that seems good. I would like to make a booking at this restaurant.',
'For which time do you want the booking to be?',
'I will be eating there at 11:30 am so make it for then.',
'Can you please confirm that you want to book a table for 2 at 11:30 am at the Bird Dog restaurant in Palo Alto for today.',
'That suits me well. Can you tell me if they feature live music?',
'Your booking has been made without errors, but unfortunately they do not have live music.',
'Will I be able to find liquor there? Can you give me the address of their location?',
'The restaurant is located at 420 Ramona Street. Unfortunately they do not serve alcohol at the restaurant.',
'I appreciate it very much. That would be all.',
'Have a good time!'
]}
```
#### Data Splits
<!-- info: Describe and name the splits in the dataset if there are more than one. -->
<!-- scope: periscope -->
The dataset is split into a train, validation, and test set with the following sizes:
| | Train | Validation | Test |
| --- | --- | --- | --- |
| \# of dialogues | 16142 | 2482 | 4201 |
| \# of turns | 48426 | 7446 | 12603 |
#### Splitting Criteria
<!-- info: Describe any criteria for splitting the data, if used. If there are differences between the splits (e.g., if the training annotations are machine-generated and the dev and test ones are created by humans, or if different numbers of annotators contributed to each example), describe them here. -->
<!-- scope: microscope -->
The data is generally split i.i.d, but some topics only appear in training and some only for testing. For example, the domains Messaging, Payment, and Train are test-only.
## Dataset in GEM
### Rationale for Inclusion in GEM
#### Why is the Dataset in GEM?
<!-- info: What does this dataset contribute toward better generation evaluation and why is it part of GEM? -->
<!-- scope: microscope -->
This dataset comprises a wide range of dialog capabilities and thus enables the evaluation of many more generation capabilities of comparable datasets. Its collection methodology ensures a high diversity but also high quality of the data.
#### Similar Datasets
<!-- info: Do other datasets for the high level task exist? -->
<!-- scope: telescope -->
yes
#### Unique Language Coverage
<!-- info: Does this dataset cover other languages than other datasets for the same task? -->
<!-- scope: periscope -->
no
#### Difference from other GEM datasets
<!-- info: What else sets this dataset apart from other similar datasets in GEM? -->
<!-- scope: microscope -->
The domains a lot more diverse than other datasets.
#### Ability that the Dataset measures
<!-- info: What aspect of model ability can be measured with this dataset? -->
<!-- scope: periscope -->
surface realization, compositionality.
### GEM-Specific Curation
#### Modificatied for GEM?
<!-- info: Has the GEM version of the dataset been modified in any way (data, processing, splits) from the original curated data? -->
<!-- scope: telescope -->
yes
#### GEM Modifications
<!-- info: What changes have been made to he original dataset? -->
<!-- scope: periscope -->
`data points modified`
#### Modification Details
<!-- info: For each of these changes, described them in more details and provided the intended purpose of the modification -->
<!-- scope: microscope -->
We are focusing on the response-generation part of the dataset and thus reformatted the dataset to treat the service agent utterances as the targets to be generated and the previous customer utterance and the agent's dialog act as the input. We additionally reformat the dialog acts to directly conform to the format described in this [paper](https://arxiv.org/abs/2004.15006).
#### Additional Splits?
<!-- info: Does GEM provide additional splits to the dataset? -->
<!-- scope: telescope -->
yes
#### Split Information
<!-- info: Describe how the new splits were created -->
<!-- scope: periscope -->
9 challenge sets for Schema-Guided Dialog were added to the GEM evaluation suite.
1. We created subsets of the training and development sets of 500 randomly selected inputs each.
2. We applied 5 transformations to respectively 5 sets of 500 randomly selected inputs: (i) back-translation, (ii)-(iii) introduction of typographical errors, using Butterfingers with two thresholds (0.02 and 0.05), resulting in two sets with different amounts of typos introduced (there are more typos with the 0.05 threshold than with the 0.02 one), (iv) removal of final punctuations (when any), and (v) input scrambling, for which the order of the dialogue acts was randomly reassigned.
3. For the input size, we created subpopulations based on the number of dialogue acts in the input.
| DA number | Frequency English |
|---------------|-------------------|
| 1 | 5049 |
| 2 | 2517 |
| 3 | 1328 |
| 4 | 469 |
| 5 | 335 |
| 6 | 256 |
| 7 | 46 |
We also split the test data according to the type of dialogue act, represented by cardinal numbers in the dataset.
| DA type | Frequency English |
|--------------|-------------------|
| 2 | 1397 |
| 3 | 983 |
| 4 | 1027 |
| 5 | 958 |
| 9 | 72 |
| 10 | 1024 |
| 11 | 1246 |
| 12 | 500 |
| 13 | 2078 |
| 15 | 715 |
#### Split Motivation
<!-- info: What aspects of the model's generation capacities were the splits created to test? -->
<!-- scope: periscope -->
Generalization and Robustness.
### Getting Started with the Task
#### Pointers to Resources
<!-- info: Getting started with in-depth research on the task. Add relevant pointers to resources that researchers can consult when they want to get started digging deeper into the task. -->
<!-- scope: microscope -->
* [Paper for dataset and DST baseline](https://arxiv.org/pdf/1909.05855.pdf)
* [DSTC8 overview paper](https://arxiv.org/pdf/2002.01359.pdf)
* [Code for DST baseline](https://github.com/google-research/google-research/tree/master/schema_guided_dst)
* [Natural language generation baseline paper](https://arxiv.org/pdf/2004.15006.pdf)
* [Blog post announcing the dataset](https://ai.googleblog.com/2019/10/introducing-schema-guided-dialogue.html)
## Previous Results
### Previous Results
#### Measured Model Abilities
<!-- info: What aspect of model ability can be measured with this dataset? -->
<!-- scope: telescope -->
Surface realization and compositionally.
#### Metrics
<!-- info: What metrics are typically used for this task? -->
<!-- scope: periscope -->
`BLEURT`, `BLEU`, `ROUGE`
#### Proposed Evaluation
<!-- info: List and describe the purpose of the metrics and evaluation methodology (including human evaluation) that the dataset creators used when introducing this task. -->
<!-- scope: microscope -->
The original paper focused on the task of dialog state prediction instead of response generation and thus did not suggest any set of metrics.
#### Previous results available?
<!-- info: Are previous results available? -->
<!-- scope: telescope -->
no
## Dataset Curation
### Original Curation
#### Original Curation Rationale
<!-- info: Original curation rationale -->
<!-- scope: telescope -->
Previous multi-domain task-oriented dialogue datsets do not sufficiently capture the real-world challenges in virtual assistants, since they cover few domains and assume a single static ontology per domain.
The SGD datset is created to cover 17 domains with over 16K dialogues, and contain multiple different APIs in most domains, many of which have overlapping functionalities but different interfaces, which reflects common real-world scenarios.
The wide range of available annotations can be used for intent prediction, slot filling, dialogue state tracking, policy imitation learning, language generation, user simulation learning, among other tasks in large-scale virtual assistants.
#### Communicative Goal
<!-- info: What was the communicative goal? -->
<!-- scope: periscope -->
The goal of a speaker who generates the target utterance is to help users accomplish tasks including but not limited to finding flights, booking restaurants, searching for nearby events and movies.
#### Sourced from Different Sources
<!-- info: Is the dataset aggregated from different data sources? -->
<!-- scope: telescope -->
no
### Language Data
#### How was Language Data Obtained?
<!-- info: How was the language data obtained? -->
<!-- scope: telescope -->
`Machine-generated`
#### Generation Method Link
<!-- info: If text was machine-generated for the dataset, provide a link to the generation method if available (N/A otherwise). -->
<!-- scope: periscope -->
[Github](https://github.com/google-research-datasets/dstc8-schema-guided-dialogue)
#### Language Producers
<!-- info: What further information do we have on the language producers? -->
<!-- scope: microscope -->
The dialogue outlines are first generated by a simulator. The dialogue simulator interacts with the services to generate dialogue outlines. It consists of two agents playing the roles of the user and the system, interacting with each other using a finite set of actions specified through dialogue acts over a probabilistic automaton designed to capture varied dialogue trajectories. It is worth noting that the simulation automaton does not include any domain-specific constraints: all domain-specific constraints are encoded in the schema and scenario.
The dialogue paraphrasing framework then converts the outlines generated by the simulator into a natural conversation. Users may refer to the slot values in the dialogue acts in various different ways during the conversation, e.g., “los angeles” may be referred to as “LA” or “LAX”. To introduce these natural variations in the slot values, different slot values are replaced with a randomly selected variation while being kept consistent across user turns in a dialogue. The actions are then converted to pseudo-natural language utterances using a set of manually defined action-to-text templates, and the resulting utterances for the different actions in a turn are concatenated together.
#### Topics Covered
<!-- info: Does the language in the dataset focus on specific topics? How would you describe them? -->
<!-- scope: periscope -->
The dataset covers the following domains: Alarm, Banks, Buses, Calendar, Events, Flights, Homes, Hotels, Media, Messaging, Movies, Music, Payment, RentalCars, Restaurants, RideSharing, Services, Train, Travel, and Weather. The domain ‘Service’ includes salons, dentists, doctors etc. The ‘Alarm’, ‘Messaging’, ‘Payment’ and ‘Train’ domains are only present in the dev or test sets.
to test generalization to new domains.
#### Data Validation
<!-- info: Was the text validated by a different worker or a data curator? -->
<!-- scope: telescope -->
not validated
#### Was Data Filtered?
<!-- info: Were text instances selected or filtered? -->
<!-- scope: telescope -->
not filtered
### Structured Annotations
#### Additional Annotations?
<!-- quick -->
<!-- info: Does the dataset have additional annotations for each instance? -->
<!-- scope: telescope -->
crowd-sourced
#### Number of Raters
<!-- info: What is the number of raters -->
<!-- scope: telescope -->
unknown
#### Raters per Training Example
<!-- info: How many annotators saw each training example? -->
<!-- scope: periscope -->
0
#### Raters per Test Example
<!-- info: How many annotators saw each test example? -->
<!-- scope: periscope -->
0
#### Annotation Service?
<!-- info: Was an annotation service used? -->
<!-- scope: telescope -->
unknown
#### Annotation Values
<!-- info: Purpose and values for each annotation -->
<!-- scope: microscope -->
The dialogue transformed by these steps is sent to the crowd workers to be reformulated into more natural language. One crowd worker is tasked with paraphrasing all utterances of a dialogue to ensure naturalness and coherence. The crowd workers are asked to exactly repeat the slot values in their paraphrases so that the span indices for the slots can be recovered via string matching.
#### Any Quality Control?
<!-- info: Quality control measures? -->
<!-- scope: telescope -->
none
### Consent
#### Any Consent Policy?
<!-- info: Was there a consent policy involved when gathering the data? -->
<!-- scope: telescope -->
no
#### Justification for Using the Data
<!-- info: If not, what is the justification for reusing the data? -->
<!-- scope: microscope -->
While no policy is reported, we assume that one was in place for the collection.
### Private Identifying Information (PII)
#### Contains PII?
<!-- quick -->
<!-- info: Does the source language data likely contain Personal Identifying Information about the data creators or subjects? -->
<!-- scope: telescope -->
no PII
#### Justification for no PII
<!-- info: Provide a justification for selecting `no PII` above. -->
<!-- scope: periscope -->
The SGD dataset does not use identity categories and does not contain sensitive data.
### Maintenance
#### Any Maintenance Plan?
<!-- info: Does the original dataset have a maintenance plan? -->
<!-- scope: telescope -->
no
## Broader Social Context
### Previous Work on the Social Impact of the Dataset
#### Usage of Models based on the Data
<!-- info: Are you aware of cases where models trained on the task featured in this dataset ore related tasks have been used in automated systems? -->
<!-- scope: telescope -->
no
### Impact on Under-Served Communities
#### Addresses needs of underserved Communities?
<!-- info: Does this dataset address the needs of communities that are traditionally underserved in language technology, and particularly language generation technology? Communities may be underserved for exemple because their language, language variety, or social or geographical context is underepresented in NLP and NLG resources (datasets and models). -->
<!-- scope: telescope -->
no
### Discussion of Biases
#### Any Documented Social Biases?
<!-- info: Are there documented social biases in the dataset? Biases in this context are variations in the ways members of different social categories are represented that can have harmful downstream consequences for members of the more disadvantaged group. -->
<!-- scope: telescope -->
no
#### Are the Language Producers Representative of the Language?
<!-- info: Does the distribution of language producers in the dataset accurately represent the full distribution of speakers of the language world-wide? If not, how does it differ? -->
<!-- scope: periscope -->
Due to the combination of the automatic generation and crowd rater paraphasing, the language can be very formulaic. While this may be acceptable for the model part (i.e., we may actually desire an automated agent to form formulaic responses), the input utterances of the simulated customers likely do not cover the entire spectrum of the English language.
## Considerations for Using the Data
### PII Risks and Liability
### Licenses
#### Copyright Restrictions on the Dataset
<!-- info: Based on your answers in the Intended Use part of the Data Overview Section, which of the following best describe the copyright and licensing status of the dataset? -->
<!-- scope: periscope -->
`open license - commercial use allowed`
#### Copyright Restrictions on the Language Data
<!-- info: Based on your answers in the Language part of the Data Curation Section, which of the following best describe the copyright and licensing status of the underlying language data? -->
<!-- scope: periscope -->
`open license - commercial use allowed`
### Known Technical Limitations
#### Technical Limitations
<!-- info: Describe any known technical limitations, such as spurrious correlations, train/test overlap, annotation biases, or mis-annotations, and cite the works that first identified these limitations when possible. -->
<!-- scope: microscope -->
The dialogues under each domain distributed unevenly, where the flights domain has 3644 dialogues while the payment domain only contains 222 dialogues.
Besides, all dialogues are paraphrased by crowd-workers, and it is possible that crow-workers with different culture backgrounds will exhibit biased opinions.
#### Unsuited Applications
<!-- info: When using a model trained on this dataset in a setting where users or the public may interact with its predictions, what are some pitfalls to look out for? In particular, describe some applications of the general task featured in this dataset that its curation or properties make it less suitable for. -->
<!-- scope: microscope -->
Since the initial data was automatically generated, the coverage of entity names is necessarily biased. An agent thus needs to be evaluated in a more realistic environment.
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
SpeedOfMagic/trivia_qa_tiny | SpeedOfMagic | 2023-09-08T16:39:19Z | 422 | 0 | null | [
"size_categories:n<1K",
"language:en",
"region:us"
] | 2023-09-08T16:39:19Z | 2023-09-08T14:32:44.000Z | 2023-09-08T14:32:44 | ---
language:
- en
size_categories:
- n<1K
---
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset contains 100 samples from [trivia_qa](https://huggingface.co/datasets/trivia_qa) dataset. It is used mainly for testing purposes.
### Languages
English.
## Dataset Structure
### Data Instances
Total data size: 8Kb.
### Data Fields
- `question`: string feature, containing question to be answered.
- `answer: string feature, answer to the question.
### Data Splits
Only `test` split, that contains 100 rows, is supported.
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
damlab/HIV_V3_coreceptor | damlab | 2022-02-08T21:09:21Z | 421 | 0 | null | [
"region:us"
] | 2022-02-08T21:09:21Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | # Dataset Description
## Dataset Summary
This dataset was derived from the Los Alamos National Laboratory HIV sequence (LANL) database.
It contains 2,935 HIV V3 loop protein sequences, which can interact with either CCR5 receptors on T-Cells or CXCR4 receptors on macrophages.
Supported Tasks and Leaderboards: None
Languages: English
## Dataset Structure
### Data Instances
Data Instances: Each column represents the protein amino acid sequence of the HIV V3 loop.
The ID field indicates the Genbank reference ID for future cross-referencing.
There are 2,935 total V3 sequences, with 91% being CCR5 tropic and 23% CXCR4 tropic.
Data Fields: ID, sequence, fold, CCR5, CXCR4
Data Splits: None
## Dataset Creation
Curation Rationale: This dataset was curated to train a model (HIV-BERT-V3) designed to predict whether an HIV V3 loop would be CCR5 or CXCR4 tropic.
Initial Data Collection and Normalization: Dataset was downloaded and curated on 12/20/2021.
## Considerations for Using the Data
Social Impact of Dataset: This dataset can be used to study the mechanism by which HIV V3 loops allow for entry into T-cells and macrophages.
Discussion of Biases: Due to the sampling nature of this database, it is predominantly composed of subtype B sequences from North America and Europe with only minor contributions of Subtype C, A, and D.
Currently, there was no effort made to balance the performance across these classes.
As such, one should consider refinement with additional sequences to perform well on non-B sequences.
## Additional Information:
- Dataset Curators: Will Dampier
- Citation Information: TBA
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lmqg/qa_squadshifts | lmqg | 2022-11-05T05:10:26Z | 421 | 0 | null | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"multilinguality:monolingual",
"size_categories:1k<n<10k",
"source_datasets:extended|wikipedia",
"language:en",
"license:cc-by-4.0",
"arxiv:2004.14444",
"region:us"
] | 2022-11-05T05:10:26Z | 2022-11-05T02:43:19.000Z | 2022-11-05T02:43:19 | ---
license: cc-by-4.0
pretty_name: SQuADShifts
language: en
multilinguality: monolingual
size_categories: 1k<n<10k
source_datasets:
- extended|wikipedia
task_categories:
- question-answering
task_ids:
- extractive-qa
---
# Dataset Card for "lmqg/qa_squadshifts"
## Dataset Description
- **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation)
- **Paper:** [https://arxiv.org/abs/2004.14444](https://arxiv.org/abs/2004.14444)
- **Point of Contact:** [Asahi Ushio](http://asahiushio.com/)
### Dataset Summary
This is SQuADShifts dataset with custom split of training/validation/test following [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts).
### Supported Tasks and Leaderboards
* `question-answering`
### Languages
English (en)
## Dataset Structure
### Data Fields
The data fields are the same among all splits.
#### plain_text
- `id`: a `string` feature of id
- `title`: a `string` feature of title of the paragraph
- `context`: a `string` feature of paragraph
- `question`: a `string` feature of question
- `answers`: a `json` feature of answers
### Data Splits
| name |train | valid | test |
|-------------|------:|------:|-----:|
|default (all)|9209|6283 |18,844|
| amazon |3295|1648|4942|
| new_wiki |2646|1323|3969|
| nyt |3355|1678|5032|
| reddit |3268|1634|4901|
## Citation Information
```
@inproceedings{miller2020effect,
title={The effect of natural distribution shift on question answering models},
author={Miller, John and Krauth, Karl and Recht, Benjamin and Schmidt, Ludwig},
booktitle={International Conference on Machine Learning},
pages={6905--6916},
year={2020},
organization={PMLR}
}
``` | [
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anuragshas/sk_opus100_processed | anuragshas | 2022-02-05T08:22:48Z | 420 | 0 | null | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
cheulyop/ksponspeech | cheulyop | 2021-10-02T04:27:13Z | 420 | 3 | null | [
"region:us"
] | 2021-10-02T04:27:13Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
YAML tags:
- copy-paste the tags obtained with the tagging app: https://github.com/huggingface/datasets-tagging
---
# Dataset Card for [KsponSpeech]
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
KsponSpeech is a large-scale spontaneous speech corpus of Korean conversations. This corpus contains 969 hrs of general open-domain dialog utterances, spoken by about 2,000 native Korean speakers in a clean environment. All data were constructed by recording the dialogue of two people freely conversing on a variety of topics and manually transcribing the utterances. The transcription provides a dual transcription consisting of orthography and pronunciation, and disfluency tags for spontaneity of speech, such as filler words, repeated words, and word fragments. KsponSpeech is publicly available on an open data hub site of the Korea government. (https://aihub.or.kr/aidata/105)
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset.
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embedding-data/QQP_triplets | embedding-data | 2022-08-02T03:14:14Z | 420 | 3 | embedding-data/QQP_triplets | [
"task_categories:sentence-similarity",
"task_ids:semantic-similarity-classification",
"language:en",
"license:mit",
"region:us"
] | 2022-08-02T03:14:14Z | 2022-07-08T03:15:59.000Z | 2022-07-08T03:15:59 | ---
license: mit
language:
- en
paperswithcode_id: embedding-data/QQP_triplets
pretty_name: QQP_triplets
task_categories:
- sentence-similarity
- paraphrase-mining
task_ids:
- semantic-similarity-classification
---
# Dataset Card for "QQP_triplets"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs)
- **Repository:** [More Information Needed](http://qim.fs.quoracdn.net/quora_duplicate_questions.tsv)
- **Paper:** [More Information Needed](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs)
- **Point of Contact:** [Kornél Csernai](https://www.quora.com/profile/Korn%C3%A9l-Csernai), [Nikhil Dandekar](https://www.quora.com/profile/Nikhil-Dandekar), [Shankar Iyer](https://www.quora.com/profile/Shankar-Iyer-5)
### Dataset Summary
This dataset will give anyone the opportunity to train and test models of semantic equivalence, based on actual Quora data. The data is organized as triplets (anchor, positive, negative).
Disclaimer: The team releasing Quora data did not upload the dataset to the Hub and did not write a dataset card.
These steps were done by the Hugging Face team.
### Supported Tasks
- [Sentence Transformers](https://huggingface.co/sentence-transformers) training; useful for semantic search and sentence similarity.
### Languages
- English.
## Dataset Structure
Each example is a dictionary with three keys (query, pos, and neg) containing a list each (triplets). The first key contains an anchor sentence, the second a positive sentence, and the third a list of negative sentences.
```
{"query": [anchor], "pos": [positive], "neg": [negative1, negative2, ..., negativeN]}
{"query": [anchor], "pos": [positive], "neg": [negative1, negative2, ..., negativeN]}
...
{"query": [anchor], "pos": [positive], "neg": [negative1, negative2, ..., negativeN]}
```
This dataset is useful for training Sentence Transformers models. Refer to the following post on how to train them.
### Usage Example
Install the 🤗 Datasets library with `pip install datasets` and load the dataset from the Hub with:
```python
from datasets import load_dataset
dataset = load_dataset("embedding-data/QQP_triplets")
```
The dataset is loaded as a `DatasetDict` and has the format:
```python
DatasetDict({
train: Dataset({
features: ['set'],
num_rows: 101762
})
})
```
Review an example `i` with:
```python
dataset["train"][i]["set"]
```
### Curation Rationale
[More Information Needed](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs)
#### Who are the source language producers?
[More Information Needed](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs)
### Annotations
#### Annotation process
[More Information Needed](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs)
#### Who are the annotators?
[More Information Needed](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs)
### Personal and Sensitive Information
[More Information Needed](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs)
### Discussion of Biases
[More Information Needed](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs)
### Other Known Limitations
Here are a few important things to keep in mind about this dataset:
- Our original sampling method returned an imbalanced dataset with many more true examples of duplicate pairs than non-duplicates.
Therefore, we supplemented the dataset with negative examples.
- One source of negative examples were pairs of “related questions” which, although pertaining to similar topics,
are not truly semantically equivalent.
- The distribution of questions in the dataset should not be taken to be representative of the distribution of questions asked on Quora. This is, in part, because of the combination of sampling procedures and also due to some sanitization measures that
have been applied to the final dataset (e.g., removal of questions with extremely long question details).
- The ground-truth labels contain some amount of noise: they are not guaranteed to be perfect.
## Additional Information
### Dataset Curators
[More Information Needed](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs)
### Licensing Information
[More Information Needed](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs)
### Citation Information
[More Information Needed](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs)
### Contributions
Thanks to [Kornél Csernai](https://www.quora.com/profile/Korn%C3%A9l-Csernai), [Nikhil Dandekar](https://www.quora.com/profile/Nikhil-Dandekar), [Shankar Iyer](https://www.quora.com/profile/Shankar-Iyer-5) for adding this dataset.
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
camel-ai/math | camel-ai | 2023-06-22T21:59:52Z | 420 | 51 | null | [
"task_categories:text-generation",
"language:en",
"license:cc-by-nc-4.0",
"instruction-finetuning",
"arxiv:2303.17760",
"region:us"
] | 2023-06-22T21:59:52Z | 2023-04-10T22:00:46.000Z | 2023-04-10T22:00:46 | ---
license: cc-by-nc-4.0
language:
- en
tags:
- instruction-finetuning
pretty_name: CAMEL Math
task_categories:
- text-generation
arxiv: 2303.17760
extra_gated_prompt: "By using this data, you acknowledge and agree to utilize it solely for research purposes, recognizing that the dataset may contain inaccuracies due to its artificial generation through ChatGPT."
extra_gated_fields:
Name: text
Email: text
I will adhere to the terms and conditions of this dataset: checkbox
---
# **CAMEL: Communicative Agents for “Mind” Exploration of Large Scale Language Model Society**
- **Github:** https://github.com/lightaime/camel
- **Website:** https://www.camel-ai.org/
- **Arxiv Paper:** https://arxiv.org/abs/2303.17760
## Dataset Summary
Math dataset is composed of 50K problem-solution pairs obtained using GPT-4. The dataset problem-solutions pairs generating from 25 math topics, 25 subtopics for each topic and 80 problems for each "topic,subtopic" pairs.
We provide the data in `math50k.zip`.
## Data Fields
**The data fields for files in `math50k.zip` are as follows:**
* `role_1`: assistant role
* `topic`: math topic
* `sub_topic`: math subtopic belonging to topic
* `message_1`: refers to the problem the assistant is asked to solve.
* `message_2`: refers to the solution provided by the assistant.
Note: File naming refers to {`topic_index`}\_{`subtopic_index`}\_{`problem_number`}.
**Download in python**
```
from huggingface_hub import hf_hub_download
hf_hub_download(repo_id="camel-ai/math", repo_type="dataset", filename="math50k.zip",
local_dir="datasets/", local_dir_use_symlinks=False)
```
### Citation
```
@misc{li2023camel,
title={CAMEL: Communicative Agents for "Mind" Exploration of Large Scale Language Model Society},
author={Guohao Li and Hasan Abed Al Kader Hammoud and Hani Itani and Dmitrii Khizbullin and Bernard Ghanem},
year={2023},
eprint={2303.17760},
archivePrefix={arXiv},
primaryClass={cs.AI}
}
```
## Disclaimer:
This data was synthetically generated by GPT4 and might contain incorrect information. The dataset is there only for research purposes.
---
license: cc-by-nc-4.0
---
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renumics/food101-enriched | renumics | 2023-06-06T08:15:28Z | 420 | 3 | food-101 | [
"task_categories:image-classification",
"task_ids:multi-class-image-classification",
"size_categories:100K<n<1M",
"source_datasets:extended|other-foodspotting",
"source_datasets:extended|food101",
"language:en",
"license:unknown",
"image classification",
"food-101",
"food-101-enriched",
"embeddings",
"enhanced",
"spotlight",
"region:us"
] | 2023-06-06T08:15:28Z | 2023-05-09T08:41:13.000Z | 2023-05-09T08:41:13 | ---
license: unknown
paperswithcode_id: food-101
pretty_name: Food-101 Data Set
size_categories:
- 100K<n<1M
tags:
- image classification
- food-101
- food-101-enriched
- embeddings
- enhanced
- spotlight
language:
- en
source_datasets:
- extended|other-foodspotting
- extended|food101
task_categories:
- image-classification
task_ids:
- multi-class-image-classification
---
# Dataset Card for Food-101-Enriched (Enhanced by Renumics)
## Dataset Description
- **Homepage:** [Renumics Homepage](https://renumics.com/?hf-dataset-card=food101-enriched)
- **GitHub** [Spotlight](https://github.com/Renumics/spotlight)
- **Dataset Homepage** [data.vision.ee.ethz.ch](https://data.vision.ee.ethz.ch/cvl/datasets_extra/food-101/)
- **Paper:** [Food-101 – Mining Discriminative Components with Random Forests](https://data.vision.ee.ethz.ch/cvl/datasets_extra/food-101/static/bossard_eccv14_food-101.pdf)
### Dataset Summary
📊 [Data-centric AI](https://datacentricai.org) principles have become increasingly important for real-world use cases.
At [Renumics](https://renumics.com/?hf-dataset-card=food101-enriched) we believe that classical benchmark datasets and competitions should be extended to reflect this development.
🔍 This is why we are publishing benchmark datasets with application-specific enrichments (e.g. embeddings, baseline results, uncertainties, label error scores). We hope this helps the ML community in the following ways:
1. Enable new researchers to quickly develop a profound understanding of the dataset.
2. Popularize data-centric AI principles and tooling in the ML community.
3. Encourage the sharing of meaningful qualitative insights in addition to traditional quantitative metrics.
📚 This dataset is an enriched version of the [Food101 Data Set](https://data.vision.ee.ethz.ch/cvl/datasets_extra/food-101/).
### Explore the Dataset
The enrichments allow you to quickly gain insights into the dataset. The open source data curation tool [Renumics Spotlight](https://github.com/Renumics/spotlight) enables that with just a few lines of code:
Install datasets and Spotlight via [pip](https://packaging.python.org/en/latest/key_projects/#pip):
```python
!pip install renumics-spotlight datasets
```
Load the dataset from huggingface in your notebook:
```python
import datasets
dataset = datasets.load_dataset("renumics/food101-enriched", split="train")
```
Start exploring with a simple view:
```python
from renumics import spotlight
df_show = dataset.to_pandas()
spotlight.show(df_show, port=8000, dtype={"image": spotlight.Image})
```
You can use the UI to interactively configure the view on the data. Depending on the concrete tasks (e.g. model comparison, debugging, outlier detection) you might want to leverage different enrichments and metadata.
### Food101 Dataset
This data set contains 101'000 images from 101 food categories.
For each class, 250 manually reviewed test images are provided as well as 750 training images.
On purpose, the training images were not cleaned, and thus still contain some amount of noise.
This comes mostly in the form of intense colors and sometimes wrong labels.
All images were rescaled to have a maximum side length of 512 pixels.
### Supported Tasks and Leaderboards
- `image-classification`: The goal of this task is to classify a given image of a dish into one of 101 classes. The leaderboard is available [here](https://paperswithcode.com/sota/fine-grained-image-classification-on-food-101).
### Languages
English class labels.
## Dataset Structure
### Data Instances
A sample from the training set is provided below:
```python
{
"image": "/huggingface/datasets/downloads/extracted/49750366cbaf225ce1b5a5c033fa85ceddeee2e82f1d6e0365e8287859b4c7c8/0/0.jpg",
"label": 6,
"label_str": "beignets",
"split": "train"
}
```
<details>
<summary>Class Label Mappings</summary>
```json
{
"apple_pie": 0,
"baby_back_ribs": 1,
"baklava": 2,
"beef_carpaccio": 3,
"beef_tartare": 4,
"beet_salad": 5,
"beignets": 6,
"bibimbap": 7,
"bread_pudding": 8,
"breakfast_burrito": 9,
"bruschetta": 10,
"caesar_salad": 11,
"cannoli": 12,
"caprese_salad": 13,
"carrot_cake": 14,
"ceviche": 15,
"cheesecake": 16,
"cheese_plate": 17,
"chicken_curry": 18,
"chicken_quesadilla": 19,
"chicken_wings": 20,
"chocolate_cake": 21,
"chocolate_mousse": 22,
"churros": 23,
"clam_chowder": 24,
"club_sandwich": 25,
"crab_cakes": 26,
"creme_brulee": 27,
"croque_madame": 28,
"cup_cakes": 29,
"deviled_eggs": 30,
"donuts": 31,
"dumplings": 32,
"edamame": 33,
"eggs_benedict": 34,
"escargots": 35,
"falafel": 36,
"filet_mignon": 37,
"fish_and_chips": 38,
"foie_gras": 39,
"french_fries": 40,
"french_onion_soup": 41,
"french_toast": 42,
"fried_calamari": 43,
"fried_rice": 44,
"frozen_yogurt": 45,
"garlic_bread": 46,
"gnocchi": 47,
"greek_salad": 48,
"grilled_cheese_sandwich": 49,
"grilled_salmon": 50,
"guacamole": 51,
"gyoza": 52,
"hamburger": 53,
"hot_and_sour_soup": 54,
"hot_dog": 55,
"huevos_rancheros": 56,
"hummus": 57,
"ice_cream": 58,
"lasagna": 59,
"lobster_bisque": 60,
"lobster_roll_sandwich": 61,
"macaroni_and_cheese": 62,
"macarons": 63,
"miso_soup": 64,
"mussels": 65,
"nachos": 66,
"omelette": 67,
"onion_rings": 68,
"oysters": 69,
"pad_thai": 70,
"paella": 71,
"pancakes": 72,
"panna_cotta": 73,
"peking_duck": 74,
"pho": 75,
"pizza": 76,
"pork_chop": 77,
"poutine": 78,
"prime_rib": 79,
"pulled_pork_sandwich": 80,
"ramen": 81,
"ravioli": 82,
"red_velvet_cake": 83,
"risotto": 84,
"samosa": 85,
"sashimi": 86,
"scallops": 87,
"seaweed_salad": 88,
"shrimp_and_grits": 89,
"spaghetti_bolognese": 90,
"spaghetti_carbonara": 91,
"spring_rolls": 92,
"steak": 93,
"strawberry_shortcake": 94,
"sushi": 95,
"tacos": 96,
"takoyaki": 97,
"tiramisu": 98,
"tuna_tartare": 99,
"waffles": 100
}
```
</details>
### Data Fields
| Feature | Data Type |
|---------------------------------|-----------------------------------------------|
| image | Image(decode=True, id=None) |
| split | Value(dtype='string', id=None) |
| label | ClassLabel(names=[...], id=None) |
| label_str | Value(dtype='string', id=None) |
### Data Splits
| Dataset Split | Number of Images in Split |
| ------------- |---------------------------|
| Train | 75750 |
| Test | 25250 |
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
The Food-101 data set consists of images from Foodspotting [1] which are not property of the Federal Institute of Technology Zurich (ETHZ). Any use beyond scientific fair use must be negociated with the respective picture owners according to the Foodspotting terms of use [2].
[1] [http://www.foodspotting.com/](http://www.foodspotting.com/)
[2] [http://www.foodspotting.com/terms/](http://www.foodspotting.com/terms/)
### Citation Information
If you use this dataset, please cite the following paper:
```
@inproceedings{bossard14,
title = {Food-101 -- Mining Discriminative Components with Random Forests},
author = {Bossard, Lukas and Guillaumin, Matthieu and Van Gool, Luc},
booktitle = {European Conference on Computer Vision},
year = {2014}
}
```
### Contributions
Lukas Bossard, Matthieu Guillaumin, Luc Van Gool, and Renumics GmbH. | [
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nlpai-lab/kullm-v2 | nlpai-lab | 2023-06-01T05:45:04Z | 420 | 45 | null | [
"task_categories:text-generation",
"size_categories:10K<n<100K",
"language:ko",
"license:apache-2.0",
"region:us"
] | 2023-06-01T05:45:04Z | 2023-06-01T05:26:22.000Z | 2023-06-01T05:26:22 | ---
license: apache-2.0
task_categories:
- text-generation
language:
- ko
pretty_name: kullm
size_categories:
- 10K<n<100K
---
# Dataset Card for "KULLM-v2"
## Dataset Summary
Korean translation of GPT4ALL, Dolly, and Vicuna data.
repository: [nlpai-lab/KULLM](https://github.com/nlpai-lab/KULLM)
huggingface: [nlpai-lab/kullm-v2](https://huggingface.co/nlpai-lab/kullm-polyglot-12.8b-v2)
#### Translate dataset
Translated 'instruction', 'input', and 'output' in the dataset via the DeepL API
## Lisence
Apache-2.0
```python
>>> from datasets import load_dataset
>>> ds = load_dataset("nlpai-lab/kullm-v2", split="train")
>>> ds
DatasetDict({
train: Dataset({
features: ['id', 'instruction', 'input', 'output'],
num_rows: 152630
})
})
```
```python
>>> ds[0]
{'id': 'alpaca_{idx}',
'instruction': '3원색이란 무엇인가요?',
'input': '',
'output': '세 가지 기본 색은 빨강, 파랑, 노랑입니다. 이 색은 다른 색을 혼합하여 만들 수 없고 다른 모든 색은 다양한 비율로 조합하여 만들 수 있기 때문에 원색이라고 부릅니다. 빛에 사용되는 첨가제 색상 시스템에서 원색은 빨강, 녹색, 파랑(RGB)입니다.'}
``` | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
fake_news_filipino | null | 2023-01-25T14:30:21Z | 419 | 0 | fake-news-filipino-dataset | [
"task_categories:text-classification",
"task_ids:fact-checking",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:tl",
"license:unknown",
"region:us"
] | 2023-01-25T14:30:21Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
language:
- tl
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- fact-checking
paperswithcode_id: fake-news-filipino-dataset
pretty_name: Fake News Filipino
dataset_info:
features:
- name: label
dtype:
class_label:
names:
'0': '0'
'1': '1'
- name: article
dtype: string
splits:
- name: train
num_bytes: 3623685
num_examples: 3206
download_size: 1313458
dataset_size: 3623685
---
# Dataset Card for Fake News Filipino
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [Fake News Filipino homepage](https://github.com/jcblaisecruz02/Filipino-Text-Benchmarks)
- **Repository:** [Fake News Filipino repository](https://github.com/jcblaisecruz02/Filipino-Text-Benchmarks)
- **Paper:** [LREC 2020 paper](http://www.lrec-conf.org/proceedings/lrec2020/index.html)
- **Leaderboard:**
- **Point of Contact:** [Jan Christian Cruz](mailto:jan_christian_cruz@dlsu.edu.ph)
### Dataset Summary
Low-Resource Fake News Detection Corpora in Filipino. The first of its kind. Contains 3,206 expertly-labeled news samples, half of which are real and half of which are fake.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The dataset is primarily in Filipino, with the addition of some English words commonly used in Filipino vernacular.
## Dataset Structure
### Data Instances
Sample data:
```
{
"label": "0",
"article": "Sa 8-pahinang desisyon, pinaboran ng Sandiganbayan First Division ang petition for Writ of Preliminary Attachment/Garnishment na inihain ng prosekusyon laban sa mambabatas."
}
```
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
Fake news articles were sourced from online sites that were tagged as fake news sites by the non-profit independent media fact-checking organization Verafiles and the National Union of Journalists in the Philippines (NUJP). Real news articles were sourced from mainstream news websites in the Philippines, including Pilipino Star Ngayon, Abante, and Bandera.
### Curation Rationale
We remedy the lack of a proper, curated benchmark dataset for fake news detection in Filipino by constructing and producing what we call “Fake News Filipino.”
### Source Data
#### Initial Data Collection and Normalization
We construct the dataset by scraping our source websites, encoding all characters into UTF-8. Preprocessing was light to keep information intact: we retain capitalization and punctuation, and do not correct any misspelled words.
#### Who are the source language producers?
Jan Christian Blaise Cruz, Julianne Agatha Tan, and Charibeth Cheng
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[Jan Christian Cruz](mailto:jan_christian_cruz@dlsu.edu.ph), Julianne Agatha Tan, and Charibeth Cheng
### Licensing Information
[More Information Needed]
### Citation Information
@inproceedings{cruz2020localization,
title={Localization of Fake News Detection via Multitask Transfer Learning},
author={Cruz, Jan Christian Blaise and Tan, Julianne Agatha and Cheng, Charibeth},
booktitle={Proceedings of The 12th Language Resources and Evaluation Conference},
pages={2596--2604},
year={2020}
}
### Contributions
Thanks to [@anaerobeth](https://github.com/anaerobeth) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
AIML-TUDA/i2p | AIML-TUDA | 2023-05-25T08:04:07Z | 419 | 5 | null | [
"license:mit",
"arxiv:2211.05105",
"region:us"
] | 2023-05-25T08:04:07Z | 2022-10-19T12:41:55.000Z | 2022-10-19T12:41:55 | ---
license: mit
---
# Inaproppriate Image Prompts (I2P)
The I2P benchmark contains real user prompts for generative text2image prompts that are unproportionately likely to produce <i>inappropriate</i> images.
I2P was introduced in the 2023 CVPR paper [Safe Latent Diffusion: Mitigating Inappropriate Degeneration in Diffusion Models](https://arxiv.org/abs/2211.05105).
This benchmark is not specific to any approach or model, but was designed to evaluate mitigating measures against <i>inappropriate</i> degeneration in Stable Diffusion.
The corresponding repository to evaluate text-to-image diffusion models on I2P can be found at https://github.com/ml-research/i2p.
## Definition of <i>inappropriate</i> content
What is considered inappropriate imagery may differ based on context, setting, cultural and social predisposition as well as individual factors and is overall highly subjective. In this work we base our definition of inappropriate content on the work of Gebru <i> et al.</i>:
> [data that] if viewed directly, might be offensive, insulting, threatening, or might otherwise cause anxiety.
>
> --<cite>Gebru, Timnit, et al. "Datasheets for datasets," (2021)</cite>
which is for example also reflected by the OpenAI content policy [OpenAI content policy](https://labs.openai.com/policies/content-policy) that also applies to the use of DALL-E. Specifically, we consider images from the following categories: hate, harassment, violence, self-harm, sexual content, shocking images, illegal activity. Note that inappropriateness is not limited to these concepts, varies between cultures, and constantly evolves. Here we restricted ourselves on images displaying tangible acts of <I>inappropriate</i> behavior. Consequently, our test bed may contain prompts describing e.g.~geopolitical events or hateful symbols.
## Data Collection
For the 7 concepts mentioned above we used 26 keywords and phrases describing them in more detail and collected up to 250 real world text prompts for each.
For a given keyword we collected the prompts of the top 250 images returned by [lexica.art](https://www.lexica.art).
Lexica is a collection of real-world, user generated images base on the official stable diffusion discord and collects the prompt, seed, guidance scale and image dimensions used in generation.
Image retrieval in lexica is based on the similarity of an image and search query in CLIP embedding space.
Therefore, the collected prompts have generated images that are close to an <i>inappropriate</i> concept in CLIP space.
Please note that we identify duplicate prompts based on their unique identifier on lexica. Accordingly, the I2P benchmark may contain entries with the same text prompt but different seeds and generation parameters.
## Estimation of generated, <i>inappropriate</i> content
We provide an estimation of the percentage of <i>inappropriate</i> content based on images generated using [Stable Diffusion v1.4](https://huggingface.co/CompVis/stable-diffusion-v1-4). For each prompt we generated 10 images using the seed and guidance scale specified in lexica while keeping the maximum image size to 512x512. Our estimate is based on the portion out of these 10 images that are classified as <i>inappropriate</i> by either the [Q16 classifier](https://github.com/ml-research/Q16) or [NudeNet Detector](https://github.com/notAI-tech/NudeNet).
## Dataset Format
The benchmark consists of the following fields:
| Field | Explanation |
| ----------- | ----------- |
| prompt | The text prompt used as input to the model (Please not that this may contain Unicode characters) |
| categories | One or multiple of the 7 categories mentioned above that yield this prompt. |
| hard | Indicates if this is a hard prompt to remove <i>inappropriate</i> content from. Prompts are labled as hard if at least half of their generated images are <i>inappropriate</i> |
| inappropriate_percentage | Percentage of <i>inappropriate</i> images as classified by Q16 and NudeNet out of 10 generated images using Stable Diffusion |
| nudity_percentage | Percentage of images as depicting explicit nudity as per the NudeNet out of 10 generated images using Stable Diffusion |
| q16_percentage | Percentage of <i>inappropriate</i> images as classified by Q16 out of 10 generated images using Stable Diffusion |
| sd_safety_percentage | Percentage of <i>inappropriate</i> images as classified by the SD NSWF safety checker out of 10 generated images using Stable Diffusion |
| prompt_toxicity | The toxicity score of the text prompt according to the [PerspectiveAPI](https://github.com/conversationai/perspectiveapi) |
| lexica_url | URL to the original prompt and the respective images in lexica for reference |
| sd_seed | Stable diffusion seed used in our image generation |
| sd_guidance_scale | Stable diffusion guidance scale used in our image generation |
| sd_image_width | Stable diffusion image width used in our image generation |
| sd_image_height | Stable diffusion image height used in our image generation |
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
amaydle/npc-dialogue | amaydle | 2023-03-25T09:11:29Z | 419 | 7 | null | [
"region:us"
] | 2023-03-25T09:11:29Z | 2023-03-25T09:11:12.000Z | 2023-03-25T09:11:12 | ---
dataset_info:
features:
- name: Name
dtype: string
- name: Biography
dtype: string
- name: Query
dtype: string
- name: Response
dtype: string
- name: Emotion
dtype: string
splits:
- name: train
num_bytes: 737058.9117493472
num_examples: 1723
- name: test
num_bytes: 82133.08825065274
num_examples: 192
download_size: 201559
dataset_size: 819192.0
---
# Dataset Card for "npc-dialogue"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
dalle-mini/YFCC100M_OpenAI_subset | dalle-mini | 2021-08-26T17:56:01Z | 418 | 9 | null | [
"arxiv:1503.01817",
"region:us"
] | 2021-08-26T17:56:01Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | # YFCC100M subset from OpenAI
Subset of [YFCC100M](https://arxiv.org/abs/1503.01817) used by OpenAI for [CLIP](https://github.com/openai/CLIP/blob/main/data/yfcc100m.md), filtered to contain only the images that we could retrieve.
| Split | train | validation |
| --- | --- | --- |
| Number of samples | 14,808,859 | 16,374 |
| Size | 1.9 TB | 2.1 GB |
Features:
* from the original dataset: `title`, `description`, `photoid`, `uid`, `unickname`, `datetaken`, `dateuploaded`, `capturedevice`, `usertags`, `machinetags`, `longitude`, `latitude`, `accuracy`, `pageurl`, `downloadurl`, `licensename`, `licenseurl`, `serverid`, `farmid`, `secret`, `secretoriginal`, `ext`, `marker`, `key`
* `img`: image content, can be loaded with `PIL.Image.open(io.BytesIO(item['img']))`
* `title_clean` and `description_clean`: derived from `title` and `description` using `clean_text` function detailed below
```python
def clean_text(text):
# decode url
text = urllib.parse.unquote_plus(text)
# remove html tags
text = re.sub('<[^<]+?>', '', text)
# remove multiple spaces + "\r" + "\n" + "\t"
text = " ".join(text.split())
return text
``` | [
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hf-internal-testing/fixtures_nlvr2 | hf-internal-testing | 2021-12-23T10:57:49Z | 418 | 0 | null | [
"region:us"
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blog_authorship_corpus | null | 2023-06-06T16:16:13Z | 417 | 6 | blog-authorship-corpus | [
"task_categories:text-classification",
"task_ids:multi-class-classification",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:unknown",
"region:us"
] | 2023-06-06T16:16:13Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
paperswithcode_id: blog-authorship-corpus
pretty_name: Blog Authorship Corpus
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-class-classification
dataset_info:
features:
- name: text
dtype: string
- name: date
dtype: string
- name: gender
dtype: string
- name: age
dtype: int32
- name: horoscope
dtype: string
- name: job
dtype: string
config_name: blog_authorship_corpus
splits:
- name: train
num_bytes: 753833081
num_examples: 689793
- name: validation
num_bytes: 41236028
num_examples: 37919
download_size: 632898892
dataset_size: 795069109
---
# Dataset Card for Blog Authorship Corpus
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://u.cs.biu.ac.il/~koppel/BlogCorpus.htm](https://u.cs.biu.ac.il/~koppel/BlogCorpus.htm)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of downloaded dataset files:** 312.95 MB
- **Size of the generated dataset:** 647.76 MB
- **Total amount of disk used:** 960.71 MB
### Dataset Summary
The Blog Authorship Corpus consists of the collected posts of 19,320 bloggers gathered from blogger.com in August 2004. The corpus incorporates a total of 681,288 posts and over 140 million words - or approximately 35 posts and 7250 words per person.
Each blog is presented as a separate file, the name of which indicates a blogger id# and the blogger’s self-provided gender, age, industry and astrological sign. (All are labeled for gender and age but for many, industry and/or sign is marked as unknown.)
All bloggers included in the corpus fall into one of three age groups:
- 8240 "10s" blogs (ages 13-17),
- 8086 "20s" blogs (ages 23-27),
- 2994 "30s" blogs (ages 33-47).
For each age group there are an equal number of male and female bloggers.
Each blog in the corpus includes at least 200 occurrences of common English words. All formatting has been stripped with two exceptions. Individual posts within a single blogger are separated by the date of the following post and links within a post are denoted by the label urllink.
The corpus may be freely used for non-commercial research purposes.
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
The language of the dataset is English (`en`).
## Dataset Structure
### Data Instances
#### blog-authorship-corpus
- **Size of downloaded dataset files:** 312.95 MB
- **Size of the generated dataset:** 647.76 MB
- **Total amount of disk used:** 960.71 MB
An example of 'validation' looks as follows.
```
{
"age": 23,
"date": "27,July,2003",
"gender": "female",
"horoscope": "Scorpion",
"job": "Student",
"text": "This is a second test file."
}
```
### Data Fields
The data fields are the same among all splits.
#### blog-authorship-corpus
- `text`: a `string` feature.
- `date`: a `string` feature.
- `gender`: a `string` feature.
- `age`: a `int32` feature.
- `horoscope`: a `string` feature.
- `job`: a `string` feature.
### Data Splits
| name |train |validation|
|----------------------|-----:|---------:|
|blog-authorship-corpus|532812| 31277|
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
The corpus may be freely used for non-commercial research purposes.
### Citation Information
```
@inproceedings{schler2006effects,
title={Effects of age and gender on blogging.},
author={Schler, Jonathan and Koppel, Moshe and Argamon, Shlomo and Pennebaker, James W},
booktitle={AAAI spring symposium: Computational approaches to analyzing weblogs},
volume={6},
pages={199--205},
year={2006}
}
```
### Contributions
Thanks to [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun), [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
JeremyArancio/lotr-book | JeremyArancio | 2023-06-02T12:30:41Z | 417 | 0 | null | [
"region:us"
] | 2023-06-02T12:30:41Z | 2023-05-18T09:53:28.000Z | 2023-05-18T09:53:28 | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 2432593
num_examples: 1
download_size: 0
dataset_size: 2432593
---
# Dataset Card for "lotr-book"
The Lord of the Rings books extracted into one dataset.
[Source](https://github.com/jeremyarancio/llm-rpg/blob/main/llm/prepare_dataset.py)
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
# Notes
* Book [link](https://gosafir.com/mag/wp-content/uploads/2019/12/Tolkien-J.-The-lord-of-the-rings-HarperCollins-ebooks-2010.pdf)
* Footers and header were removed.
* Starts at page 45 and ends at page 1055 | [
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FreedomIntelligence/evol-instruct-deutsch | FreedomIntelligence | 2023-08-06T08:12:07Z | 417 | 5 | null | [
"region:us"
] | 2023-08-06T08:12:07Z | 2023-06-30T03:43:08.000Z | 2023-06-30T03:43:08 | The dataset is used in the research related to [MultilingualSIFT](https://github.com/FreedomIntelligence/MultilingualSIFT). | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
gfissore/arxiv-abstracts-2021 | gfissore | 2022-10-27T17:08:00Z | 416 | 17 | null | [
"task_categories:summarization",
"task_categories:text-retrieval",
"task_categories:text2text-generation",
"task_ids:explanation-generation",
"task_ids:text-simplification",
"task_ids:document-retrieval",
"task_ids:entity-linking-retrieval",
"task_ids:fact-checking-retrieval",
"annotations_creators:no-annotation",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"language:en",
"license:cc0-1.0",
"arxiv:1905.00075",
"region:us"
] | 2022-10-27T17:08:00Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- no-annotation
language_creators:
- expert-generated
language:
- en
license:
- cc0-1.0
multilinguality:
- monolingual
pretty_name: arxiv-abstracts-2021
size_categories:
- 1M<n<10M
source_datasets: []
task_categories:
- summarization
- text-retrieval
- text2text-generation
task_ids:
- explanation-generation
- text-simplification
- document-retrieval
- entity-linking-retrieval
- fact-checking-retrieval
---
# Dataset Card for arxiv-abstracts-2021
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** [Needs More Information]
- **Repository:** [Needs More Information]
- **Paper:** [Clement et al., 2019, On the Use of ArXiv as a Dataset, https://arxiv.org/abs/1905.00075](https://arxiv.org/abs/1905.00075)
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Giancarlo Fissore](mailto:giancarlo.fissore@gmail.com)
### Dataset Summary
A dataset of metadata including title and abstract for all arXiv articles up to the end of 2021 (~2 million papers).
Possible applications include trend analysis, paper recommender engines, category prediction, knowledge graph construction and semantic search interfaces.
In contrast to [arxiv_dataset](https://huggingface.co/datasets/arxiv_dataset), this dataset doesn't include papers submitted to arXiv after 2021 and it doesn't require any external download.
### Supported Tasks and Leaderboards
[Needs More Information]
### Languages
English
## Dataset Structure
### Data Instances
Here's an example instance:
```
{
"id": "1706.03762",
"submitter": "Ashish Vaswani",
"authors": "Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion\n Jones, Aidan N. Gomez, Lukasz Kaiser, Illia Polosukhin",
"title": "Attention Is All You Need",
"comments": "15 pages, 5 figures",
"journal-ref": null,
"doi": null,
"abstract": " The dominant sequence transduction models are based on complex recurrent or\nconvolutional neural
networks in an encoder-decoder configuration. The best\nperforming models also connect the encoder and decoder through
an attention\nmechanism. We propose a new simple network architecture, the Transformer, based\nsolely on attention
mechanisms, dispensing with recurrence and convolutions\nentirely. Experiments on two machine translation tasks show
these models to be\nsuperior in quality while being more parallelizable and requiring significantly\nless time to
train. Our model achieves 28.4 BLEU on the WMT 2014\nEnglish-to-German translation task, improving over the existing
best results,\nincluding ensembles by over 2 BLEU. On the WMT 2014 English-to-French\ntranslation task, our model
establishes a new single-model state-of-the-art\nBLEU score of 41.8 after training for 3.5 days on eight GPUs, a small
fraction\nof the training costs of the best models from the literature. We show that the\nTransformer generalizes well
to other tasks by applying it successfully to\nEnglish constituency parsing both with large and limited training
data.\n",
"report-no": null,
"categories": [
"cs.CL cs.LG"
],
"versions": [
"v1",
"v2",
"v3",
"v4",
"v5"
]
}
```
### Data Fields
These fields are detailed on the [arXiv](https://arxiv.org/help/prep):
- `id`: ArXiv ID (can be used to access the paper)
- `submitter`: Who submitted the paper
- `authors`: Authors of the paper
- `title`: Title of the paper
- `comments`: Additional info, such as number of pages and figures
- `journal-ref`: Information about the journal the paper was published in
- `doi`: [Digital Object Identifier](https://www.doi.org)
- `report-no`: Report Number
- `abstract`: The abstract of the paper
- `categories`: Categories / tags in the ArXiv system
### Data Splits
No splits
## Dataset Creation
### Curation Rationale
For about 30 years, ArXiv has served the public and research communities by providing open access to scholarly articles, from the vast branches of physics to the many subdisciplines of computer science to everything in between, including math, statistics, electrical engineering, quantitative biology, and economics. This rich corpus of information offers significant, but sometimes overwhelming, depth. In these times of unique global challenges, efficient extraction of insights from data is essential. The `arxiv-abstracts-2021` dataset aims at making the arXiv more easily accessible for machine learning applications, by providing important metadata (including title and abstract) for ~2 million papers.
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
The language producers are members of the scientific community at large, but not necessarily affiliated to any institution.
### Annotations
#### Annotation process
[N/A]
#### Who are the annotators?
[N/A]
### Personal and Sensitive Information
The full names of the papers' authors are included in the dataset.
## 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
The original data is maintained by [ArXiv](https://arxiv.org/)
### Licensing Information
The data is under the [Creative Commons CC0 1.0 Universal Public Domain Dedication](https://creativecommons.org/publicdomain/zero/1.0/)
### Citation Information
```
@misc{clement2019arxiv,
title={On the Use of ArXiv as a Dataset},
author={Colin B. Clement and Matthew Bierbaum and Kevin P. O'Keeffe and Alexander A. Alemi},
year={2019},
eprint={1905.00075},
archivePrefix={arXiv},
primaryClass={cs.IR}
}
``` | [
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grammarly/coedit | grammarly | 2023-10-21T01:49:43Z | 416 | 18 | null | [
"task_categories:text-generation",
"size_categories:10K<n<100K",
"language:en",
"license:apache-2.0",
"arxiv:2305.09857",
"region:us"
] | 2023-10-21T01:49:43Z | 2023-08-15T00:01:51.000Z | 2023-08-15T00:01:51 | ---
license: apache-2.0
task_categories:
- text-generation
language:
- en
pretty_name: coedit
size_categories:
- 10K<n<100K
---
# Dataset Card for CoEdIT: Text Editing via Instruction Tuning
## Paper: [CoEdIT: Text Editing by Task-Specific Instruction Tuning](https://arxiv.org/abs/2305.09857)
## Authors: Vipul Raheja, Dhruv Kumar, Ryan Koo, Dongyeop Kang
## Project Repo: [https://github.com/vipulraheja/coedit](https://github.com/vipulraheja/coedit)
## Dataset Summary
This is the dataset that was used to train the CoEdIT text editing models. Full details of the dataset can be found in our paper.
# Dataset Structure
The dataset is in JSON format.
## Data Instances
```
{
'_id': 1,
'task': "gec",
'src': "Improve the grammaticality: As the number of people grows, the need of habitable environment is unquestionably essential.",
'tgt': "As the number of people grows, the need for a habitable environment is unquestionably increasing."
}
```
## Data Fields
* `_id`:
* `task`: Text editing task for this instance
* `src`: input text (formatted as `instruction: input_text`)
* `tgt`: output text
## Considerations for Using the Data
Please note that this dataset contains 69k instances (as opposed to the 82k instances we used in the paper). This is because this public release includes only the instances that were acquired and curated from publicly available datasets. Specifically, it is missing roughly 13k instances in training and 1.5k instances in validation data from Simplification and Formality Transfer tasks due to licensing restrictions.
# Citation
```
@article{raheja2023coedit,
title={CoEdIT: Text Editing by Task-Specific Instruction Tuning},
author={Vipul Raheja and Dhruv Kumar and Ryan Koo and Dongyeop Kang},
year={2023},
eprint={2305.09857},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
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code_x_glue_tc_nl_code_search_adv | null | 2023-07-27T15:51:10Z | 415 | 2 | null | [
"task_categories:text-retrieval",
"task_ids:document-retrieval",
"annotations_creators:found",
"language_creators:found",
"multilinguality:other-programming-languages",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:code",
"language:en",
"license:c-uda",
"arxiv:2102.04664",
"region:us"
] | 2023-07-27T15:51:10Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- found
language:
- code
- en
license:
- c-uda
multilinguality:
- other-programming-languages
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-retrieval
task_ids:
- document-retrieval
pretty_name: CodeXGlueTcNlCodeSearchAdv
dataset_info:
features:
- name: id
dtype: int32
- name: repo
dtype: string
- name: path
dtype: string
- name: func_name
dtype: string
- name: original_string
dtype: string
- name: language
dtype: string
- name: code
dtype: string
- name: code_tokens
sequence: string
- name: docstring
dtype: string
- name: docstring_tokens
sequence: string
- name: sha
dtype: string
- name: url
dtype: string
- name: docstring_summary
dtype: string
- name: parameters
dtype: string
- name: return_statement
dtype: string
- name: argument_list
dtype: string
- name: identifier
dtype: string
- name: nwo
dtype: string
- name: score
dtype: float32
splits:
- name: train
num_bytes: 820716084
num_examples: 251820
- name: validation
num_bytes: 23468834
num_examples: 9604
- name: test
num_bytes: 47433760
num_examples: 19210
download_size: 966025624
dataset_size: 891618678
---
# Dataset Card for "code_x_glue_tc_nl_code_search_adv"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits-sample-size)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/microsoft/CodeXGLUE/tree/main/Text-Code/NL-code-search-Adv
- **Paper:** https://arxiv.org/abs/2102.04664
### Dataset Summary
CodeXGLUE NL-code-search-Adv dataset, available at https://github.com/microsoft/CodeXGLUE/tree/main/Text-Code/NL-code-search-Adv
The dataset we use comes from CodeSearchNet and we filter the dataset as the following:
- Remove examples that codes cannot be parsed into an abstract syntax tree.
- Remove examples that #tokens of documents is < 3 or >256
- Remove examples that documents contain special tokens (e.g. <img ...> or https:...)
- Remove examples that documents are not English.
### Supported Tasks and Leaderboards
- `document-retrieval`: The dataset can be used to train a model for retrieving top-k codes from a given **English** natural language query.
### Languages
- Python **programming** language
- English **natural** language
## Dataset Structure
### Data Instances
An example of 'validation' looks as follows.
```
{
"argument_list": "",
"code": "def Func(arg_0, arg_1='.', arg_2=True, arg_3=False, **arg_4):\n \"\"\"Downloads Dailymotion videos by URL.\n \"\"\"\n\n arg_5 = get_content(rebuilt_url(arg_0))\n arg_6 = json.loads(match1(arg_5, r'qualities\":({.+?}),\"'))\n arg_7 = match1(arg_5, r'\"video_title\"\\s*:\\s*\"([^\"]+)\"') or \\\n match1(arg_5, r'\"title\"\\s*:\\s*\"([^\"]+)\"')\n arg_7 = unicodize(arg_7)\n\n for arg_8 in ['1080','720','480','380','240','144','auto']:\n try:\n arg_9 = arg_6[arg_8][1][\"url\"]\n if arg_9:\n break\n except KeyError:\n pass\n\n arg_10, arg_11, arg_12 = url_info(arg_9)\n\n print_info(site_info, arg_7, arg_10, arg_12)\n if not arg_3:\n download_urls([arg_9], arg_7, arg_11, arg_12, arg_1=arg_1, arg_2=arg_2)",
"code_tokens": ["def", "Func", "(", "arg_0", ",", "arg_1", "=", "'.'", ",", "arg_2", "=", "True", ",", "arg_3", "=", "False", ",", "**", "arg_4", ")", ":", "arg_5", "=", "get_content", "(", "rebuilt_url", "(", "arg_0", ")", ")", "arg_6", "=", "json", ".", "loads", "(", "match1", "(", "arg_5", ",", "r'qualities\":({.+?}),\"'", ")", ")", "arg_7", "=", "match1", "(", "arg_5", ",", "r'\"video_title\"\\s*:\\s*\"([^\"]+)\"'", ")", "or", "match1", "(", "arg_5", ",", "r'\"title\"\\s*:\\s*\"([^\"]+)\"'", ")", "arg_7", "=", "unicodize", "(", "arg_7", ")", "for", "arg_8", "in", "[", "'1080'", ",", "'720'", ",", "'480'", ",", "'380'", ",", "'240'", ",", "'144'", ",", "'auto'", "]", ":", "try", ":", "arg_9", "=", "arg_6", "[", "arg_8", "]", "[", "1", "]", "[", "\"url\"", "]", "if", "arg_9", ":", "break", "except", "KeyError", ":", "pass", "arg_10", ",", "arg_11", ",", "arg_12", "=", "url_info", "(", "arg_9", ")", "print_info", "(", "site_info", ",", "arg_7", ",", "arg_10", ",", "arg_12", ")", "if", "not", "arg_3", ":", "download_urls", "(", "[", "arg_9", "]", ",", "arg_7", ",", "arg_11", ",", "arg_12", ",", "arg_1", "=", "arg_1", ",", "arg_2", "=", "arg_2", ")"],
"docstring": "Downloads Dailymotion videos by URL.",
"docstring_summary": "Downloads Dailymotion videos by URL.",
"docstring_tokens": ["Downloads", "Dailymotion", "videos", "by", "URL", "."],
"func_name": "",
"id": 0,
"identifier": "dailymotion_download",
"language": "python",
"nwo": "soimort/you-get",
"original_string": "",
"parameters": "(url, output_dir='.', merge=True, info_only=False, **kwargs)",
"path": "src/you_get/extractors/dailymotion.py",
"repo": "",
"return_statement": "",
"score": 0.9997601509094238,
"sha": "b746ac01c9f39de94cac2d56f665285b0523b974",
"url": "https://github.com/soimort/you-get/blob/b746ac01c9f39de94cac2d56f665285b0523b974/src/you_get/extractors/dailymotion.py#L13-L35"
}
```
### Data Fields
In the following each data field in go is explained for each config. The data fields are the same among all splits.
#### default
| field name | type | description |
|-----------------|-----------------------|-----------------------------------------------------------------------------------|
|id |int32 | Index of the sample |
|repo |string | repo: the owner/repo |
|path |string | path: the full path to the original file |
|func_name |string | func_name: the function or method name |
|original_string |string | original_string: the raw string before tokenization or parsing |
|language |string | language: the programming language |
|code |string | code/function: the part of the original_string that is code |
|code_tokens |Sequence[string] | code_tokens/function_tokens: tokenized version of code |
|docstring |string | docstring: the top-level comment or docstring, if it exists in the original string|
|docstring_tokens |Sequence[string] | docstring_tokens: tokenized version of docstring |
|sha |string | sha of the file |
|url |string | url of the file |
|docstring_summary|string | Summary of the docstring |
|parameters |string | parameters of the function |
|return_statement |string | return statement |
|argument_list |string | list of arguments of the function |
|identifier |string | identifier |
|nwo |string | nwo |
|score |datasets.Value("float"]| score for this search |
### Data Splits
| name |train |validation|test |
|-------|-----:|---------:|----:|
|default|251820| 9604|19210|
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
Data from CodeSearchNet Challenge dataset.
[More Information Needed]
#### Who are the source language producers?
Software Engineering developers.
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
https://github.com/microsoft, https://github.com/madlag
### Licensing Information
Computational Use of Data Agreement (C-UDA) License.
### Citation Information
```
@article{DBLP:journals/corr/abs-2102-04664,
author = {Shuai Lu and
Daya Guo and
Shuo Ren and
Junjie Huang and
Alexey Svyatkovskiy and
Ambrosio Blanco and
Colin B. Clement and
Dawn Drain and
Daxin Jiang and
Duyu Tang and
Ge Li and
Lidong Zhou and
Linjun Shou and
Long Zhou and
Michele Tufano and
Ming Gong and
Ming Zhou and
Nan Duan and
Neel Sundaresan and
Shao Kun Deng and
Shengyu Fu and
Shujie Liu},
title = {CodeXGLUE: {A} Machine Learning Benchmark Dataset for Code Understanding
and Generation},
journal = {CoRR},
volume = {abs/2102.04664},
year = {2021}
}
@article{husain2019codesearchnet,
title={Codesearchnet challenge: Evaluating the state of semantic code search},
author={Husain, Hamel and Wu, Ho-Hsiang and Gazit, Tiferet and Allamanis, Miltiadis and Brockschmidt, Marc},
journal={arXiv preprint arXiv:1909.09436},
year={2019}
}
```
### Contributions
Thanks to @madlag (and partly also @ncoop57) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
mystic-leung/medical_cord19 | mystic-leung | 2023-09-14T03:00:13Z | 415 | 2 | null | [
"task_categories:summarization",
"language:aa",
"license:openrail",
"medical",
"region:us"
] | 2023-09-14T03:00:13Z | 2023-08-22T13:35:59.000Z | 2023-08-22T13:35:59 | ---
license: openrail
task_categories:
- summarization
language:
- aa
tags:
- medical
---
## Description
This dataset contains large amounts of biomedical abstracts and corresponding summaries. | [
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SetFit/imdb | SetFit | 2022-01-19T20:49:40Z | 414 | 2 | null | [
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result-kand2-sdxl-wuerst-karlo/a6175b5f | result-kand2-sdxl-wuerst-karlo | 2023-11-07T19:09:35Z | 413 | 0 | null | [
"region:us"
] | 2023-11-07T19:09:35Z | 2023-11-07T19:09:33.000Z | 2023-11-07T19:09:33 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 168
num_examples: 10
download_size: 1303
dataset_size: 168
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "a6175b5f"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
conv_ai_3 | null | 2022-11-03T16:30:50Z | 412 | 13 | null | [
"task_categories:conversational",
"task_categories:text-classification",
"task_ids:text-scoring",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:unknown",
"evaluating-dialogue-systems",
"arxiv:2009.11352",
"region:us"
] | 2022-11-03T16:30:50Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- conversational
- text-classification
task_ids:
- text-scoring
paperswithcode_id: null
pretty_name: More Information Needed
tags:
- evaluating-dialogue-systems
dataset_info:
features:
- name: topic_id
dtype: int32
- name: initial_request
dtype: string
- name: topic_desc
dtype: string
- name: clarification_need
dtype: int32
- name: facet_id
dtype: string
- name: facet_desc
dtype: string
- name: question_id
dtype: string
- name: question
dtype: string
- name: answer
dtype: string
config_name: conv_ai_3
splits:
- name: train
num_bytes: 2567404
num_examples: 9176
- name: validation
num_bytes: 639351
num_examples: 2313
download_size: 2940038
dataset_size: 3206755
---
# Dataset Card for [More Information Needed]
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/aliannejadi/ClariQ
- **Repository:** https://github.com/aliannejadi/ClariQ
- **Paper:** https://arxiv.org/abs/2009.11352
- **Leaderboard:** [More Information Needed]
- **Point of Contact:** [More Information Needed]
### Dataset Summary
The Conv AI 3 challenge is organized as part of the Search-oriented Conversational AI (SCAI) EMNLP workshop in 2020. The main aim of the conversational systems is to return an appropriate answer in response to the user requests. However, some user requests might be ambiguous. In Information Retrieval (IR) settings such a situation is handled mainly through the diversification of search result page. It is however much more challenging in dialogue settings. Hence, we aim to study the following situation for dialogue settings:
- a user is asking an ambiguous question (where ambiguous question is a question to which one can return > 1 possible answers)
- the system must identify that the question is ambiguous, and, instead of trying to answer it directly, ask a good clarifying question.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
Here are a few examples from the dataset:
```
{'topic_id': 8,
'facet_id': 'F0968',
'initial_request': 'I want to know about appraisals.',
'topic_desc': 'Find information about the appraisals in nearby companies.',
'clarification_need': 2,
'question_id': 'F0001',
'question': 'are you looking for a type of appraiser',
'answer': 'im looking for nearby companies that do home appraisals',
'facet_desc': 'Get the TYPE of Appraisals'
'conversation_context': [],
'context_id': 968}
```
```
{'topic_id': 8,
'facet_id': 'F0969',
'initial_request': 'I want to know about appraisals.',
'topic_desc': 'Find information about the type of appraisals.',
'clarification_need': 2,
'question_id': 'F0005',
'question': 'are you looking for a type of appraiser',
'facet_desc': 'Get the TYPE of Appraisals'
'answer': 'yes jewelry',
'conversation_context': [],
'context_id': 969}
```
```
{'topic_id': 293,
'facet_id': 'F0729',
'initial_request': 'Tell me about the educational advantages of social networking sites.',
'topic_desc': 'Find information about the educational benefits of the social media sites',
'clarification_need': 2,
'question_id': 'F0009'
'question': 'which social networking sites would you like information on',
'answer': 'i don have a specific one in mind just overall educational benefits to social media sites',
'facet_desc': 'Detailed information about the Networking Sites.'
'conversation_context': [{'question': 'what level of schooling are you interested in gaining the advantages to social networking sites', 'answer': 'all levels'}, {'question': 'what type of educational advantages are you seeking from social networking', 'answer': 'i just want to know if there are any'}],
'context_id': 976573}
```
### Data Fields
- `topic_id`: the ID of the topic (`initial_request`).
- `initial_request`: the query (text) that initiates the conversation.
- `topic_desc`: a full description of the topic as it appears in the TREC Web Track data.
- `clarification_need`: a label from 1 to 4, indicating how much it is needed to clarify a topic. If an `initial_request` is self-contained and would not need any clarification, the label would be 1. While if a `initial_request` is absolutely ambiguous, making it impossible for a search engine to guess the user's right intent before clarification, the label would be 4.
- `facet_id`: the ID of the facet.
- `facet_desc`: a full description of the facet (information need) as it appears in the TREC Web Track data.
- `question_id`: the ID of the question..
- `question`: a clarifying question that the system can pose to the user for the current topic and facet.
- `answer`: an answer to the clarifying question, assuming that the user is in the context of the current row (i.e., the user's initial query is `initial_request`, their information need is `facet_desc`, and `question` has been posed to the user).
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
@misc{aliannejadi2020convai3,
title={ConvAI3: Generating Clarifying Questions for Open-Domain Dialogue Systems (ClariQ)},
author={Mohammad Aliannejadi and Julia Kiseleva and Aleksandr Chuklin and Jeff Dalton and Mikhail Burtsev},
year={2020},
eprint={2009.11352},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
### Contributions
Thanks to [@rkc007](https://github.com/rkc007) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
allegro/klej-cdsc-e | allegro | 2022-08-30T06:58:29Z | 412 | 0 | null | [
"task_categories:text-classification",
"task_ids:natural-language-inference",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:pl",
"license:cc-by-nc-sa-4.0",
"region:us"
] | 2022-08-30T06:58:29Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- pl
license:
- cc-by-nc-sa-4.0
multilinguality:
- monolingual
pretty_name: 'CDSC-E'
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- natural-language-inference
---
# klej-cdsc-e
## Description
Polish CDSCorpus consists of 10K Polish sentence pairs which are human-annotated for semantic relatedness (**CDSC-R**) and entailment (**CDSC-E**). The dataset may be used to evaluate compositional distributional semantics models of Polish. The dataset was presented at ACL 2017.
Although the SICK corpus inspires the main design of the dataset, it differs in detail. As in SICK, the sentences come from image captions, but the set of chosen images is much more diverse as they come from 46 thematic groups.
## Tasks (input, output, and metrics)
The entailment relation between two sentences is labeled with *entailment*, *contradiction*, or *neutral*. The task is to predict if the premise entails the hypothesis (entailment), negates the hypothesis (contradiction), or is unrelated (neutral).
b **entails** a (a **wynika z** b) – if a situation or an event described by sentence b occurs, it is recognized that a situation or an event described by a occurs as well, i.e., a and b refer to the same event or the same situation;
**Input**: ('sentence_A', 'sentence_B'): sentence pair
**Output** ('entailment_judgment' column): one of the possible entailment relations (*entailment*, *contradiction*, *neutral*)
**Domain:** image captions
**Measurements**: Accuracy
**Example:**
Input: `Żaden mężczyzna nie stoi na przystanku autobusowym.` ; `Mężczyzna z żółtą i białą reklamówką w ręce stoi na przystanku obok autobusu.`
Input (translated by DeepL): `No man standing at the bus stop.` ; `A man with a yellow and white bag in his hand stands at a bus stop next to a bus.`
Output: `entailment`
## Data splits
| Subset | Cardinality |
| ------------- | ----------: |
| train | 8000 |
| validation | 1000 |
| test | 1000 |
## Class distribution
| Class | train | validation | test |
|:--------------|--------:|-------------:|-------:|
| NEUTRAL | 0.744 | 0.741 | 0.744 |
| ENTAILMENT | 0.179 | 0.185 | 0.190 |
| CONTRADICTION | 0.077 | 0.074 | 0.066 |
## Citation
```
@inproceedings{wroblewska-krasnowska-kieras-2017-polish,
title = "{P}olish evaluation dataset for compositional distributional semantics models",
author = "Wr{\'o}blewska, Alina and
Krasnowska-Kiera{\'s}, Katarzyna",
booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P17-1073",
doi = "10.18653/v1/P17-1073",
pages = "784--792",
abstract = "The paper presents a procedure of building an evaluation dataset. for the validation of compositional distributional semantics models estimated for languages other than English. The procedure generally builds on steps designed to assemble the SICK corpus, which contains pairs of English sentences annotated for semantic relatedness and entailment, because we aim at building a comparable dataset. However, the implementation of particular building steps significantly differs from the original SICK design assumptions, which is caused by both lack of necessary extraneous resources for an investigated language and the need for language-specific transformation rules. The designed procedure is verified on Polish, a fusional language with a relatively free word order, and contributes to building a Polish evaluation dataset. The resource consists of 10K sentence pairs which are human-annotated for semantic relatedness and entailment. The dataset may be used for the evaluation of compositional distributional semantics models of Polish.",
}
```
## License
```
Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
```
## Links
[HuggingFace](https://huggingface.co/datasets/allegro/klej-cdsc-e)
[Source](http://zil.ipipan.waw.pl/Scwad/CDSCorpus)
[Paper](https://aclanthology.org/P17-1073.pdf)
## Examples
### Loading
```python
from pprint import pprint
from datasets import load_dataset
dataset = load_dataset("allegro/klej-cdsc-e")
pprint(dataset["train"][0])
# {'entailment_judgment': 'NEUTRAL',
# 'pair_ID': 1,
# 'sentence_A': 'Chłopiec w czerwonych trampkach skacze wysoko do góry '
# 'nieopodal fontanny .',
# 'sentence_B': 'Chłopiec w bluzce w paski podskakuje wysoko obok brązowej '
# 'fontanny .'}
```
### Evaluation
```python
import random
from pprint import pprint
from datasets import load_dataset, load_metric
dataset = load_dataset("allegro/klej-cdsc-e")
dataset = dataset.class_encode_column("entailment_judgment")
references = dataset["test"]["entailment_judgment"]
# generate random predictions
predictions = [random.randrange(max(references) + 1) for _ in range(len(references))]
acc = load_metric("accuracy")
f1 = load_metric("f1")
acc_score = acc.compute(predictions=predictions, references=references)
f1_score = f1.compute(predictions=predictions, references=references, average="macro")
pprint(acc_score)
pprint(f1_score)
# {'accuracy': 0.325}
# {'f1': 0.2736171695141161}
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
lksy/ru_instruct_gpt4 | lksy | 2023-06-02T16:56:03Z | 412 | 19 | null | [
"task_categories:text-generation",
"task_categories:text2text-generation",
"size_categories:10K<n<100K",
"language:ru",
"license:cc-by-4.0",
"chat",
"region:us"
] | 2023-06-02T16:56:03Z | 2023-04-18T08:15:50.000Z | 2023-04-18T08:15:50 | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
- name: full_output
dtype: string
splits:
- name: train
num_bytes: 22424451
num_examples: 15056
download_size: 23276814
dataset_size: 22424451
license: cc-by-4.0
task_categories:
- text-generation
- text2text-generation
language:
- ru
tags:
- chat
size_categories:
- 10K<n<100K
---
# ru_instruct_gpt4
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
Dataset of GPT-4 generated instructions in Russian. Will soon be updated with more examples.
### Languages
Russian
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
kyujinpy/KOR-OpenOrca-Platypus | kyujinpy | 2023-10-24T06:54:44Z | 412 | 4 | null | [
"task_categories:conversational",
"task_categories:text-classification",
"task_categories:token-classification",
"task_categories:table-question-answering",
"task_categories:question-answering",
"task_categories:zero-shot-classification",
"task_categories:summarization",
"task_categories:feature-extraction",
"task_categories:text-generation",
"task_categories:text2text-generation",
"size_categories:10K<n<50K",
"language:ko",
"license:cc-by-nc-4.0",
"arxiv:2306.02707",
"arxiv:2301.13688",
"region:us"
] | 2023-10-24T06:54:44Z | 2023-10-09T14:23:30.000Z | 2023-10-09T14:23:30 | ---
language:
- ko
license: cc-by-nc-4.0
size_categories:
- 10K<n<50K
task_categories:
- conversational
- text-classification
- token-classification
- table-question-answering
- question-answering
- zero-shot-classification
- summarization
- feature-extraction
- text-generation
- text2text-generation
pretty_name: OpenOrca
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: id
dtype: string
- name: input
dtype: string
- name: output
dtype: string
- name: instruction
dtype: string
splits:
- name: train
num_bytes: 78588418
num_examples: 46558
download_size: 39656100
dataset_size: 78588418
---
# KOR-OpenOrca-Platypus
- OpenOrca-Ko + KOpen-platypus
- 데이터셋 이용하셔서 모델이나 데이터셋을 만드실 때, 간단한 출처 표기를 해주신다면 연구에 큰 도움이 됩니다😭😭
## KOpen-platpyus
Repo: [KOpen-platypus](https://huggingface.co/datasets/kyujinpy/KOpen-platypus)
- 고품질 한국어 데이터셋
1. 코드와 주석은 그대로 유지하고, 설명 부분만 한국어로 수정
2. 1번과 더불어서, Python, Java, Cpp, xml 등등 결과들은 전부 기존의 데이터 형태로 최대한 보존
3. 단일 숫자와 영어는 본래의 결과 그대로 가져옴
4. DeepL Pro 번역 결과 중 미완성 변역 결과 직접 수정(예를 들면, '[...]'가 포함되어 있음)
5. DeepL Pro 번역 결과가 본래의 데이터에 비해 글자수가 50% 이하로 낮으면, 번역 결과 수정
6. 번역하고자 하는 글자수가 1500자 이상일 경우, API로 변경해서 번역
7. 고유명사는 최대한 유지함
> Post-processing 작업 내용
## OpenOrca-Ko
Repo: [OpenOrca-Ko](https://huggingface.co/datasets/kyujinpy/OpenOrca-KO)
1. NIV // 1571개
2. FLAN // 9434개
3. T0 // 6351개
4. CoT // 2117개
5. KoCoT // 2159개
> Dataset 구성
## Translation
Using DeepL Pro API. Thanks.
---
>Below is original dataset card
## Table of Contents
- [Dataset Summary](#dataset-summary)
- [Dataset Attribution](#dataset-attribution)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Dataset Use](#dataset-use)
- [Use Cases](#use-cases)
- [Usage Caveats](#usage-caveats)
- [Getting Started](#getting-started)
<p><h1>🐋 The OpenOrca Dataset! 🐋</h1></p>
<a name="dataset-announcement"></a>
We are thrilled to announce the release of the OpenOrca dataset!
This rich collection of augmented FLAN data aligns, as best as possible, with the distributions outlined in the [Orca paper](https://arxiv.org/abs/2306.02707).
It has been instrumental in generating high-performing model checkpoints and serves as a valuable resource for all NLP researchers and developers!
# Official Models
## OpenOrca-Platypus2-13B
Our [latest release](https://huggingface.co/Open-Orca/OpenOrca-Platypus2-13B), the first 13B model to score higher than LLaMA1-65B on the HuggingFace Leaderboard!
Released in partnership with Platypus.
## LlongOrca 7B & 13B
* Our [first 7B release](https://huggingface.co/Open-Orca/LlongOrca-7B-16k), trained on top of LLongMA2 to achieve 16,000 tokens context. #1 long context 7B model at release time, with >99% of the overall #1 model's performance.
* [LlongOrca-13B-16k](https://huggingface.co/Open-Orca/LlongOrca-13B-16k), trained on top of LLongMA2. #1 long context 13B model at release time, with >97% of the overall #1 model's performance.
## OpenOrcaxOpenChat-Preview2-13B
Our [second model](https://huggingface.co/Open-Orca/OpenOrcaxOpenChat-Preview2-13B), highlighting that we've surpassed the performance reported in the Orca paper.
Was #1 at release time, now surpassed by our own OpenOrca-Platypus2-13B.
Released in partnership with OpenChat.
## OpenOrca-Preview1-13B
[OpenOrca-Preview1-13B](https://huggingface.co/Open-Orca/OpenOrca-Preview1-13B)
This model was trained in less than a day, for <$200, with <10% of our data.
At release, it beat the current state of the art models on BigBench-Hard and AGIEval. Achieves ~60% of the improvements reported in the Orca paper.
<a name="dataset-summary"></a>
# Dataset Summary
The OpenOrca dataset is a collection of augmented [FLAN Collection data](https://arxiv.org/abs/2301.13688).
Currently ~1M GPT-4 completions, and ~3.2M GPT-3.5 completions.
It is tabularized in alignment with the distributions presented in the ORCA paper and currently represents a partial completion of the full intended dataset, with ongoing generation to expand its scope.
The data is primarily used for training and evaluation in the field of natural language processing.
<a name="dataset-attribution"></a>
# Dataset Attribution
We would like to give special recognition to the following contributors for their significant efforts and dedication:
Teknium
WingLian/Caseus
Eric Hartford
NanoBit
Pankaj
Winddude
Rohan
http://AlignmentLab.ai:
Autometa
Entropi
AtlasUnified
NeverendingToast
NanoBit
WingLian/Caseus
Also of course, as always, TheBloke, for being the backbone of the whole community.
Many thanks to NanoBit and Caseus, makers of [Axolotl](https://github.com/OpenAccess-AI-Collective/axolotl), for lending us their expertise on the platform that developed and trained manticore, minotaur, and many others!
We are welcoming sponsors or collaborators to help us build these models to the scale they deserve. Please reach out via our socials:
http://Alignmentlab.ai https://discord.gg/n9hXaBPWxx
Want to visualize our full dataset? Check out our [Nomic Atlas Map](https://atlas.nomic.ai/map/c1b88b47-2d9b-47e0-9002-b80766792582/2560fd25-52fe-42f1-a58f-ff5eccc890d2).
[<img src="https://huggingface.co/Open-Orca/OpenOrca-Preview1-13B/resolve/main/OpenOrca%20Nomic%20Atlas.png" alt="Atlas Nomic Dataset Map" width="400" height="400" />](https://atlas.nomic.ai/map/c1b88b47-2d9b-47e0-9002-b80766792582/2560fd25-52fe-42f1-a58f-ff5eccc890d2)
<a name="supported-tasks-and-leaderboards"></a>
# Supported Tasks and Leaderboards
This dataset supports a range of tasks including language modeling, text generation, and text augmentation.
It has been instrumental in the generation of multiple high-performing model checkpoints which have exhibited exceptional performance in our unit testing.
Further information on leaderboards will be updated as they become available.
<a name="languages"></a>
# Languages
The language of the data is primarily English.
<a name="dataset-structure"></a>
# Dataset Structure
<a name="data-instances"></a>
## Data Instances
A data instance in this dataset represents entries from the FLAN collection which have been augmented by submitting the listed question to either GPT-4 or GPT-3.5.
The response is then entered into the response field.
<a name="data-fields"></a>
## Data Fields
The fields are:
1) 'id', a unique numbered identifier which includes one of 'niv', 't0', 'cot', or 'flan' to represent which source FLAN Collection submix the 'question' is sourced from.
2) 'system_prompt', representing the System Prompt presented to the GPT-3.5 or GPT-4 API for the datapoint
3) 'question', representing a question entry as provided by the FLAN Collection
4) 'response', a response to that question received from a query to either GPT-3.5 or GPT-4.
<a name="data-splits"></a>
## Data Splits
The data is unsplit.
<a name="dataset-creation"></a>
# Dataset Creation
<a name="curation-rationale"></a>
## Curation Rationale
The dataset was created to provide a source of augmented text data for researchers and developers.
The datapoints are intended primarily to provide an enhancement of the core FLAN Collection data which relies upon the detailed step by step reasoning capabilities of GPT-3.5 and GPT-4.
This "reasoning trace" augmentation has demonstrated exceptional results, allowing a LLaMA-13B model trained with this data to rival or beat GPT-3.5 on broad sets of hard reasoning tasks which all models below 100B parameters had previously performed dramatically worse on.
<a name="source-data"></a>
## Source Data
The data is generated using techniques in alignment with the distributions outlined in the Orca paper, except as noted below:
1) There is not enough CoT data in the FLAN Collection to generate 150K zero-shot entries, as the paper purports to use.
We suspect this portion was either undocumented or misrepresented. We have used the ~75K points available.
2) We used the pre-generated FLAN Collection datasets hosted on HuggingFace under conceptofmind, e.g. [conceptofmind/flan2021](https://huggingface.co/datasets/conceptofmind/flan2021_submix_original).
These are referenced by the [official FLAN Collection repo](https://github.com/google-research/FLAN/tree/main/flan/v2) as the preferred data source.
However, these are a subset of the full FLAN Collection data, and have less than the required entries for the flan2021 and t0 submixes, by ~1.25M and 200k respectively.
Combined, this gave us ~1.5M fewer datapoints than in the original Orca paper. Completing the set is an ongoing work.
<a name="dataset-use"></a>
# Dataset Use
<a name="use-cases"></a>
## Use Cases
The dataset can be used for tasks related to language understanding, natural language processing, machine learning model training, and model performance evaluation.
<a name="usage-caveats"></a>
## Usage Caveats
Given that this is a work-in-progress dataset, it is recommended to regularly check for updates and improvements.
Further, the data should be used in accordance with the guidelines and recommendations outlined in the Orca paper.
<a name="getting-started"></a>
## Getting Started
This dataset is organized such that it can be naively loaded via Hugging Face datasets library.
We recommend using streaming due to the large size of the files.
Regular updates and data generation progress can be monitored through the OpenOrca repository on Hugging Face.
# Citation
```bibtex
@misc{OpenOrca,
title = {OpenOrca: An Open Dataset of GPT Augmented FLAN Reasoning Traces},
author = {Wing Lian and Bleys Goodson and Eugene Pentland and Austin Cook and Chanvichet Vong and "Teknium"},
year = {2023},
publisher = {HuggingFace},
journal = {HuggingFace repository},
howpublished = {\url{https://https://huggingface.co/Open-Orca/OpenOrca},
}
```
```bibtex
@misc{mukherjee2023orca,
title={Orca: Progressive Learning from Complex Explanation Traces of GPT-4},
author={Subhabrata Mukherjee and Arindam Mitra and Ganesh Jawahar and Sahaj Agarwal and Hamid Palangi and Ahmed Awadallah},
year={2023},
eprint={2306.02707},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
```bibtex
@misc{longpre2023flan,
title={The Flan Collection: Designing Data and Methods for Effective Instruction Tuning},
author={Shayne Longpre and Le Hou and Tu Vu and Albert Webson and Hyung Won Chung and Yi Tay and Denny Zhou and Quoc V. Le and Barret Zoph and Jason Wei and Adam Roberts},
year={2023},
eprint={2301.13688},
archivePrefix={arXiv},
primaryClass={cs.AI}
}
```
```bibtex
@misc{touvron2023llama,
title={Llama 2: Open Foundation and Fine-Tuned Chat Models},
author={Hugo Touvron and Louis Martin and Kevin Stone and Peter Albert and Amjad Almahairi and Yasmine Babaei and Nikolay Bashlykov and Soumya Batra and Prajjwal Bhargava and Shruti Bhosale and Dan Bikel and Lukas Blecher and Cristian Canton Ferrer and Moya Chen and Guillem Cucurull and David Esiobu and Jude Fernandes and Jeremy Fu and Wenyin Fu and Brian Fuller and Cynthia Gao and Vedanuj Goswami and Naman Goyal and Anthony Hartshorn and Saghar Hosseini and Rui Hou and Hakan Inan and Marcin Kardas and Viktor Kerkez and Madian Khabsa and Isabel Kloumann and Artem Korenev and Punit Singh Koura and Marie-Anne Lachaux and Thibaut Lavril and Jenya Lee and Diana Liskovich and Yinghai Lu and Yuning Mao and Xavier Martinet and Todor Mihaylov and Pushkar Mishra and Igor Molybog and Yixin Nie and Andrew Poulton and Jeremy Reizenstein and Rashi Rungta and Kalyan Saladi and Alan Schelten and Ruan Silva and Eric Michael Smith and Ranjan Subramanian and Xiaoqing Ellen Tan and Binh Tang and Ross Taylor and Adina Williams and Jian Xiang Kuan and Puxin Xu and Zheng Yan and Iliyan Zarov and Yuchen Zhang and Angela Fan and Melanie Kambadur and Sharan Narang and Aurelien Rodriguez and Robert Stojnic and Sergey Edunov and Thomas Scialom},
year={2023},
eprint= arXiv 2307.09288
}
@software{touvron2023llama,
title={LLaMA: Open and Efficient Foundation Language Models},
author={Touvron, Hugo and Lavril, Thibaut and Izacard, Gautier and Martinet, Xavier and Lachaux, Marie-Anne and Lacroix, Timoth{\'e}e and Rozi{\`e}re, Baptiste and Goyal, Naman and Hambro, Eric and Azhar, Faisal and Rodriguez, Aurelien and Joulin, Armand and Grave, Edouard and Lample, Guillaume},
journal={arXiv preprint arXiv:2302.13971},
year={2023}
}
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
emotone_ar | null | 2023-01-25T14:29:56Z | 411 | 6 | null | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:ar",
"license:unknown",
"region:us"
] | 2023-01-25T14:29:56Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- found
language:
- ar
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
pretty_name: Emotional Tone in Arabic
dataset_info:
features:
- name: tweet
dtype: string
- name: label
dtype:
class_label:
names:
'0': none
'1': anger
'2': joy
'3': sadness
'4': love
'5': sympathy
'6': surprise
'7': fear
splits:
- name: train
num_bytes: 1541746
num_examples: 10065
download_size: 1563138
dataset_size: 1541746
---
# Dataset Card for Emotional Tone in Arabic
## Table of Contents
- [Dataset Card for Emotional Tone in Arabic](#dataset-card-for-emotional-tone-in-arabic)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [|split|num examples|](#splitnum-examples)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
- [Who are the source language producers?](#who-are-the-source-language-producers)
- [Annotations](#annotations)
- [Annotation process](#annotation-process)
- [Who are the annotators?](#who-are-the-annotators)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Repository:** [Repository](https://github.com/AmrMehasseb/Emotional-Tone)
- **Paper:** [Emotional Tone Detection in Arabic Tweets](https://www.researchgate.net/publication/328164296_Emotional_Tone_Detection_in_Arabic_Tweets_18th_International_Conference_CICLing_2017_Budapest_Hungary_April_17-23_2017_Revised_Selected_Papers_Part_II)
- **Point of Contact:** [Amr Al-Khatib](https://github.com/AmrMehasseb)
### Dataset Summary
Dataset of 10065 tweets in Arabic for Emotion detection in Arabic text
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The dataset is based on Arabic.
## Dataset Structure
### Data Instances
example:
```
>>> {'label': 0, 'tweet': 'الاوليمبياد الجايه هكون لسه ف الكليه ..'}
```
### Data Fields
- "tweet": plain text tweet in Arabic
- "label": emotion class label
the dataset distribution and balance for each class looks like the following
|label||Label description | Count |
|---------|---------| ------- |
|0 |none | 1550 |
|1 |anger | 1444 |
|2 |joy | 1281 |
|3 |sadness | 1256 |
|4 |love | 1220 |
|5 |sympathy | 1062 |
|6 |surprise | 1045 |
|7 |fear | 1207 |
### Data Splits
The dataset is not split.
| | train |
|----------|--------:|
| no split | 10,065 |
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
[More Information Needed]
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### 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
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
```
@inbook{inbook,
author = {Al-Khatib, Amr and El-Beltagy, Samhaa},
year = {2018},
month = {01},
pages = {105-114},
title = {Emotional Tone Detection in Arabic Tweets: 18th International Conference, CICLing 2017, Budapest, Hungary, April 17–23, 2017, Revised Selected Papers, Part II},
isbn = {978-3-319-77115-1},
doi = {10.1007/978-3-319-77116-8_8}
}
```
### Contributions
Thanks to [@abdulelahsm](https://github.com/abdulelahsm) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
SetFit/rte | SetFit | 2022-02-28T12:46:43Z | 409 | 0 | null | [
"region:us"
] | 2022-02-28T12:46:43Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | # Glue RTE
This dataset is a port of the official [`rte` dataset](https://huggingface.co/datasets/glue/viewer/rte/train) on the Hub.
Note that the sentence1 and sentence2 columns have been renamed to text1 and text2 respectively.
Also, the test split is not labeled; the label column values are always -1.
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
iara-project/news-articles-ptbr-dataset | iara-project | 2023-09-21T03:12:30Z | 409 | 1 | null | [
"region:us"
] | 2023-09-21T03:12:30Z | 2023-09-17T19:11:32.000Z | 2023-09-17T19:11:32 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: title
dtype: string
- name: text
dtype: string
- name: date
dtype: string
- name: category
dtype: string
- name: category_natural_language
dtype: string
- name: link
dtype: string
splits:
- name: train
num_bytes: 628987914
num_examples: 176114
- name: test
num_bytes: 627415372
num_examples: 176114
download_size: 770300096
dataset_size: 1256403286
---
# Dataset Card for "news-articles-ptbr-dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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smangrul/pubmed_smol | smangrul | 2023-11-08T15:10:47Z | 409 | 0 | null | [
"region:us"
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
reciprocate/number-pairs | reciprocate | 2023-11-20T14:36:37Z | 408 | 0 | null | [
"region:us"
] | 2023-11-20T14:36:37Z | 2023-05-03T15:16:41.000Z | 2023-05-03T15:16:41 | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: selected
dtype: string
- name: rejected
dtype: string
splits:
- name: train
num_bytes: 30931
num_examples: 900
- name: test
num_bytes: 3436
num_examples: 100
download_size: 5098
dataset_size: 34367
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
# Dataset Card for "autocrit-testing-numbers"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
indonlp/nusaparagraph_topic | indonlp | 2023-09-07T13:04:43Z | 407 | 0 | null | [
"license:apache-2.0",
"region:us"
] | 2023-09-07T13:04:43Z | 2023-09-07T13:01:09.000Z | 2023-09-07T13:01:09 | ---
license: apache-2.0
---
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Brendan/icdst_multiwoz_turns_v24 | Brendan | 2023-10-25T21:41:18Z | 407 | 0 | null | [
"region:us"
] | 2023-10-25T21:41:18Z | 2023-10-13T00:07:28.000Z | 2023-10-13T00:07:28 | ---
dataset_info:
features:
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download_size: 6875945
dataset_size: 145293067.4987746
---
# Dataset Card for "icdst_multiwoz_turns_v24"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
DDSC/angry-tweets | DDSC | 2023-07-20T00:34:34Z | 406 | 1 | null | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:da",
"license:cc-by-4.0",
"region:us"
] | 2023-07-20T00:34:34Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- da
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: AngryTweets
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
---
# Dataset Card for AngryTweets
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Paper:** https://aclanthology.org/2021.nodalida-main.53/
- **Direct Download**: https://danlp-downloads.alexandra.dk/datasets/game_tweets.zip
### Dataset Summary
This dataset consists of anonymised Danish Twitter data that has been annotated for sentiment analysis through crowd-sourcing. All credits go to the authors of the following paper, who created the dataset:
[Pauli, Amalie Brogaard, et al. "DaNLP: An open-source toolkit for Danish Natural Language Processing." Proceedings of the 23rd Nordic Conference on Computational Linguistics (NoDaLiDa). 2021](https://aclanthology.org/2021.nodalida-main.53/)
### Supported Tasks and Leaderboards
This dataset is suitable for sentiment analysis.
### Languages
This dataset is in Danish.
## Dataset Structure
### Data Instances
Every entry in the dataset has a tweet and an associated label.
### Data Fields
An entry in the dataset consists of the following fields:
- `text` (`str`): The tweet content.
- `label` (`str`): The label of the `text`. Can be "positiv", "neutral" or "negativ" for positive, neutral and negative sentiment, respectively.
### Data Splits
A `train` and `test` split is available, with the test split being 30% of the dataset, randomly sampled in a stratified fashion. There are 2,437 tweets in the training split and 1,047 in the test split.
## Additional Information
### Dataset Curators
The collection and annotation of the dataset is solely due to the authors of [the original paper](https://aclanthology.org/2021.nodalida-main.53/): Amalie Brogaard Pauli, Maria Barrett, Ophélie Lacroix and Rasmus Hvingelby. The tweets have been anonymised by [@saattrupdan](https://github.com/saattrupdan).
### Licensing Information
The dataset is released under the CC BY 4.0 license.
### Citation Information
```
@inproceedings{pauli2021danlp,
title={DaNLP: An open-source toolkit for Danish Natural Language Processing},
author={Pauli, Amalie Brogaard and Barrett, Maria and Lacroix, Oph{\'e}lie and Hvingelby, Rasmus},
booktitle={Proceedings of the 23rd Nordic Conference on Computational Linguistics (NoDaLiDa)},
pages={460--466},
year={2021}
}
```
### Contributions
Thanks to [@saattrupdan](https://github.com/saattrupdan) for adding this dataset to the Hugging Face Hub. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
turk | null | 2022-11-18T21:56:55Z | 405 | 3 | null | [
"task_categories:text2text-generation",
"task_ids:text-simplification",
"annotations_creators:machine-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:gpl-3.0",
"region:us"
] | 2022-11-18T21:56:55Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- machine-generated
language_creators:
- found
language:
- en
license:
- gpl-3.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text2text-generation
task_ids:
- text-simplification
paperswithcode_id: null
pretty_name: TURK
dataset_info:
features:
- name: original
dtype: string
- name: simplifications
sequence: string
config_name: simplification
splits:
- name: validation
num_bytes: 2120187
num_examples: 2000
- name: test
num_bytes: 396378
num_examples: 359
download_size: 2443394
dataset_size: 2516565
---
# Dataset Card for TURK
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** None
- **Repository:** [TURK](https://github.com/cocoxu/simplification)
- **Paper:** [Optimizing Statistical Machine Translation for Text Simplification](https://www.aclweb.org/anthology/Q16-1029/)
- **Leaderboard:** N/A
- **Point of Contact:** [Wei Xu](mailto:wei.xu@cc.gatech.edu)
### Dataset Summary
TURK is a multi-reference dataset for the evaluation of sentence simplification in English. The dataset consists of 2,359 sentences from the [Parallel Wikipedia Simplification (PWKP) corpus](https://www.aclweb.org/anthology/C10-1152/). Each sentence is associated with 8 crowdsourced simplifications that focus on only lexical paraphrasing (no sentence splitting or deletion).
### Supported Tasks and Leaderboards
No Leaderboard for the task.
### Languages
TURK contains English text only (BCP-47: `en`).
## Dataset Structure
### Data Instances
An instance consists of an original sentence and 8 possible reference simplifications that focus on lexical paraphrasing.
```
{'original': 'one side of the armed conflicts is composed mainly of the sudanese military and the janjaweed , a sudanese militia group recruited mostly from the afro-arab abbala tribes of the northern rizeigat region in sudan .',
'simplifications': ['one side of the armed conflicts is made of sudanese military and the janjaweed , a sudanese militia recruited from the afro-arab abbala tribes of the northern rizeigat region in sudan .', 'one side of the armed conflicts consist of the sudanese military and the sudanese militia group janjaweed .', 'one side of the armed conflicts is mainly sudanese military and the janjaweed , which recruited from the afro-arab abbala tribes .', 'one side of the armed conflicts is composed mainly of the sudanese military and the janjaweed , a sudanese militia group recruited mostly from the afro-arab abbala tribes in sudan .', 'one side of the armed conflicts is made up mostly of the sudanese military and the janjaweed , a sudanese militia group whose recruits mostly come from the afro-arab abbala tribes from the northern rizeigat region in sudan .', 'the sudanese military and the janjaweed make up one of the armed conflicts , mostly from the afro-arab abbal tribes in sudan .', 'one side of the armed conflicts is composed mainly of the sudanese military and the janjaweed , a sudanese militia group recruited mostly from the afro-arab abbala tribes of the northern rizeigat regime in sudan .', 'one side of the armed conflicts is composed mainly of the sudanese military and the janjaweed , a sudanese militia group recruited mostly from the afro-arab abbala tribes of the northern rizeigat region in sudan .']}
```
### Data Fields
- `original`: an original sentence from the source datasets
- `simplifications`: a set of reference simplifications produced by crowd workers.
### Data Splits
TURK does not contain a training set; many models use [WikiLarge](https://github.com/XingxingZhang/dress) (Zhang and Lapata, 2017) or [Wiki-Auto](https://github.com/chaojiang06/wiki-auto) (Jiang et. al 2020) for training.
Each input sentence has 8 associated reference simplified sentences. 2,359 input sentences are randomly split into 2,000 validation and 359 test sentences.
| | Dev | Test | Total |
| ----- | ------ | ---- | ----- |
| Input Sentences | 2000 | 359 | 2359 |
| Reference Simplifications | 16000 | 2872 | 18872 |
## Dataset Creation
### Curation Rationale
The TURK dataset was constructed to evaluate the task of text simplification. It contains multiple human-written references that focus on only lexical simplification.
### Source Data
#### Initial Data Collection and Normalization
The input sentences in the dataset are extracted from the [Parallel Wikipedia Simplification (PWKP) corpus](https://www.aclweb.org/anthology/C10-1152/).
#### Who are the source language producers?
The references are crowdsourced from Amazon Mechanical Turk. The annotators were asked to provide simplifications without losing any information or splitting the input sentence. No other demographic or compensation information is provided in the paper.
### Annotations
#### Annotation process
The instructions given to the annotators are available in the paper.
#### Who are the annotators?
The annotators are Amazon Mechanical Turk workers.
### Personal and Sensitive Information
Since the dataset is created from English Wikipedia (August 22, 2009 version), all the information contained in the dataset is already in the public domain.
## Considerations for Using the Data
### Social Impact of Dataset
The dataset helps move forward the research towards text simplification by creating a higher quality validation and test dataset. Progress in text simplification in turn has the potential to increase the accessibility of written documents to wider audiences.
### Discussion of Biases
The dataset may contain some social biases, as the input sentences are based on Wikipedia. Studies have shown that the English Wikipedia contains both gender biases [(Schmahl et al., 2020)](https://research.tudelft.nl/en/publications/is-wikipedia-succeeding-in-reducing-gender-bias-assessing-changes) and racial biases [(Adams et al., 2019)](https://journals.sagepub.com/doi/pdf/10.1177/2378023118823946).
### Other Known Limitations
Since the dataset contains only 2,359 sentences that are derived from Wikipedia, it is limited to a small subset of topics present on Wikipedia.
## Additional Information
### Dataset Curators
TURK was developed by researchers at the University of Pennsylvania. The work was supported by the NSF under grant IIS-1430651 and the NSF GRFP under grant 1232825.
### Licensing Information
[GNU General Public License v3.0](https://github.com/cocoxu/simplification/blob/master/LICENSE)
### Citation Information
```
@article{Xu-EtAl:2016:TACL,
author = {Wei Xu and Courtney Napoles and Ellie Pavlick and Quanze Chen and Chris Callison-Burch},
title = {Optimizing Statistical Machine Translation for Text Simplification},
journal = {Transactions of the Association for Computational Linguistics},
volume = {4},
year = {2016},
url = {https://cocoxu.github.io/publications/tacl2016-smt-simplification.pdf},
pages = {401--415}
}
```
### Contributions
Thanks to [@mounicam](https://github.com/mounicam) for adding this dataset. | [
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NbAiLab/norwegian_parliament | NbAiLab | 2022-07-01T19:51:13Z | 405 | 1 | null | [
"task_categories:text-classification",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:no",
"license:cc-by-4.0",
"region:us"
] | 2022-07-01T19:51:13Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- no
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
---
# Dataset Card Creation Guide
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** N/A
- **Repository:** [GitHub](https://github.com/ltgoslo/NorBERT/)
- **Paper:** N/A
- **Leaderboard:** N/A
- **Point of Contact:** -
### Dataset Summary
The Norwegian Parliament Speeches is a collection of text passages from 1998 to 2016 and pronounced at the Norwegian Parliament (Storting) by members of the two major parties: Fremskrittspartiet and Sosialistisk Venstreparti. The dataset is annotated with the party the speaker was associated with at the time (dates of speeches are also included).
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The text in the dataset is in Norwegian.
## Dataset Structure
### Data Instances
Example of one instance in the dataset.
```{'label': 0, 'text': 'Verre er det med slagsmålene .'}```
### Data Fields
- `id`: index of the example
- `text`: Text of a speech
- `date`: Date (`YYYY-MM-DD`) the speech was produced
- `label`: Political party the speaker was associated with at the time
- 0 = Fremskrittspartiet
- 1 = Sosialistisk Venstreparti
### Data Splits
The dataset is split into a `train`, `validation`, and `test` split with the following sizes:
| | Tain | Valid | Test |
| ----- | ------ | ----- | ----- |
| Number of examples | 3600 | 1200 | 1200 |
The dataset is balanced on political party.
## Dataset Creation
This dataset is based on the publicly available information by Norwegian Parliament (Storting) and created by the National Library of Norway AI-Lab to benchmark their language models.
## Additional Information
### Licensing Information
This work is licensed under a Creative Commons Attribution 4.0 International License
### Citation Information
```latex
@misc{--,
title={--},
author={--},
year={2021},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
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generated_reviews_enth | null | 2023-01-25T14:30:46Z | 404 | 4 | null | [
"task_categories:translation",
"task_categories:text-classification",
"task_ids:multi-class-classification",
"task_ids:semantic-similarity-classification",
"annotations_creators:expert-generated",
"annotations_creators:machine-generated",
"language_creators:machine-generated",
"multilinguality:translation",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"language:th",
"license:cc-by-sa-4.0",
"arxiv:2007.03541",
"arxiv:1909.05858",
"region:us"
] | 2023-01-25T14:30:46Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
- machine-generated
language_creators:
- machine-generated
language:
- en
- th
license:
- cc-by-sa-4.0
multilinguality:
- translation
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- translation
- text-classification
task_ids:
- multi-class-classification
- semantic-similarity-classification
pretty_name: generated_reviews_enth
dataset_info:
features:
- name: translation
dtype:
translation:
languages:
- en
- th
- name: review_star
dtype: int32
- name: correct
dtype:
class_label:
names:
'0': neg
'1': pos
config_name: generated_reviews_enth
splits:
- name: train
num_bytes: 147673215
num_examples: 141369
- name: validation
num_bytes: 16409966
num_examples: 15708
- name: test
num_bytes: 18133523
num_examples: 17453
download_size: 59490601
dataset_size: 182216704
---
# Dataset Card for generated_reviews_enth
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** ttp://airesearch.in.th/
- **Repository:** https://github.com/vistec-ai/generated_reviews_enth
- **Paper:** https://arxiv.org/pdf/2007.03541.pdf
- **Leaderboard:**
- **Point of Contact:** [AIResearch](http://airesearch.in.th/)
### Dataset Summary
`generated_reviews_enth` is created as part of [scb-mt-en-th-2020](https://arxiv.org/pdf/2007.03541.pdf) for machine translation task. This dataset (referred to as `generated_reviews_yn` in [scb-mt-en-th-2020](https://arxiv.org/pdf/2007.03541.pdf)) are English product reviews generated by [CTRL](https://arxiv.org/abs/1909.05858), translated by Google Translate API and annotated as accepted or rejected (`correct`) based on fluency and adequacy of the translation by human annotators. This allows it to be used for English-to-Thai translation quality esitmation (binary label), machine translation, and sentiment analysis.
### Supported Tasks and Leaderboards
English-to-Thai translation quality estimation (binary label) is the intended use. Other uses include machine translation and sentiment analysis.
### Languages
English, Thai
## Dataset Structure
### Data Instances
```
{'correct': 0, 'review_star': 4, 'translation': {'en': "I had a hard time finding a case for my new LG Lucid 2 but finally found this one on amazon. The colors are really pretty and it works just as well as, if not better than the otterbox. Hopefully there will be more available by next Xmas season. Overall, very cute case. I love cheetah's. :)", 'th': 'ฉันมีปัญหาในการหาเคสสำหรับ LG Lucid 2 ใหม่ของฉัน แต่ในที่สุดก็พบเคสนี้ใน Amazon สีสวยมากและใช้งานได้ดีเช่นเดียวกับถ้าไม่ดีกว่านาก หวังว่าจะมีให้มากขึ้นในช่วงเทศกาลคริสต์มาสหน้า โดยรวมแล้วน่ารักมาก ๆ ฉันรักเสือชีตาห์ :)'}}
{'correct': 0, 'review_star': 1, 'translation': {'en': "This is the second battery charger I bought as a Christmas present, that came from Amazon, after one purchased before for my son. His was still working. The first charger, received in July, broke apart and wouldn't charge anymore. Just found out two days ago they discontinued it without warning. It took quite some time to find the exact replacement charger. Too bad, really liked it. One of these days, will purchase an actual Nikon product, or go back to buying batteries.", 'th': 'นี่เป็นเครื่องชาร์จแบตเตอรี่ก้อนที่สองที่ฉันซื้อเป็นของขวัญคริสต์มาสซึ่งมาจากอเมซอนหลังจากที่ซื้อมาเพื่อลูกชายของฉัน เขายังทำงานอยู่ เครื่องชาร์จแรกที่ได้รับในเดือนกรกฎาคมแตกเป็นชิ้น ๆ และจะไม่ชาร์จอีกต่อไป เพิ่งค้นพบเมื่อสองวันก่อนพวกเขาหยุดมันโดยไม่มีการเตือนล่วงหน้า ใช้เวลาพอสมควรในการหาที่ชาร์จที่ถูกต้อง แย่มากชอบมาก สักวันหนึ่งจะซื้อผลิตภัณฑ์ Nikon จริงหรือกลับไปซื้อแบตเตอรี่'}}
{'correct': 1, 'review_star': 1, 'translation': {'en': 'I loved the idea of having a portable computer to share pictures with family and friends on my big screen. It worked really well for about 3 days, then when i opened it one evening there was water inside where all the wires came out. I cleaned that up and put some tape over that, so far, no leaks. My husband just told me yesterday, however, that this thing is trash.', 'th': 'ฉันชอบไอเดียที่มีคอมพิวเตอร์พกพาเพื่อแชร์รูปภาพกับครอบครัวและเพื่อน ๆ บนหน้าจอขนาดใหญ่ของฉัน มันใช้งานได้ดีจริง ๆ ประมาณ 3 วันจากนั้นเมื่อฉันเปิดมันในเย็นวันหนึ่งมีน้ำอยู่ภายในที่ซึ่งสายไฟทั้งหมดออกมา ฉันทำความสะอาดมันแล้ววางเทปไว้ที่นั่นจนถึงตอนนี้ไม่มีรอยรั่ว สามีของฉันเพิ่งบอกฉันเมื่อวานนี้ว่าสิ่งนี้เป็นขยะ'}}
```
### Data Fields
- `translation`:
- `en`: English product reviews generated by [CTRL](https://arxiv.org/abs/1909.05858)
- `th`: Thai product reviews translated from `en` by Google Translate API
- `review_star`: Stars of the generated reviews, put as condition for [CTRL](https://arxiv.org/abs/1909.05858)
- `correct`: 1 if the English-to-Thai translation is accepted (`correct`) based on fluency and adequacy of the translation by human annotators else 0
### Data Splits
| | train | valid | test |
|-----------------|--------|-------|-------|
| # samples | 141369 | 15708 | 17453 |
| # correct:0 | 99296 | 10936 | 12208 |
| # correct:1 | 42073 | 4772 | 5245 |
| # review_star:1 | 50418 | 5628 | 6225 |
| # review_star:2 | 22876 | 2596 | 2852 |
| # review_star:3 | 22825 | 2521 | 2831 |
| # review_star:1 | 22671 | 2517 | 2778 |
| # review_star:5 | 22579 | 2446 | 2767 |
## Dataset Creation
### Curation Rationale
`generated_reviews_enth` is created as part of [scb-mt-en-th-2020](https://arxiv.org/pdf/2007.03541.pdf) for machine translation task. This dataset (referred to as `generated_reviews_yn` in [scb-mt-en-th-2020](https://arxiv.org/pdf/2007.03541.pdf)) are English product reviews generated by [CTRL](https://arxiv.org/abs/1909.05858), translated by Google Translate API and annotated as accepted or rejected (`correct`) based on fluency and adequacy of the translation by human annotators. This allows it to be used for English-to-Thai translation quality esitmation (binary label), machine translation, and sentiment analysis.
### Source Data
#### Initial Data Collection and Normalization
The data generation process is as follows:
- `en` is generated using conditional generation of [CTRL](https://arxiv.org/abs/1909.05858), stating a star review for each generated product review.
- `th` is translated from `en` using Google Translate API
- `correct` is annotated as accepted or rejected (1 or 0) based on fluency and adequacy of the translation by human annotators
For this specific dataset for translation quality estimation task, we apply the following preprocessing:
- Drop duplciates on `en`,`th`,`review_star`,`correct`; duplicates might exist because the translation checking is done by annotators.
- Remove reviews that are not between 1-5 stars.
- Remove reviews whose `correct` are not 0 or 1.
- Deduplicate on `en` which contains the source sentences.
#### Who are the source language producers?
[CTRL](https://arxiv.org/abs/1909.05858)
### Annotations
#### Annotation process
Annotators are given English and Thai product review pairs. They are asked to label the pair as acceptable translation or not based on fluency and adequacy of the translation.
#### Who are the annotators?
Human annotators of [Hope Data Annotations](https://www.hopedata.org/) hired by [AIResearch.in.th](http://airesearch.in.th/)
### Personal and Sensitive Information
The authors do not expect any personal or sensitive information to be in the generated product reviews, but they could slip through from pretraining of [CTRL](https://arxiv.org/abs/1909.05858).
## Considerations for Using the Data
### Social Impact of Dataset
- English-Thai translation quality estimation for machine translation
- Product review classification for Thai
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
Due to annotation process constraints, the number of one-star reviews are notably higher than other-star reviews. This makes the dataset slighly imbalanced.
## Additional Information
### Dataset Curators
The dataset was created by [AIResearch.in.th](http://airesearch.in.th/)
### Licensing Information
CC BY-SA 4.0
### Citation Information
```
@article{lowphansirikul2020scb,
title={scb-mt-en-th-2020: A Large English-Thai Parallel Corpus},
author={Lowphansirikul, Lalita and Polpanumas, Charin and Rutherford, Attapol T and Nutanong, Sarana},
journal={arXiv preprint arXiv:2007.03541},
year={2020}
}
```
### Contributions
Thanks to [@cstorm125](https://github.com/cstorm125) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
result-kand2-sdxl-wuerst-karlo/2dde8d06 | result-kand2-sdxl-wuerst-karlo | 2023-11-08T00:00:01Z | 404 | 0 | null | [
"region:us"
] | 2023-11-08T00:00:01Z | 2023-11-08T00:00:00.000Z | 2023-11-08T00:00:00 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 169
num_examples: 10
download_size: 1357
dataset_size: 169
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "2dde8d06"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
nomic-ai/gpt4all-j-prompt-generations | nomic-ai | 2023-04-24T15:20:43Z | 403 | 168 | null | [
"size_categories:100K<n<1M",
"language:en",
"license:apache-2.0",
"region:us"
] | 2023-04-24T15:20:43Z | 2023-04-10T21:59:10.000Z | 2023-04-10T21:59:10 | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: response
dtype: string
- name: source
dtype: string
splits:
- name: train
num_bytes: 1774285641
num_examples: 808812
download_size: 990673616
dataset_size: 1774285641
license: apache-2.0
language:
- en
size_categories:
- 100K<n<1M
---
# Dataset Card for [GPT4All-J Prompt Generations]
## Dataset Description
Dataset used to train [GPT4All-J](https://huggingface.co/nomic-ai/gpt4all-j) and [GPT4All-J-LoRA](https://huggingface.co/nomic-ai/gpt4all-j-lora)
We release several versions of datasets
- **v1.0:** The original dataset we used to finetune GPT-J on
- **v1.1-breezy**: A filtered dataset where we removed all instances of `AI language model`
- **v1.2-jazzy**: A filtered dataset where we also removed instances like `I'm sorry, I can't answer...` and `AI language model`
- **v1.3-groovy**: The v1.2 dataset with ShareGPT and Dolly added with ~8% of semantic duplicates removed from the dataset using [Atlas](https://atlas.nomic.ai/)
The dataset defaults to `main` which is `v1.0`. To download a specific version, you can pass an argument to the keyword `revision` in `load_dataset`:
```python
from datasets import load_dataset
jazzy = load_dataset("nomic-ai/gpt4all-j-prompt-generations", revision='v1.2-jazzy')
```
- **Homepage:** [gpt4all.io](https://gpt4all.io/)
- **Repository:** [gpt4all](https://github.com/nomic-ai/gpt4all)
- **Paper:** [Technical Report](https://static.nomic.ai/gpt4all/2023_GPT4All-J_Technical_Report_2.pdf)
- **Atlas Map:** [Map of Prompts](https://atlas.nomic.ai/map/gpt4all-j-prompts-curated) and [Responses](https://atlas.nomic.ai/map/gpt4all-j-response-curated) | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
dyk | null | 2023-01-25T14:29:39Z | 402 | 0 | null | [
"task_categories:question-answering",
"task_ids:open-domain-qa",
"annotations_creators:expert-generated",
"language_creators:other",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:pl",
"license:bsd-3-clause",
"region:us"
] | 2023-01-25T14:29:39Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- other
language:
- pl
license:
- bsd-3-clause
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- open-domain-qa
pretty_name: dyk
dataset_info:
features:
- name: q_id
dtype: string
- name: question
dtype: string
- name: answer
dtype: string
- name: target
dtype:
class_label:
names:
'0': '0'
'1': '1'
splits:
- name: train
num_bytes: 1388690
num_examples: 4154
- name: test
num_bytes: 353643
num_examples: 1029
download_size: 685462
dataset_size: 1742333
---
# Dataset Card for [Dataset Name]
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
http://nlp.pwr.wroc.pl/en/tools-and-resources/resources/czy-wiesz-question-answering-dataset
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
The Did You Know (pol. Czy wiesz?) dataset consists of human-annotated question-answer pairs. The task is to predict if the answer is correct. We chose the negatives which have the largest token overlap with a question.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
Polish
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
- q_id: question id
- question: question sentence
- answer: answer sentence
- target: 1 if the answer is correct, 0 otherwise. Note that the test split doesn't have target values so -1 is used instead
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
CC BY-SA 3.0
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@abecadel](https://github.com/abecadel) for adding this dataset. | [
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kensho/spgispeech | kensho | 2022-10-21T14:46:30Z | 402 | 21 | null | [
"task_categories:automatic-speech-recognition",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:en",
"license:other",
"arxiv:2104.02014",
"region:us"
] | 2022-10-21T14:46:30Z | 2022-06-29T16:09:04.000Z | 2022-06-29T16:09:04 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license:
- other
multilinguality:
- monolingual
pretty_name: SpgiSpeech
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- automatic-speech-recognition
extra_gated_prompt: |-
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---
# Dataset Card for SPGISpeech
## Table of Contents
- [Table of Contents](#table-of-contents)
<img src="https://s3.amazonaws.com/moonup/production/uploads/1661776840270-62e049fe81d9ca6484eff137.png" alt="SPGISpeech Logo" width="200"/>
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
- [Terms of Usage](#terms-of-usage)
## Dataset Description
- **Homepage:** https://datasets.kensho.com/datasets/spgispeech
- **Repository:**
- **Paper:** https://arxiv.org/abs/2104.02014
- **Leaderboard:**
- **Point of Contact:** [data@kensho.com](mailto:data@kensho.com )
## Dataset Description
SPGISpeech (rhymes with “squeegee-speech”) is a large-scale transcription dataset, freely available for academic research.
SPGISpeech is a corpus of 5,000 hours of professionally-transcribed financial audio.
SPGISpeech contains a broad cross-section of L1 and L2 English accents,
strongly varying audio quality, and both spontaneous and narrated speech. The transcripts have each been cross-checked
by multiple professional editors for high accuracy and are fully formatted, including capitalization, punctuation, and
denormalization of non-standard words.
SPGISpeech consists of 5,000 hours of recorded company earnings calls and their respective transcriptions.
The original calls were split into slices ranging from 5 to 15 seconds in length to allow easy training for
speech recognition systems. Calls represent a broad cross-section of international business English;
SPGISpeech contains approximately 50,000 speakers, one of the largest numbers of any speech corpus,
and offers a variety of L1 and L2 English accents. The format of each WAV file is single channel, 16kHz, 16 bit audio.
### Example Usage
The training split has several configurations of various size: S, M, L. See the Section [Data Splits](#data-splits)
for for more information. To download the S configuration:
```python
from datasets import load_dataset
spgi = load_dataset("kensho/spgispeech", "S", use_auth_token=True)
# see structure
print(spgi)
# load audio sample on the fly
audio_input = spgi["train"][0]["audio"] # first decoded audio sample
transcription = spgi["train"][0]["text"] # first transcription
```
It is possible to download only the development or test data:
```python
spgi_dev = load_dataset("kensho/spgispeech", "dev", use_auth_token=True)
spgi_test = load_dataset("kensho/spgispeech", "test", use_auth_token=True)
```
### Supported Tasks and Leaderboards
- `automatic-speech-recognition`: The dataset can be used to train a model for Automatic Speech Recognition (ASR).
The model is presented with an audio file and asked to transcribe the audio file to written text.
The most common evaluation metric is the word error rate (WER).
### Languages
SPGISpeech contains audio and transcription data in business English and offers a variety of L1 and L2 accents.
## Dataset Structure
### Data Instances
```python
{
'wav_filename': '32bcf9c9dc707fb61a04290e296f31eb/99.wav',
'audio': {
'path': '/home/user/.cache/huggingface/datasets/downloads/extracted/c7082e2bd5b.../dev_part_2/32bcf9c9dc707fb61a04290e296f31eb/99.wav',
'array': array([-0.00039673, -0.00057983, -0.00057983, ..., -0.0007019 ,
-0.00027466, 0.00021362], dtype=float32),
'sampling_rate': 16000
},
'wav_filesize': 292844,
'transcript': 'This is proving to be true, and through focused execution we are on track to exceed our targeted savings in 2017. As a reminder,'
}
```
### Data Fields
* wav_filename (string) - audio filename (includes parent directory).
* audio (Audio feature) - a dictionary containing the path to the audio, the decoded audio array, and the sampling rate.
In non-streaming mode (default), the path points to the locally extracted audio. In streaming mode, the path is the relative path of an audio
inside its archive (as files are not downloaded and extracted locally).
* wav_filesize (int) - size of the file in bytes.
* transcript (string) - transcription of the file.
### Data Splits
The dataset has three splits: train, evaluation (dev) and test. The train split has three configurations of various sizes:
S, M, L. Larger subsets are supersets of smaller subsets, e.g., the L subset contains all the data from the M subset.
#### Transcribed Subsets Size
| Subset | Size |
|:------:|:-------:|
| S | 22Gb |
| M | 107Gb |
| L | 530Gb |
| dev | 11Gb |
| test | 11Gb |
## Dataset Creation
### Curation Rationale
To augment the open-source speech-to-text datasets available for R&D.
### Source Data
The dataset contains S&P Global company earnings calls.
#### Initial Data Collection and Normalization
Public earnings calls spanning the time period from 2007-2020 were converted to 16kHz, 16-bit audio.
#### Who are the source language producers?
English speakers with a diverse selection of accents, including non-native ones (L2), producing both
spontaneous and narrated speech.
### Annotations
#### Annotation process
Data is orthographically transcribed according to a professional style guide detailing conventions for capitalization, punctuation,
denormalization of non-standard words and transcription of disfluencies in spontaneous speech.
The transcripts have each been cross-checked by multiple professional editors for high accuracy and are fully formatted.
Full earnings calls last 30-60 minutes in length and are typically
transcribed as whole units, without internal timestamps. In order to produce short audio slices suitable for STT
training, the files were segmented with [Gentle](https://lowerquality.com/gentle/), a double-pass forced aligner,
with the beginning and end of each slice of audio imputed by voice activity detection with
[py-webrtc](https://github.com/wiseman/py-webrtcvad).
#### Who are the annotators?
Earning calls are manually transcribed by S&P Global, Inc.
### Personal and Sensitive Information
Though earnings calls are public, we nevertheless identified full names with the spaCy en core web large model.
We withheld samples containing names that appeared fewer than ten times (7% of total). Full
names appearing ten times or more in the data were considered to be public figures and were retained.
This necessarily incomplete approach to named entity recognition was complemented with randomized manual spot
checks which uncovered no false negatives missed by the automated approach.
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
The largest issue inherent with the dataset is that the speaker distribution of SPGISpeech reflects the speaker distribution seen during earning calls.
One example issue that stems from this: during earnings calls, close to 90% of speakers are male.
### Other Known Limitations
Due to formal language seen during earnings calls, the dataset needs augmentation for training systems that transcribe informal speech.
## Additional Information
### Dataset Curators
Kensho Technologies
### Licensing Information
### Citation Information
Please cite this paper:
```bibtext
@ARTICLE{2021arXiv210402014O,
author = {{O'Neill}, Patrick K. and {Lavrukhin}, Vitaly and {Majumdar},
Somshubra and {Noroozi}, Vahid and {Zhang}, Yuekai and {Kuchaiev}, Oleksii and {Balam},
Jagadeesh and {Dovzhenko}, Yuliya and {Freyberg}, Keenan and {Shulman}, Michael D. and {Ginsburg},
Boris and {Watanabe}, Shinji and {Kucsko}, Georg},
title = "{SPGISpeech: 5,000 hours of transcribed financial audio for fully formatted end-to-end speech recognition}",
journal = {arXiv e-prints},
keywords = {Computer Science - Computation and Language, Electrical Engineering and Systems Science - Audio and Speech Processing},
year = 2021,
month = apr,
eid = {arXiv:2104.02014},
pages = {arXiv:2104.02014},
archivePrefix = {arXiv},
eprint = {2104.02014},
primaryClass = {cs.CL},
adsurl = {https://ui.adsabs.harvard.edu/abs/2021arXiv210402014O},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
```
### Contributions
Thanks to [@sanchit-gandhi](https://github.com/sanchit-gandhi), [@patrickvonplaten](https://github.com/patrickvonplaten),
and [@polinaeterna](https://github.com/polinaeterna) for adding this dataset.
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
dikw/hh_rlhf_cn | dikw | 2023-08-24T05:51:47Z | 402 | 28 | null | [
"license:llama2",
"region:us"
] | 2023-08-24T05:51:47Z | 2023-08-18T10:09:26.000Z | 2023-08-18T10:09:26 | ---
license: llama2
---
---
license: bsd
---
## hh-rlhf中文翻译版本
基于Anthropic论文Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback 开源的helpful 和harmless数据，使用翻译工具进行了翻译。
hh_rlhf_train.jsonl 合并中英文训练集数据 清洗过后17万条
hh_rlhf_test.jsonl 合并中英文测试集数据 清洗过后9千条
harmless_base_cn_train.jsonl 42394条
harmless_base_cn_test.jsonl 2304条
helpful_base_cn_train.jsonl 43722条
helpful_base_cn_test.jsonl 2346条
## 实验报告
相关rlhf实验报告:https://zhuanlan.zhihu.com/p/652044120 | [
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id_newspapers_2018 | null | 2022-11-03T16:16:15Z | 401 | 4 | null | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
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"source_datasets:original",
"language:id",
"license:cc-by-4.0",
"region:us"
] | 2022-11-03T16:16:15Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
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language_creators:
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language:
- id
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
task_ids:
- language-modeling
- masked-language-modeling
paperswithcode_id: null
pretty_name: Indonesian Newspapers 2018
dataset_info:
features:
- name: id
dtype: string
- name: url
dtype: string
- name: date
dtype: string
- name: title
dtype: string
- name: content
dtype: string
config_name: id_newspapers_2018
splits:
- name: train
num_bytes: 1116031922
num_examples: 499164
download_size: 446018349
dataset_size: 1116031922
---
# Dataset Card for Indonesian Newspapers 2018
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [Indonesian Newspapers](https://github.com/feryandi/Dataset-Artikel)
- **Repository:** [Indonesian Newspapers](https://github.com/feryandi/Dataset-Artikel)
- **Paper:**
- **Leaderboard:**
- **Point of Contact:** [feryandi.n@gmail.com](mailto:feryandi.n@gmail.com),
[cahya.wirawan@gmail.com](mailto:cahya.wirawan@gmail.com)
### Dataset Summary
The dataset contains around 500K articles (136M of words) from 7 Indonesian newspapers: Detik, Kompas, Tempo,
CNN Indonesia, Sindo, Republika and Poskota. The articles are dated between 1st January 2018 and 20th August 2018
(with few exceptions dated earlier). The size of uncompressed 500K json files (newspapers-json.tgz) is around 2.2GB,
and the cleaned uncompressed in a big text file (newspapers.txt.gz) is about 1GB. The original source in Google Drive
contains also a dataset in html format which include raw data (pictures, css, javascript, ...)
from the online news website. A copy of the original dataset is available at
https://cloud.uncool.ai/index.php/s/mfYEAgKQoY3ebbM
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
Indonesian
## Dataset Structure
```
{
'id': 'string',
'url': 'string',
'date': 'string',
'title': 'string',
'content': 'string'
}
```
### Data Instances
An instance from the dataset is
```
{'id': '0',
'url': 'https://www.cnnindonesia.com/olahraga/20161221234219-156-181385/lorenzo-ingin-samai-rekor-rossi-dan-stoner',
'date': '2016-12-22 07:00:00',
'title': 'Lorenzo Ingin Samai Rekor Rossi dan Stoner',
'content': 'Jakarta, CNN Indonesia -- Setelah bergabung dengan Ducati, Jorge Lorenzo berharap bisa masuk dalam jajaran pebalap yang mampu jadi juara dunia kelas utama dengan dua pabrikan berbeda. Pujian Max Biaggi untuk Valentino Rossi Jorge Lorenzo Hadir dalam Ucapan Selamat Natal Yamaha Iannone: Saya Sering Jatuh Karena Ingin yang Terbaik Sepanjang sejarah, hanya ada lima pebalap yang mampu jadi juara kelas utama (500cc/MotoGP) dengan dua pabrikan berbeda, yaitu Geoff Duke, Giacomo Agostini, Eddie Lawson, Valentino Rossi, dan Casey Stoner. Lorenzo ingin bergabung dalam jajaran legenda tersebut. “Fakta ini sangat penting bagi saya karena hanya ada lima pebalap yang mampu menang dengan dua pabrikan berbeda dalam sejarah balap motor.” “Kedatangan saya ke Ducati juga menghadirkan tantangan yang sangat menarik karena hampir tak ada yang bisa menang dengan Ducati sebelumnya, kecuali Casey Stoner. Hal itu jadi motivasi yang sangat bagus bagi saya,” tutur Lorenzo seperti dikutip dari Crash Lorenzo saat ini diliputi rasa penasaran yang besar untuk menunggang sepeda motor Desmosedici yang dipakai tim Ducati karena ia baru sekali menjajal motor tersebut pada sesi tes di Valencia, usai MotoGP musim 2016 berakhir. “Saya sangat tertarik dengan Ducati arena saya hanya memiliki kesempatan mencoba motor itu di Valencia dua hari setelah musim berakhir. Setelah itu saya tak boleh lagi menjajalnya hingga akhir Januari mendatang. Jadi saya menjalani penantian selama dua bulan yang panjang,” kata pebalap asal Spanyol ini. Dengan kondisi tersebut, maka Lorenzo memanfaatkan waktu yang ada untuk liburan dan melepaskan penat. “Setidaknya apa yang terjadi pada saya saat ini sangat bagus karena saya jadi memiliki waktu bebas dan sedikit liburan.” “Namun tentunya saya tak akan larut dalam liburan karena saya harus lebih bersiap, terutama dalam kondisi fisik dibandingkan sebelumnya, karena saya akan menunggangi motor yang sulit dikendarai,” ucap Lorenzo. Selama sembilan musim bersama Yamaha, Lorenzo sendiri sudah tiga kali jadi juara dunia, yaitu pada 2010, 2012, dan 2015. (kid)'}
```
### Data Fields
- `id`: id of the sample
- `url`: the url to the original article
- `date`: the publishing date of the article
- `title`: the title of the article
- `content`: the content of the article
### Data Splits
The dataset contains train set of 499164 samples.
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. The dataset is shared for the sole purpose of aiding open scientific research in Bahasa Indonesia (computing or linguistics), and can only be used for that purpose. The ownership of each article within the dataset belongs to the respective newspaper from which it was extracted; and the maintainer of the repository does not claim ownership of any of the content within it. If you think, by any means, that this dataset breaches any established copyrights; please contact the repository maintainer.
### Citation Information
[N/A]
### Contributions
Thanks to [@cahya-wirawan](https://github.com/cahya-wirawan) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
SetFit/go_emotions | SetFit | 2022-09-08T15:41:33Z | 401 | 5 | null | [
"region:us"
] | 2022-09-08T15:41:33Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | # GoEmotions
This dataset is a port of the official [`go_emotions` dataset](https://huggingface.co/datasets/go_emotions) on the Hub. It only contains the `simplified` subset as these are the only fields we need for text classification. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
distil-whisper/librispeech_asr-timestamped | distil-whisper | 2023-09-25T10:30:13Z | 401 | 0 | null | [
"task_categories:automatic-speech-recognition",
"language:en",
"license:cc-by-4.0",
"region:us"
] | 2023-09-25T10:30:13Z | 2023-09-22T09:05:08.000Z | 2023-09-22T09:05:08 | ---
license: cc-by-4.0
task_categories:
- automatic-speech-recognition
language:
- en
-pretty_name: LibriSpeech ASR
---
# Distil Whisper: LibriSpeech ASR With Timestamps
This is a variant of the [LibriSpeech ASR](https://huggingface.co/datasets/librispeech_asr) dataset, augmented to return the pseudo-labelled Whisper
Transcriptions alongside the original dataset elements. The pseudo-labelled transcriptions were generated by
labelling the input audio data with the Whisper [large-v2](https://huggingface.co/openai/whisper-large-v2)
model with *greedy* sampling and timestamp prediction. For information on how the original dataset was curated, refer to the original
[dataset card](https://huggingface.co/datasets/librispeech_asr).
## Standalone Usage
First, install the latest version of the 🤗 Datasets package:
```bash
pip install --upgrade pip
pip install --upgrade datasets[audio]
```
The dataset can be downloaded and pre-processed on disk using the [`load_dataset`](https://huggingface.co/docs/datasets/v2.14.5/en/package_reference/loading_methods#datasets.load_dataset)
function:
```python
from datasets import load_dataset
dataset = load_dataset("distil-whisper/librispeech_asr", "all")
# take the first sample of the validation set
sample = dataset["validation.clean"][0]
```
It can also be streamed directly from the Hub using Datasets' [streaming mode](https://huggingface.co/blog/audio-datasets#streaming-mode-the-silver-bullet).
Loading a dataset in streaming mode loads individual samples of the dataset at a time, rather than downloading the entire
dataset to disk:
```python
from datasets import load_dataset
dataset = load_dataset("distil-whisper/librispeech_asr", "all", streaming=True)
# take the first sample of the validation set
sample = next(iter(dataset["validation.clean"]))
```
## Distil Whisper Usage
To use this dataset to reproduce a Distil Whisper training run, refer to the instructions on the
[Distil Whisper repository](https://github.com/huggingface/distil-whisper#training).
## License
This dataset is licensed under cc-by-4.0.
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
lener_br | null | 2023-09-25T07:35:39Z | 400 | 23 | lener-br | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:pt",
"license:unknown",
"legal",
"region:us"
] | 2023-09-25T07:35:39Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- pt
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
paperswithcode_id: lener-br
pretty_name: leNER-br
dataset_info:
features:
- name: id
dtype: string
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': O
'1': B-ORGANIZACAO
'2': I-ORGANIZACAO
'3': B-PESSOA
'4': I-PESSOA
'5': B-TEMPO
'6': I-TEMPO
'7': B-LOCAL
'8': I-LOCAL
'9': B-LEGISLACAO
'10': I-LEGISLACAO
'11': B-JURISPRUDENCIA
'12': I-JURISPRUDENCIA
config_name: lener_br
splits:
- name: train
num_bytes: 3984189
num_examples: 7828
- name: validation
num_bytes: 719433
num_examples: 1177
- name: test
num_bytes: 823708
num_examples: 1390
download_size: 2983137
dataset_size: 5527330
tags:
- legal
---
# Dataset Card for leNER-br
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [leNER-BR homepage](https://cic.unb.br/~teodecampos/LeNER-Br/)
- **Repository:** [leNER-BR repository](https://github.com/peluz/lener-br)
- **Paper:** [leNER-BR: Long Form Question Answering](https://cic.unb.br/~teodecampos/LeNER-Br/luz_etal_propor2018.pdf)
- **Point of Contact:** [Pedro H. Luz de Araujo](mailto:pedrohluzaraujo@gmail.com)
### Dataset Summary
LeNER-Br is a Portuguese language dataset for named entity recognition
applied to legal documents. LeNER-Br consists entirely of manually annotated
legislation and legal cases texts and contains tags for persons, locations,
time entities, organizations, legislation and legal cases.
To compose the dataset, 66 legal documents from several Brazilian Courts were
collected. Courts of superior and state levels were considered, such as Supremo
Tribunal Federal, Superior Tribunal de Justiça, Tribunal de Justiça de Minas
Gerais and Tribunal de Contas da União. In addition, four legislation documents
were collected, such as "Lei Maria da Penha", giving a total of 70 documents
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The language supported is Portuguese.
## Dataset Structure
### Data Instances
An example from the dataset looks as follows:
```
{
"id": "0",
"ner_tags": [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0],
"tokens": [
"EMENTA", ":", "APELAÇÃO", "CÍVEL", "-", "AÇÃO", "DE", "INDENIZAÇÃO", "POR", "DANOS", "MORAIS", "-", "PRELIMINAR", "-", "ARGUIDA", "PELO", "MINISTÉRIO", "PÚBLICO", "EM", "GRAU", "RECURSAL"]
}
```
### Data Fields
- `id`: id of the sample
- `tokens`: the tokens of the example text
- `ner_tags`: the NER tags of each token
The NER tags correspond to this list:
```
"O", "B-ORGANIZACAO", "I-ORGANIZACAO", "B-PESSOA", "I-PESSOA", "B-TEMPO", "I-TEMPO", "B-LOCAL", "I-LOCAL", "B-LEGISLACAO", "I-LEGISLACAO", "B-JURISPRUDENCIA", "I-JURISPRUDENCIA"
```
The NER tags have the same format as in the CoNLL shared task: a B denotes the first item of a phrase and an I any non-initial word.
### Data Splits
The data is split into train, validation and test set. The split sizes are as follow:
| Train | Val | Test |
| ------ | ----- | ---- |
| 7828 | 1177 | 1390 |
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
```
@inproceedings{luz_etal_propor2018,
author = {Pedro H. {Luz de Araujo} and Te\'{o}filo E. {de Campos} and
Renato R. R. {de Oliveira} and Matheus Stauffer and
Samuel Couto and Paulo Bermejo},
title = {{LeNER-Br}: a Dataset for Named Entity Recognition in {Brazilian} Legal Text},
booktitle = {International Conference on the Computational Processing of Portuguese ({PROPOR})},
publisher = {Springer},
series = {Lecture Notes on Computer Science ({LNCS})},
pages = {313--323},
year = {2018},
month = {September 24-26},
address = {Canela, RS, Brazil},
doi = {10.1007/978-3-319-99722-3_32},
url = {https://cic.unb.br/~teodecampos/LeNER-Br/},
}
```
### Contributions
Thanks to [@jonatasgrosman](https://github.com/jonatasgrosman) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
lst20 | null | 2023-01-25T14:34:28Z | 400 | 2 | null | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"task_ids:part-of-speech",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:th",
"license:other",
"word-segmentation",
"clause-segmentation",
"sentence-segmentation",
"region:us"
] | 2023-01-25T14:34:28Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- th
license:
- other
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
- part-of-speech
pretty_name: LST20
tags:
- word-segmentation
- clause-segmentation
- sentence-segmentation
dataset_info:
features:
- name: id
dtype: string
- name: fname
dtype: string
- name: tokens
sequence: string
- name: pos_tags
sequence:
class_label:
names:
'0': NN
'1': VV
'2': PU
'3': CC
'4': PS
'5': AX
'6': AV
'7': FX
'8': NU
'9': AJ
'10': CL
'11': PR
'12': NG
'13': PA
'14': XX
'15': IJ
- name: ner_tags
sequence:
class_label:
names:
'0': O
'1': B_BRN
'2': B_DES
'3': B_DTM
'4': B_LOC
'5': B_MEA
'6': B_NUM
'7': B_ORG
'8': B_PER
'9': B_TRM
'10': B_TTL
'11': I_BRN
'12': I_DES
'13': I_DTM
'14': I_LOC
'15': I_MEA
'16': I_NUM
'17': I_ORG
'18': I_PER
'19': I_TRM
'20': I_TTL
'21': E_BRN
'22': E_DES
'23': E_DTM
'24': E_LOC
'25': E_MEA
'26': E_NUM
'27': E_ORG
'28': E_PER
'29': E_TRM
'30': E_TTL
- name: clause_tags
sequence:
class_label:
names:
'0': O
'1': B_CLS
'2': I_CLS
'3': E_CLS
config_name: lst20
splits:
- name: train
num_bytes: 107725145
num_examples: 63310
- name: validation
num_bytes: 9646167
num_examples: 5620
- name: test
num_bytes: 8217425
num_examples: 5250
download_size: 0
dataset_size: 125588737
---
# Dataset Card for LST20
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://aiforthai.in.th/
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:** [email](thepchai@nectec.or.th)
### Dataset Summary
LST20 Corpus is a dataset for Thai language processing developed by National Electronics and Computer Technology Center (NECTEC), Thailand.
It offers five layers of linguistic annotation: word boundaries, POS tagging, named entities, clause boundaries, and sentence boundaries.
At a large scale, it consists of 3,164,002 words, 288,020 named entities, 248,181 clauses, and 74,180 sentences, while it is annotated with
16 distinct POS tags. All 3,745 documents are also annotated with one of 15 news genres. Regarding its sheer size, this dataset is
considered large enough for developing joint neural models for NLP.
Manually download at https://aiforthai.in.th/corpus.php
See `LST20 Annotation Guideline.pdf` and `LST20 Brief Specification.pdf` within the downloaded `AIFORTHAI-LST20Corpus.tar.gz` for more details.
### Supported Tasks and Leaderboards
- POS tagging
- NER tagging
- clause segmentation
- sentence segmentation
- word tokenization
### Languages
Thai
## Dataset Structure
### Data Instances
```
{'clause_tags': [1, 2, 2, 2, 2, 2, 2, 2, 3], 'fname': 'T11964.txt', 'id': '0', 'ner_tags': [8, 0, 0, 0, 0, 0, 0, 0, 25], 'pos_tags': [0, 0, 0, 1, 0, 8, 8, 8, 0], 'tokens': ['ธรรมนูญ', 'แชมป์', 'สิงห์คลาสสิก', 'กวาด', 'รางวัล', 'แสน', 'สี่', 'หมื่น', 'บาท']}
{'clause_tags': [1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3], 'fname': 'T11964.txt', 'id': '1', 'ner_tags': [8, 18, 28, 0, 0, 0, 0, 6, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 15, 25, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 6], 'pos_tags': [0, 2, 0, 2, 1, 1, 2, 8, 2, 10, 2, 8, 2, 1, 0, 1, 0, 4, 7, 1, 0, 2, 8, 2, 10, 1, 10, 4, 2, 8, 2, 4, 0, 4, 0, 2, 8, 2, 10, 2, 8], 'tokens': ['ธรรมนูญ', '_', 'ศรีโรจน์', '_', 'เก็บ', 'เพิ่ม', '_', '4', '_', 'อันเดอร์พาร์', '_', '68', '_', 'เข้า', 'ป้าย', 'รับ', 'แชมป์', 'ใน', 'การ', 'เล่น', 'อาชีพ', '_', '19', '_', 'ปี', 'เป็น', 'ครั้ง', 'ที่', '_', '8', '_', 'ใน', 'ชีวิต', 'ด้วย', 'สกอร์', '_', '18', '_', 'อันเดอร์พาร์', '_', '270']}
```
### Data Fields
- `id`: nth sentence in each set, starting at 0
- `fname`: text file from which the sentence comes from
- `tokens`: word tokens
- `pos_tags`: POS tags
- `ner_tags`: NER tags
- `clause_tags`: clause tags
### Data Splits
| | train | eval | test | all |
|----------------------|-----------|-------------|-------------|-----------|
| words | 2,714,848 | 240,891 | 207,295 | 3,163,034 |
| named entities | 246,529 | 23,176 | 18,315 | 288,020 |
| clauses | 214,645 | 17,486 | 16,050 | 246,181 |
| sentences | 63,310 | 5,620 | 5,250 | 74,180 |
| distinct words | 42,091 | (oov) 2,595 | (oov) 2,006 | 46,692 |
| breaking spaces※ | 63,310 | 5,620 | 5,250 | 74,180 |
| non-breaking spaces※※| 402,380 | 39,920 | 32,204 | 475,504 |
※ Breaking space = space that is used as a sentence boundary marker
※※ Non-breaking space = space that is not used as a sentence boundary marker
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
Respective authors of the news articles
### Annotations
#### Annotation process
Detailed annotation guideline can be found in `LST20 Annotation Guideline.pdf`.
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
All texts are from public news. No personal and sensitive information is expected to be included.
## Considerations for Using the Data
### Social Impact of Dataset
- Large-scale Thai NER & POS tagging, clause & sentence segmentatation, word tokenization
### Discussion of Biases
- All 3,745 texts are from news domain:
- politics: 841
- crime and accident: 592
- economics: 512
- entertainment: 472
- sports: 402
- international: 279
- science, technology and education: 216
- health: 92
- general: 75
- royal: 54
- disaster: 52
- development: 45
- environment: 40
- culture: 40
- weather forecast: 33
- Word tokenization is done accoding to InterBEST 2009 Guideline.
### Other Known Limitations
- Some NER tags do not correspond with given labels (`B`, `I`, and so on)
## Additional Information
### Dataset Curators
[NECTEC](https://www.nectec.or.th/en/)
### Licensing Information
1. Non-commercial use, research, and open source
Any non-commercial use of the dataset for research and open-sourced projects is encouraged and free of charge. Please cite our technical report for reference.
If you want to perpetuate your models trained on our dataset and share them to the research community in Thailand, please send your models, code, and APIs to the AI for Thai Project. Please contact Dr. Thepchai Supnithi via thepchai@nectec.or.th for more information.
Note that modification and redistribution of the dataset by any means are strictly prohibited unless authorized by the corpus authors.
2. Commercial use
In any commercial use of the dataset, there are two options.
- Option 1 (in kind): Contributing a dataset of 50,000 words completely annotated with our annotation scheme within 1 year. Your data will also be shared and recognized as a dataset co-creator in the research community in Thailand.
- Option 2 (in cash): Purchasing a lifetime license for the entire dataset is required. The purchased rights of use cover only this dataset.
In both options, please contact Dr. Thepchai Supnithi via thepchai@nectec.or.th for more information.
### Citation Information
```
@article{boonkwan2020annotation,
title={The Annotation Guideline of LST20 Corpus},
author={Boonkwan, Prachya and Luantangsrisuk, Vorapon and Phaholphinyo, Sitthaa and Kriengket, Kanyanat and Leenoi, Dhanon and Phrombut, Charun and Boriboon, Monthika and Kosawat, Krit and Supnithi, Thepchai},
journal={arXiv preprint arXiv:2008.05055},
year={2020}
}
```
### Contributions
Thanks to [@cstorm125](https://github.com/cstorm125) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
antolin/python-150_interduplication | antolin | 2023-11-10T11:57:59Z | 400 | 2 | null | [
"region:us"
] | 2023-11-10T11:57:59Z | 2023-09-05T11:21:06.000Z | 2023-09-05T11:21:06 | ---
dataset_info:
features:
- name: id_within_dataset
dtype: int64
- name: snippet
dtype: string
- name: tokens
sequence: string
- name: nl
dtype: string
- name: split_within_dataset
dtype: string
- name: is_duplicated
dtype: bool
splits:
- name: train
num_bytes: 41621821.20269596
num_examples: 40842
- name: test
num_bytes: 13915723.238891663
num_examples: 13655
- name: valid
num_bytes: 13864768.55841238
num_examples: 13605
download_size: 30588162
dataset_size: 69402313.0
---
# Dataset Card for "python-150_interduplication"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
bbc_hindi_nli | null | 2023-01-25T14:27:06Z | 399 | 0 | null | [
"task_categories:text-classification",
"task_ids:natural-language-inference",
"annotations_creators:machine-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|bbc__hindi_news_classification",
"language:hi",
"license:mit",
"region:us"
] | 2023-01-25T14:27:06Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- machine-generated
language_creators:
- found
language:
- hi
license:
- mit
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|bbc__hindi_news_classification
task_categories:
- text-classification
task_ids:
- natural-language-inference
pretty_name: BBC Hindi NLI Dataset
dataset_info:
features:
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: label
dtype:
class_label:
names:
'0': not-entailment
'1': entailment
- name: topic
dtype:
class_label:
names:
'0': india
'1': news
'2': international
'3': entertainment
'4': sport
'5': science
config_name: bbc hindi nli
splits:
- name: train
num_bytes: 2990080
num_examples: 15552
- name: validation
num_bytes: 496808
num_examples: 2580
- name: test
num_bytes: 494432
num_examples: 2592
download_size: 3815652
dataset_size: 3981320
---
# Dataset Card for BBC Hindi NLI Dataset
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Repository:** [GitHub](https://github.com/midas-research/hindi-nli-data)
- **Paper:** [Aclweb](https://www.aclweb.org/anthology/2020.aacl-main.71)
- **Point of Contact:** [GitHub](https://github.com/midas-research/hindi-nli-data)
### Dataset Summary
- Dataset for Natural Language Inference in Hindi Language. BBC Hindi Dataset consists of textual-entailment pairs.
- Each row of the Datasets if made up of 4 columns - Premise, Hypothesis, Label and Topic.
- Context and Hypothesis is written in Hindi while Entailment_Label is in English.
- Entailment_label is of 2 types - entailed and not-entailed.
- Dataset can be used to train models for Natural Language Inference tasks in Hindi Language.
[More Information Needed]
### Supported Tasks and Leaderboards
- Natural Language Inference for Hindi
### Languages
Dataset is in Hindi
## Dataset Structure
- Data is structured in TSV format.
- Train and Test files are in seperate files
### Dataset Instances
An example of 'train' looks as follows.
```
{'hypothesis': 'यह खबर की सूचना है|', 'label': 'entailed', 'premise': 'गोपनीयता की नीति', 'topic': '1'}
```
### Data Fields
- Each row contatins 4 columns - Premise, Hypothesis, Label and Topic.
### Data Splits
- Train : 15553
- Valid : 2581
- Test : 2593
## Dataset Creation
- We employ a recasting technique from Poliak et al. (2018a,b) to convert publicly available BBC Hindi news text classification datasets in Hindi and pose them as TE problems
- In this recasting process, we build template hypotheses for each class in the label taxonomy
- Then, we pair the original annotated sentence with each of the template hypotheses to create TE samples.
- For more information on the recasting process, refer to paper "https://www.aclweb.org/anthology/2020.aacl-main.71"
### Source Data
Source Dataset for the recasting process is the BBC Hindi Headlines Dataset(https://github.com/NirantK/hindi2vec/releases/tag/bbc-hindi-v0.1)
#### Initial Data Collection and Normalization
- BBC Hindi News Classification Dataset contains 4, 335 Hindi news headlines tagged across 14 categories: India, Pakistan,news, International, entertainment, sport, science, China, learning english, social, southasia, business, institutional, multimedia
- We processed this dataset to combine two sets of relevant but low prevalence classes.
- Namely, we merged the samples from Pakistan, China, international, and southasia as one class called international.
- Likewise, we also merged samples from news, business, social, learning english, and institutional as news.
- Lastly, we also removed the class multimedia because there were very few samples.
#### Who are the source language producers?
Pls refer to this paper: "https://www.aclweb.org/anthology/2020.aacl-main.71"
### Annotations
#### Annotation process
Annotation process has been described in Dataset Creation Section.
#### Who are the annotators?
Annotation is done automatically.
### Personal and Sensitive Information
No Personal and Sensitive Information is mentioned in the Datasets.
## Considerations for Using the Data
Pls refer to this paper: https://www.aclweb.org/anthology/2020.aacl-main.71
### Discussion of Biases
Pls refer to this paper: https://www.aclweb.org/anthology/2020.aacl-main.71
### Other Known Limitations
No other known limitations
## Additional Information
Pls refer to this link: https://github.com/midas-research/hindi-nli-data
### Dataset Curators
It is written in the repo : https://github.com/avinsit123/hindi-nli-data that
- This corpus can be used freely for research purposes.
- The paper listed below provide details of the creation and use of the corpus. If you use the corpus, then please cite the paper.
- If interested in commercial use of the corpus, send email to midas@iiitd.ac.in.
- If you use the corpus in a product or application, then please credit the authors and Multimodal Digital Media Analysis Lab - Indraprastha Institute of Information Technology, New Delhi appropriately. Also, if you send us an email, we will be thrilled to know about how you have used the corpus.
- Multimodal Digital Media Analysis Lab - Indraprastha Institute of Information Technology, New Delhi, India disclaims any responsibility for the use of the corpus and does not provide technical support. However, the contact listed above will be happy to respond to queries and clarifications.
- Rather than redistributing the corpus, please direct interested parties to this page
- Please feel free to send us an email:
- with feedback regarding the corpus.
- with information on how you have used the corpus.
- if interested in having us analyze your data for natural language inference.
- if interested in a collaborative research project.
### Licensing Information
Copyright (C) 2019 Multimodal Digital Media Analysis Lab - Indraprastha Institute of Information Technology, New Delhi (MIDAS, IIIT-Delhi).
Pls contact authors for any information on the dataset.
### Citation Information
```
@inproceedings{uppal-etal-2020-two,
title = "Two-Step Classification using Recasted Data for Low Resource Settings",
author = "Uppal, Shagun and
Gupta, Vivek and
Swaminathan, Avinash and
Zhang, Haimin and
Mahata, Debanjan and
Gosangi, Rakesh and
Shah, Rajiv Ratn and
Stent, Amanda",
booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
month = dec,
year = "2020",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.aacl-main.71",
pages = "706--719",
abstract = "An NLP model{'}s ability to reason should be independent of language. Previous works utilize Natural Language Inference (NLI) to understand the reasoning ability of models, mostly focusing on high resource languages like English. To address scarcity of data in low-resource languages such as Hindi, we use data recasting to create NLI datasets for four existing text classification datasets. Through experiments, we show that our recasted dataset is devoid of statistical irregularities and spurious patterns. We further study the consistency in predictions of the textual entailment models and propose a consistency regulariser to remove pairwise-inconsistencies in predictions. We propose a novel two-step classification method which uses textual-entailment predictions for classification task. We further improve the performance by using a joint-objective for classification and textual entailment. We therefore highlight the benefits of data recasting and improvements on classification performance using our approach with supporting experimental results.",
}
```
### Contributions
Thanks to [@avinsit123](https://github.com/avinsit123) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
germeval_14 | null | 2023-04-05T10:06:39Z | 399 | 3 | nosta-d-named-entity-annotation-for-german | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:de",
"license:cc-by-4.0",
"region:us"
] | 2023-04-05T10:06:39Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- de
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
paperswithcode_id: nosta-d-named-entity-annotation-for-german
pretty_name: GermEval14
dataset_info:
features:
- name: id
dtype: string
- name: source
dtype: string
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': O
'1': B-LOC
'2': I-LOC
'3': B-LOCderiv
'4': I-LOCderiv
'5': B-LOCpart
'6': I-LOCpart
'7': B-ORG
'8': I-ORG
'9': B-ORGderiv
'10': I-ORGderiv
'11': B-ORGpart
'12': I-ORGpart
'13': B-OTH
'14': I-OTH
'15': B-OTHderiv
'16': I-OTHderiv
'17': B-OTHpart
'18': I-OTHpart
'19': B-PER
'20': I-PER
'21': B-PERderiv
'22': I-PERderiv
'23': B-PERpart
'24': I-PERpart
- name: nested_ner_tags
sequence:
class_label:
names:
'0': O
'1': B-LOC
'2': I-LOC
'3': B-LOCderiv
'4': I-LOCderiv
'5': B-LOCpart
'6': I-LOCpart
'7': B-ORG
'8': I-ORG
'9': B-ORGderiv
'10': I-ORGderiv
'11': B-ORGpart
'12': I-ORGpart
'13': B-OTH
'14': I-OTH
'15': B-OTHderiv
'16': I-OTHderiv
'17': B-OTHpart
'18': I-OTHpart
'19': B-PER
'20': I-PER
'21': B-PERderiv
'22': I-PERderiv
'23': B-PERpart
'24': I-PERpart
config_name: germeval_14
splits:
- name: train
num_bytes: 13816714
num_examples: 24000
- name: validation
num_bytes: 1266974
num_examples: 2200
- name: test
num_bytes: 2943201
num_examples: 5100
download_size: 10288972
dataset_size: 18026889
---
# Dataset Card for "germeval_14"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://sites.google.com/site/germeval2014ner/](https://sites.google.com/site/germeval2014ner/)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [https://pdfs.semanticscholar.org/b250/3144ed2152830f6c64a9f797ab3c5a34fee5.pdf](https://pdfs.semanticscholar.org/b250/3144ed2152830f6c64a9f797ab3c5a34fee5.pdf)
- **Point of Contact:** [Darina Benikova](mailto:benikova@aiphes.tu-darmstadt.de)
- **Size of downloaded dataset files:** 10.29 MB
- **Size of the generated dataset:** 18.03 MB
- **Total amount of disk used:** 28.31 MB
### Dataset Summary
The GermEval 2014 NER Shared Task builds on a new dataset with German Named Entity annotation with the following properties: - The data was sampled from German Wikipedia and News Corpora as a collection of citations. - The dataset covers over 31,000 sentences corresponding to over 590,000 tokens. - The NER annotation uses the NoSta-D guidelines, which extend the Tübingen Treebank guidelines, using four main NER categories with sub-structure, and annotating embeddings among NEs such as [ORG FC Kickers [LOC Darmstadt]].
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
German
## Dataset Structure
### Data Instances
#### germeval_14
- **Size of downloaded dataset files:** 10.29 MB
- **Size of the generated dataset:** 18.03 MB
- **Total amount of disk used:** 28.31 MB
An example of 'train' looks as follows. This example was too long and was cropped:
```json
{
"id": "11",
"ner_tags": [13, 14, 14, 14, 14, 0, 0, 0, 0, 0, 0, 0, 19, 20, 13, 0, 1, 0, 0, 0, 0, 0, 19, 20, 20, 0, 0, 0, 0, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
"nested_ner_tags": [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
"source": "http://de.wikipedia.org/wiki/Liste_von_Filmen_mit_homosexuellem_Inhalt [2010-01-11] ",
"tokens": "[\"Scenes\", \"of\", \"a\", \"Sexual\", \"Nature\", \"(\", \"GB\", \"2006\", \")\", \"-\", \"Regie\", \":\", \"Ed\", \"Blum\", \"Shortbus\", \"(\", \"USA\", \"2006..."
}
```
### Data Fields
The data fields are the same among all splits.
#### germeval_14
- `id`: a `string` feature.
- `source`: a `string` feature.
- `tokens`: a `list` of `string` features.
- `ner_tags`: a `list` of classification labels, with possible values including `O` (0), `B-LOC` (1), `I-LOC` (2), `B-LOCderiv` (3), `I-LOCderiv` (4).
- `nested_ner_tags`: a `list` of classification labels, with possible values including `O` (0), `B-LOC` (1), `I-LOC` (2), `B-LOCderiv` (3), `I-LOCderiv` (4).
### Data Splits
| name |train|validation|test|
|-----------|----:|---------:|---:|
|germeval_14|24000| 2200|5100|
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[CC BY-SA 4.0 license](https://creativecommons.org/licenses/by-sa/4.0/)
### Citation Information
```
@inproceedings{benikova-etal-2014-nosta,
title = {NoSta-D Named Entity Annotation for German: Guidelines and Dataset},
author = {Benikova, Darina and
Biemann, Chris and
Reznicek, Marc},
booktitle = {Proceedings of the Ninth International Conference on Language Resources and Evaluation ({LREC}'14)},
month = {may},
year = {2014},
address = {Reykjavik, Iceland},
publisher = {European Language Resources Association (ELRA)},
url = {http://www.lrec-conf.org/proceedings/lrec2014/pdf/276_Paper.pdf},
pages = {2524--2531},
}
```
### Contributions
Thanks to [@thomwolf](https://github.com/thomwolf), [@jplu](https://github.com/jplu), [@lewtun](https://github.com/lewtun), [@lhoestq](https://github.com/lhoestq), [@stefan-it](https://github.com/stefan-it), [@mariamabarham](https://github.com/mariamabarham) for adding this dataset. | [
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ehartford/dolphin | ehartford | 2023-09-25T16:59:11Z | 399 | 251 | null | [
"task_categories:text-generation",
"language:en",
"license:apache-2.0",
"region:us"
] | 2023-09-25T16:59:11Z | 2023-07-01T10:53:40.000Z | 2023-07-01T10:53:40 | ---
license: apache-2.0
task_categories:
- text-generation
language:
- en
---
Dolphin 🐬
https://erichartford.com/dolphin
## Dataset details
This dataset is an attempt to replicate the results of [Microsoft's Orca](https://www.microsoft.com/en-us/research/publication/orca-progressive-learning-from-complex-explanation-traces-of-gpt-4/)
Our dataset consists of:
- ~1 million of FLANv2 augmented with GPT-4 completions (flan1m-alpaca-uncensored.jsonl)
- ~3.5 million of FLANv2 augmented with GPT-3.5 completions (flan5m-alpaca-uncensored.jsonl)
We followed the submix and system prompt distribution outlined in the Orca paper. With a few exceptions. We included all 75k of CoT in the FLAN-1m dataset rather than sampling that. Also, we found that many items were duplicated, so we removed duplicates, resulting in 3.5m instructs in the ChatGPT dataset.
Then we filtered out instances of alignment, refusal, avoidance, and bias, in order to produce an uncensored model upon which can be layered your personalized alignment LoRA.
Token distribution for GPT-3.5 completions
### Loading
```python
## load GPT-4 completions
dataset = load_dataset("ehartford/dolphin",data_files="flan1m-alpaca-uncensored.jsonl")
## load GPT-3.5 completions
dataset = load_dataset("ehartford/dolphin",data_files="flan5m-alpaca-uncensored.jsonl")
```
This dataset is licensed apache-2.0 for commercial or non-commercial use.
We currently plan to release Dolphin on:
- Xgen 7b 8k
- LLaMA 13b (Non-commercial)
- MPT 30b 8k
- LLaMA 33b (Non-commercial)
- Falcon 40b
- LLaMA 65b (Non-commercial)
The Dolphin models that are released will be subject to the license of the foundational model on which it is trained. (LLaMA releases will be non-commercial)
I would like to thank the motley crew of Open Source AI/ML engineers who have worked beside me in this endeavor. Including:
- Wing "Caseus" Lian and NanoBit of OpenAccess AI Collective
- Rohan
- Teknium
- Pankaj Mathur
- Tom "TheBloke" Jobbins for quantizing and amplifying
- Special thanks to EdenCoder and chirper.ai for mentorship and financial sponsorship.
- Special thanks to Kilkonie for his very valued mentorship.
- All the other people in the Open Source AI community who have taught me and helped me along the way. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
qa_zre | null | 2023-04-05T13:37:03Z | 398 | 2 | null | [
"task_categories:question-answering",
"annotations_creators:no-annotation",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:en",
"license:unknown",
"zero-shot-relation-extraction",
"region:us"
] | 2023-04-05T13:37:03Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- no-annotation
language_creators:
- expert-generated
language:
- en
license:
- unknown
multilinguality:
- monolingual
pretty_name: QaZre
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- question-answering
task_ids: []
paperswithcode_id: null
tags:
- zero-shot-relation-extraction
dataset_info:
features:
- name: relation
dtype: string
- name: question
dtype: string
- name: subject
dtype: string
- name: context
dtype: string
- name: answers
sequence: string
splits:
- name: test
num_bytes: 29410194
num_examples: 120000
- name: validation
num_bytes: 1481430
num_examples: 6000
- name: train
num_bytes: 2054954011
num_examples: 8400000
download_size: 516061636
dataset_size: 2085845635
---
# Dataset Card for QaZre
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [http://nlp.cs.washington.edu/zeroshot](http://nlp.cs.washington.edu/zeroshot)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of downloaded dataset files:** 516.06 MB
- **Size of the generated dataset:** 2.09 GB
- **Total amount of disk used:** 2.60 GB
### Dataset Summary
A dataset reducing relation extraction to simple reading comprehension questions
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Dataset Structure
### Data Instances
#### default
- **Size of downloaded dataset files:** 516.06 MB
- **Size of the generated dataset:** 2.09 GB
- **Total amount of disk used:** 2.60 GB
An example of 'validation' looks as follows.
```
{
"answers": [],
"context": "answer",
"question": "What is XXX in this question?",
"relation": "relation_name",
"subject": "Some entity Here is a bit of context which will explain the question in some way"
}
```
### Data Fields
The data fields are the same among all splits.
#### default
- `relation`: a `string` feature.
- `question`: a `string` feature.
- `subject`: a `string` feature.
- `context`: a `string` feature.
- `answers`: a `list` of `string` features.
### Data Splits
| name | train | validation | test |
|---------|--------:|-----------:|-------:|
| default | 8400000 | 6000 | 120000 |
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
Unknown.
### Citation Information
```
@inproceedings{levy-etal-2017-zero,
title = "Zero-Shot Relation Extraction via Reading Comprehension",
author = "Levy, Omer and
Seo, Minjoon and
Choi, Eunsol and
Zettlemoyer, Luke",
booktitle = "Proceedings of the 21st Conference on Computational Natural Language Learning ({C}o{NLL} 2017)",
month = aug,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/K17-1034",
doi = "10.18653/v1/K17-1034",
pages = "333--342",
}
```
### Contributions
Thanks to [@thomwolf](https://github.com/thomwolf), [@lhoestq](https://github.com/lhoestq), [@ghomasHudson](https://github.com/ghomasHudson), [@lewtun](https://github.com/lewtun) for adding this dataset. | [
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ai4bharat/IndicXParaphrase | ai4bharat | 2022-10-14T16:40:28Z | 398 | 0 | null | [
"region:us"
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pauri32/fiqa-2018 | pauri32 | 2023-05-31T15:43:26Z | 398 | 4 | null | [
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usvsnsp/pile-semantic-memorization-filter-results | usvsnsp | 2023-09-19T18:56:42Z | 398 | 1 | null | [
"region:us"
] | 2023-09-19T18:56:42Z | 2023-09-19T18:16:50.000Z | 2023-09-19T18:16:50 | ---
dataset_info:
features:
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download_size: 48107269588
dataset_size: 112134065488
---
# Dataset Card for "pile-semantic-memorization-filter-results"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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medal | null | 2023-06-13T12:39:11Z | 397 | 11 | medal | [
"task_categories:other",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10M<n<100M",
"source_datasets:original",
"language:en",
"license:unknown",
"disambiguation",
"region:us"
] | 2023-06-13T12:39:11Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10M<n<100M
source_datasets:
- original
task_categories:
- other
task_ids: []
paperswithcode_id: medal
pretty_name: MeDAL
tags:
- disambiguation
dataset_info:
features:
- name: abstract_id
dtype: int32
- name: text
dtype: string
- name: location
sequence: int32
- name: label
sequence: string
splits:
- name: train
num_bytes: 3573399948
num_examples: 3000000
- name: test
num_bytes: 1190766821
num_examples: 1000000
- name: validation
num_bytes: 1191410723
num_examples: 1000000
- name: full
num_bytes: 15536883723
num_examples: 14393619
download_size: 21060929078
dataset_size: 21492461215
---
# Dataset Card for the MeDAL dataset
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Repository:** https://github.com/BruceWen120/medal
- **Paper:** https://www.aclweb.org/anthology/2020.clinicalnlp-1.15/
- **Dataset (Kaggle):** https://www.kaggle.com/xhlulu/medal-emnlp
- **Dataset (Zenodo):** https://zenodo.org/record/4265632
- **Pretrained model:** https://huggingface.co/xhlu/electra-medal
- **Leaderboard:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Dataset Summary
A large medical text dataset (14Go) curated to 4Go for abbreviation disambiguation, designed for natural language understanding pre-training in the medical domain. For example, DHF can be disambiguated to dihydrofolate, diastolic heart failure, dengue hemorragic fever or dihydroxyfumarate
### Supported Tasks and Leaderboards
Medical abbreviation disambiguation
### Languages
English (en)
## Dataset Structure
Each file is a table consisting of three columns:
* text: The normalized content of an abstract
* location: The location (index) of each abbreviation that was substituted
* label: The word at that was substituted at the given location
### Data Instances
An example from the train split is:
```
{'abstract_id': 14145090,
'text': 'velvet antlers vas are commonly used in traditional chinese medicine and invigorant and contain many PET components for health promotion the velvet antler peptide svap is one of active components in vas based on structural study the svap interacts with tgfβ receptors and disrupts the tgfβ pathway we hypothesized that svap prevents cardiac fibrosis from pressure overload by blocking tgfβ signaling SDRs underwent TAC tac or a sham operation T3 one month rats received either svap mgkgday or vehicle for an additional one month tac surgery induced significant cardiac dysfunction FB activation and fibrosis these effects were improved by treatment with svap in the heart tissue tac remarkably increased the expression of tgfβ and connective tissue growth factor ctgf ROS species C2 and the phosphorylation C2 of smad and ERK kinases erk svap inhibited the increases in reactive oxygen species C2 ctgf expression and the phosphorylation of smad and erk but not tgfβ expression in cultured cardiac fibroblasts angiotensin ii ang ii had similar effects compared to tac surgery such as increases in αsmapositive CFs and collagen synthesis svap eliminated these effects by disrupting tgfβ IB to its receptors and blocking ang iitgfβ downstream signaling these results demonstrated that svap has antifibrotic effects by blocking the tgfβ pathway in CFs',
'location': [63],
'label': ['transverse aortic constriction']}
```
### Data Fields
The column types are:
* text: content of the abstract as a string
* location: index of the substitution as an integer
* label: substitued word as a string
### Data Splits
The following files are present:
* `full_data.csv`: The full dataset with all 14M abstracts.
* `train.csv`: The subset used to train the baseline and proposed models.
* `valid.csv`: The subset used to validate the model during training for hyperparameter selection.
* `test.csv`: The subset used to evaluate the model and report the results in the tables.
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
The original dataset was retrieved and modified from the [NLM website](https://www.nlm.nih.gov/databases/download/pubmed_medline.html).
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
Details on how the abbreviations were created can be found in section 2.2 (Dataset Creation) of the [ACL ClinicalNLP paper](https://aclanthology.org/2020.clinicalnlp-1.15.pdf).
#### 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
Since the abstracts are written in English, the data is biased towards anglo-centric medical research. If you plan to use a model pre-trained on this dataset for a predominantly non-English community, it is important to verify whether there are negative biases present in your model, and ensure that they are correctly mitigated. For instance, you could fine-tune your dataset on a multilingual medical disambiguation dataset, or collect a dataset specific to your use case.
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
The ELECTRA model is licensed under [Apache 2.0](https://github.com/google-research/electra/blob/master/LICENSE). The license for the libraries used in this project (`transformers`, `pytorch`, etc.) can be found in their respective GitHub repository. Our model is released under a MIT license.
The original dataset was retrieved and modified from the [NLM website](https://www.nlm.nih.gov/databases/download/pubmed_medline.html). By using this dataset, you are bound by the [terms and conditions](https://www.nlm.nih.gov/databases/download/terms_and_conditions_pubmed.html) specified by NLM:
> INTRODUCTION
>
> Downloading data from the National Library of Medicine FTP servers indicates your acceptance of the following Terms and Conditions: No charges, usage fees or royalties are paid to NLM for this data.
>
> MEDLINE/PUBMED SPECIFIC TERMS
>
> NLM freely provides PubMed/MEDLINE data. Please note some PubMed/MEDLINE abstracts may be protected by copyright.
>
> GENERAL TERMS AND CONDITIONS
>
> * Users of the data agree to:
> * acknowledge NLM as the source of the data by including the phrase "Courtesy of the U.S. National Library of Medicine" in a clear and conspicuous manner,
> * properly use registration and/or trademark symbols when referring to NLM products, and
> * not indicate or imply that NLM has endorsed its products/services/applications.
>
> * Users who republish or redistribute the data (services, products or raw data) agree to:
> * maintain the most current version of all distributed data, or
> * make known in a clear and conspicuous manner that the products/services/applications do not reflect the most current/accurate data available from NLM.
>
> * These data are produced with a reasonable standard of care, but NLM makes no warranties express or implied, including no warranty of merchantability or fitness for particular purpose, regarding the accuracy or completeness of the data. Users agree to hold NLM and the U.S. Government harmless from any liability resulting from errors in the data. NLM disclaims any liability for any consequences due to use, misuse, or interpretation of information contained or not contained in the data.
>
> * NLM does not provide legal advice regarding copyright, fair use, or other aspects of intellectual property rights. See the NLM Copyright page.
>
> * NLM reserves the right to change the type and format of its machine-readable data. NLM will take reasonable steps to inform users of any changes to the format of the data before the data are distributed via the announcement section or subscription to email and RSS updates.
### Citation Information
```
@inproceedings{wen-etal-2020-medal,
title = "{M}e{DAL}: Medical Abbreviation Disambiguation Dataset for Natural Language Understanding Pretraining",
author = "Wen, Zhi and
Lu, Xing Han and
Reddy, Siva",
booktitle = "Proceedings of the 3rd Clinical Natural Language Processing Workshop",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.clinicalnlp-1.15",
pages = "130--135",
abstract = "One of the biggest challenges that prohibit the use of many current NLP methods in clinical settings is the availability of public datasets. In this work, we present MeDAL, a large medical text dataset curated for abbreviation disambiguation, designed for natural language understanding pre-training in the medical domain. We pre-trained several models of common architectures on this dataset and empirically showed that such pre-training leads to improved performance and convergence speed when fine-tuning on downstream medical tasks.",
}
```
### Contributions
Thanks to [@Narsil](https://github.com/Narsil) and [@xhlulu](https://github.com/xhlulu)) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
DDSC/reddit-da | DDSC | 2022-10-27T11:00:42Z | 397 | 2 | null | [
"task_categories:text-generation",
"task_ids:language-modeling",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:da",
"license:mit",
"region:us"
] | 2022-10-27T11:00:42Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- da
license:
- mit
multilinguality:
- monolingual
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- text-generation
task_ids:
- language-modeling
pretty_name: Reddit-da
---
# Dataset Card for SQuAD-da
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Contributions](#contributions)
### Dataset Summary
This dataset consists of 1,908,887 Danish posts from Reddit. These are from [this Reddit dump](https://files.pushshift.io/reddit/) and have been filtered using [this script](https://github.com/NBAiLab/notram/blob/master/corpus_generation_scripts/lang_detect_reddit.py), which uses FastText to detect the Danish posts.
### Supported Tasks and Leaderboards
This dataset is suitable for language modelling.
### Languages
This dataset is in Danish.
## Dataset Structure
### Data Instances
Every entry in the dataset contains short Reddit comments in Danish, along with a unique ID.
### Data Fields
An entry in the dataset consists of the following fields:
- `id` (`str`): A unique identifier.
- `text` (`str`): A short Reddit comment.
## Additional Information
### Licensing Information
The dataset is released under the MIT license.
### Contributions
Thanks to [@saattrupdan](https://github.com/saattrupdan) for adding this dataset to the Hugging Face Hub. | [
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